diff --git a/.gitignore b/.gitignore
index c559d356..996fc5d8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,13 @@
+docs/equations/*.aux
+docs/equations/*.log
+docs/equations/*.out
+docs/equations/*.synctex.gz
+code/ch12/mnist
+code/datasets/mnist/t10k-images-idx3-ubyte
+code/datasets/mnist/t10k-labels-idx1-ubyte
+code/datasets/mnist/train-images-idx3-ubyte
+code/datasets/mnist/train-labels-idx1-ubyte
+
.ipynb_checkpoints
.DS_Store
code/datasets/movie/aclImdb_v1.tar.gz
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 00000000..00c66beb
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2015, 2016 SEBASTIAN RASCHKA (mail@sebastianraschka.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
index 616d3656..13ce8fd4 100644
--- a/README.md
+++ b/README.md
@@ -1,24 +1,141 @@
-# python-machine-learning-book
+# Python Machine Learning book code repository
-*Python Machine Learning* code repository.
+
+[](https://groups.google.com/forum/#!forum/python-machine-learning-reader-discussion-board)
+
+---
+
+#### IMPORTANT NOTE (09/21/2017):
+
+This GitHub repository contains the code examples of the **1st Edition** of Python Machine Learning book. If you are looking for the code examples of the **2nd Edition**, please refer to [this](https://github.com/rasbt/python-machine-learning-book-2nd-edition#whats-new-in-the-second-edition-from-the-first-edition) repository instead.
+
+---
What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and
we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.
You are not sure if this book is for you? Please checkout the excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/preface_sr.pdf), or take a look at the [FAQ](#faq) section for further information.
+
+
---
-
+[](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_1?ie=UTF8&qid=1470882464&sr=8-1&keywords=python+machine+learning)
-
-1st edition, published September 23rd 2015
+1st edition, published September 23rd 2015
Paperback: 454 pages
Publisher: Packt Publishing
Language: English
ISBN-10: 1783555130
ISBN-13: 978-1783555130
-Kindle ASIN: B00YSILNL0
+Kindle ASIN: B00YSILNL0
+
+
+
+[](http://www.computingreviews.com/recommend/bestof/notableitems.cfm?bestYear=2016)
+
+
+
+German ISBN-13: 978-3958454224
+Japanese ISBN-13: 978-4844380603
+Italian ISBN-13: 978-8850333974
+Chinese (traditional) ISBN-13: 978-9864341405
+Chinese (mainland) ISBN-13: 978-7111558804
+Korean ISBN-13: 979-1187497035
+Russian ISBN-13: 978-5970604090
+
+
+
+## Table of Contents and Code Notebooks
+
+
+Simply click on the `ipynb`/`nbviewer` links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by the NbViewer version).
+**Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**
+
+
+- Excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/preface_sr.pdf)
+- [Instructions for setting up Python and the Jupiter Notebook](./code/ch01/README.md)
+
+
+
+1. Machine Learning - Giving Computers the Ability to Learn from Data [[dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
+2. Training Machine Learning Algorithms for Classification [[dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
+3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]
+4. Building Good Training Sets – Data Pre-Processing [[dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)]
+5. Compressing Data via Dimensionality Reduction [[dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)]
+6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)]
+7. Combining Different Models for Ensemble Learning [[dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)]
+8. Applying Machine Learning to Sentiment Analysis [[dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb)]
+9. Embedding a Machine Learning Model into a Web Application [[dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb)]
+10. Predicting Continuous Target Variables with Regression Analysis [[dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch10/ch10.ipynb)]
+11. Working with Unlabeled Data – Clustering Analysis [[dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch11/ch11.ipynb)]
+12. Training Artificial Neural Networks for Image Recognition [[dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb)]
+13. Parallelizing Neural Network Training via Theano [[dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch13/ch13.ipynb)]
+
+
+
+#### Equation Reference
+
+
+
+[[PDF](./docs/equations/pymle-equations.pdf)] [[TEX](./docs/equations/pymle-equations.tex)]
+
+#### Slides for Teaching
+
+A big thanks to [Dmitriy Dligach](dmitriydligach) for sharing his slides from his machine learning course that is currently offered at [Loyola University Chicago](http://www.luc.edu/cs/).
+
+- [https://github.com/dmitriydligach/PyMLSlides](https://github.com/dmitriydligach/PyMLSlides)
+-
+
+
+
+#### Additional Math and NumPy Resources
+
+Some readers were asking about Math and NumPy primers, since they were not included due to length limitations. However, I recently put together such resources for another book, but I made these *chapters* freely available online in hope that they also serve as helpful background material for this book:
+
+
+- Algebra Basics [[PDF](https://sebastianraschka.com/pdf/books/dlb/appendix_b_algebra.pdf)] [[EPUB](https://sebastianraschka.com/pdf/books/dlb/appendix_b_algebra.epub)]
+
+- A Calculus and Differentiation Primer [[PDF](https://sebastianraschka.com/pdf/books/dlb/appendix_d_calculus.pdf)] [[EPUB](https://sebastianraschka.com/pdf/books/dlb/appendix_d_calculus.epub)]
+
+- Introduction to NumPy [[PDF](https://sebastianraschka.com/pdf/books/dlb/appendix_f_numpy-intro.pdf)] [[EPUB](https://sebastianraschka.com/pdf/books/dlb/appendix_f_numpy-intro.epub)] [[Code Notebook](https://github.com/rasbt/deep-learning-book/blob/master/code/appendix_f_numpy-intro/appendix_f_numpy-intro.ipynb)]
+
+
+
+---
+
+#### Citing this Book
+
+You are very welcome to re-use the code snippets or other contents from this book
+in scientific publications and other works;
+in this case, I would appreciate citations to the original source:
+
+**BibTeX**:
+
+```
+@Book{raschka2015python,
+ author = {Raschka, Sebastian},
+ title = {Python Machine Learning},
+ publisher = {Packt Publishing},
+ year = {2015},
+ address = {Birmingham, UK},
+ isbn = {1783555130}
+ }
+```
+
+
+**MLA**:
+
+
+Raschka, Sebastian. *Python machine learning*. Birmingham, UK: Packt Publishing, 2015. Print.
+
+---
+
+### [Feedback & Reviews](./docs/feedback.md)
+
+#### [Short review snippets](./docs/feedback.md)
+
+[](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_1?ie=UTF8&qid=1472342570&sr=8-1&keywords=sebastian+raschka)
---
> *Sebastian Raschka’s new book, Python Machine Learning, has just been released. I got a chance to read a review copy and it’s just as I expected - really great! It’s well organized, super easy to follow, and it not only offers a good foundation for smart, non-experts, practitioners will get some ideas and learn new tricks here as well.*
@@ -30,59 +147,289 @@ Kindle ASIN: B00YSILNL0
> *I've read (virtually) every Machine Learning title based around Scikit-learn and this is hands-down the best one out there.*
– [Jason Wolosonovich](https://www.linkedin.com/pulse/python-machine-learning-sebastian-raschka-review-jason-wolosonovich?trk=prof-post)
-### [Feedback & Reviews](./docs/feedback.md)
+> *The best book I've seen to come out of PACKT Publishing. This is a very well written introduction to machine learning with Python. As others have noted, a perfect mixture of theory and application.*
+– [Josh D.](https://www.amazon.com/gp/customer-reviews/R27WB1GWTNGIR2/ref=cm_cr_getr_d_rvw_ttl?ie=UTF8&ASIN=1783555130)
+
+> *A book with a blend of qualities that is hard to come by: combines the needed mathematics to control the theory with the applied coding in Python. Also great to see it doesn't waste paper in giving a primer on Python as many other books do just to appeal to the greater audience. You can tell it's been written by knowledgeable writers and not just DIY geeks.*
+– [Amazon Customer](https://www.amazon.com/gp/customer-reviews/RZWY4TF66Z6V0/ref=cm_cr_getr_d_rvw_ttl?ie=UTF8&ASIN=1783555130)
+
+> *Sebastian Raschka created an amazing machine learning tutorial which combines theory with practice. The book explains machine learning from a theoretical perspective and has tons of coded examples to show how you would actually use the machine learning technique. It can be read by a beginner or advanced programmer.*
+- William P. Ross, [7 Must Read Python Books](http://williampross.com/7-must-read-python-books/)
+
+#### Longer reviews
+
+If you need help to decide whether this book is for you, check out some of the "longer" reviews linked below. (If you wrote a review, please let me know, and I'd be happy to add it to the list).
+
+- [Python Machine Learning Review](http://www.bcs.org/content/conWebDoc/55586) by Patrick Hill at the Chartered Institute for IT
+- [Book Review: Python Machine Learning by Sebastian Raschka](http://whatpixel.com/python-machine-learning-book-review/) by Alex Turner at WhatPixel
+
+---
## Links
- ebook and paperback at [Amazon.com](http://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_2?ie=UTF8&qid=1437754343&sr=8-2&keywords=python+machine+learning+essentials), [Amazon.co.uk](http://www.amazon.co.uk/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130), [Amazon.de](http://www.amazon.de/s/ref=nb_sb_noss_2?__mk_de_DE=ÅMÅŽÕÑ&url=search-alias%3Daps&field-keywords=python+machine+learning)
- [ebook and paperback](https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning) from Packt (the publisher)
-- at other book stores: [O'Reilly](http://shop.oreilly.com/product/9781783555130.do), [Safari](https://www.safaribooksonline.com/library/view/python-machine-learning/9781783555130/), [Barnes & Noble](http://www.barnesandnoble.com/w/python-machine-learning-essentials-sebastian-raschka/1121999969?ean=9781783555130), [Apple iBooks](https://itunes.apple.com/us/book/python-machine-learning/id1028207310?mt=11), ...
-- free [sample chapter](http://www.slideshare.net/Products123/python-machine-learning-sample-chapter)
+- at other book stores: [Google Books](https://books.google.com/books?id=GOVOCwAAQBAJ&source=gbs_slider_cls_metadata_7_mylibrary), [O'Reilly](http://shop.oreilly.com/product/9781783555130.do), [Safari](https://www.safaribooksonline.com/library/view/python-machine-learning/9781783555130/), [Barnes & Noble](http://www.barnesandnoble.com/w/python-machine-learning-essentials-sebastian-raschka/1121999969?ean=9781783555130), [Apple iBooks](https://itunes.apple.com/us/book/python-machine-learning/id1028207310?mt=11), ...
+- social platforms: [Goodreads](https://www.goodreads.com/book/show/25545994-python-machine-learning)
+#### Translations
+- [Italian translation](https://www.amazon.it/learning-Costruire-algoritmi-generare-conoscenza/dp/8850333978/) via "Apogeo"
+- [German translation](https://www.amazon.de/Machine-Learning-Python-mitp-Professional/dp/3958454224/) via "mitp Verlag"
+- [Japanese translation](http://www.amazon.co.jp/gp/product/4844380605/) via "Impress Top Gear"
+- [Chinese translation (traditional Chinese)](https://taiwan.kinokuniya.com/bw/9789864341405)
+- [Chinese translation (simple Chinese)](https://book.douban.com/subject/27000110/)
+- [Korean translation](http://www.kyobobook.co.kr/product/detailViewKor.laf?mallGb=KOR&ejkGb=KOR&barcode=9791187497035) via "Kyobo"
+- [Polish translation](https://www.amazon.de/Python-Uczenie-maszynowe-Sebastian-Raschka/dp/8328336138/ref=sr_1_11?ie=UTF8&qid=1513601461&sr=8-11&keywords=sebastian+raschka) via "Helion"
+---
### [Literature References & Further Reading Resources](./docs/references.md)
-### [Image Gallery](./images/image_gallery/README.md)
-
### [Errata](./docs/errata.md)
-## Table of Contents and Code Notebooks
+---
+### Bonus Notebooks (not in the book)
-Simply click on the `ipynb`/`nbviewer` links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by the NbViewer version).
-**Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**
+- Logistic Regression Implementation [[dir](./code/bonus)] [[ipynb](./code/bonus/logistic_regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/logistic_regression.ipynb)]
+- A Basic Pipeline and Grid Search Setup [[dir](./code/bonus)] [[ipynb](./code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)]
+- An Extended Nested Cross-Validation Example [[dir](./code/bonus)] [[ipynb](./code/bonus/nested_cross_validation.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/nested_cross_validation.ipynb)]
+- A Simple Barebones Flask Webapp Template [[view directory](./code/bonus/flask_webapp_ex01)][[download as zip-file](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip)]
+- Reading handwritten digits from MNIST into NumPy arrays [[GitHub ipynb](./code/bonus/reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)]
+- Scikit-learn Model Persistence using JSON [[GitHub ipynb](./code/bonus/scikit-model-to-json.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/scikit-model-to-json.ipynb)]
+- Multinomial logistic regression / softmax regression [[GitHub ipynb](./code/bonus/softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)]
+
-Excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/preface_sr.pdf)
+**"Related Content" (not in the book)**
-1. Machine Learning - Giving Computers the Ability to Learn from Data [[dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
-2. Training Machine Learning Algorithms for Classification [[dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
-3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]
-4. Building Good Training Sets – Data Pre-Processing [[dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)]
-5. Compressing Data via Dimensionality Reduction [[dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)]
-6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)]
-7. Combining Different Models for Ensemble Learning [[dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)]
-8. Applying Machine Learning to Sentiment Analysis [[dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb)]
-9. Embedding a Machine Learning Model into a Web Application [[dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb)]
-10. Predicting Continuous Target Variables with Regression Analysis [[dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch10/ch10.ipynb)]
-11. Working with Unlabeled Data – Clustering Analysis [[dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch11/ch11.ipynb)]
-12. Training Artificial Neural Networks for Image Recognition [[dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb)]
-13. Parallelizing Neural Network Training via Theano [[dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch13/ch13.ipynb)]
+- [Model evaluation, model selection, and algorithm selection in machine learning - Part I](http://sebastianraschka.com/blog/2016/model-evaluation-selection-part1.html)
+- [Model evaluation, model selection, and algorithm selection in machine learning - Part II](http://sebastianraschka.com/blog/2016/model-evaluation-selection-part2.html)
+- [Model evaluation, model selection, and algorithm selection in machine learning - Part III](http://sebastianraschka.com/blog/2016/model-evaluation-selection-part3.html)
+
+---
+
+#### SciPy 2016
+
+We had such a great time at [SciPy 2016](http://scipy2016.scipy.org/ehome/index.php?eventid=146062&tabid=332930&) in Austin! It was a real pleasure to meet and chat with so many readers of my book. Thanks so much for all the nice words and feedback! And in case you missed it, Andreas Mueller and I gave an **Introduction to Machine Learning with Scikit-learn**; if you are interested, the video recordings of [Part I](https://www.youtube.com/watch?v=OB1reY6IX-o&index=91&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6) and [Part II](https://www.youtube.com/watch?v=Cte8FYCpylk&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6&index=90) are now online!
+
+[](https://www.youtube.com/watch?v=OB1reY6IX-o&index=91&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6)
+
+#### PyData Chicago 2016
+
+I attempted the rather challenging task of introducing scikit-learn & machine learning in *just* 90 minutes at PyData Chicago 2016. The slides and tutorial material are available at "[Learning scikit-learn -- An Introduction to Machine Learning in Python](https://github.com/rasbt/pydata-chicago2016-ml-tutorial)."
+
+
+---
+
+**Note**
+
+I have set up a separate library, [`mlxtend`](http://rasbt.github.io/mlxtend/), containing additional implementations of machine learning (and general "data science") algorithms. I also added implementations from this book (for example, the decision region plot, the artificial neural network, and sequential feature selection algorithms) with additional functionality.
+
+[](http://rasbt.github.io/mlxtend/)
+
+
+
+
+
+
+### Translations
+
+[](https://www.amazon.it/learning-Costruire-algoritmi-generare-conoscenza/dp/8850333978/)
+[](https://www.amazon.de/Machine-Learning-Python-mitp-Professional/dp/3958454224/)
+[](http://www.amazon.co.jp/gp/product/4844380605/)
+[](https://taiwan.kinokuniya.com/bw/9789864341405)
+[](https://book.douban.com/subject/27000110/)
+[](http://www.kyobobook.co.kr/product/detailViewKor.laf?ejkGb=KOR&mallGb=KOR&barcode=9791187497035&orderClick=LEA&Kc=)
+[](http://www.ozon.ru/context/detail/id/140152222/)
+[](https://www.amazon.de/Python-Uczenie-maszynowe-Sebastian-Raschka/dp/8328336138/ref=sr_1_11?ie=UTF8&qid=1513601461&sr=8-11&keywords=sebastian+raschka)
+
+
+
+---
+
+***Dear readers***,
+first of all, I want to thank all of you for the great support! I am really happy about all the great feedback you sent me so far, and I am glad that the book has been so useful to a broad audience.
+
+Over the last couple of months, I received hundreds of emails, and I tried to answer as many as possible in the available time I have. To make them useful to other readers as well, I collected many of my answers in the FAQ section (below).
+
+In addition, some of you asked me about a platform for readers to discuss the contents of the book. I hope that this would provide an opportunity for you to discuss and share your knowledge with other readers:
+
+#### [Google Groups Discussion Board](https://groups.google.com/forum/#!forum/python-machine-learning-reader-discussion-board)
+
+(And I will try my best to answer questions myself if time allows! :))
+
+> The only thing to do with good advice is to pass it on. It is never of any use to oneself.
+— Oscar Wilde
+
+---
+
+## Examples and Applications by Readers
+Once again, I have to say (big!) THANKS for all the nice feedback about the book. I've received many emails from readers, who
+put the concepts and examples from this book out into the real world and make good use of them in their projects. In this section, I am
+starting to gather some of these great applications, and I'd be more than happy to add your project to this list -- just shoot me a quick mail!
+
+- [40 scripts on Optical Character Recognition](https://github.com/rrlyman/PythonMachineLearingExamples) by [Richard Lyman](https://github.com/rrlyman)
+- [Code experiments](https://github.com/jeremyn/python-machine-learning-book) by [Jeremy Nation](https://github.com/jeremyn)
+- [What I Learned Implementing a Classifier from Scratch in Python](http://www.jeannicholashould.com) by [Jean-Nicholas Hould](http://www.jeannicholashould.com)
## FAQ
+### General Questions
+
+- [What are machine learning and data science?](./faq/datascience-ml.md)
+- [Why do you and other people sometimes implement machine learning algorithms from scratch?](./faq/implementing-from-scratch.md)
+- [What learning path/discipline in data science I should focus on?](./faq/data-science-career.md)
+- [At what point should one start contributing to open source?](./faq/open-source.md)
+- [How important do you think having a mentor is to the learning process?](./faq/mentor.md)
+- [Where are the best online communities centered around data science/machine learning or python?](./faq/ml-python-communities.md)
+- [How would you explain machine learning to a software engineer?](./faq/ml-to-a-programmer.md)
+- [How would your curriculum for a machine learning beginner look like?](./faq/ml-curriculum.md)
+- [What is the Definition of Data Science?](./faq/definition_data-science.md)
+- [How do Data Scientists perform model selection? Is it different from Kaggle?](./faq/model-selection-in-datascience.md)
+
+### Questions about the Machine Learning Field
+
+- [How are Artificial Intelligence and Machine Learning related?](./faq/ai-and-ml.md)
+- [What are some real-world examples of applications of machine learning in the field?](./faq/ml-examples.md)
+- [What are the different fields of study in data mining?](./faq/datamining-overview.md)
+- [What are differences in research nature between the two fields: machine learning & data mining?](./faq/datamining-vs-ml.md)
+- [How do I know if the problem is solvable through machine learning?](./faq/ml-solvable.md)
+- [What are the origins of machine learning?](./faq/ml-origins.md)
+- [How was classification, as a learning machine, developed?](./faq/classifier-history.md)
+- [Which machine learning algorithms can be considered as among the best?](./faq/best-ml-algo.md)
+- [What are the broad categories of classifiers?](./faq/classifier-categories.md)
+- [What is the difference between a classifier and a model?](./faq/difference_classifier_model.md)
+- [What is the difference between a parametric learning algorithm and a nonparametric learning algorithm?](./faq/parametric_vs_nonparametric.md)
+- [What is the difference between a cost function and a loss function in machine learning?](./faq/cost-vs-loss.md)
+
+### Questions about ML Concepts and Statistics
+
+##### Cost Functions and Optimization
+
+- [Fitting a model via closed-form equations vs. Gradient Descent vs Stochastic Gradient Descent vs Mini-Batch Learning -- what is the difference?](./faq/closed-form-vs-gd.md)
+- [How do you derive the Gradient Descent rule for Linear Regression and Adaline?](./faq/linear-gradient-derivative.md)
+
+##### Regression Analysis
+
+- [What is the difference between Pearson R and Simple Linear Regression?](./faq/pearson-r-vs-linear-regr.md)
+
+##### Tree models
+
+- [How does the random forest model work? How is it different from bagging and boosting in ensemble models?](./faq/bagging-boosting-rf.md)
+- [What are the disadvantages of using classic decision tree algorithm for a large dataset?](./faq/decision-tree-disadvantages.md)
+- [Why are implementations of decision tree algorithms usually binary, and what are the advantages of the different impurity metrics?](./faq/decision-tree-binary.md)
+- [Why are we growing decision trees via entropy instead of the classification error?](./faq/decisiontree-error-vs-entropy.md)
+- [When can a random forest perform terribly?](./faq/random-forest-perform-terribly.md)
+
+##### Model evaluation
+
+- [What is overfitting?](./faq/overfitting.md)
+- [How can I avoid overfitting?](./faq/avoid-overfitting.md)
+- [Is it always better to have the largest possible number of folds when performing cross validation?](./faq/number-of-kfolds.md)
+- [When training an SVM classifier, is it better to have a large or small number of support vectors?](./faq/num-support-vectors.md)
+- [How do I evaluate a model?](./faq/evaluate-a-model.md)
+- [What is the best validation metric for multi-class classification?](./faq/multiclass-metric.md)
+- [What factors should I consider when choosing a predictive model technique?](./faq/choosing-technique.md)
+- [What are the best toy datasets to help visualize and understand classifier behavior?](./faq/clf-behavior-data.md)
+- [How do I select SVM kernels?](./faq/select_svm_kernels.md)
+- [Interlude: Comparing and Computing Performance Metrics in Cross-Validation -- Imbalanced Class Problems and 3 Different Ways to Compute the F1 Score](./faq/computing-the-f1-score.md)
+
+##### Logistic Regression
+
+- [What is Softmax regression and how is it related to Logistic regression?](./faq/softmax_regression.md)
+- [Why is logistic regression considered a linear model?](./faq/logistic_regression_linear.md)
+- [What is the probabilistic interpretation of regularized logistic regression?](./faq/probablistic-logistic-regression.md)
+- [Does regularization in logistic regression always results in better fit and better generalization?](./faq/regularized-logistic-regression-performance.md)
+- [What is the major difference between naive Bayes and logistic regression?](./faq/naive-bayes-vs-logistic-regression.md)
+- [What exactly is the "softmax and the multinomial logistic loss" in the context of machine learning?](./faq/softmax.md)
+- [What is the relation between Logistic Regression and Neural Networks and when to use which?](./faq/logisticregr-neuralnet.md)
+- [Logistic Regression: Why sigmoid function?](./faq/logistic-why-sigmoid.md)
+- [Is there an analytical solution to Logistic Regression similar to the Normal Equation for Linear Regression?](./faq/logistic-analytical.md)
+
+
+##### Neural Networks and Deep Learning
+
+- [What is the difference between deep learning and usual machine learning?](./faq/difference-deep-and-normal-learning.md)
+- [Can you give a visual explanation for the back propagation algorithm for neural networks?](./faq/visual-backpropagation.md)
+- [Why did it take so long for deep networks to be invented?](./faq/inventing-deeplearning.md)
+- [What are some good books/papers for learning deep learning?](./faq/deep-learning-resources.md)
+- [Why are there so many deep learning libraries?](./faq/many-deeplearning-libs.md)
+- [Why do some people hate neural networks/deep learning?](./faq/deeplearning-criticism.md)
+- [How can I know if Deep Learning works better for a specific problem than SVM or random forest?](./faq/deeplearn-vs-svm-randomforest.md)
+- [What is wrong when my neural network's error increases?](./faq/neuralnet-error.md)
+- [How do I debug an artificial neural network algorithm?](./faq/nnet-debugging-checklist.md)
+- [What is the difference between a Perceptron, Adaline, and neural network model?](./faq/diff-perceptron-adaline-neuralnet.md)
+- [What is the basic idea behind the dropout technique?](./faq/dropout.md)
+
+
+##### Other Algorithms for Supervised Learning
+
+- [Why is Nearest Neighbor a Lazy Algorithm?](./faq/lazy-knn.md)
+
+##### Unsupervised Learning
+
+- [What are some of the issues with clustering?](./faq/issues-with-clustering.md)
+
+##### Semi-Supervised Learning
+
+- [What are the advantages of semi-supervised learning over supervised and unsupervised learning?](./faq/semi-vs-supervised.md)
+
+##### Ensemble Methods
+
+- [Is Combining Classifiers with Stacking Better than Selecting the Best One?](./faq/logistic-boosting.md)
+
+##### Preprocessing, Feature Selection and Extraction
+
+- [Why do we need to re-use training parameters to transform test data?](./faq/scale-training-test.md)
+- [What are the different dimensionality reduction methods in machine learning?](./faq/dimensionality-reduction.md)
+- [What is the difference between LDA and PCA for dimensionality reduction?](./faq/lda-vs-pca.md)
+- [When should I apply data normalization/standardization?](./faq/when-to-standardize.md)
+- [Does mean centering or feature scaling affect a Principal Component Analysis?](./faq/pca-scaling.md)
+- [How do you attack a machine learning problem with a large number of features?](./faq/large-num-features.md)
+- [What are some common approaches for dealing with missing data?](./faq/missing-data.md)
+- [What is the difference between filter, wrapper, and embedded methods for feature selection?](./faq/feature_sele_categories.md)
+- [Should data preparation/pre-processing step be considered one part of feature engineering? Why or why not?](./faq/dataprep-vs-dataengin.md)
+- [Is a bag of words feature representation for text classification considered as a sparse matrix?](./faq/bag-of-words-sparsity.md)
+
+##### Naive Bayes
+
+- [Why is the Naive Bayes Classifier naive?](./faq/naive-naive-bayes.md)
+- [What is the decision boundary for Naive Bayes?](./faq/naive-bayes-boundary.md)
+- [Can I use Naive Bayes classifiers for mixed variable types?](./faq/naive-bayes-vartypes.md)
+- [Is it possible to mix different variable types in Naive Bayes, for example, binary and continues features?](./naive-bayes-vartypes.md)
+
+##### Other
+
+- [What is Euclidean distance in terms of machine learning?](./faq/euclidean-distance.md)
+- [When should one use median, as opposed to the mean or average?](./faq/median-vs-mean.md)
+
+##### Programming Languages and Libraries for Data Science and Machine Learning
+
+- [Is R used extensively today in data science?](./faq/r-in-datascience.md)
+- [What is the main difference between TensorFlow and scikit-learn?](./faq/tensorflow-vs-scikitlearn.md)
+
+
+
+
+
+
+
+### Questions about the Book
+
- [Can I use paragraphs and images from the book in presentations or my blog?](./faq/copyright.md)
- [How is this different from other machine learning books?](./faq/different.md)
- [Which version of Python was used in the code examples?](./faq/py2py3.md)
- [Which technologies and libraries are being used?](./faq/technologies.md)
- [Which book version/format would you recommend?](./faq/version.md)
-- [Why did you choose Python for machine learning?](./faq/why_python.md)
-- [Why do you use so many leading and trailing underscores in the code examples?](./faq/underscore_convention.md)
-- [Are There Any Prerequisites and Recommended Pre-Readings?](./faq/prerequisites.md)
+- [Why did you choose Python for machine learning?](./faq/why-python.md)
+- [Why do you use so many leading and trailing underscores in the code examples?](./faq/underscore-convention.md)
+- [What is the purpose of the `return self` idioms in your code examples?](./faq/return_self_idiom.md)
+- [Are there any prerequisites and recommended pre-readings?](./faq/prerequisites.md)
+- [How can I apply SVM to categorical data?](./faq/svm_for_categorical.md)
## Contact
diff --git a/code/README.md b/code/README.md
index 72d9a11c..02f17e16 100644
--- a/code/README.md
+++ b/code/README.md
@@ -1,10 +1,27 @@
-## Table of Contents and Code Notebooks
+## Resources for setting up your coding environment
+
+- [Instructions for setting up Python and the Jupyter Notebook](./ch01/README.md)
+
+***If you need help with opening the Jupyter notebooks, I made a short [step by step guide](../docs/running_jupyter_nb.pdf) that illustrates this process***
+
+- A [quick and great NumPy refresher](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html) that covers everything (and more) you'd need for this book
+
+- Recommended! To check your coding environment, open the `check_environment.ipynb` (it can be found in this directory) in Jupyter Notebook and execute the code cell:
+
+
+
+
+## Table of contents and code notebooks
Simply click on the `ipynb`/`nbviewer` links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by the NbViewer version).
**Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**
+
+
+
+
1. Machine Learning - Giving Computers the Ability to Learn from Data [[dir](./ch01)] [[ipynb](./ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
2. Training Machine Learning Algorithms for Classification [[dir](./ch02)] [[ipynb](./ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[dir](./ch03)] [[ipynb](./ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]
@@ -19,6 +36,21 @@ Simply click on the `ipynb`/`nbviewer` links next to the chapter headlines to vi
12. Training Artificial Neural Networks for Image Recognition [[dir](./ch12)] [[ipynb](./ch12/ch12.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb)]
13. Parallelizing Neural Network Training via Theano [[dir](./ch13)] [[ipynb](./ch13/ch13.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch13/ch13.ipynb)]
+
+
+**Bonus Notebooks (not in the book)**
+
+- Logistic Regression Implementation [[dir](./bonus)] [[ipynb](./bonus/logistic_regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/logistic_regression.ipynb)]
+- A Basic Pipeline and Grid Search Setup [[dir](./bonus)] [[ipynb](./bonus/svm_iris_pipeline_and_gridsearch.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)]
+- An Extended Nested Cross-Validation Example [[dir](./bonus)] [[ipynb](./bonus/nested_cross_validation.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/nested_cross_validation.ipynb)]
+- A Simple(r) Barebones Flask Webapp Template [[view directory](./bonus/flask_webapp_ex01)][[download as zip-file](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip)]
+- Reading handwritten digits from MNIST into NumPy arrays [[GitHub ipynb](./bonus/reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)]
+- Scikit-learn Model Persistence using JSON [[GitHub ipynb](./bonus/scikit-model-to-json.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/scikit-model-to-json.ipynb)]
+- Multinomial logistic regression / softmax regression [[GitHub ipynb](./bonus/softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)]
+
+
+
+
## Contact
I am happy to answer questions! Just write me an [email](mailto:mail@sebastianraschka.com)
diff --git a/code/bonus/README.md b/code/bonus/README.md
new file mode 100644
index 00000000..77f73baf
--- /dev/null
+++ b/code/bonus/README.md
@@ -0,0 +1,20 @@
+Sebastian Raschka, 2015
+
+Python Machine Learning - Code Examples
+
+## Bonus Material
+
+A collection of additional notebooks and code examples to clarify and explain concepts based on reader feedback.
+
+
+- A Basic Pipeline and Grid Search Setup [[GitHub ipynb](./svm_iris_pipeline_and_gridsearch.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)]
+
+- An Extended Nested Cross-Validation Example [[GitHub ipynb](./nested_cross_validation.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/nested_cross_validation.ipynb)]
+
+- A Simple(r) Barebones Flask Webapp Template [[view directory](./flask_webapp_ex01)][[download as zip-file](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip)]
+
+- Reading handwritten digits from MNIST into NumPy arrays [[GitHub ipynb](./reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)]
+
+- Scikit-learn Model Persistence using JSON [[GitHub ipynb](./scikit-model-to-json.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/scikit-model-to-json.ipynb)]
+
+- Multinomial logistic regression / softmax regression [[GitHub ipynb](./softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)]
diff --git a/code/bonus/flask_webapp_ex01/README.md b/code/bonus/flask_webapp_ex01/README.md
new file mode 100644
index 00000000..d19666ac
--- /dev/null
+++ b/code/bonus/flask_webapp_ex01/README.md
@@ -0,0 +1,16 @@
+Sebastian Raschka, 2015
+
+Python Machine Learning - Code Examples (Bonus Material)
+
+
+# A Simple(r) Barebones Flask Webapp Template
+
+A simple Flask app that calculates the sum of two numbers entered in the respective input fields.
+
+You can run the app locally by executing `python app.py` within this directory.
+
+
+
+
+
+Click [here](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip) to download this example as zip-file.
\ No newline at end of file
diff --git a/code/bonus/flask_webapp_ex01/app.py b/code/bonus/flask_webapp_ex01/app.py
new file mode 100644
index 00000000..5314e37c
--- /dev/null
+++ b/code/bonus/flask_webapp_ex01/app.py
@@ -0,0 +1,31 @@
+from flask import Flask, render_template, request
+from wtforms import Form, DecimalField, validators
+
+app = Flask(__name__)
+
+
+class EntryForm(Form):
+ x_entry = DecimalField('x:',
+ places=10,
+ validators=[validators.NumberRange(-1e10, 1e10)])
+ y_entry = DecimalField('y:',
+ places=10,
+ validators=[validators.NumberRange(-1e10, 1e10)])
+
+@app.route('/')
+def index():
+ form = EntryForm(request.form)
+ return render_template('entry.html', form=form, z='')
+
+@app.route('/results', methods=['POST'])
+def results():
+ form = EntryForm(request.form)
+ z = ''
+ if request.method == 'POST' and form.validate():
+ x = request.form['x_entry']
+ y = request.form['y_entry']
+ z = float(x) + float(y)
+ return render_template('entry.html', form=form, z=z)
+
+if __name__ == '__main__':
+ app.run(debug=True)
\ No newline at end of file
diff --git a/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip b/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip
new file mode 100644
index 00000000..4f1e07d8
Binary files /dev/null and b/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip differ
diff --git a/code/bonus/flask_webapp_ex01/img/img_1.png b/code/bonus/flask_webapp_ex01/img/img_1.png
new file mode 100644
index 00000000..bfc35109
Binary files /dev/null and b/code/bonus/flask_webapp_ex01/img/img_1.png differ
diff --git a/code/bonus/flask_webapp_ex01/static/style.css b/code/bonus/flask_webapp_ex01/static/style.css
new file mode 100644
index 00000000..8abda7b6
--- /dev/null
+++ b/code/bonus/flask_webapp_ex01/static/style.css
@@ -0,0 +1,7 @@
+body{
+ width:600px;
+}
+
+#button{
+ padding-top: 20px;
+}
\ No newline at end of file
diff --git a/code/bonus/flask_webapp_ex01/templates/_formhelpers.html b/code/bonus/flask_webapp_ex01/templates/_formhelpers.html
new file mode 100644
index 00000000..57908946
--- /dev/null
+++ b/code/bonus/flask_webapp_ex01/templates/_formhelpers.html
@@ -0,0 +1,12 @@
+{% macro render_field(field) %}
+
{{ field.label }}
+
{{ field(**kwargs)|safe }}
+ {% if field.errors %}
+
+ {% for error in field.errors %}
+
{{ error }}
+ {% endfor %}
+
+ {% endif %}
+
+{% endmacro %}
\ No newline at end of file
diff --git a/code/bonus/flask_webapp_ex01/templates/entry.html b/code/bonus/flask_webapp_ex01/templates/entry.html
new file mode 100644
index 00000000..c31100d8
--- /dev/null
+++ b/code/bonus/flask_webapp_ex01/templates/entry.html
@@ -0,0 +1,26 @@
+
+
+
+ Webapp Ex 1
+
+
+
+
+{% from "_formhelpers.html" import render_field %}
+
+
+
+
+
\ No newline at end of file
diff --git a/code/bonus/images/logistic_regression_schematic.png b/code/bonus/images/logistic_regression_schematic.png
new file mode 100755
index 00000000..1bf92d66
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diff --git a/code/bonus/images/logistic_regression_schematic_2.png b/code/bonus/images/logistic_regression_schematic_2.png
new file mode 100644
index 00000000..5d99287d
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diff --git a/code/bonus/images/softmax_schematic_1.png b/code/bonus/images/softmax_schematic_1.png
new file mode 100644
index 00000000..f6960fe5
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diff --git a/code/bonus/logistic_regression.ipynb b/code/bonus/logistic_regression.ipynb
new file mode 100644
index 00000000..cc89f1b9
--- /dev/null
+++ b/code/bonus/logistic_regression.ipynb
@@ -0,0 +1,457 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[Sebastian Raschka](http://sebastianraschka.com), 2015\n",
+ "\n",
+ "https://github.com/rasbt/python-machine-learning-book"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Python Machine Learning Essentials - Code Examples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Bonus Material - A Simple Logistic Regression Implementation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sebastian Raschka \n",
+ "Last updated: 12/22/2015 \n",
+ "\n",
+ "CPython 3.5.1\n",
+ "IPython 4.0.1\n",
+ "\n",
+ "numpy 1.10.2\n",
+ "pandas 0.17.1\n",
+ "matplotlib 1.5.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext watermark\n",
+ "%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# to install watermark just uncomment the following line:\n",
+ "#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Overview"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Please see Chapter 3 for more details on logistic regression.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Implementing logistic regression in Python"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following implementation is similar to the Adaline implementation in [Chapter 2](../ch02/ch02.ipynb) except that we replace the sum of squared errors cost function with the logistic cost function\n",
+ "\n",
+ "$$J(\\mathbf{w}) = \\sum_{i=1}^{m} - y^{(i)} log \\bigg( \\phi\\big(z^{(i)}\\big) \\bigg) - \\big(1 - y^{(i)}\\big) log\\bigg(1-\\phi\\big(z^{(i)}\\big)\\bigg).$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "class LogisticRegression(object):\n",
+ " \"\"\"LogisticRegression classifier.\n",
+ "\n",
+ " Parameters\n",
+ " ------------\n",
+ " eta : float\n",
+ " Learning rate (between 0.0 and 1.0)\n",
+ " n_iter : int\n",
+ " Passes over the training dataset.\n",
+ "\n",
+ " Attributes\n",
+ " -----------\n",
+ " w_ : 1d-array\n",
+ " Weights after fitting.\n",
+ " cost_ : list\n",
+ " Cost in every epoch.\n",
+ "\n",
+ " \"\"\"\n",
+ " def __init__(self, eta=0.01, n_iter=50):\n",
+ " self.eta = eta\n",
+ " self.n_iter = n_iter\n",
+ "\n",
+ " def fit(self, X, y):\n",
+ " \"\"\" Fit training data.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : {array-like}, shape = [n_samples, n_features]\n",
+ " Training vectors, where n_samples is the number of samples and\n",
+ " n_features is the number of features.\n",
+ " y : array-like, shape = [n_samples]\n",
+ " Target values.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " self : object\n",
+ "\n",
+ " \"\"\"\n",
+ " self.w_ = np.zeros(1 + X.shape[1])\n",
+ " self.cost_ = [] \n",
+ " for i in range(self.n_iter):\n",
+ " y_val = self.activation(X)\n",
+ " errors = (y - y_val)\n",
+ " neg_grad = X.T.dot(errors)\n",
+ " self.w_[1:] += self.eta * neg_grad\n",
+ " self.w_[0] += self.eta * errors.sum()\n",
+ " self.cost_.append(self._logit_cost(y, self.activation(X)))\n",
+ " return self\n",
+ "\n",
+ " def _logit_cost(self, y, y_val):\n",
+ " logit = -y.dot(np.log(y_val)) - ((1 - y).dot(np.log(1 - y_val)))\n",
+ " return logit\n",
+ " \n",
+ " def _sigmoid(self, z):\n",
+ " return 1.0 / (1.0 + np.exp(-z))\n",
+ " \n",
+ " def net_input(self, X):\n",
+ " \"\"\"Calculate net input\"\"\"\n",
+ " return np.dot(X, self.w_[1:]) + self.w_[0]\n",
+ "\n",
+ " def activation(self, X):\n",
+ " \"\"\" Activate the logistic neuron\"\"\"\n",
+ " z = self.net_input(X)\n",
+ " return self._sigmoid(z)\n",
+ " \n",
+ " def predict_proba(self, X):\n",
+ " \"\"\"\n",
+ " Predict class probabilities for X.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
+ " Training vectors, where n_samples is the number of samples and\n",
+ " n_features is the number of features.\n",
+ " \n",
+ " Returns\n",
+ " ----------\n",
+ " Class 1 probability : float\n",
+ " \n",
+ " \"\"\"\n",
+ " return activation(X)\n",
+ "\n",
+ " def predict(self, X):\n",
+ " \"\"\"\n",
+ " Predict class labels for X.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
+ " Training vectors, where n_samples is the number of samples and\n",
+ " n_features is the number of features.\n",
+ " \n",
+ " Returns\n",
+ " ----------\n",
+ " class : int\n",
+ " Predicted class label.\n",
+ " \n",
+ " \"\"\"\n",
+ " # equivalent to np.where(self.activation(X) >= 0.5, 1, 0)\n",
+ " return np.where(self.net_input(X) >= 0.0, 1, 0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Reading-in the Iris data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
@@ -251,17 +296,30 @@
"2 ***SPOILER*** Do not read this, if you think a... 0"
]
},
- "execution_count": 1,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
+ "\n",
"df = pd.read_csv('./movie_data.csv')\n",
"df.head(3)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "### Note\n",
+ "\n",
+ "If you have problems with creating the `movie_data.csv` file in the previous chapter, you can find a download a zip archive at \n",
+ "https://github.com/rasbt/python-machine-learning-book/tree/master/code/datasets/movie\n",
+ ""
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -291,36 +349,50 @@
"## Transforming documents into feature vectors"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By calling the fit_transform method on CountVectorizer, we just constructed the vocabulary of the bag-of-words model and transformed the following three sentences into sparse feature vectors:\n",
+ "1. The sun is shining\n",
+ "2. The weather is sweet\n",
+ "3. The sun is shining, the weather is sweet, and one and one is two\n"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 6,
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
+ "\n",
"count = CountVectorizer()\n",
"docs = np.array([\n",
" 'The sun is shining',\n",
" 'The weather is sweet',\n",
- " 'The sun is shining and the weather is sweet'])\n",
+ " 'The sun is shining, the weather is sweet, and one and one is two'])\n",
"bag = count.fit_transform(docs)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let us print the contents of the vocabulary to get a better understanding of the underlying concepts:"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 7,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "{'sweet': 4, 'is': 1, 'shining': 2, 'weather': 6, 'sun': 3, 'the': 5, 'and': 0}\n"
+ "{'one': 2, 'sweet': 5, 'the': 6, 'shining': 3, 'weather': 8, 'and': 0, 'two': 7, 'is': 1, 'sun': 4}\n"
]
}
],
@@ -328,20 +400,32 @@
"print(count.vocabulary_)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see from executing the preceding command, the vocabulary is stored in a Python dictionary, which maps the unique words that are mapped to integer indices. Next let us print the feature vectors that we just created:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each index position in the feature vectors shown here corresponds to the integer values that are stored as dictionary items in the CountVectorizer vocabulary. For example, the rst feature at index position 0 resembles the count of the word and, which only occurs in the last document, and the word is at index position 1 (the 2nd feature in the document vectors) occurs in all three sentences. Those values in the feature vectors are also called the raw term frequencies: *tf (t,d)*—the number of times a term t occurs in a document *d*."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 8,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[[0 1 1 1 0 1 0]\n",
- " [0 1 0 0 1 1 1]\n",
- " [1 2 1 1 1 2 1]]\n"
+ "[[0 1 0 1 1 0 1 0 0]\n",
+ " [0 1 0 0 0 1 1 0 1]\n",
+ " [2 3 2 1 1 1 2 1 1]]\n"
]
}
],
@@ -365,7 +449,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 9,
"metadata": {
"collapsed": true
},
@@ -374,20 +458,36 @@
"np.set_printoptions(precision=2)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we are analyzing text data, we often encounter words that occur across multiple documents from both classes. Those frequently occurring words typically don't contain useful or discriminatory information. In this subsection, we will learn about a useful technique called term frequency-inverse document frequency (tf-idf) that can be used to downweight those frequently occurring words in the feature vectors. The tf-idf can be de ned as the product of the term frequency and the inverse document frequency:\n",
+ "\n",
+ "$$\\text{tf-idf}(t,d)=\\text{tf (t,d)}\\times \\text{idf}(t,d)$$\n",
+ "\n",
+ "Here the tf(t, d) is the term frequency that we introduced in the previous section,\n",
+ "and the inverse document frequency *idf(t, d)* can be calculated as:\n",
+ "\n",
+ "$$\\text{idf}(t,d) = \\text{log}\\frac{n_d}{1+\\text{df}(d, t)},$$\n",
+ "\n",
+ "where $n_d$ is the total number of documents, and *df(d, t)* is the number of documents *d* that contain the term *t*. Note that adding the constant 1 to the denominator is optional and serves the purpose of assigning a non-zero value to terms that occur in all training samples; the log is used to ensure that low document frequencies are not given too much weight.\n",
+ "\n",
+ "Scikit-learn implements yet another transformer, the `TfidfTransformer`, that takes the raw term frequencies from `CountVectorizer` as input and transforms them into tf-idfs:"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 12,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[[ 0. 0.43 0.56 0.56 0. 0.43 0. ]\n",
- " [ 0. 0.43 0. 0. 0.56 0.43 0.56]\n",
- " [ 0.4 0.48 0.31 0.31 0.31 0.48 0.31]]\n"
+ "[[ 0. 0.43 0. 0.56 0.56 0. 0.43 0. 0. ]\n",
+ " [ 0. 0.43 0. 0. 0. 0.56 0.43 0. 0.56]\n",
+ " [ 0.5 0.45 0.5 0.19 0.19 0.19 0.3 0.25 0.19]]\n"
]
}
],
@@ -398,43 +498,108 @@
"print(tfidf.fit_transform(count.fit_transform(docs)).toarray())"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we saw in the previous subsection, the word is had the largest term frequency in the 3rd document, being the most frequently occurring word. However, after transforming the same feature vector into tf-idfs, we see that the word is is\n",
+ "now associated with a relatively small tf-idf (0.45) in document 3 since it is\n",
+ "also contained in documents 1 and 2 and thus is unlikely to contain any useful, discriminatory information.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "However, if we'd manually calculated the tf-idfs of the individual terms in our feature vectors, we'd have noticed that the `TfidfTransformer` calculates the tf-idfs slightly differently compared to the standard textbook equations that we de ned earlier. The equations for the idf and tf-idf that were implemented in scikit-learn are:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\\text{idf} (t,d) = log\\frac{1 + n_d}{1 + \\text{df}(d, t)}$$\n",
+ "\n",
+ "The tf-idf equation that was implemented in scikit-learn is as follows:\n",
+ "\n",
+ "$$\\text{tf-idf}(t,d) = \\text{tf}(t,d) \\times (\\text{idf}(t,d)+1)$$\n",
+ "\n",
+ "While it is also more typical to normalize the raw term frequencies before calculating the tf-idfs, the `TfidfTransformer` normalizes the tf-idfs directly.\n",
+ "\n",
+ "By default (`norm='l2'`), scikit-learn's TfidfTransformer applies the L2-normalization, which returns a vector of length 1 by dividing an un-normalized feature vector *v* by its L2-norm:\n",
+ "\n",
+ "$$v_{\\text{norm}} = \\frac{v}{||v||_2} = \\frac{v}{\\sqrt{v_{1}^{2} + v_{2}^{2} + \\dots + v_{n}^{2}}} = \\frac{v}{\\big (\\sum_{i=1}^{n} v_{i}^{2}\\big)^\\frac{1}{2}}$$\n",
+ "\n",
+ "To make sure that we understand how TfidfTransformer works, let us walk\n",
+ "through an example and calculate the tf-idf of the word is in the 3rd document.\n",
+ "\n",
+ "The word is has a term frequency of 3 (tf = 3) in document 3, and the document frequency of this term is 3 since the term is occurs in all three documents (df = 3). Thus, we can calculate the idf as follows:\n",
+ "\n",
+ "$$\\text{idf}(\"is\", d3) = log \\frac{1+3}{1+3} = 0$$\n",
+ "\n",
+ "Now in order to calculate the tf-idf, we simply need to add 1 to the inverse document frequency and multiply it by the term frequency:\n",
+ "\n",
+ "$$\\text{tf-idf}(\"is\",d3)= 3 \\times (0+1) = 3$$"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 13,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "tf-idf of term \"is\" = 2.00\n"
+ "tf-idf of term \"is\" = 3.00\n"
]
}
],
"source": [
- "tf_is = 2 \n",
+ "tf_is = 3\n",
"n_docs = 3\n",
- "idf_is = np.log((n_docs+1) / (3+1) )\n",
+ "idf_is = np.log((n_docs+1) / (3+1))\n",
"tfidf_is = tf_is * (idf_is + 1)\n",
"print('tf-idf of term \"is\" = %.2f' % tfidf_is)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we repeated these calculations for all terms in the 3rd document, we'd obtain the following tf-idf vectors: [3.39, 3.0, 3.39, 1.29, 1.29, 1.29, 2.0 , 1.69, 1.29]. However, we notice that the values in this feature vector are different from the values that we obtained from the TfidfTransformer that we used previously. The nal step that we are missing in this tf-idf calculation is the L2-normalization, which can be applied as follows:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\\text{tfi-df}_{norm} = \\frac{[3.39, 3.0, 3.39, 1.29, 1.29, 1.29, 2.0 , 1.69, 1.29]}{\\sqrt{[3.39^2, 3.0^2, 3.39^2, 1.29^2, 1.29^2, 1.29^2, 2.0^2 , 1.69^2, 1.29^2]}}$$\n",
+ "\n",
+ "$$=[0.5, 0.45, 0.5, 0.19, 0.19, 0.19, 0.3, 0.25, 0.19]$$\n",
+ "\n",
+ "$$\\Rightarrow \\text{tfi-df}_{norm}(\"is\", d3) = 0.45$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see, the results match the results returned by scikit-learn's `TfidfTransformer` (below). Since we now understand how tf-idfs are calculated, let us proceed to the next sections and apply those concepts to the movie review dataset."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 14,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([ 1.69, 2. , 1.29, 1.29, 1.29, 2. , 1.29])"
+ "array([ 3.39, 3. , 3.39, 1.29, 1.29, 1.29, 2. , 1.69, 1.29])"
]
},
- "execution_count": 8,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -447,18 +612,16 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 15,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([ 0.4 , 0.48, 0.31, 0.31, 0.31, 0.48, 0.31])"
+ "array([ 0.5 , 0.45, 0.5 , 0.19, 0.19, 0.19, 0.3 , 0.25, 0.19])"
]
},
- "execution_count": 9,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -484,10 +647,8 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 16,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -495,7 +656,7 @@
"'is seven.
\r\n",
+ "\r\n",
+ " \r\n",
+ ""
+ ]
+ }
+ ],
+ "source": [
+ "!cat ./movieclassifier/templates/thanks.html"
]
},
{
@@ -829,10 +1217,8 @@
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 25,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -841,7 +1227,7 @@
""
]
},
- "execution_count": 14,
+ "execution_count": 25,
"metadata": {
"image/png": {
"width": 600
@@ -851,7 +1237,7 @@
}
],
"source": [
- "Image(filename='./images/09_08.png', width=600) "
+ "Image(filename='../images/09_08.png', width=600) "
]
},
{
@@ -876,18 +1262,6 @@
"Change current directory to `movieclassifier`:"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import os\n",
- "os.chdir('movieclassifier')"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -897,10 +1271,8 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 26,
+ "metadata": {},
"outputs": [],
"source": [
"import pickle\n",
@@ -940,10 +1312,8 @@
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 27,
+ "metadata": {},
"outputs": [],
"source": [
"cur_dir = '.'\n",
@@ -968,6 +1338,66 @@
"# , protocol=4)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "import pickle\r\n",
+ "import sqlite3\r\n",
+ "import numpy as np\r\n",
+ "import os\r\n",
+ "\r\n",
+ "# import HashingVectorizer from local dir\r\n",
+ "from vectorizer import vect\r\n",
+ "\r\n",
+ "def update_model(db_path, model, batch_size=10000):\r\n",
+ "\r\n",
+ " conn = sqlite3.connect(db_path)\r\n",
+ " c = conn.cursor()\r\n",
+ " c.execute('SELECT * from review_db')\r\n",
+ "\r\n",
+ " results = c.fetchmany(batch_size)\r\n",
+ " while results:\r\n",
+ " data = np.array(results)\r\n",
+ " X = data[:, 0]\r\n",
+ " y = data[:, 1].astype(int)\r\n",
+ "\r\n",
+ " classes = np.array([0, 1])\r\n",
+ " X_train = vect.transform(X)\r\n",
+ " model.partial_fit(X_train, y, classes=classes)\r\n",
+ " results = c.fetchmany(batch_size)\r\n",
+ "\r\n",
+ " conn.close()\r\n",
+ " return model\r\n",
+ "\r\n",
+ "cur_dir = os.path.dirname(__file__)\r\n",
+ "\r\n",
+ "clf = pickle.load(open(os.path.join(cur_dir,\r\n",
+ " 'pkl_objects',\r\n",
+ " 'classifier.pkl'), 'rb'))\r\n",
+ "db = os.path.join(cur_dir, 'reviews.sqlite')\r\n",
+ "\r\n",
+ "clf = update_model(db_path=db, model=clf, batch_size=10000)\r\n",
+ "\r\n",
+ "# Uncomment the following lines if you are sure that\r\n",
+ "# you want to update your classifier.pkl file\r\n",
+ "# permanently.\r\n",
+ "\r\n",
+ "# pickle.dump(clf, open(os.path.join(cur_dir,\r\n",
+ "# 'pkl_objects', 'classifier.pkl'), 'wb')\r\n",
+ "# , protocol=4)\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cat ./movieclassifier_with_update/update.py"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -990,6 +1420,7 @@
"metadata": {},
"source": [
" \n",
+ "...\n",
" "
]
}
@@ -1010,9 +1441,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.4.3"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/code/ch09/movieclassifier/pkl_objects/classifier.pkl b/code/ch09/movieclassifier/pkl_objects/classifier.pkl
index 28aca72a..844975ad 100644
Binary files a/code/ch09/movieclassifier/pkl_objects/classifier.pkl and b/code/ch09/movieclassifier/pkl_objects/classifier.pkl differ
diff --git a/code/ch09/movieclassifier/pkl_objects/stopwords.pkl b/code/ch09/movieclassifier/pkl_objects/stopwords.pkl
index 91be340d..f1b186fa 100644
Binary files a/code/ch09/movieclassifier/pkl_objects/stopwords.pkl and b/code/ch09/movieclassifier/pkl_objects/stopwords.pkl differ
diff --git a/code/ch09/movieclassifier/reviews.sqlite b/code/ch09/movieclassifier/reviews.sqlite
index e72271b2..5eb25271 100644
Binary files a/code/ch09/movieclassifier/reviews.sqlite and b/code/ch09/movieclassifier/reviews.sqlite differ
diff --git a/code/ch09/movieclassifier/static/style.css b/code/ch09/movieclassifier/static/style.css
index 8abda7b6..5c35cf02 100644
--- a/code/ch09/movieclassifier/static/style.css
+++ b/code/ch09/movieclassifier/static/style.css
@@ -2,6 +2,6 @@ body{
width:600px;
}
-#button{
+.button{
padding-top: 20px;
-}
\ No newline at end of file
+}
diff --git a/code/ch09/movieclassifier/vectorizer.py b/code/ch09/movieclassifier/vectorizer.py
index 00d6e745..a4017c20 100644
--- a/code/ch09/movieclassifier/vectorizer.py
+++ b/code/ch09/movieclassifier/vectorizer.py
@@ -5,8 +5,8 @@
cur_dir = os.path.dirname(__file__)
stop = pickle.load(open(
- os.path.join(cur_dir,
- 'pkl_objects',
+ os.path.join(cur_dir,
+ 'pkl_objects',
'stopwords.pkl'), 'rb'))
def tokenizer(text):
@@ -21,4 +21,4 @@ def tokenizer(text):
vect = HashingVectorizer(decode_error='ignore',
n_features=2**21,
preprocessor=None,
- tokenizer=tokenizer)
+ tokenizer=tokenizer)
\ No newline at end of file
diff --git a/code/ch09/movieclassifier_with_update/static/style.css b/code/ch09/movieclassifier_with_update/static/style.css
index 8abda7b6..5c35cf02 100644
--- a/code/ch09/movieclassifier_with_update/static/style.css
+++ b/code/ch09/movieclassifier_with_update/static/style.css
@@ -2,6 +2,6 @@ body{
width:600px;
}
-#button{
+.button{
padding-top: 20px;
-}
\ No newline at end of file
+}
diff --git a/code/ch09/movieclassifier_with_update/update.py b/code/ch09/movieclassifier_with_update/update.py
index 58a904c9..d7eec9e7 100644
--- a/code/ch09/movieclassifier_with_update/update.py
+++ b/code/ch09/movieclassifier_with_update/update.py
@@ -20,20 +20,20 @@ def update_model(db_path, model, batch_size=10000):
classes = np.array([0, 1])
X_train = vect.transform(X)
- clf.partial_fit(X_train, y, classes=classes)
+ model.partial_fit(X_train, y, classes=classes)
results = c.fetchmany(batch_size)
conn.close()
- return None
+ return model
cur_dir = os.path.dirname(__file__)
clf = pickle.load(open(os.path.join(cur_dir,
- 'pkl_objects',
- 'classifier.pkl'), 'rb'))
+ 'pkl_objects',
+ 'classifier.pkl'), 'rb'))
db = os.path.join(cur_dir, 'reviews.sqlite')
-update_model(db_path=db, model=clf, batch_size=10000)
+clf = update_model(db_path=db, model=clf, batch_size=10000)
# Uncomment the following lines if you are sure that
# you want to update your classifier.pkl file
diff --git a/code/ch09/pickle-test-scripts/README.md b/code/ch09/pickle-test-scripts/README.md
new file mode 100644
index 00000000..c69e84dd
--- /dev/null
+++ b/code/ch09/pickle-test-scripts/README.md
@@ -0,0 +1,21 @@
+**Note**
+
+The pickling-section may be a bit tricky so that I included simpler test scripts in this directory (pickle-test-scripts/) to check if your environment is set up correctly. Basically, it is just a trimmed-down version of the relevant sections from Ch08, including a very small movie_review_data subset.
+
+Executing
+
+ python pickle-dump-test.py
+
+will train a small classification model from the `movie_data_small.csv` and create the 2 pickle files
+
+ stopwords.pkl
+ classifier.pkl
+
+Next, if you execute
+
+ python pickle-load-test.py
+
+You should see the following 2 lines as output:
+
+ Prediction: positive
+ Probability: 85.71%
\ No newline at end of file
diff --git a/code/ch09/pickle-test-scripts/movie_data_small.csv b/code/ch09/pickle-test-scripts/movie_data_small.csv
new file mode 100644
index 00000000..675ab1cf
--- /dev/null
+++ b/code/ch09/pickle-test-scripts/movie_data_small.csv
@@ -0,0 +1,102 @@
+review,sentiment
+"In 1974, the teenager Martha Moxley (Maggie Grace) moves to the high-class area of Belle Haven, Greenwich, Connecticut. On the Mischief Night, eve of Halloween, she was murdered in the backyard of her house and her murder remained unsolved. Twenty-two years later, the writer Mark Fuhrman (Christopher Meloni), who is a former LA detective that has fallen in disgrace for perjury in O.J. Simpson trial and moved to Idaho, decides to investigate the case with his partner Stephen Weeks (Andrew Mitchell) with the purpose of writing a book. The locals squirm and do not welcome them, but with the support of the retired detective Steve Carroll (Robert Forster) that was in charge of the investigation in the 70's, they discover the criminal and a net of power and money to cover the murder.
""Murder in Greenwich"" is a good TV movie, with the true story of a murder of a fifteen years old girl that was committed by a wealthy teenager whose mother was a Kennedy. The powerful and rich family used their influence to cover the murder for more than twenty years. However, a snoopy detective and convicted perjurer in disgrace was able to disclose how the hideous crime was committed. The screenplay shows the investigation of Mark and the last days of Martha in parallel, but there is a lack of the emotion in the dramatization. My vote is seven.
Title (Brazil): Not Available",1
+"OK... so... I really like Kris Kristofferson and his usual easy going delivery of lines in his movies. Age has helped him with his soft spoken low energy style and he will steal a scene effortlessly. But, Disappearance is his misstep. Holy Moly, this was a bad movie!
I must give kudos to the cinematography and and the actors, including Kris, for trying their darndest to make sense from this goofy, confusing story! None of it made sense and Kris probably didn't understand it either and he was just going through the motions hoping someone would come up to him and tell him what it was all about!
I don't care that everyone on this movie was doing out of love for the project, or some such nonsense... I've seen low budget movies that had a plot for goodness sake! This had none, zilcho, nada, zippo, empty of reason... a complete waste of good talent, scenery and celluloid!
I rented this piece of garbage for a buck, and I want my money back! I want my 2 hours back I invested on this Grade F waste of my time! Don't watch this movie, or waste 1 minute of your valuable time while passing through a room where it's playing or even open up the case that is holding the DVD! Believe me, you'll thank me for the advice!",0
+"***SPOILER*** Do not read this, if you think about watching that movie, although it would be a waste of time. (By the way: The plot is so predictable that it does not make any difference if you read this or not anyway)
If you are wondering whether to see ""Coyote Ugly"" or not: don't! It's not worth either the money for the ticket or the VHS / DVD. A typical ""Chick-Feel-Good-Flick"", one could say. The plot itself is as shallow as it can be, a ridiculous and uncritical version of the American Dream. The young good-looking girl from a small town becoming a big success in New York. The few desperate attempts of giving the movie any depth fail, such as the ""tragic"" accident of the father, the ""difficulties"" of Violet's relationship with her boyfriend, and so on. McNally (Director) tries to arouse the audience's pity and sadness put does not have any chance to succeed in this attempt due to the bad script and the shallow acting. Especially Piper Perabo completely fails in convincing one of ""Jersey's"" fear of singing in front of an audience. The only good (and quite funny thing) about ""Coyote Ugly"" is John Goodman, who represents the small ray of hope of this movie.
I was very astonished, that Jerry Bruckheimer produced this movie. First ""Gone In 60 Seconds"" and now this... what happened to great movies like ""The Rock"" and ""Con Air""? THAT was true Bruckheimer stuff.
If you are looking for a superficial movie with good looking women just to have a relaxed evening, you should better go and see ""Charlie's Angels"" (it's much more funny, entertaining and self-ironic) instead of this flick.
Two thumbs down (3 out of 10).",0
+hi for all the people who have seen this wonderful movie im sure thet you would have liked it as much as i. i love the songs once you have seen the show you can sing along as though you are part of the show singing and dancing . dancing and singing. the song ONE is an all time fave musical song too and the strutters at the end with the mirror its so oh you have to watch this one,1
+"I recently bought the DVD, forgetting just how much I hated the movie version of ""A Chorus Line."" Every change the director Attenborough made to the story failed.
By making the Director-Cassie relationship so prominent, the entire ensemble-premise of the musical sails out the window.
Some of the musical numbers are sped up and rushed. The show's hit song gets the entire meaning shattered when it is given to Cassie's character.
The overall staging is very self-conscious.
The only reason I give it a 2, is because a few of the great numbers are still able to be enjoyed despite the film's attempt to squeeze every bit of joy and spontaneity out of it.",0
+"Leave it to Braik to put on a good show. Finally he and Zorak are living their own lives outside of Spac Ghost Coast To Coast. I have to say that I love both of these shows a whole lot. They are completely what started Adult Swim. Brak made it big with an album that came out in the year 2000. It may not have been platinum, but his show was really popular to tons of people out there that love Adult Swims shows. I have to say that out of all the Adult Swim shows with no plot, this has to be the one with the most none plot ever made. That is why I like it so much, it is just such a classic in the Adult Swim history. I believe this is just such a great show, if you don't like it. Hey there were tons who hated it and tons who loved it.",1
+"Nathan Detroit (Frank Sinatra) is the manager of the New York's longest- established floating craps game, and he needs $1000 to secure a new location. Confident of his odds, he bets the city's highest-roller, Sky Masterson (Marlon Brando), that he can't woo uptight missionary Sarah Brown (Jean Simmons). 'Guys and Dolls (1955)' is such a great musical because it deftly blends the contrasting styles of film and stage. During a dazzling opening sequence, crowds of pedestrians move in rhythm, stopping and starting as though responding to backstage cues. Even the walking movements themselves are stylised and angular, halfway between a walk and a dance. Mankiewicz's New York City is a glittering flurry of art deco colour and movement, a fantasy world so completely removed from reality that even the business of underground gambling and criminal thuggery seems perfectly genial.
As I write this review, I've just received word that Jean Simmons has passed away, age 80. This, unbelievably, was the first time I'd seen her in a film, yet she dazzled me from the beginning. Her idealistic and sexually-repressed Sarah comes out of her shell following an alcohol binge in Havana, letting loose with an adorably playful rendition of ""If I Were A Bell."" Even though both Simmons and Brando were non-singers, producer Sam Goldwyn decided not to dub their vocals, contending that ""maybe you don't sound so good, but at least it's you."" Despite Goldwyn's backhanded confidence, the pair both do well to carry entire musical numbers themselves. Simmons suggests the same child-like liveliness that Audrey Hepburn might have brought to the role, and Brando exudes such self-assurance and charisma that it doesn't matter that his singing voice isn't quite there.",1
+"To understand ""Crash Course"" in the right context, you must understand the 80's in TV. Most TV shows didn't have any point. The sitcom outpopulated the drama at least 3 to 1. They were still figuring out where the lines were so that they could cross them. (TV Shows like ""Hail to the Chief"" was quite the bold step!) This made-for-TV movie ""Crash Course"" featured an All-Star cast, bringing together members from all the 80's classics: ""227"", ""Family Ties"", ""Who's the Boss?"", et al. Directors must've had a certain penchant for those all-star movies then. Still, this movie offered very light fare and a simplistic view of heroism and maturity. And that's not bad sometimes. Viva Soleil Moon Frye.",1
+"I've been impressed with Chavez's stance against globalisation for sometime now, but it wasn't until I saw the film at the Amsterdam documentary international film festival that I realize what he has really achieved. This film tells the story of coup/conspiracy by Venezuela's elite, the oil companies and oil loving corrupt western governments, to remove democratically elected president Chavez, and return Venezuela back to a brutal dictatorship. This film is must for anyone who believes in freedom and justice, and is also a lesson to the rest of world ! I commend the people of Venezuela for taking matter into their own hands, and saving their country from the likes of Halliburton and the Bush regime.",1
+"This movie is directed by Renny Harlin the finnish miracle. Stallone is Gabe Walker. Cat and Mouse on the mountains with ruthless terrorists. Renny Harlin knows how to direct actionmovie. Stallone needed this role to get back on track. Snowy mountain is very good place for action movie and who is better to direct movie where is snow, ice, cold and bad weather than finnish man. Action is good! Music in the film is spectacular. The bad guy is John Litghow, other stars Micheal Rooker ( The portrait of serialkiller), Janine Turner ( Strong Medicine). The is placed in beautiful place and it is very exciting movie. Overall good movie ****/*****
Remember Extreme ääliöt: special collectors edition, with good extras. Comig soon in Finland straight to video.",1
+"I once lived in the u.p and let me tell you what. I didn't have the foggyest idea what the heck this ""bear walk "" is. I never heard of it the whole 10 years I was up there. It was really funny in the beginning but went down hill quickly.",0
+"Hidden Frontier is notable for being the longest running internet-based Star Trek fan series. While the production quality is not on a par with fan productions like Starship Exeter, or New Voyages, Hidden Frontier concentrates largely on story, and in that regard it does very well indeed.
Hidden Frontier has no physical sets; instead actors are filmed against a greenscreen, and the backgrounds inserted digitally. One of Hidden Frontier's greatest achievements is the sheer volume of work they have produced. One of the ways in which this is achieved is by inserting the virtual sets at the time of filming, instead of in post-production. While this does save a great deal of time, it's also worth noting that the quality of the resultant footage is not as high as if it had been produced in post-production, though it still serves its purpose.
While it may not be everyone's cup of tea, Hidden Frontier is well worth a shot, though you might be best to start off watching the third season, since this is where the producers really start to hit their stride.",1
+"It's a while ago, that I have seen Sleuth (1972) with two great actors Michael Caine and Laurence Olivier. Michael Caine is back, but he is now the husband and Jude Law the lover of his wife. The story is still the same and it's a fantastic play.
During the movie I always had the feeling to watch a play. That's one of the reasons I dislike this remake of a classic. When I watch a movie adapted a play I still must feel to see a movie and not just a play. Director Kenneth Brannigan did some marvelous movies in the past, but this time he missed. Another reason was the look of the movie. The design was modern, stylish clean, uncomfortable and cold. I never got the feeling that somebody ever lived in that house. The photography wasn't bad, but the lightening was awful. Sometimes there was blue light, dark, green light, to round it up not friendly for eyes.
The acting was really good. Michael Caine's and Jude Law's perform at their best. I really would like to see these 2 guys playing together on stage. But I have to confess I never was a fan of Jude Law. The weakest part was the mid part. I remember that in the original that this part was still very mysterious and just marvelous directed. I tried to watch it twice and always in the mid part I felt asleep. The end part is better and more interesting. Sleuth (2007) isn't awful, but it seems to be more a movie for critics than for the audience. Sleuth (1972) is still a masterpiece and much more entertaining than Sleuth (2007).",0
+"What is it about the French? First, they (apparently) like Jerry Lewis a lot more than the US does. Second, they (seem) to like Edgar Allan Poe's work more than just about anyone else does. It's got to be the ""Beaudelaire effect"".
Don't get me wrong...I'm a Poe fan myself. But this trilogy manages to make three of Poe's below-average stories into...well, I'm not sure what they're made into.
""Toby Dammit"" is a fine Fellini film, but it has nothing to do with Poe's story, at least in terms of theme. It's enjoyable on the first viewing. Terence Stamp does a good job with an interesting role. However, it has nothing whatsoever to do with Poe or spirits of the dead.
""Metzergenstein"" is a big mess. How did Vadim's films get produced? It's just awful...not even up to amateur film school standards. Depending on the DVD menu you have, try to skip it and save your time.
""William Wilson"" is actually the segment that is most faithful to Poe's work. It does not have much style, though, even if it includes the strangest snowball fight I think that I have ever seen on film. (It looks like the boys are throwing tissues, or maybe handkerchiefs, that have been rolled up into balls.)
My advice is to skip ""Metzergenstein"", watch ""William Wilson"", and then, if you're a Fellini fan (I'm not) keep ""Toby Dammit"" on while you cook dinner or make a snack.",0
+"This very strange movie is unlike anything made in the west at the time. With its tumultuous emotions and net of visions, dreams, and startling images, its effect is both beautiful and unsettling. The actors are choreographed more like dance than acting. It contains the only dream sequence I know of that actually resembles a real nightmare (sorry, Dali fans).",1
+"I saw this movie on the strength of the single positive review and I can only imagine that guy is a shill.
The acting of the female lead is actually quite good, but the entire film is just so excruciatingly boring I could hardly bear to sit through it. This is the very definition of dullness.
So far, this film is rated as 8 out of 10 on 7 votes. That must mean the director, director's girlfriend, producer, actress and drinking buddies have given their own film a 10.
For the rest of you, who simply want to be entertained or enjoy a good story, avoid this.
This man on the street shall give it a 2 out of 10.
FDA note: while this movie can be used as an aide to obtaining a good nights sleep, no medicinal value is implied or offered.",0
+"There are some great philosophical questions. What is the purpose of life? What happens when we die? And WHY DO THEY MAKE MOVIES THIS BAD??? The premise is absurd. Thre acting is one dimensional. The special effects are overdone. And the movie is one unending gun battle among some of the lousiest shots Hollywood ever produced. But then, if they had been good shots, everybody would have been dead in the first five minutes and there would be no movie. Too bad it didn't happen that way. Tempted to turn it off several times, I stuck with it to see just how bad it could get. Glad I did because (SPOILER?) the last line is the crowning stupidity of the whole dopey, dismal scenario.It is not even worthy of second feature status at a third rate drive-in in off season. Apart from the general awfulness of the film, I worry deeply about its impact on young audiences. The Americans crank out crap like this and then wonder why events like Columbine happen. This is truly banal cinema on a Brobdingnagian scale!",0
+"I was cast as the Surfer Dude in the beach scenes. Almost got cast as the muscle guy, since the real muscle guy was really really late that day. Pauly had my brother and I (the skateboarder in front of the tattoo place) do some vj stuff in between takes live from Venice since he was still doing his MTV thing. This movie is really good as well. Would it have made my top 100 if I wasn't in it........?",1
+"I had high hopes for this one until they changed the name to 'The Shepherd : Border Patrol, the lamest movie name ever, what was wrong with just 'The Shepherd'. This is a by the numbers action flick that tips its hat at many classic Van Damme films. There is a nice bit of action in a bar which reminded me of hard target and universal soldier but directed with no intensity or flair which is a shame. There is one great line about 'being p*ss drunk and carrying a rabbit' and some OK action scenes let down by the cheapness of it all. A lot of the times the dialogue doesn't match the characters mouth and the stunt men fall down dead a split second before even being shot. The end fight is one of the better Van Damme fights except the Director tries to go a bit too John Woo and fails also introducing flashbacks which no one really cares about just gets in the way of the action which is the whole point of a van Damme film.
Not good, not bad, just average generic action.",0
+"Set in and near a poor working class town in the mountains of rural Italy, it's a story of madness. The landscape may be quite picturesque, but there's madness herein, concealed behind the mask of a person who seems outwardly normal. This person kills little children.
In style and tone this film resembles Dario Argento's famous Italian giallos, those fascinating whodunit horror films, except that Argento's films are much better looking. Still, the visuals in Fulci's ""Don't Torture A Duckling"" are competent, with some interesting compositions and lighting. Lightning and thunder on a rainy night enhances suspense in one sequence wherein one of the ""ducklings"" is vulnerably alone.
In one sequence the gore is a bit overdone. But this is no slasher film. A legitimate theme undergirds the story. And that theme is that madness can take many unexpected forms, not just the obvious delusions of people who practice voodoo or black magic.
Plenty of red herrings render the puzzle solution difficult if the viewer doesn't assume an agenda on the part of the director. Don't dismiss someone who might not seem to be a suspect. The twist near the end provides good misdirection. However, in one scene midway through, a line of dialogue could have been added to clarify the relationship between two characters, one of whom is the murderer. The film's finale takes place on a beautiful mountaintop with the wind whistling in the background. We see flashbacks to clues and get insights into the killer's mindset.
I don't care for the film's widescreen projection. But background music is effective, and ranges from jarringly creepy at the beginning to low-key jazz, to indigenous Italian songs. Acting is generally average, though in a couple of cases, it's a bit overdone.
Though not as visually brilliant as Argento's giallos, ""Don't Torture A Duckling"" nevertheless is a fine film, one that contains a thematic storyline and enough of a whodunit puzzle to interest most viewers who like thrillers and murder mysteries.",1
+"Opulent sets and sumptuous costumes well photographed by Theodor Sparkuhl, and a good (not great) performance by Jannings as Henry cannot overcome poor writing and static camera-work. Henny Porten chews the scenery as Anne.
It's all very beautiful; but it's all surface and no depth. The melodramatic tale of a woman wronged made it a hit in America where the expressionistic ""The Cabinet of Dr. Caligari"" flopped in the same year (1920), proving that what is popular is not what endures. Lubitsch would be remembered for his lively comedies, not sterile spectacles like this.",0
+"i saw the film and i got screwed, because the film was foolish and boring. i thought ram gopal varma will justify his work but unfortunately he failed and the whole film got spoiled and they spoiled ""sholay"". the cast and crew was bad. the whole theater slept while watching the movie some people ran away in the middle. amithab bachan's acting is poor, i thought this movie will be greatest hit of the year but this film will be the greatest flop of the year,sure. nobody did justice to their work, including Ajay devagan. this film don't deserve any audiences. i bet that this film will flop.
""FINALLY THIS MOVIE SUCKS""",0
+"I'm getting a little tired of people misusing God's name to perpetuate their own bigoted view on the world. Well I don't dismiss the idea of Armageddon, or the coming of the anti-Christ, I do dismiss the idea that only certain people who live truly good lives(They seem to be mostly white Christian children) will go to Heaven, while the rest of us must suffer through a millenia of Hell on Earth, just because we weren't good enough. God may be a judge, but I don't think He is going to measure every level of goodness. Give the Creator some credit.",0
+"How offensive! Those who liked this movie have probably never opened a bible. I can imagine those at NBC saying, ""OK. Let's make a movie to appease those pesky Christians, but they'll never know the difference if we don't have anything factual or in the correct chronological order."" Well, they were wrong. Anybody associated with this atrocity needs to find a church and repent for their involvement in this blasphemous atrocity. I only gave this movie a 1 because I couldn't give it a 0.",0
+"What else can you say about this movie,except that it's plain awful.Tina Louise and Adam West are the reasons why to see this,but,that's it,but their talents are wasted in this junk.I think that they used a double in some of Adam's scenes,like when he's running because you can't see his face.If Adam was embarrassed in being in Zombie Nightmare,just think what he must've felt about appearing in this??? If it was before or after,I'm not sure,but,still,Zombie Nightmare is a classic(check out the Mystery Science Theater 3000 version first and last)compared to this.The gang is very annoying and over-acting by some of the actors.A rip-off of The Wild One starring Marlon Brando,of course.Tina looks stunning though.I hope her and Adam got a good paycheck!! Pass!",0
+"Certain aspects of Punishment Park are less than perfect, specifically some of the acting. However I feel that this is probably the most important movie of the ""war on terror"" era. I grew up hating hippies and in some respects I still do. It wasn't until the United States was started down the path of an unnecessary and deceitful war in Iraq that I began to see the world through their eyes. I can feel what they must have felt. Although the film is somewhat dated, watching it brings those uncomfortable emotions about our present situation right to the surface. It's clear enough early in the film that Punishment Park is designed to be a concentration and death camp for all the ""unpatriotic"" elements of American society. This is certainly an exaggerated and extreme view of our polarized society, but it is CREDIBLE. At times I find myself believing that the USA could easily slip into fascism. As I watched this film I could only think about how I hear similar sentiments from people on both sides of the political spectrum almost daily. This movie is a raw, concentrated distillation of America's PRESENT political scene. I am both impressed and saddened that something this relevant (and yes, accurate) was filmed more than 30 years ago. If you take a more moderate view of the movie and choose to believe that this couldn't happen here, look more closely at Guantanamo Bay, some of our ""enemy combatants,"" the rumored CIA secret prisons and the many incidents similar to the one in Greensboro, NC in 1979 (8 full years AFTER the making of this movie).",1
+"First of all, I'd like to tell you that I'm into comics, anime, animation and such stuff. It is true that everyone has his own preferences, but you can trust me on this movie. I'll be objective. To begin with the story - it's OK. Follows the story line of the comic books as far as I'm familiar with them. But the animation... Well, it's not actually terrible, but it's definitely cheap and mediocre. It would be a lot better if they didn't try to imitate the anime style and sticked to the original comic book style drawings. If we pretend not to see the rare sloppy effects like fire and lightnings you could tell that the movie is made about 10 years ago and even more. Looks a little bit like the original Vampire Hunter D from 1985. Take a look at Heavy Metal FAKK 2000 for instance - 4 years ago they made a movie that looks a hell lot better! In addition to this the voice talents do nothing remarkable, the music is nothing special. So all in all - it lacks atmosphere. I watched it, but I cannot tell I really enjoyed it. It just does not capture you. There's plenty of blood and violence, but that does not impress me at all. May be it will be shocking for someone who was never watched more mature oriented animations and sees animated blood for the first time (is there anyone around?), but I don't think this is the audience for this movie. So they could add a little nudity and spice to it. The chicks around Lucifer were quite tasty, and hell, we have Lady Death herself! There are few sexy looks, but that's not enough. Instead of Bill Brown's music I think it would look better on a hard rock / heavy metal soundtrack. All in all - the movie isn't that bad, but if you want something better take the original Heavy Metal, Heavy Metal FAKK 2000, Ralph Bakshi's Fire and Ice or Wizards maybe. And of course - Vampire Hunter D: Bloodlust",0
+"You should not take what I am about to say lightly. I've seen many, many films and have reviewed a great deal of them, in print. So when I tell you that this film has the single funniest scene I have ever seen in a movie, you might want to listen to me. There's a lot of diversity of opinion as to what makes this INCREDIBLY stupid movie as funny as it is. And to those who just didn't get, well, I can't blame them, too much. The scene I speak of, comes at about the 30 minute mark and involves a dead convict shackled to John Candy. Up until that point, I had found the film dumb, confusing and it was beginning to lose me. When this scene came up, I laughed so hard, I peed my pants. No movie has ever done that to me before. When the project began, ""Going Berserk"" was supposed to be the SCTV movie. I remember it being announced. As time went on, the cast was whittled down To John Candy, Joe Flaherty & Eugene Levy. There also must have been a regime change at Universal, while it was being shot, because upon being released, it was shown in nearly ZERO theaters. When watching this a second time, I listened to the theme song (which actually flaunts how incomprehensible the plot is, in the lyrics), relaxed my logic nerve and figured out what was going on. Aside from the aforementioned routine, ""Going Berserk"" has many other hilarious scenes to recommend it. This is almost a 3 Stooges flick, except it's much funnier. Director David Steinberg has razor sharp timing, and he must have been laughing all through this. As for Candy, who's basically in charge here, he has NEVER been funnier. With all the plot devices and explanatory scenes thrown out the window, he absolutely runs wild. Flaherty and Levy follow him effortlessly. There is a plot, but it's a plot like ""Animal House"" had a plot, and yeah, the script is uneven, and a little slow to start. Once you know this, however, you can well appreciate the full SCTV style craziness that transpires. It IS stupid, but it's stupid on purpose, and you need to remember that when you see it. DO see it, and discover for yourself, if it has the funniest scene of all time in it.",1
+"I love the Jurassic Park movies, they are three of my all time favorite movies.
And I hate this game, if there was one game I wish I never own for the Super Nintendo was this one.
How can a game based on a classic movie be just too awful? And to make it worst, I was scare of this game when I was a kid.
How dumb was that but then again I was a kid when this game was first out.
The game play in this game is just odd. One minute it's a action game and then it's a shooter. What in the world is wrong with making up your mind when making a video game.
The Sound in the game is just terrible to listen.
The music is just too sick to listen to.
The Controllers in the game don't work most of the time.
Jurassic Park the game is just a waste of time and money and won't be a classic.
Avoid at all cost",0
+"The first series of Lost kicked off with a bang... literally and slowly decreased in pace. This may have put some viewers off and people who started to watch halfway through would either be bored or just plain confused.
I would advise people new to the world of Lost to simply watch from the beginning and don't get pt off by the slower episodes. The acting throughout is excellent but why have 5 series' planned... WHY??? All this means is that there will be no answers for at least 4 years, oh well, i'll keep watching if it keeps the tension up and dialogue flowing.",1
+"I first saw this movie on a local station on the Sunday afternoon horror show back around 1969 or 1970. Uncut. I was just a little kid at the time, but I loved it and wasn't really that scared by it. I thought it had such a cool and highly original storyline. Thinking back, I'm still surprised that it was shown during the day on T.V. uncut in those years. I've sought out this film ever since, seen it over and over again, and always loved it. One would think John Waters would have idolized this film. It's got to be not only a scary film, but one of the sleaziest, trashiest films ever made at that time. And surprisingly, you don't hear about this one as having the cult following that a movie such as ""Blood Feast"" or ""The Hills Have Eyes"" have acquired over the years. It has a cult following, but it should have really become a cult classic, in my opinion. As far as I know, this came out a little before Blood Feast came out, making this probably one of the first true ""gore"" films. In fact, this movie has elements of Hershell Gordon Lewis AND a little Russ Meyer thrown in for good measure.
Anyway, I recommend this for anyone who likes trashy, sleazy, black and white horror films from the early '60's (I think the date at the end of it read 1960).",1
+"I have read a lot of books in my short lifetime but this is by far the WORST!!! I just got done reading this worthless piece of trash and when I finished it I threw it across the room! I hated it and let me state the reasons! 1.The soldier dies. Why would the author make the soldier die?! Why couldn't she have kept him alive like a good love story author would do?! I deeply applaud Patty for trying to claw that FBI agent's eyes out.
2.Ruth get's fired. Ruth (the black housekeeper) get's fired and for no apparent reason too! She tried to comfort Patty and then Patty's SOB dad fires her for no good reason! Ruth and Anton and Patty were the only bright spots in the book. Oh and the grandparents too! 3. The perm. Yes. The perm. Now you people might think why would the perm upset you? Well here's why. Patty's mom asks the girl if she wants her hair done. Patty says no but the mom calls Mrs. Reeves (the horrible hairdresser) and tells her to give Patty a perm. Why on God's green earth would she do that?! Why would a mother ask her daughter if she wants a perm only to have her get a perm anyway! The mom always pretends that Patty has a say when she dosen't have a say at all!!! She should be given the ""Worst Mother of the Year Award"" for the stuff she dose to Patty. Thank God Ruth cut her perm off! 4. Discrimination, Racisem, and Prejudious. I hate the discrimination in this book. They use the word *beep* too much. Yes.I know that in those days blacks were free but had basically no rights but come on! Why teach todays children that word! It just teaches them how to discriminate people. Not only were blacks discriminated but the Chinese too. In the book people refer to Mr.Lee (a Chinese man) as ""The *beep* That is really despicable and last but not least... Jews and Nazies. I hate the town for spitting on a little girl. What was so wrong for her liking Anton. SHE IS A 12 YEAR OLD GIRL!!! It was just a crush. Like a 12 year old can really love a 22 year old. Come on! This isn't ""Lolita"". And ""Lolita"" is a good book not a piece of filth! I'm surprised that this movie isn't considered ""dirty"" like ""Lolita"" is.
5. Patty going to a reformatory. Patty should not have gone to that reformatory. Refirmitories are for thieves and murders, not innocent 12 year olds! The teacher or whatever she was called Patty an ungrateful, spoiled brat. Ungrateful spoiled brat my butt! Patty was not a spoiled brat because her father and mother never gave a rip about her! Patty should of got community service or something. She did nothing wrong. She just helped a friend.
6. Favortisem. The parents played favoritism with their children. Patty, their firstborn daughter is clearly the least favored while Sharon, the five year old brat is their favorite daughter. The dad says that he wanted to take Sharon to Hollywood but clearly forgets his other daughter.
7. The dad. I hated him! He was so mean Patty might as well had Hitler himself as her father. Her dad beats her for no apparent reason and the way he talks to her in the end will make you so mad you'll be caught thinking ""Patty would get better treatment in a concentration camp"".
Well there you have it folks. 7 reasons I hate this book. Instead of reading this book read ""The Diaries of Anne Frank"" or anything else because I warn you, it is very depressing and it will leave you really mad! The only reason it gets 4 stars is because of Anton, Patty, Ruth, and the grandparents!",0
+"The Shining starts with Jack Torrance (Jack Nicholson) driving to an isolated hotel named the 'Overlook' situated high in the Colorado mountains for an interview with it's manager Stuart Ullman (Barry Nelson) about becoming the Winter caretaker. Ullman tells Jack that he will be responsible for the basic upkeep of the hotel but will be almost totally isolated from the rest of the world for six months as the harsh Winter sets in. Together with his wife Wendy (Shelley Duvall) & young son Danny (Danny Lloyd) Jack moves into the hotel & at first everything seems fine, it's a beautiful hotel, absolutely huge & whatever they need is at their disposal. However the Overlook hotel has a murky past with a previous caretaker murdering his entire family before committing suicide & Danny has the ability to 'shine' which means he has psychic powers that let him see & hear things 'ordinary' people can't. As the days, weeks & months begin to pass Jack become more & more insane, Danny keeps 'seeing' things & people while Wendy becomes frantic as she doesn't have a clue what's happening to her family, as a heavy snowstorm leaves them trapped Jack finally loses it...
This English production was co-written, co-produced & directed by Stanley Kubrick & is a fine horror film. It appears that The Shining is another film that exists in two distinct different versions & the one I will be commenting on is the shorter European cut that runs just under 2 hours in length. The script by Kubrick & Diane Johnson, is based on the novel by Stephen King which I have not read so I can't compare them, goes for psychological horror rather than visual with only one murder during the entire film. There are very few character's in The Shining with Jack, Wendy & Danny the only ones that really matter, since the film concentrates on them almost exclusively you care for them, become involved with them & what they go through. The pace is somewhat slow but this is one film that didn't feel that long & keeps you interested throughout. On the negative side I don't think the reasoning behind Jack going crazy & wanting to kill his family was strong enough to convince me, the fact that Jack escapes from the freezer without any explanation bugs me & I don't know if I missed something but that ending didn't make any sense to me whatsoever, I'm still trying to work out what that picture is all about! There is very little in the way of violence or gore, a couple of rotten zombie ghosts & someone is killed with an axe but The Shining is a horror film that doesn't need to rely on blood & special effects as it has a gripping story. With a budget of about $19,000,000 The Shining is technically flawless as you would expect from an obsessive filmmaker such as Kubrick, the cinematography is brilliant with some fantastic free-flowing & smooth steadicam shots as the camera effortlessly follows the character's around the maze of corridors, the sets look absolutely real & instead of clichéd old haunted house themes like dark corners, basements & cobwebs Kubrick brings things right up-to-date with brightly lit corridors, massive open expansive spaces & a modern decor (well 80's modern, just check that red toilet out!). The acting is good from everyone involved although as usual in horror films the little kid is highly annoying & Nicholson seems crazy from the very start. The Shining is an absorbing film that I enjoyed watching although I'm not sure I'd watch it again anytime soon. For those looking for explosions & fancy special effects you will be disappointed, for those looking for a good haunted house type horror with a strong story I definitely think The Shining is for you, well worth a watch in my humble opinion.",1
+"The story is extremely unique.It's about these 2 pilots saving Earth from alien beings but they have to use a special speed that makes everything around them age rapidly.The whole series is about the pilots dealing with the loss of time,friends,and mentors.
The ending COULD have been fantastic.It started to end on a total down note and leave a real mark but instead ended on a super happy Disney note and annoyed me VERY bad.
The animation is decent for 89 but can't compare to nowadays.I have also heard many complain about the cheesiness of the nudity.I actually found it to be somewhat decent.The nudity for the most part was warranted except in episode 2 where there was an excess.
Overall it deserves a look but the ending keeps it from being a classic.",1
+"I guess those who have been in a one-sided relationship of some sort before will be able identify with the lead character Minako (Yuko Tanaka), a 50 year old woman who is still in the pink of good health, as demonstrated by her daily, grinding routine of waking up extremely early in the morning to prepare for her milk delivery work, where she has to lug bottles of Megmilk in a bag in a route around her town like clockwork, to exchange empty bottles for full ones, and to collect payment and issue receipt. And there's always be that one delivery stop that's right at the top, needing to scale a long flight of stairs in order to achieve customer satisfaction.
And peculiar enough, that stop happened to be a stop delivering to a man with whom she has been in love with for almost all her teenage to adult life, and not having the product appreciated, but poured down the sink. Having gone to the same school, we see that they're not talking to each other, and in their daily life always seem so close physically, but yet so far away. There's no eye contact, save for cursory glances by chance, and little acknowledgement of each other's existence. We learn that they share a past that probably destroyed all notions of being together, where clear attraction between the two was hampered from developing further by the earlier generation.
While I thought Minako was an interesting woman in herself, one who has kept her feelings suppressed for so long, one can only wonder what kind of damage it would do. If I read that the original Japanese title means ""At some time the days you read books"" and it's accurate, I felt the movie had a wonderful finale with that shot of her well stocked bookcase, likely alluding to the fact that she's not alone after all, and had probably fallen back on her crutch of sorts to deal with the pain of being alone, and back to a lifestyle which she had already been accustomed to for 50 years. Besides immersing herself in two jobs, she has those books which serve as a form of escapism, and occasionally pens little sweet nothings to song dedication shows on the radio.
Yuko Tanaka did a commendable job as the emotionally strong woman resigned to her fate and her decision to love none other, her object of affection, Takanashi (Ittoku Kishibe) was a more interesting character who has more facets. Staying true to marriage vows, he spends significant amount of screen time looking after his sickly bedridden wife (played by Akiko Nishina), while juggling with his job of social welfare in the Children's Affairs department in City Hall. I felt that as a childless couple, the job provided him a means to care, not for his own, but for other people's children, the troubled ones who are neglected and left to fend for themselves. In a rare moment of rage, we see how he angrily chides such wayward parents who don't appreciate and wastes their children's lives away.
The story by Kenji Aoki provides little quirks to make its characters appeal and successfully attempted to provide a lot more glimpses and dimension into them as well, such as how Takanashi is a hopeless Haiku poet despite being a member of the Haiku club, and supporting characters such as the aged Minagawa couple, where Masao (Koichi Ueda) lent some comical though sad moments as he slowly turned senile, while wife Toshiko (Misako Watanabe) narrates and brings us through this love story of a single woman at 50. Even Akiko Nishina's performance as the bedridden wife was nothing short of arresting, with her character's enlightened state of knowing her husband's past, and making unselfish, and painful decisions in her sickly state.
It's what you can expect from a typical Japanese romantic movie, sans young, nubile leads as star-crossed lovers, but with all other elements in place such as romantic set ups, love songs and those quintessential restrained but affectionate behaviour. I thought the story was in danger of going down the beaten track when unrequited love gets consummated, but director Akira Ogata managed to steer clear of the usual melodramatic moments in such stories, though the story did call for some obvious plot development into the final act that you can predict, especially if you're already way past your Romance Movie 101.
Not being your average lovey-dovey story, I thought The Milkwoman told a strong story with unrequited love as a central theme, and frankly a recommended romance movie (though told at a measured pace) if you're in the mood for some bittersweet loving, reminiscence, and seeking to live without regrets.",1
+"Cates is insipid and unconvincing, Kline over-acts as always, as does Lithgow while butchering an English accent (at least, I assume that's what he's attempting), and the tone staggers uneasily between farcical and maudlin. As with most pet projects showcasing a celebrity couple, it's a relief when this shoddy piece grinds to it's forced and jarring conclusion.",0
+"I am so excited that Greek is back! This season looks really eventful. Im glad that Casey is trying to get serious about school but is still involved in the sorority. Its really funny that she wants to go into politics & that they're highlighting her 'scheming talent.' I loved Calvin's new haircut! It makes him look more mature. They should shave Cappy's head, as well. All the guys are hot but Calvin is definitely the hottest! I cant wait to see more of him! I'm especially interested in what happens between Calvin, Adam, & Rusty! I also love Rebecca. She's really pretty. I actually think that Rebecca & Calvin should hook up. go for it, Calvin! Join my team!",1
+"I wanted to watch this, to get a inside look at the show. It told the story more of Robin Williams, then Mork & Mindy. Still, thought it was great. We got to see, Robin always being 'on', no matter what. The performance of Diamontopolous was awesome.
The introductions of the main players, seem so real to me. Roebuck as Garry Marshall was wonderful. He was so charming in this, which helped me get through all the Williams energy. The little behind the scenes pieces of his other shows (Happy Days, and Laverne & Shirley), was enlightening. I also thought Richmond-Peck's Harvey was also a nice rock in the pond. (This is a good thing).
This movie told the age old story of Hollywood folks, going through the ups and downs of stardom. It kept me glued to my TV, and I learned to love Robin, well hell, mostly everybody seem to be the super people I sometimes think Hollywood is. Go figure.
I sometimes wonder why the network people are always played to be idiots. We never saw the head of ABC. Just heard him, like Charlie from Charlie Angels (I wonder if this way planed?). It seems so sad, that a show at number 1, could be so destroy by their own network.
I think this story could be told about anyone's life, as they climb the ladder of any job. Movie, and TV stars are always loved or hated by so many people, that you grew up with, you just want to reach back in their past, to remember your own past. I Remember watching the show, and always wondering what does happen in their personal lives.
Mork and Mindy, will always be part of me, and I got to see part of them. It may not all be the truth, it's also all not a lie, but in the end, it told me a wonderful sad, happy story.",1
+"Imagine yourself trapped inside a museum of the dark middle Ages and a resurrected vampire and his maniacal sidekick are chasing you. Where is the absolute last place you want to hide? I'd say inside the uncanny Virgin of Nuremberg torture device, because there's a good risk you'll get brutally spiked to death. And yet, the elderly lady in this film stupidly runs into her spiked coffin. ""The Vampire's Coffin"" is a rather disappointing sequel, as director Fernando Méndez doesn't re-create the Gothic atmosphere of the 1957-original but puts the emphasis on comical situations and dialogs. No more ominous castles with eerie cobwebs and dark vaults, but confused doctors and clumsy assistants that provoke laughs instead of frights. The story opens inside Count de Lavud's final resting place, where an eminent doctor and a hired assistant steal the coffin in order to examine the corpse at a private clinic. Naturally the wooden stake gets removed from his heart, and the vampire count comes to live again, immediately enslaving the petty thief to do his dirty work. The vampire has his eye on a beautiful female patient at the clinic, and it's up to Dr. Enrique Saldívar to rescue her soul and to destroy the bloodsucker. ""The Vampire's Coffin"" uses a limited amount of locations and there's very little action. The whole film would actually be pretty boring if it weren't for a handful of memorable sequences and decent acting performances. The photography is amazing, though, with the sublime use of shadows and darkness. This is most notably during the scene in which Count de Lavud stalks a young woman through the deserted streets of little town at night. It's the only truly worthwhile scene of the whole film, the rest is fairly mediocre and déjà-vu.",0
+"SKELETON MAN was okay for the first 5 minutes but as soon as the so-called ""Special Force Agents"" hit the screen, it went down hill faster than a fat kid on a sled.
The opening makes us think we might have a corny, yet fun, horror flick on our hands but no...the film makers ruin any hope of that when the ""Special Force Agents"" show up. I wish the screenwriter took a different route and had the ""Skeleton Man"" chase down some dim witted teenagers until one of them finally gets the upper hand. Instead, the ""Skeleton Man"" chases down some dim witted ""Special Force Agents"" and offs them until their Captain finally gets the upper hand.
I know the whole ""stalking of dim witted teenagers by a killer"" thing as been done before but it would of been more suited for a movie like this.
When the ""Skeleton Man"" finally does meet his ""so called"" demise, in a building that blows up, the Captain of the ""Special Force Agents"" is asked the following by a police officer outside of the building: ""What the hell happened in there?"" My answer to that question: ""Who the hell cares?""",0
+"""COSBY,"" in my opinion, is a must-see CBS hit! I'm not sure if I've never seen every episode, but I still enjoyed it. It's hard to say which one is my favorite. Also, I really loved the theme song. If you ask me, even though I liked everyone, it would have been nice if Madeline Kahn hadn't passed away during the show's run. Since that happened, I've always wondered what the show would have been like. Everyone always gave a good performance, the production design was spectacular, the costumes were well-designed, and the writing was always very strong. In conclusion, even though it can be seen on TBS now, I strongly recommend you catch it just in case it goes off the air for good",1
+"Just as the new BSG wasn't what fans of the original series were expecting, Caprica may not deliver what fans of the new BSG were expecting (for the most part). It is a very interesting, if not somewhat self-involved show, or at least the pilot is.
If you're looking for the big CGI thrills of the (new) BSG, you'll be sorely disappointed. If you liked the drama, you'll probably find something you like and maybe even identify with.
The storyline does examine on how the Cylons were developed, why Adama hates them and the origins of a monotheistic society. The writers also manage to tackle humans 'playing God(s)' and the creation or re-creation of 'human' life. It will be interesting to see how this plays out.
I found it to plod along in some parts and too preachy in others, but all in all it was promising. A small part of me wishes (or hopes) there might be some minor inklings of BSG in there (aside from the back story I mentioned), but that would probably convolute the storyline too much. Like BSG, I'll have to wait and see if Caprica grows on me, but it's way too early to tell.
It would really easy to chalk this up as a failure if you compare it to the previous series, but I'm willing to give it a chance. Overall, I thought it was interesting enough to make me see how the actual series is before 'throwing in the (proverbial) towel'.",1
+"This is a big step down after the surprisingly enjoyable original. This sequel isn't nearly as fun as part one, and it instead spends too much time on plot development. Tim Thomerson is still the best thing about this series, but his wisecracking is toned down in this entry. The performances are all adequate, but this time the script lets us down. The action is merely routine and the plot is only mildly interesting, so I need lots of silly laughs in order to stay entertained during a ""Trancers"" movie. Unfortunately, the laughs are few and far between, and so, this film is watchable at best.",0
+"The only possible way to enjoy this flick is to bang your head against the wall, allow some internal hemorrhaging of the brain, let a bunch of your brain cells die and once you are officially mentally retarded, perhaps then you *MIGHT* enjoy this film.
The only saving grace was the story between Raju and Stephanie. Govinda was excellent in the role of the cab driver and so was the Brit girl. Perhaps if they would have created the whole movie on their escapades in India and how they eventually fall in love would have made it a much more enjoyable film.
The only reason I gave it a 3 rating is because of Govida and his ability as an actor when it comes to comedy.
Juhi Chawla and Anil Kapoor were wasted needlessly. Plus the scene at Heathrow of the re-union was just too much to digest. Being an international traveler in the post 9/11 world, Anil Kapoor would have got himself shot much before he even reached the sky bridge to profess his true love :) But then again the point of the movie was to defy logic, gravity, physics and throw an egg on the face of the *GENERAL* audience.
Watch it at your own peril. At least I know I have been scarred for life :(",0
+"This is the kind of film they used to make, amusing, heart-warming, troubling, authentic, with convincing performances by people without nose jobs, boob jobs, eye jobs, in other words real people. Shauna Macdonald plays the female love interest, and she is so real you want to give her a cuddle at the very least. Imagine that, a real girl in a movie, whatever next? Hollywood would hate her, because her freshness is a sharp rebuke to every false starlet in Tinseltown. This story has the same hilarious feel as Sandy Mackendrick's classic 'Whisky Galore', with the gnomic humour of remote Scottish islanders puncturing the pretensions of intruders from outside and enjoying a wee dram from time to time (the actual intervals between those times often being rather short). Director Stephen Whittaker displays a rare skill in pulling this off just right, and it is shocking to discover that he died before his film's release, aged only 56, which was clearly a substantial loss to the screen. Ulrich Thomsen does very well at playing a German rocket scientist who in the late 1930s goes to Scarp in the Isle of Harris to build a small rocket to carry postal packets between the islands. There he falls in love with the alluring Macdonald lass, and she reciprocates the affection. Some wonderfully colourful local characters decorate the tale, and the film is pure delight. There is of course the threat of imminent war with Hitler, and we learn that Hitler executed 1000 rocket scientists who refused to build weapons of war, which is a shocking statistic. Tragic love is never far from view, but lips must remain sealed in a review as to what happens in the end. This film is a magnificent example of just the kind of films which people in Britain should be making. But are they being properly released? In a nation whose tastes have been so corrupted by reality TV shows, where repulsive nonentities have become the national heroes, is there even a market anymore for a film like this? After all, there is no grunting sex, there are no close-ups of suppurating wounds or of anyone's genitals, there are no drugs taken, there are no mindless celebrities prancing around wanting to be looked at, and so one wonders whether there is anything to interest a public which has become so decadent and jaded that only the most extreme sensations can briefly alleviate the tedium of their pointless existence. Anyone who is looking for an antidote to the vacuity of contemporary Britain can take refuge in this refreshing and honest film.",1
+"Unlike some movies which you can wonder around and do other things, this movie kept me in front of the screen for the entire two hours. I loved every minute of it.
However, I have to say that the story is not very believable. Especially when the foreigner was expelled by the government, and then later on, actually sent a package to the guy who helped him. Xiao Liu is a very good actor, he shows his emotions, and he shows his silliness, and his love toward that girl.",1
+"""Dahmer"" is an interesting film although I wouldn't use ""horror"" or ""thriller"" do describe it. It's more a minor character study that seems oddly sympathetic of the killer.
Jeremy Renner portrays serial killer Jeffrey Dahmer, who drugged, murdered and dismembered his male victims. The film centers on the relationship between Dahmer and ""Rodney"", well-played by Artel Kayàru.
Rodney is almost the more interesting character: enamored of Dahmer and having once escaped an attack, he returns to Dahmer for sex and survives a second attack.
I think the film is disjointed because it does little to portray Dahmer's formative years, how events may have created the human monster we see on screen and offers no insight into Dahmer's belief that he could create sexual zombies of his victims.
The roles are well played but the story is thin.",0
+"This was obviously a low budget film. It shows in every scene. What is nice to see is where it was made. A lot of the film was shot in Columbia, CA, in the Sierra Nevada Mountains near Sonora, CA. Some of the film was also shot in Jamestown, CA, very near Columbia. There is a railroad museum in Jamestown and they used some of the old trains in the picture. ""High Noon"" was also shot in Jamestown, as was ""Back to the Future III"".",0
+"I just found the entire 3 DVD set at Wal-Mart in the bargain bin for $5.50, so I thought I would take another look. Total of 13 hours to watch it all (26 episodes). I was born in 1948 and saw most of them on TV in the sixties. Many independent stations repeated them for many years.
Better than I expected actually, time has been kind to the obvious sincerity of it's creators, and to the obvious gratitude and respect they give to all the Allied fighting men and women. More abstract and arty than a straight forward documentary, but very truthful in it's depiction of the causes and final results of WWII. That war was greatly dependent on sea transportation, and the final victory was dependent on who achieved the final mastery of the world's oceans. The Allies were the ones who were able to do it.
Interesting too, to see how they try to strike a balance between big events, and the individual soldiers and sailors that made them happen. The score is impressive, if a bit too much by today's standards. I read somewhere that Robert Russell Bennett contributed just as much as Richard Rodgers to final score. I imagine that Rodgers provided all the major themes, and it was up to Bennett to fit them to the images. Great job!
Should be seen by every ruler, or potential ruler. A warning to tyrants that wars are eventually won by ideals, determination, and the supplies to back them up. Logistics: their quality and delivery will determine the eventual victors. The Allies outproduced and surpassed the material quality of the Axis, attacked their very source in the process, and insured their eventual defeat.
Sorry to see that the producer, Henry Salomon, lived a very short life. IMDb's facts were rather skimpy, I have to find out more about him. He did a few more outstanding documentaries before his early death. Might have more to say at a later time
Trivia: I had all 3 LP records made of the background music, pretty good overall. Unfortunately, the producers decided to add sound effects to the last one, relegating immediately to just novelty status, rather than for serious music listening. Too bad too, because it contained some interesting but more minor themes in the series. Silly stuff like 16 inch guns firing, torpedoes being fired, bulldozers, planes...just for kids mainly.
RSGRE",1
+"If you want to watch a film that is oddly shot, oddly lit, weird stories of these men (and one woman) who enjoy beating the crap out of each other, if you want to enjoy a story that goes nowhere of these two guys, one a boxer and the other a gay man, then you should watch this film.
After watching this film, I almost felt as badly bruised up and cut up, like the director (of the film) himself beat the hell out of me.
This is a movie where one is not meant to watch for plot or for great acting, this is a film to gawk at in horror and wonder. A lot like watching an airplane crash or a train wreck.
If you want to watch a great movie, a good movie, a ""B"" movie, or even a mediocre movie, this movie is not it.
A warning to all who watch this film, please don't eat beforehand. You might want to puke by the end of the film.",0
+"after my daughter was born in 1983, i needed to lose weight. i tried the 20 minute workout and i was hooked. i lost about 50 lbs. it was the most weight i ever lost in my life. i can't believe this show is forgotten. it would be a blessing if you started a cable channel strictly for exercise and included the 20 minute workout. i think this was the best workout video ever made. i wish i could purchase it somehow and somewhere. the routine was easy to learn and you did work up quite a sweat. the workouts they have today are too complicated and too hard to learn. please do your best to get this video back in circulation. i pray it will be a blessing to all who see and use it.",1
+"It's not just that the movie is lame. It's more than that. This movie is just unnecessary. Do we need another Western? How about a western with afro-Americans in the titles roles? Sound stupid, implausible and a lame attempt at modernizing the genre? It is. Incredibly lame and simple minded. It's like that lame Baz Luhrman film ""Romeo and Juliet"" where he set it in modern times to attract young folks and create some hype with his revamping of a classic tale. Well, Baz Luhrman failed miserably and so does this mess. The story is actually not bad however the whole idea of removing the racism out of a racist genre by casting an all afro-American cast is racist in itself. It's also puerile and simple minded (like Baz Luhrman-man he's a bad director). Hey (I hear you say) this was directed by Mario Van Peebles! He's also IN the film! How can it be racist? It's not. I said the idea of casting all afro-Americans instead of Caucasians was. The film isn't racist, it's just pointless, stupid and very very boring.",0
+"Manna From Heaven is a light comedy that uses exaggeration of human foibles to entertain the audience. Throughout the film there is the expectation that goodness will surface in each situation. The result is that the movie goer finds himself/herself sitting with this silly grin on his/her face, peace in his/her heart, and high expectations for human kind. Watching this movie was a most pleasant experience. (I would venture to say uplifting experience, but some would say that sounds corny!!)",1
+"I would love to comment on this film. Alas , my search has always endeth in vain. If any good citizen could help a desperate inhabitant of this ailing planet and restore his confidence in humanity by offering the whereabouts of either a UK VHS or loan him a DVD copy of the VHS; he would, without reservation, be eternally grateful.....
Blake wrote ""The road to excess is the path to wisdom"", one hopes my weary road of excess will offer the path to fruition .... If not, I will have to replay the excellent Mr Russel's Gothic in the knowledge that those who have seen Haunted Summer (for better or for worse) have enriched their viewing pleasure of the events of July 1816 whilst I, a fellow member of this melodious plot, rests his lonely case in solitude ...",1
+"If Jean Renoir's first film ""Whirlpool of Fate"" first takes us into the world of the countryside, the rivers, the lives of the peasantry that he will continue to explore, it seems only fitting that his second film deals for the most part with the wealthy and the privileged, the upper classes and those who are trying to claw their way upwards. Put the characters from the first two films together and you have the seeds of his great ""Grand Illusion"" and ""Rules of the Game."" This is beautifully filmed, with the restless camera making full use of the amazingly huge apartments and backstage areas that dominate the film's interiors, and the acting though frequently overwrought offers some great moments as well, particularly from Werner Krauss' Muffat. But the glamorous and sultry Ms. Hessling, who at first appears as if she might give Louise Brooks a run for her money in vampishness, never goes beyond a one note, selfish harlot portrayal. Perhaps this is in part a problem with the script, which does seem to mostly go for high points and outraged emotions; not having read the novel I'm not really clear on whether the choices were well-made or not.
Still, the differences between Nana's suitors are well-drawn, and I particularly liked the relationship between Muffat and Jean Angelo's Vandeuvres -- the tragic understandings that each seems to have of his ultimate fate and their sympathy with each other, particularly in the scene at the bottom of the enormous staircase where Vandeuvres warns Muffat, and we wonder if violence will erupt -- this and other gleanings of the ridiculousness of the idle rich help give the film the depth it has.
Far from his greatest achievement, and for me probably just shy overall of ""Whirlpool of Fate"", this is still well worth seeing for Renoir fans or those interested in silent cinema generally.",1
+"I had the pleasure to view this film when I was 10 years old,(having an existing interest in Egyptology). I know that there are subtle mistakes to the art direction and costuming, but over all this is the best film, to date with the look of the 18th dynasty.
The film only approximates Mika Walteri's ""The Egyptian"", in plot. A good portion of the text never made it to film, as we have to consider the running length.
The music score by B. Hermann and Alfred Newman is beautiful!!! Performances as follows. The late Edmond Purdom gave an excellent performance as an orphaned child adopted by parents past their child bearing years. He states that he keeps to himself,has the best education available and lets' face it is a rather emotionally distant person, given his upbringing and high intellect.
Jean Simmons is fine as a humble tavern maid; honest loving and sincere. Bella Darvi, people complained about her accent, well she is a Babylonian. It is not that apparent in the film as to why Sinhue is so insanely obsessed with Nefer Nefer Nefer. Her correct name. In the book Sinhue is enjoying her carnal fruits and gets his revenge early in the plot by leaving Nefer Nefer Nefer's drugged body with the ""House of the Dead's "" workers.
Gene Tierney as Baketaten, is brilliant! When she tells Sinhue that he is pharoah, she looks like she could devour him (in his weakness). She is intense, brilliant and coldly beautiful.
Michael Wilding is heartbreakingly tragic in his mission to bring all people to know his one God. I believe that we are viewing Ankhnaten thru the lens of Egyptologist A. Weigall. A view at the time that had a pre-messiah feeling about Ankhnaten's vocation. Did his monotheism influence the Jewish people? Note Psalm 104. and other Egyption imagery in the psalms?
Mr. Peter Ustinov provided the alter ego to Sinhue. He is street wise and cunning a survivor. Excellent acting as always.
Mature never thought much of his acting personally, His Horemheb is fine as an ambitious ""super patriot"" who ultimately has Sinhue murder more than one person in his quest for power, (Walteri's book).
I felt that the ending to The Egyptian was confusing as Sinhue's personality changes too easily. He has a living son (Toth dies in the novel), power is handed to him through is half sister Baketaten, he world savvy now and has a grip on international affairs. So he became enlightened? He could have modified the Amon Priesthood as he was capable.
But NO! Sinhue gives everything up, everything including his son's future to become a ragged beggar preaching monotheistic love?
This change was too immediate and the major flaw in the script!
Again the look of the film,colour, most of the costumes(Nefer Nefer Nefer's gold dress was too over the top as she is more richly dressed than the royal family), music is beautiful.
I will watch this film again easily.
P.S. I know that you porbably know that Horemheb did not directly succceed Ankhnaten, but I could not resist stating this fact.",1
+"Damon Runyon's world of Times Square, in New York, prior to its Disneyfication, is the basis for this musical. Joseph L. Mankiewicz, a man who knew about movies, directed this nostalgic tribute to the ""crossroads of the world"" that show us that underside of New York of the past. Frank Loesser's music sounds great. We watch a magnificent cast of characters that were typical of the area. People at the edges of society tended to gravitate toward that area because of the lights, the action, the possibilities in that part of town. This underbelly of the city made a living out of the street life that was so intense.
Some of the songs from the original production were not included in the film. We don't know whether this makes sense, but this is not unusual for a Hollywood musical to change and alter what worked on the stage. That original cast included the wonderful Vivian Blaine and Stubby Kaye, and we wonder about the decision of not letting Robert Alda, Sam Levene, Isabel Bigley repeat their original roles. These were distinguished actors that could have made an amazing contribution.
The film, visually, is amazing. The look follows closely the fashions of the times. As far as the casting of Marlon Brando, otherwise not known for his singing abilities, Frank Sinatra and Jean Simmons, seem to work in the film. Sky Masterson is, after all, a man's man, who would look otherwise sissy if he presented a different 'look'. Frank Sinatra is good as Nathan Detroit. Jean Simmons, as Sarah Brown, does a nice job portraying the woman from the Salvation Army who suddenly finds fulfillment with the same kind of man she is trying to save.
Vivian Blaine is a delight. She never ceases to amaze as Miss Adelaide, a woman with a heart of gold who's Nathan Detroit's love interest. Ms. Blaine makes a fantastic impression as the show girl who is wiser than she lets out to be. Stubby Kaye makes a wonderful job out of reprising his Nicely Nicely Johnson.
The wonderful production owes a lot to the talented Abe Burrows, who made the adaptation to the screen. The costumes by Irene Sharaff set the right tone.",1
+"The script is so so laughable... this in turn, makes the actors' lines sound stiff and unrealistic and not to be believed. There's repetition of phrases -- ""my sweet little god daughter"" and minor variations of that line which comes to mind... and it's just sloppy soap opera dialog.
Worse yet, the music is so WRONG! Plus, the main bluesy ""theme"" is horribly quaint and entirely wrong for this. And it feels overused mostly because the instrumentation, texture and arrangement of this theme never changes, even when the scene's emotional context does.
Subsequently, whenever it appears, it sticks out like a sore thumb as the main transition from one scene to another.
The music's corny, and it's as if the writer were writing music for a soap or a sitcom -- a low budget 80's Canadian sitcom at that -- and this makes it feel as if we're always on the brink of throwing to a commercial.
This is so miscast, there's a lot of overacting and it's a real stretch that so many of these characters are employing only ONE type of NY accent -- a thick Bronx accent. I don't know if it's a question of the actors' limited capacity in only knowing *one* NY accent -- or whether it's a question of the director's ability to notice such an glaring anomaly.
In the end, it's the amateur script with it's leaden lines which makes this entire ""movie""... blow. When any foundation is shaky and unstable, it's impossible to build upon it without it's flaws revealing themselves in exponentially more damaging and unflattering ways.",0
+"Time For A Hit!
Waqt Dir- Vipul Amrutlal Shah Cast- Amitabh Bachchan, Akshay Kumar, Priyanka Chopra, Shefali Shah, Rajpal Yadav and Boman Irani. Written by- Aatish Kapadia Rating- ***
Eureka! We've got it! Yes, ladies and gentlemen in Vipul Shah's 'Waqt', we have probably found this year's first bona fide hit. Replete with all the necessary ingredients of a commercial Bollywood fare, 'Waqt' has all that it takes for a movie to click with the Indian audiences. It's the kinda film that makes a distributor feel happy and contemplate his next phoren visit! In this 'saga of Indian emotions' then, we have a happy family(isn't it always?) of three. Ishwar(Amitabh Bachchan), the postman-turned-millionaire(don't ask how!...there's something about selling toys while delivering letters and all that seriously- who gives a damn!), married to Sumi(Shefali Shah) is a doting father to Aditya(Akshay Kumar). Ishwar has to make a serious decision about his son's careless attitude towards the responsibilities of life. His love for Aditya though, results in his procrastination of the grave issue. However, when faced with a situation that will test his race against time, Ishwar has no alternative but to throw Aditya out of the house- hoping that the new predicament might make him more conscientious of his own life. But this presumed solution becomes a problem in itself, as the rift between the loving father-son increases and the fences continue to grow.
You don't have to be a rocket-scientist to realize that such a story provides ample opportunities to infuse comedy and drama alike. So, pre-interval you have the initially funny, later annoying comedy track of Boman Irani and Rajpal Yadav; and post-interval there are the go for your kerchief moments between Aby and Akki! Writer Aatish Kapadia(he also penned the original Gujarati play 'Aavjo Vhala Fari Malishu' on which the film is based) does a good job of keeping the narrative fluid. The dialogues tend to get inconsistent at times. It doesn't help that songs appear like acne on a teenage face and mar the proceedings. Clearly, a couple of numbers could've been done away with. On the directing front, Vipul shows that he possesses a natural flair for story-telling. 'Waqt', as well as his earlier debut effort 'Aankhen', manage to keep you interested till the last reel. On a personal note- the seesaw of emotions was a tad jerky for me. But gauging from the audience reactions, it was working to the hilt.
Finally, 'Waqt' is all about its performances which amount to one whole point in the overall rating! Amitabh Bachchan is dependable as always. His energy is visible and so is his age! Shefali pitches in a finely nuanced performance and matches the superstar at every step. Boman and Rajpal bring the house down with their histrionics. Priyanka has little to do than fulfill the perfunctory role of a heroine. When it all boils down though, 'Waqt' is Akshay's vehicle. I have always maintained that Akki is as good as the role suits him. Put him in a 'Mujhse Shaadi Karogi' and he's fantastic, but in a 'Bewafaa' he is woefully bad. Here, Akki is probably at his best. Whether it is his comic timing or his emotional renderings, he is near-perfect. There's also an action scene for his fans! Ironically, his previous best endeavour was in 'Aankhen'- with the same director and Big B at his side!
'Waqt' is by no means a memorable movie. It's not one that will feature in the better films of our industry. But it is one for the masses. And at a time when the industry is waiting desperately for a universal hit, 'Waqt' might just do the trick!
- Abhishek Bandekar
Trivia- This is Akshay Kumar's second consecutive film after 'Bewafaa', in which he performs on stage during the climax!
Rating- ***
* Poor ** Average *** Good **** Very Good ***** Excellent
22nd April, 2005",1
+"There is absolutely no plot in this movie ...no character development...no climax...nothing. But has a few good fighting scenes that are actually pretty good. So there you go...as a movie overall is pretty bad, but if you like a brainless flick that offer nothing but just good action scene then watch this movie. Do not expect nothing more that just that.Decent acting and a not so bad direction..A couple of cameos from Kimbo and Carano...I was looking to see Carano a little bit more in this movie..she is a good fighter and a really hot girl.... White is a great martial artist and a decent actor. I really hope he can land a better movie in the future so we can really enjoy his art..Imagine a film with White and Jaa together...that would be awesome",0
+"I don't like boxing, don't understand the attraction. I did like this movie. Positive portrayals of Latinos, with no drugs, sex or street violence. The plot actually showed stable, loving families. The fight sequences are violent, as is boxing, but not as over the top as Rocky films. Nothing wrong with attempting familiar themes with a different angle and ethnicity. It's a good rent.",1
+"Eisenstein describes his collaboration with Prokeviev as an equal partnership, where they worked together to match image and music, scene by scene. Unfortunately, the sound recording was a disaster, so for once the devotion to authenticity in Criterion DVD's backfires. Fortunately, there is at least one restored version of the film on VHS (BMG Classics) with an excellent re-recording of the music (by the St. Petersburg Philharmonic Orchestra and Chorus).
It is interesting to compare this film with contemporary propaganda films in England, Germany, and the United States. Eisenstein's film was made in 1938, in response to the fear of a German invasion; and Olivier's in 1943, when a German invasion of England was still expected. Both films are stagey, but in different ways. Olivier begins by showing a staged performance of the play in the Globe Theater by Shakespeare's own company, then takes us out of the theater to a more cinematic (though still stylized) setting. Eisenstein's film is cinematic from the beginning, but the dialog and speeches are still influenced by the melodramatic acting conventions of the old Russian theater. This works very well for Cerkassov's speeches as Alexander, because part of his job as a prince and military leader was to play a role in public.
In Nazi Germany, the first major propaganda film was Leni Riefenstahl's tedious Triumph of the Will, which recorded a huge political spectacle - massed crowds cheering Hitler's ranting speeches. The propaganda in her masterpiece, the film of the 1936 Berlin Olympics, is much subtler, with its worship of the athletic male body carrying disturbing undertones of the Aryan superiority myth. But wartime German propaganda films could also be subtle. Karl Ritter's Urlaub auf Ehrenwort (Furlough on Word of Honor) is typical. It shows a young lieutenant letting the men in his company go on a 24-hour leave before returning to the WWI trenches (and almost certain death). Against the advice of veterans, he accepts their word of honor to return, though he will be courtmartialed and shot if they don't. Naturally, they all return, (though some of them berate themselves for it), presumably inspiring the audiences to similar displays of duty to their country.
In the United States, one of the better WWII propaganda films was Howard Hawks' Air Force. In it, we follow the mismatched crew of a bomber as they bond to each other with the experience of battle, and overcome obstacles to continue their part in the war. Typically for Hawks' films, however, their real loyalty is more to each other than to their country.
Eisenstein has to reach far back in history to find any Russian military triumphs. Ironically, Alexander (like the other Russian princes) is descended from the Vikings who sailed up the Russian rivers to conquer and rule their own fiefdoms. So he is a conquerer repelling another would-be conquerer. Physically, they are not that different (though the actors portraying the German princes were obviously chosen for their ugliness and smirking stupidity). But the real contrast is between the common soldiers. The Russian peasants are as tall and strong as the nobles; whereas the German peasants who scuttle out of the shield wall to kill wounded Russians are a foot shorter than their masters. There is some historical truth in this contrast. Russian serfs in the Middle Ages were much better off than their European counterparts, because they could always escape into the wilderness and clear their own land.
Eisenstein's film also cleverly gives us our first sight of Alexander as a fisherman. In the battle with the Germans, he uses his fisherman's knowledge of the ice as well as his knowledge of their military tactics to defeat them. When Gavrilo breaks the shield wall, they are forced to regroup and mass on the West side of the lake, where the ice is thinner.
One of the other pleasures of Eisenstein's film (which most audiences miss) is the historically accurate way that he portrays the politics of medieval Russia. Cities like Pskov and Novgorod owed their growing wealth and prominence largely to trade, which put the merchants into power, and sidelined the princes until their military expertise and feudal levies were needed to repel invaders. In the film, Alexander is shown not only as a military leader, but also as a master politician, who knows how to wait for his time, and how to make the most of his popularity after the victory.",1
+"It tries to be the epic adventure of the century. And with a cast like Shô Kasugi, Christopher Lee and John-Rhys Davies it really is the perfect B-adventure of all time. It's actually is a pretty fun, swashbuckling adventure that, even with it's flaws, captures your interest. It must have felt as the biggest movie ever for the people who made it. Even if it's made in the 90s, it doesn't have a modern feel. It more has the same feeling that a old Errol Flynn movie had. Big adventure movie are again the big thing in Hollywood but I'm afraid that the feeling in them will never be the same as these old movies had. This on the other hand, just has the real feeling. You just can't hate it. I think it's an okay adventure movie. And I really love the soundtrack. Damn, I want the theme song.",1
+"If you are having a bad day,or bad week. If you are looking for a film that will make you laugh and forget about your troubles. I don't think Role Models is that movie for you.
The film centers around Danny(Paul Rudd) and Wheeler(Seann William Scott) Two juice promoters, who go to schools promoting the product, telling kids to stay off drugs, and more juice. But Danny is having the worst week ever, and crashes his company car, with Wheeler in the seat next to him. His soon to be ex girlfriend Beth(Elizabeth Banks) who is a lawyer, manages to avoid getting them jail time, by doing hours of community service, volunteering at a big brother place called Sturdy Wings led by Gayle(Jane Lynch). Wheeler is assigned to Ronnie(Bobb'e J Thompson) who is 10 years old, and has a foul mouth like he's Chris Rock. Danny is assigned to Augie(McLovins, Christopher Mintz-Plasse) who likes to dress like a knight, and fight like he is in medieval times. But will this be good for Danny and Wheeler, or will they be better off in jail?
Okay I'm not gonna beat around the bush, this movie was very unpleasant in many ways. Namely the Ronnie character, hearing those bad words coming out of a kid that young, was very shocking. If he was a little bit older, it would not have mater'd as much. I mean what where his parents thinking, when they sign'd him on to this. Elizabeth Banks character is so unwatchable, maybe I was supposed to feel bad for her character, but I felt nothing, because she is annoyingly predictably portrayed as a female who would be played in these types of comedies. And Jane Lynch, who's the worst of the worst. She delivers the most overacting performance ever. Playing a former drug addict, who acts like she still is on drugs. Listening to her give all that annoying dialog, made me want to throw my head up against the wall. Seann William Scott once again playing another Stifler like character, he should really try to separate himself, and this film won't do it. And the more Scott tries to hard to be funny, is what keeps him from being funny.
Now Paul Rudd on the other hand, I'm gonna separate from the others in the film, cause he manages to deliver a solid performance, although he does not get higher laughs, but he is the most interesting character from the rest. Cause Rudd does not overact, and does not try so hard. The scenes with him and Mintz-Plasse are watchable. But the rest of the film is so stupid, it picks up at times. But it becomes so predictable and uninteresting. It is a reminder that these types of comedies try nothing new, there all the same, they take no chances. Role Models is an example of that.",0
+"No laughs whatsoever. Yes, I watched this entire train wreck but only so that I wouldn't later wonder if Cleese had come to his senses in the latter part. (No, he had not.)
This may be historically interesting to you youngsters out there, to see that British ""humor"" included black ""jokes"" like these, thirty years ago.
What amazes me even more though, is to read the other reviewers' comments, which acknowledge this isn't very good, yet then turn around and give it high votes. If the vast majority of the comedies that you have seen are even much worse than this one, then I certainly pity your torturous existences.
The humor level of this show appears aimed at little kids, yet the subject matter does not. So who is this for? People who enjoy repeated & drawn-out double-takes, pratfalls, drug jokes (interesting only as a short trip back to '77), and other ""low"" humor. The Three Stooges are still funny, and were to me as a kid, too. THEY exerted some effort in making jokes work. This however is sloughed off schlock. I fear that it IS the end of civilization, if this stuff really is accepted as worthwhile. Next you'll be telling me that tabloid TV is popular. :(",0
+"Apparently, a massive head wound is the cure for homicidal tendencies, turning a murderous sociopath into a lovable and oafish dog catcher. Also (this ones for the ladies), it seems that the front gate of a psychiatric hospital is an overlooked hot spot for meeting potential mates. Those are just two of the approximately 23 absurdities we're supposed to accept for this movie to have any meaning. I love movies and I believed, as I'm assuming many Americans do (forgive me if I'm wrong), that Hollywood turned out the best product. I've come to learn how sadly naive and brainwashed I was and 2) how much more sophisticated European/Asian Cinema is in comparison to its American counterpart.
I watched this allegedly disturbing psychological ""thriller"" the night following a viewing of a Japanese movie called Suicide Club. As the camera faded on Walter Sparrow's happy little family enjoying some quality time around a prison visiting room table (not to mention the patronizing voice-over extolling the virtue of ""doing the right thing""), I suddenly had an epiphany. I had just finished watching a movie that left me feeling as though I'd just had a glass of water when I really wanted a beer. My thirst was sated, but it was strictly utilitarian. The premise was mildly interesting, but the story itself, with its innumerable ""coincidences"" (How do we explain her finding the book? We'll just say something like,""...Or did the book find her?."" They'll buy that), gaping plot holes (why did wifey take the skeleton?), predictability, and obligatory happy ending, turned out to be just another Hollywood hack job. Additionally, the casting of Jim Carrey was just wrong. At any moment, I felt he was capable of breaking into some shtick from one of his stupid comedies or In Living Color. Jim Carrey as a tattooed hard-boiled police detective who enjoys bondage and rough sex? Didn't buy it for a second.
You want disturbing? Deeply disturbing? Watch Suicide Club. The story surrounds the mysterious mass suicide of 54 school girls. The film opens with a group of giggling high schoolers mulling about on a subway train platform. We then watch in horror as they line up, hold hands, and happily throw themselves in front of a fast moving commuter train. Needless to say, much chaos ensues. That's as far as I'm going to go with the story line because I encourage the reader to see the film. In fact, I'm not sure if I could outline the plot even if I wanted to. What begins as a straightforward mystery quickly descends into a madhouse of grotesque imagery. Did I understand the movie? No not initially like many of the foreign films my girlfriend has introduced me to. So naturally, I thought it was ""bad."" But this one lingered in my mind. I went to bed thinking on the film and awoke the next morning and looked it up on IMDb. I read some of the viewer comments and was astonished at 1) the insights others had derived from the film and 2) the fact that I had so thoroughly missed the whole point of the movie. I realized that I was so used to being spoon fed the ""message"" from Hollywood, that when confronted with a film that actually required the viewer to participate to actually think for themselves, I was totally unequipped. It's as if I had been conditioned to ""check my brain at the door"" of the theater.
Am I saying that Suicide Club is the greatest movie ever made? Of course not. It has its flaws, many of which were reported adroitly by the IMDb reviewers. Am I saying that all American movies are bad and all foreign movies are good? Again of course not. My point is that there's a whole world of film-making outside of Hollywood a body of work that engages the viewer; forces them to think and question movies that don't telegraph plot twists, follow a strict linear sequence, and above all, don't insult the intelligence of the person watching. I look forward to expanding my mind while exploring this new world of film that doesn't ""do the thinking for me.""",0
+"The best Treasure Island ever made. They just don't make films
like this anymore, or ever. No one makes films like this. More
than a novelty, this film is funny, frank and fascinating, yet moody,
mysterious and morose. This is one of my favorite pictures. The
director must have had some idea what it is all about, but he
certainly leaves room for your own impressions and interpretations, while leaving little left to the imagination. Why he
has not made more films like this, I have no idea. While
reminding me of some of the best noir, it is one of a kind. But this
is not for the lazy or simple.",1
+"Even if it won't give one more than previous posts here (like Ruby Liang's very good one) i wanted to share my own point of view. Hope my English is understandable.
Bon voyage is a rhythmic, light but deep presentation of the French unorganized come-down, but also courage and charm. All along in a brilliantly reconstituted 1940 France with many details (from Bordeaux luxurious hotel occupied by Government HQ and attacked by useless high class French, to Parisian coffees near Le Pantheon / rue Mouffetard and 1930s cars) Gérard Depardieu and Yvan Attal give their second roles a brilliant taste;) Isabelle Adjani and Virgnie Ledoyen are very credible in their drastically different roles, and Grégori Derangère makes an bewitching performance:)
Much lighter than average (e.g. American) war times movies, and focused on the civilians, Bon voyage shows a lot of things about french issues (even to a French guy like me), some of them quite deep.",1
+"I thought it would at least be aesthetically beautiful. It was slow, pretentious, and boring. I almost fell asleep. There are some decent songs, but there is this one song at the end which is just some guy yelling out ""Yaowwww!"" while someone taps randomly on a wooden object. That being said, there are some pretty songs, but it's not worth seeing hte movie over. Go on itunes (they have the album), preview it, and choose the good ones.
Half the movie is some guy making tea. Well, that's a slight exaggeration. But you'll see what I mean if you see it. That being said: DON'T SEE IT!",0
+"a movie about the cruelty of this world. I found it liberating, as only truth can be. It also contains some quite funny bits. Some of the acting is extraordinary, see Maria Hofstätter for instance. The director has tried to depict life as realistically as possible, succeeding. Coherently, the sex scenes are explicit and no more fake than those of a hard-core movie. Although I hardly understood a sentence, I found the vision of the movie in the original language with subtitles much more rewarding, because with the dubbing half the great work of the actors gets lost. The voice of the character played by Maria Hofstätter is particularly hard to duplicate by a dubber.
My favorite movie",1
+"Wow. I felt like I needed to shower off after watching this one, but maybe there were other reasons that I will leave to your imagination. I felt used and abused after wacking, I mean watching this film. Hairy chests, thick mustaches, and well, hairy everything describes this porn/horror movie, but hey, it was 1981, you can't call it ""porn"" in the 70s and 80s without the hair.
As a horror flick, this bites. But as a piece of exploitation/porn from Italy's rich cinematic history- it definitely has a place in my library. The copy I have is in Italian with English subtitles. I wish it had the really poorly dubbed English, I think it would have added to the sleaziness factor that already existed. The only white guy who gets laid in the movie is ""Mark Shannon""- he is the moustache wearing, hairy chested piece of machismo who really does try and give a performance every time he ""steps up to bat"". This was at the end of an era where porn producers were actually trying to make something artistic. Nothing like panning the camera from a tropical backdrop to a hairy man having ""doggie-style"" sex with a woman. I can't help but laugh.
This is one of those movies that I pray my future wife and kids never find.",0
+"Quite simply a well-made, well-written and wonderfully acted movie. Eastwood is classic as grizzled Secret Service Agent Frank Horrigan and Rene Russo
holds her own as partner (and love interest) Lilly Raines. But the movie's
greatness rests on the shoulders of John Malkovich as ""Booth"". He captures
this character's rage and hatred, as well as his humanity oddly enough.
Personally I think this was his best performance and should have received an
Oscar for it (But I loved Tommy Lee Jones in The Fugitive as well that year). Overall a great movie to see you want to peek into an assassin's mind and be
on the edge of your seat the whole way through. Enjoy!!",1
+"Bestselling writer George Plimpton(Alan Alda)takes on an assignment for Sports Illustrated. He is to go incognito to the Detroit Lions training camp and try out for a position as third string Quarterback. He is quickly found out by the team members featuring Alex Karras and Mike Lucci. The entire team finds it amusing to cause stumbling blocks in the writer's determination to Quarterback for a series in a real game.
This movie is Alda's debut and also helped Karras leave the gridiron for acting. Besides the 1968 Detroit Lions, the cast also includes ""Sugar Ray"" Robinson, Roy Schieder and Lauren Hutton.
Alex March directs this story based on Plimton's book.",0
+"Being raised at the time this movie was released has probably influenced my shallow mind, but still, this isn't a bad movie by any means. It's a movie about a hostage situation involving a prep school populated to some extent by endearing teenage boys who can't seem to get out of trouble. What's wrong with that? It doesn't have any big special effects, but so what? Who needs special effects? Cinema's decline began around the same time that special effects were popularized. A coincidence? I think not. It turned movies with potentially good plot and feelings and turned them into a big, substance-less light show for innocent kids and the self-medicated. Well, you know, not all movies need special effects. About three fourths of the movies on the IMDb top 250 are without special effects, but almost all of the Top Grossing movies of all time have some special effects. Think about it: Star Wars, E.T., Ghostbusters, etc. All good movies, but the rest of the top-grossing movies are usually cliched tripe with non-sensical plots and lots of eye candy. Well some movies don't need ny of that junk.
Excuse me for going off on a tangent, which I normally do, but I'm just so fed up with that special effects junk. Back to the point: Toy Soldiers is simply a great movie. I admit, some of the content is a little corny and ripped off, but so what, every movie rips off another to some extent. Think of Resovoir Dogs. Countless ""appreciation"" sites dictate the fact that beloved Quentin Tarentino, who I admit I like, has copied many, many, many movies in the making of his first major film Reservoir Dogs. Many say that the entire plot is ripped off almost scene for scene from japanese and chinese gangster movies which Mr. Tarentino loved so much, and probably still does. Sorry once again for the tangent.
Toy Soldiers is fun. It has the whole insubordination from teenagers to unwanted members of authority, i.e. hostage takers. It's fun to see kids take over when they're being held to something they don't want to do. Hell, teenage angst-inspired rebelion was the key topic to a great majority to 80's comedies. Plus there's the tension and thrill of having the characters use fire-arms and knock out the bad guys, etc. Plus there's some emotional points to the film. When one of the characters dies the others have to cope and adjust. It's not perfect acting but it beats most of the other tripe out there.
In short, Toy Soldiers is exciting, interesting, and fun. How dare you jaded blowhards rate this movie poorly! Shame on you all!
Personal rating: 8/10",1
+"this movie has lot of downsides and thats all i could see. it is painfully long and awfully directed. i could see whole audience getting impatient and waiting for it to end. run time is way over 3 hrs which could have been edited to less then 2 hrs.
transition between stories is average. most people confessed being on seating expecting something better to come out.
its funny only in pockets. ambitious project and a below par execution. govinda does a fair job, anil kapoor disappointed me, rest we as expected. if u r expecting anything close to babel or love actually then its no where close.",0
+"I've been a huge fan of the Cky videos, Jackass, and Viva La Bam for a long time. They've had a great run and I expected my laughter to end, eventually. But, it hasn't yet. This movie kept my mouth open the entire time. I'm still laughing, randomly. I went to the theater with low expectations, thinking it wasn't going to be better than the first. Oh, how incredibly wrong I was.
There were many great moments in the movie. If you're squeamish, don't like randomly placed raw humor, or if you disliked the first movie, you probably won't like this. But, with that said, I almost wet my pants from laughing so hard. It had all kinds of different pranks, masochistic humor, toilet humor, puking, laughing, some great falls and massive damage done to all of the cast. Ryan Dunn even branded Bam's rear end with an image that will be stuck there for a long time. I'm sure you can only imagine how raw this movie is.
No pain, no gain? Right? This movie has already done well, causing theaters all over America to laugh so hard, they'll be wishing it could last longer. I know I did. This movie did not feel short, at all, especially with the credits continuing the footage. But, I still wish it could've gone on forever. Now, let's just wait and see when they release Jackass Number 3! Overall, an excellent film, if you can get past the male nudity and a few sickening images. Keep your kids out of this film. They don't need to see this, at least until they are older. Support the crew and BUY THIS when it comes out on DVD! I know I will.",1
+"Going for something far away from the deliberately gross stuff that he usually makes, John Waters (happy birthday, John!) made this parody of the celebrity/art world. Edward Furlong plays the title character, a working-class teenager in Baltimore who loves to photograph things. When a New York agent (Lili Taylor) discovers his work, she offers him his big break, which he accepts. But once he hits it big, he has to reconsider everything.
Basically, ""Pecker"" looks at how he loses his friends and his normal life once he becomes a celebrity. The sort of thing that we might expect, sure, but with Waters directing, there's always a few things to shock us (you'll know them when you see them). I certainly recommend it. Also starring Christina Ricci, Mink Stole and Patty Hearst.",1
+"A DAMN GOOD MOVIE! One that is seriously underrated. The songs that the children sing in the movie gave me a sense of their pain, but also their hope for the future. Whoopi Goldberg puts in a good performance here, but the best performance throughout the whole movie is that of the actress who plays the title character. I wish she was in more movies.
This movie should have a higher rating. I give it a 10/10.",1
+"K Murli Mohan Rao made the much better BANDHAN in 1998 This film is an awful remake of THE WEDDING SINGER
Basically in short, the film consists of: Salman Khan who in those days used to have the role of a dejected lover who looses his girl and also he had his comic scenes where he hammed badly even today he does well he does it all here too and also looses his shirt in scenes
Jackie Shroff- wasted, bored and tired, his role is so stupid He is shown as a lover of Pooja Bhatra then in 1 scene he is shown as a womanizer?
Inder Kumar- confusing characterization again
Rani Mukherjee- boring, overweight and does nothing special Pooja Bhatra- tall, fair and actress worthy but lacks talent
Kashmira Shah- says a dial as if a poetry
Mohinish Behl- poor fellow the 2 kids were awful too
The story is the same and has awful comic scenes, a sudden love story and boring drunken scenes plus a forced comic track of Shakti Kapoor
Direction is poor Music is decent
Salman khan just goes through the motions, Jackie is bad, Rani is as usual, Pooja is bad, Mohinish and Kashmira are nothing great Inder is awful",0
+"Talk about marketing. The poster/home video cover of 'The New Twenty' broadcasts a half-naked male in a ""Wolfe Video."" For those familiar with the gay-themed movies this broadcasts a ""must-see."" (I loved reading one reviewer (from another site) stating they had been ""tricked"" into seeing a ""Sodomite"" movie. Are you serious? The tagline itself as the word ""gay."" The Lord gives you eyes, yet you cannot see ) That being said, despite the number of gay characters, stereotyped, no less (see: the lonely gay, the AIDS victim gay and the closeted gay) it's more about long-term friendship and characters that grow apart. In fact, if anything, there's more (here's one for Christians to complain about) heterosexual couples having sex outside of, gasp!, marriage. Not to mention backstabbing, drinking to excess and drug usage. I see this more of a made for TV-Logo or Showtime movie than big screen effort. Sure, I loved the cinematography, some of the actors could act and I always love seeing a big-group-of-friends that actually act like they've known each other for a million years. But we've see this all before. Nothing really ""new"" here. Barely an original idea hence bringing back the same 'ole ""I have AIDS, let's deal with that"" for a good portion of the movie and boy, our friend has a serious drug problem, but let's not deal with that until it's almost too late. That's so (US) 'Queer as Folk' and 'Broken Hearts Club,' respectfully. The film deals with a group of college buddies, now grown (in size not minds) who have to eventually grow up and each trying their best while failing. Strangely, as in most of these independent movies, the most interesting, to me at least, was the heavier-set one, Ben. He stole each scene, but, again, there wasn't much to take.",0
+"This movie is just plain dumb.
From the casting of Ralph Meeker as Mike Hammer to the fatuous climax, the film is an exercise in wooden predictability.
Mike Hammer is one of detective fiction's true sociopaths. Unlike Marlow and Spade, who put pieces together to solve the mystery, Hammer breaks things apart to get to the truth. This film turns Hammer into a boob by surrounding him with bad guys who are ... well, too dumb to get away with anything. One is so poorly drawn that he succumbs to a popcorn attack.
Other parts of the movie are right out of the Three Stooges play book. Velda's dance at the barre, for instance, or the bad guy who accidentally stabs his boss in the back. And the continuity breaks are shameful: Frau Blucher is running down the centerline of the road when the camera is tight on her lower legs but she's way over the side when the camera pulls back for a wider shot. The worst break, however, precedes the popcorn attack. The bad guy stalking Hammer passes a clock seconds after our hero, except the clock shows he was seven minutes behind our guy.
To be fair, there were some interesting camera angles and lighting, and the grand finale is so bad that it must been seen, which is the only reason that it gets two points out of 10.",0
+"This is the best direct-to-DVD effort from Van Damme that I have seen yet. Van Damme plays a border patrol agent who is out to stop heroin smugglers trying to cross into the United States. The action in this movie is great and the fight scenes rank with Van Damme's best. Costar Scott Adkins shows why he should be the next big star in the martial arts genre. For further evidence check out ""Undisputed 2"". Adkins is so good in fact that before I watched ""The Shepherd"", I thought that Van Damme might not look very believable in defeating him on screen. Van Damme holds his own though and although he isn't quite as athletic as Adkins is, he can still kick with the best of them. All of the fight scenes in this film are very well done and the gun battles are above average for this type of film as well. The only negative thing I can say about this movie is that the story is a little underdeveloped. I think Van Damme's character's motives should have been presented earlier in the movie, especially in regard to why he carries around a rabbit. The reason he does is very cool but you don't find out until the very end. There are a couple of other things that are never really explained either but this is a Van Damme movie so you know where the priority lies in making this kind of movie and it ain't character development. Overall though, this is a solid action movie that I recommend. So run for the Damme border!",1
+"I remember my dad hiring these episodes on video. My whole family loved them, and now that I have moved away from home and have my own life I am trying to share these fabulous Jim Henson creations with my Husband and stepson but as I am starting to find out not everyone is a Henson fan. Which is a pity since it means they will just have to put up with me searching for this series. But even though they don't find these interesting, I would highly recommend anybody getting hold of the Storyteller. You will be lost in a world of tales from a time when people could only talk about unexplained situations through stories and how people need to care if they were ever confronted with these situations.",1
+"This is the second movie based on the life and times of ultra hung porn star, John Curtis Estes, better known as John Holmes. Boogie Nights is also roughly based on his life. Maybe someday someone is going to do a movie on the life of Tommy Byron instead.
The problem is, that the story is not very well told. There are many Law & Order episodes that have more twists and turns than Wonderland, and the director never gets the criminal case going with any kind of gusto. Val Kilmer has two problems - he is not nearly as hung as Holmes is (and no prosthesis this time around, unlike in Boogie Nights), and he is much better looking than mope Holmes.
The director does not introduce one single likable individual among the cast. The racist, immature lowlifes he hangs out with, or his wife, and the police don't get much in the way of characterization.
The best part of the movie is Eric Bogosian telling Paris Hilton to ""get lost"".
Having said all that, anyone interested in the sleaziest side of the porn business in the 1980s or true crime shouldn't miss it.",1
+"I am listening to Istanbul, intent, my eyes closed: At first there is a gentle breeze And the leaves on the trees Softly sway; Out there, far away, The bells of water-carriers unceasingly ring; I am listening to Istanbul, intent, my eyes closed.
I am listening to Istanbul, intent, my eyes closed; Then suddenly birds fly by, Flocks of birds, high up, with a hue and cry, While the nets are drawn in the fishing grounds And a woman's feet begin to dabble in the water. I am Iistening to Istanbul, intent, my eyes closed.
I am listening to Istanbul, intent, my eyes closed. The Grand Bazaar's serene and cool, An uproar at the hub of the Market, Mosque yards are full of pigeons. While hammers bang and clang at the docks Spring winds bear the smell of sweat; I am listening to Istanbul, intent, my eyes closed.
I am listening to Istanbul, intent, my eyes closed; Still giddy from the revelries of the past, A seaside mansion with dingy boathouses is fast asleep. Amid the din and drone of southern winds, reposed, I am listening to Istanbul, intent, my eyes closed.
I am listening to Istanbul, intent, my eyes closed. A pretty girl walks by on the sidewalk: Four-letter words, whistles and songs, rude remarks; Something falls out of her hand It is a rose, I guess. I am listening to Istanbul, intent, my eyes closed.
I am listening to Istanbul, intent, my eyes closed. A bird flutters round your skirt; On your brow, is there sweat? Or not? I know. Are your lips wet? Or not? I know. A silver moon rises beyond the pine trees: I can sense it all in your heart's throbbing. I am listening to Istanbul, intent, my eyes closed.
FOR YOU
For you, my fellow humans, Everything is for you, Nights are for you, days are for you; Daylight is for you, moonlight is for you; Leaves in the moonlight; Wonder and wisdom in the leaves, Myriad greens in daylight, Yellow is for you, and pink. The feel of the skin on the palm, Its warmth, Its softness, The comfort of lying down; For you are all the greetings And the masts winnowing in the harbor; Names of the days, Names of the months, Fresh paint on rowboats is for you Mailman's feet, Potter's hands Sweat on foreheads, Bullets fired on battlefronts; Graves are for you and tombstones, Jails and handcuffs and death sentences Are for you Everything is for you.
SEA NOSTALGIA
Vessels sail along my dreams, Over the roofs, ships in a feast of color, And poor me, Yearning for the sea year in year out, I gaze and weep. I recall my first sight of the world Through a mussel shell I pried open: The greenest water and the bluest sky And the rippliest of lump-fish... My blood still flows salty Where the oysters slit my skin. What a mad speed plunge was ours Into the high seas on the whitest foam! Foam bears no malice, Like lips Whose adultery with men Is no disgrace.
Vessels sail along our dreams Over the roofs, ships in a feast of color, And poor me, Yearning for the sea year in year out.
-- Orhan Veli
I could not have said anything better than what Orhal Veli Kanik said about Istanbul. About this movie, all I have is praise. A very nice and balanced introduction to a city and its music that connected Asia, Europe and Africa at one point of time.",1
+"I am a new convert you might as well say. I borrowed the dvds from my local library. I have been interested in samurai since watching 'The Last Samurai.' My dad told me he used to watch Shintaro when he was a kid. He said that it was pretty good. We are up to series 3. I absolutely love it. It takes a little to get used to the dubbed English voices over the characters speaking Japanese but I really enjoy it all the same. It is a little strange to watch the slight pauses when the ninja stars are thrown at characters and they stick into a tree or wall. I was not used to this but I am now. But I suppose that's the technology they had in the 60s. I've noticed that Shintaro is kind, friendly, willing to help those in need, he's very humble, most of the time he doesn't big note himself (he only says he is better than the enemy ninja). I admire Shintaro for these qualities. It's really interesting to watch the swordsmanship that Koichi Ose has. It is amazing. This series is for anyone who are interested in samurai.",1
+"There's hardly anything at all to recommend this movie. Chase Masterson is always nice to look at and actually can act, though her role in this clunker is a waste. Unfortunately the rest of the cast ranges from bad to mediocre. In a lot of films like this someone will shine through the material and you make a note of them for future reference. No such luck here. Creature Unknown"" a clichéd monster-on-the-loose flick with the kids getting knocked off one after the other. The monster is a man in a rubber suit which hearkens back to the days of Paul Blaisdell. So bad it's good! The rest of the show is just so bad it's bad. A little humor might have made this more palatable, but everyone plays the deadly dull material straight up. There is a twist or two at the end, but by then you won't care anymore.",0
+"Jean Rollin artistic nonsense about vampires, aliens and the quest for immortality.
The women are beautiful and the photography stunning. The dialog is inane. Its a laughable mess. Great to look at but as any semblance of a horror film or thriller purely awful. I'm trying to figure out if we're suppose to be scared or not. At the same time is it a put on or not? Its an odd mix of art film and horror that never quite meshes and while its nice to look at it never seems to ""mean"" anything, and its by no means scary even if the occasional shot or sequence creates a moment of frisson Its well made pretentious twaddle. Something to leave on in the background as a living wall paper for those who like naked women.",0
+"This movie is amazing. It is funny, sexy, violent and sick, but it all holds together for a brilliant Troma rendition of Romeo and Juliet. If you don't mind being grossed out a bit (ok a lot, but it's funny grossed out), see this movie. It's worth it!There's not one level on which it doesn't deliver. I've seen it thrice now, and it is still amazing. I recommend it. Go! Get it!",1
+"Unlike the other spaghetti Westerns, this one has characters that almost make sense, and can be identified to some degree. It still has the goofy gunplay of other spaghettis Westerns. A spaghetti, by the way, is another word for a Western with no plot, no characters you can care about, and goofy gunplay that doesn't make a bit of sense for the era, and relying on great music to make audiences feel something. This one is more lighthearted, like the ones that Bud Spencer and Terence Hill made together. They, too, were superior to the junk made by Eastwood and others, which sado-masochists make their friends watch, if they get a chance. It looks like everyone had a lot of fun making the movie, too. It was good to see a giant actor like Gilbert Roland, who wasn't even mentioned on the movie rental box, yet who was clearly the biggest name. His character was very enjoyable. There is a three way standoff at the end, which is much superior to the one it spoofs (The Good the Bad and the Ugly), simply because the characters are at least a bit likable and a bit identifiable. Not a good movie, but has a bit of fun to it.",0
+"Well, TiVo recorded this because of Angelina Jolie. It had 2.5 stars. It seemed promising. It went downhill fast.
There is much overacting, even from Angelina. She's about 20 and playing a 16 year old. There are three characters that are supposed to be Italian. Everyone else is Italian- American. The native Italian accents were good, I thought. The young male lead is cute, my wife says. Everyone else in this movie is a fat Italian woman. Even the men.
I should have known that when Dick Van Patten was cast as a randy doctor, that that was a bad sign. The two couples chasing their kids around are like the four Italian Stooges.
My wife would not let go of the remote. Hopefully she was not taking makeup, clothing or decorating tips. It was a sick and twisted combination of hideous and garish. It was hidegarishous.
Cutting off my left ventricle was not sufficient to distract from the pain of watching this movie. If this movie shows up on your TV, do yourself a favor and ram your head through the TV screen instead. You'll be glad you did. The only movie I've ever seen that was worse than this was ""Hamburger: The Movie"". Or maybe ""Deadly Friend"".",0
+"This was a marvelously funny comedy with a great cast. John Ritter and Katey Sagal were perfectly cast as the parents, and the kids were great too. Kaley Cuoco was a good choice to play Bridget, who was sort of a toned-down version of Kelly Bundy from Married with Children. The writing and performances were both first-rate.
Sadly, John Ritter died during the series, and it put a damper on things. They had to scramble to change the show and bring in more cast members, and it was obviously an uncomfortable situation, but they handled it well. James Garner was a good addition. It could have lasted longer had Ritter lived.
I especially loved it when they brought in Ed O'Neill in a guest spot. That was great.
*** out of ****",1
+"
What is left of Planet Earth is populated by a few poor and starving rag-tag survivors. They must eat bugs and insects, or whatever, after a poison war, or something, has nearly wiped out all human civilization. In these dark times, one of the few people on Earth still able to live in comfort, we will call him the All Knowing Big Boss, has a great quest to prevent some secret spore seeds from being released into the air. It seems that the All Knowing Big Boss is the last person on Earth that knows that these spores even exist. The spores are located far away from any living soul, and they are highly protected by many layers of deadly defense systems.
The All Knowing Big Boss wants the secret spores to remain in their secret protected containers. So, he makes a plan to send in a macho action team to remove the spore containers from all of the protective systems and secret location. Sending people to the location of secret spores makes them no longer a secret. Sending people to disable all of the protective systems makes it possible for the spores to be easily released into the air. How about letting sleeping dogs lie?!
The one pleasant feature of ENCRYPT is the radiant and elegant Vivian Wu. As the unremarkable macho action team members drop off with mechanically paced predictable timing, engaging Vivian Wu's charm makes acceptable the plot idea of her old employer wanting her so much. She is an object of love, an object of desire -- a very believable concept!
Fans of Vivian Wu may want to check out an outstanding B-movie she is in from a couple years back called DINNER RUSH. DINNER RUSH is highly recommended. ENCRYPT is not.",0
+"Being a fan of ZaSu Pitts comedies, I thought this one looked like it was worth a try. I was quite disappointed.
(The version I saw was on TCM, but consisted only of the Niagara Falls movie; the Miss Polly movie was absent.) The talents of the actors, who give fine performances, is wasted on one of the stupidest stories I have ever had the misfortune of sitting through.
Tom Brown (Tom Wilson) surprised me by being the strongest actor in the show, but the spotlight is hogged by Slim Summerville (Sam Sawyer), who, if he has any talent, didn't demonstrate it here.
ZaSu Pitts (Elly Sawyer) is great, but doesn't have near big enough a part. The biggest laugh in the movie is when she ends up under Sam under a table.
The only one in the movie who has any sense at all is Tom Wilson. Margie (Marjorie Woodworth) is unreasonable in general. While she is physically quite attractive, her personality and attitudes make her completely undesirable. Elly, Sam, and the hotel desk clerk are just complete fools.
Sam and Elly give up their honeymoon suite in the crowded hotel for Tom and Margie. But then they take it back. Sam ends up imprisoning Tom and Margie in their room. Most of the movie is them trying to break out, but Sam, using a rifle, always puts them back again.
Towards the end comes the worst part. Tom, who is finally about to make good his escape, runs into a minister on a lower floor of the hotel. Now the guy, who, as I said, is the only one in the whole movie who has a head on his shoulders, suddenly, for absolutely no reason at all, decides he has to marry Margie!
He drags the minister up to the room he has just escaped from, but Margie doesn't want to marry him. He gives her a kiss, and now, after one kiss, she feels compelled to marry him.
Finally, Sam has the nerve to say to Tom, ""You deceived me,"" when practically the only line Tom had to Sam earlier was, ""We're not married,"" to which Sam replied, ""You think I'd believe that?""
Idiotic.",0
+"Well, to each his own, but I thought Gibson's Hamlet was the most god-awful rendition I had ever witnessed... as subtly nuanced as a paper bag, and as inspired as a telemarketing call. The only reason I watched the movie through to the end was that I held out hope that either it would get better or become unintentionally funny. No luck.
No disrespect for the supporting cast or for Zefferelli's staging, but nothing can make up for the bungling of the main character. I have seen Hamlet well-portrayed as an African prince, as an animated lion, as a rough-and-tumble warrior, as a romantic poet, etc. etc. etc. . But IMHO this portrayal was just a plentiful lack of wit together with most weak hams.",0
+"Yes, this film is another remake. Yes, this film can be considered a chick-flick. And yes, this film is not perfect. The Women is however a clever modern update on the social behaviors of all women, with an impressive cast of A-listers including Meg Ryan, Debra Messing, Annette Benning and Bette Midler.
The film revolves around four main characters, Mary (Ryan), her best friend, editor-in-chief, Sylvie (Benning), Alex (Jada Pinkett-Smith) and Edie (Debra Messing) and the out-of-this-world female creature who is responsible for most of the film's drama,named Crystal (Eva Mendez). Mary is trying to deal with her cheating husband (who's never actually seen in the film), by following the advice of both her friends and her mother (Candice Bergen).
Aside from Mary, there's Sylvie who's torn between her social life and her professional life. She has decisions to make that test her moral and ethic values. Then there's writer Alex who's a lesbian, with a lot of spunk, but knows her way with words. And finally Edie with four girls and another baby on the way, who loves children and has a heart of gold, with a hidden secret revealed at the end.
Together the women live for revenge, rely on each other, and give each other life lessons. But it's the cameos by Bette Midler, Candice Bergen, Cloris Leachman, Carrie Fisher, and Debi Mazar, that show the cruel and usual behavior of women. Bergen plays Ryan's mother, she's tough, silver-tongued, experienced, and yet feels she could have become what her daughter does later. There's Fisher who shows how to blackmail and test the boundaries of selfishness, morals, and betrayal. Mazar, the gossip girl, that shows no mercy for what she says and whom she says it to. Leachman who plays Ryan's sassy housekeeper, she knows her place, when and where she's needed, and how to deliver a good one-liner. Finally there's MIdler, who plays Leah Miller, a crazy eclectic but wise Hollywood agent. She's the one character who gives Ryan's Mary an epiphany on who she truly is by discovering ""what do I want."" Despite Midler's scene stealing performance and memorable quotes, she was underused.
But back to the film, together the women show the audience what it means to live in the 21st century without knowing exactly what you want until the time comes when you answer that very own question. It tackles feminism, what it means to be a woman (fierce, ruthless, bad-ass, tacky, smart, sly, clever, shy, proud, ashame, self-conscious, careless, beautiful, strong, independent); and also what it is that women want, why are women the way they are. It's funny, modern and by all means not a masterpiece. But the Bottom line is, it's worth the money and time to see veteran and younger actresses teach us all about women.",1
+"In 2054 Paris, Avalon, a computer generated system, controls the city and when a young woman is kidnapped, detective Karas (Craig) must go against Avalon to find her.
Renaissance is a splendid blend of film making mixed with a conceptual futuristic narrative that lights up the screen in a shocking manor with a noir themed ideology and conceptual montages that should delight many.
Pixar are the animation masters. Their numerous Oscar winning films are endless from the charming Toy Story to the mystifying Wall-E and so any company or director has a real challenge to knock them of their perch. Renaissance isn't a film aimed for the young audience though, and like 2007's Persepolis, brings a strong and mature approach to the genre of animation to make an older and more challenging film to its targeted older generation.
In 2005 Robert Rodriguez released a shockingly brilliant noir Sin City that shook up the whole usage of green screen with a splendid balance of filming in black and white with the odd spurts of colour and a year later, Christian Volckman took up a similar approach with this equally visually masterful stroke of film making.
Volckman's picture however is a full on animation but it doesn't half look realistic for the majority of it's strong 1 hour and 40 minutes of running time. The faces of the character's are well portrayed and in particular, this film has got to be the finest ever for the usage of shadow. The fact we never know if its night or day is irrelevant when simply gazing into the stony faces as the shadows blend across their expressions. It is almost a clever use of pathetic fallacy, and is finely directed also.
For anyone who has seen Persepolis you will have come to the conclusion it is one of the finest directed animations ever screened for the simple but highly conceptual artistic style by Marjane Satrapi
Renaissance is equally on terms with that picture and in many instances rivals it with stronger graphics and a darker tone to reflect the mood. One scene in particular when Karas appears out of darkness is beautifully shot.
The narrative revolves around a stubborn and nosey political government who keeps tabs on every citizen. The running of Paris is down to the mysterious Avalon which we don't see nearly enough to get an essence of its true dominance. Renaissance is controlling the narrative around a tired cop's attempts to rescue the mysterious woman, and then we see Craig's tired and boring cop attempt a rescue whilst battling with other elements. There are many things wrong with the scripting, not to mention the tired exasperated cop routine is now old, but there is plenty of dashing adrenaline and springy banter between characters to keep it alive right till a wonderfully shot shocking last couple of stages.",1
+"Hilarious and low-budget comedy at it's best. This set of unique individual sketches with extensive self-referential humor is reminiscent of a really raunchy Kids in the Hall. Be prepared for some of the most random and recitation worthy lines, filled with ethnic slurs and awful language. Sex toys included!
There should be more comedy like this around today. This collection of sketches on one DVD will warrant many viewings and reviewings in order to appreciate some of the parts. If you enjoyed The State and/or Wet Hot American Summer, get ready for some more glory. If you are even considering this for younger audiences I would say that every child on earth should see this.",1
+"I'll say it again... one of the worst films ever made and it was made by the director that made one of my most, favorite films - ""Excalibur"". I was floored to see it got a grade of over six. This movie sucks. It looked terrible. It looked like it was shot in 18 days and Boorman must've been sleeping when he directed this. Arquette didn't do anything. Just plain terrible, rotten, unbearable and probably the only blemish in Boorman's celebrated career.
1/10!!!!!",0
+"I firmly believe that the best Oscar ceremony in recent years was in 2003 for two reasons:
1 ) Host Steve Martin was at his most wittiest: "" I saw the teamsters help Michael Moore into the trunk of his limo "" and "" I'll better not mention the gay mafia in case I wake up with a poodle's head in my bed ""
2 ) Surprise winners: No one had Adrien Brody down for best actor ( Genuine applause ) or Roman Polanski for best director ( Genuine jeers and boos ) but they won
Last year's award ceremony wasn't too bad but there was little in the way of surprises and I was happy to see RETURN OF THE KING sweep the awards even if it wasn't the best in the trilogy ( FELLOWSHIP was much better )but what let the BBC coverage down was Jonathan Ross getting a few of his sycophantic mates round and pretending they were hilarious when they were anything but . So when I heard Sky were doing the coverage for British TV I was expecting Barry Norman and Mark Kermode to be doing the links , but instead we ended up with Jamie Theakston and Sharon Osbourne ! Oh gawd if British TV are desperate for film critics ( Obviously they are ) I'm sure both Bob The Moo and Theo Robertson will happily fly over to LA to give their honest opinions on the winners and losers
Chris Rock wasn't too bad , but he's no Steve Martin while the location seemed to resemble a sports hall with seats put in ! Not much of a glitzy arena in my opinion . The main problem I had with the ceremony was the format with the "" minor "" Oscars handed out to the winners who were sitting in their seats ! There's no such thing as a "" minor "" Oscar and just because the award is for Best Animated Short or Best Costume Design they're as well deserved as Best Picture or Best Director . All the winners should be allowed to march up to the podium . What a bunch of arrogant snobs the Academy are becoming and I quite agree with the comments that this format is disgraceful and if it wasn't for the surprises this could possibly have been the worst ceremony in history . As for the awards themselves
Best Supporting Actress - Cate Blanchett . No great surprise for a competitive category
Best Supporting Actor - Morgan Freeman . No real complaints since Freeman is one of America's greatest living character actors
Best Actor - Jamie Foxx . Most predictable award of the night . Yawn
Best Actress - Hilary Swank . Major surprise since everyone thought Annette Benning was going to win simply down to academy politics but Swank did deserve it and gave the best speech of the night
Best Director - Clint Eastwood . Major surprise since everyone thought Scorsese was going to get the award simply because he'd never won one . Actually I'm glad about this because if he didn't deserve it for TAXI DRIVER , RAGING BULL or GOODFELLAS he didn't deserve it for THE AVIATOR
Best Film - MILLION DOLLAR BABY . Again another major surprise since everyone thought the academy would split the awards for best director and best picture while I thought the Hollywood friendly plot of THE AVIATOR would have made it a dead cert for Best Picture while MDB's controversial subject matter would have turned a lot of voters off
What these awards perhaps illustrate is that this year the voters have decided to ignore Oscar politics and genuinely give out awards to people who deserve it something they haven't done in the past , I mean A BEAUTIFUL MIND beating THE FELLOWSHIP OF THE RING for gawd's sake ! And long may the academy vote with their heads instead of their hearts",0
+"Ordinarily I really enjoy movies like ""Chances Are,"" but I wasn't quite satisfied with this one for a few reasons. The first half was pretty well done overall, with Alex Finch dying and being reincarnated in a new body (played by Robert Downey Jr.). He meets up with his wife (Cybill Shepherd) and friend (Ryan O'Neal) and his daughter, who is now grown up. The scenes with them meeting again and Downey rediscovering who he once was are well done, and there is a good amount of emotion and happiness once Shepherd finally believes its really her husband reincarnated, but from there the film goes downhill. There are several sex-related scenes that turned me off completely, especially Downey and Shepherd wanting to get together again despite the difference in their age now. After that, however, the film manages to end in the most satisfying way possible, considering the circumstances of the plot. I was disappointed because I did not expect the film to become so immoral by the end. There was great potential with this story, and the scenes in heaven are well done. There is a good theme song sung by Peter Cetera and Cher, but ultimately the film is not great. For a better, similar film, try ""Heaven Can Wait."" Decent, but I really kind of wish I hadn't seen it because of the scenes in the second half.
\n",
" \n",
"\n",
@@ -951,13 +933,13 @@
],
"text/plain": [
" row label 1 row label 2 distance no. of items in clust.\n",
- "cluster 1 0 4 3.835396 2\n",
- "cluster 2 1 2 4.347073 2\n",
- "cluster 3 3 5 5.899885 3\n",
- "cluster 4 6 7 8.316594 5"
+ "cluster 1 0.0 4.0 3.835396 2.0\n",
+ "cluster 2 1.0 2.0 4.347073 2.0\n",
+ "cluster 3 3.0 5.0 5.899885 3.0\n",
+ "cluster 4 6.0 7.0 8.316594 5.0"
]
},
- "execution_count": 13,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -966,23 +948,23 @@
"# 3. correct approach: Input sample matrix\n",
"\n",
"row_clusters = linkage(df.values, method='complete', metric='euclidean')\n",
- "pd.DataFrame(row_clusters, \n",
- " columns=['row label 1', 'row label 2', 'distance', 'no. of items in clust.'],\n",
- " index=['cluster %d' %(i+1) for i in range(row_clusters.shape[0])])"
+ "pd.DataFrame(row_clusters,\n",
+ " columns=['row label 1', 'row label 2',\n",
+ " 'distance', 'no. of items in clust.'],\n",
+ " index=['cluster %d' % (i + 1)\n",
+ " for i in range(row_clusters.shape[0])])"
]
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 17,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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S5JuBf8rMBwM3As8E/gT4Mj2sLxYRtwfWA0cCv2h6HkmSpNWiSRCbBD5cb98M7JKZvwJe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\nBT4F7AY8aencMQ1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"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1024,16 +1006,14 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 18,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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sTLGxsZoyZYqKi4vl9/sDXrOhoUEPPfSQ+vXrp8jISN1xxx365JNPrPoVAMARmmaEgr2R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\nxjouQuYrsAAAAABJRU5ErkJggg==\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1042,12 +1022,14 @@
],
"source": [
"# plot row dendrogram\n",
- "fig = plt.figure(figsize=(8,8))\n",
- "axd = fig.add_axes([0.09,0.1,0.2,0.6])\n",
- "row_dendr = dendrogram(row_clusters, orientation='right')\n",
+ "fig = plt.figure(figsize=(8, 8), facecolor='white')\n",
+ "axd = fig.add_axes([0.09, 0.1, 0.2, 0.6])\n",
+ "\n",
+ "# note: for matplotlib < v1.5.1, please use orientation='right'\n",
+ "row_dendr = dendrogram(row_clusters, orientation='left')\n",
"\n",
"# reorder data with respect to clustering\n",
- "df_rowclust = df.ix[row_dendr['leaves'][::-1]]\n",
+ "df_rowclust = df.iloc[row_dendr['leaves'][::-1]]\n",
"\n",
"axd.set_xticks([])\n",
"axd.set_yticks([])\n",
@@ -1056,10 +1038,8 @@
"for i in axd.spines.values():\n",
" i.set_visible(False)\n",
"\n",
- "\n",
- " \n",
"# plot heatmap\n",
- "axm = fig.add_axes([0.23,0.1,0.6,0.6]) # x-pos, y-pos, width, height\n",
+ "axm = fig.add_axes([0.23, 0.1, 0.6, 0.6]) # x-pos, y-pos, width, height\n",
"cax = axm.matshow(df_rowclust, interpolation='nearest', cmap='hot_r')\n",
"fig.colorbar(cax)\n",
"axm.set_xticklabels([''] + list(df_rowclust.columns))\n",
@@ -1085,10 +1065,8 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 19,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1101,7 +1079,9 @@
"source": [
"from sklearn.cluster import AgglomerativeClustering\n",
"\n",
- "ac = AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='complete')\n",
+ "ac = AgglomerativeClustering(n_clusters=2, \n",
+ " affinity='euclidean', \n",
+ " linkage='complete')\n",
"labels = ac.fit_predict(X)\n",
"print('Cluster labels: %s' % labels)"
]
@@ -1123,10 +1103,8 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 20,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1135,7 +1113,7 @@
""
]
},
- "execution_count": 5,
+ "execution_count": 20,
"metadata": {
"image/png": {
"width": 500
@@ -1150,16 +1128,14 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 21,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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WNnpe+BSPnlTJkPiqpDRMKUvnT78FXVApaFLQFEjhFUcy++FZ+hLu6OjKBkfe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\nmkREREQCUNAkIiIiEsD/DzM1VYYUuee1AAAAAElFTkSuQmCC\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1170,9 +1146,9 @@
"from sklearn.datasets import make_moons\n",
"\n",
"X, y = make_moons(n_samples=200, noise=0.05, random_state=0)\n",
- "plt.scatter(X[:,0], X[:,1])\n",
+ "plt.scatter(X[:, 0], X[:, 1])\n",
"plt.tight_layout()\n",
- "#plt.savefig('./figures/moons.png', dpi=300)\n",
+ "# plt.savefig('./figures/moons.png', dpi=300)\n",
"plt.show()"
]
},
@@ -1185,16 +1161,14 @@
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 22,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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v4LMxQ5k+8nXWzv2KiGe1woAGg4GNiRuoXi2QVV9NZ+u6b6jZ4HFO/LKfUqV9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\ngg==\n",
+ "image/png": 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tBtdyMiWZ9UvfZfXsafTp04frr7/evk/79u3Ztm0b06ZN48NVKzh79iz169Vn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\ngg==\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1202,18 +1176,24 @@
}
],
"source": [
- "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,3))\n",
+ "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))\n",
"\n",
"km = KMeans(n_clusters=2, random_state=0)\n",
"y_km = km.fit_predict(X)\n",
- "ax1.scatter(X[y_km==0,0], X[y_km==0,1], c='lightblue', marker='o', s=40, label='cluster 1')\n",
- "ax1.scatter(X[y_km==1,0], X[y_km==1,1], c='red', marker='s', s=40, label='cluster 2')\n",
+ "ax1.scatter(X[y_km == 0, 0], X[y_km == 0, 1],\n",
+ " c='lightblue', marker='o', s=40, label='cluster 1')\n",
+ "ax1.scatter(X[y_km == 1, 0], X[y_km == 1, 1],\n",
+ " c='red', marker='s', s=40, label='cluster 2')\n",
"ax1.set_title('K-means clustering')\n",
"\n",
- "ac = AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='complete')\n",
+ "ac = AgglomerativeClustering(n_clusters=2,\n",
+ " affinity='euclidean',\n",
+ " linkage='complete')\n",
"y_ac = ac.fit_predict(X)\n",
- "ax2.scatter(X[y_ac==0,0], X[y_ac==0,1], c='lightblue', marker='o', s=40, label='cluster 1')\n",
- "ax2.scatter(X[y_ac==1,0], X[y_ac==1,1], c='red', marker='s', s=40, label='cluster 2')\n",
+ "ax2.scatter(X[y_ac == 0, 0], X[y_ac == 0, 1], c='lightblue',\n",
+ " marker='o', s=40, label='cluster 1')\n",
+ "ax2.scatter(X[y_ac == 1, 0], X[y_ac == 1, 1], c='red',\n",
+ " marker='s', s=40, label='cluster 2')\n",
"ax2.set_title('Agglomerative clustering')\n",
"\n",
"plt.legend()\n",
@@ -1231,16 +1211,14 @@
},
{
"cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 23,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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zGDpsGIMUaQr7oHzE3rIHzi6JYDAY8Pzzz+PNufNwS7fu6HZ/X3RM6I3qqspa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\nQohIAKsALJVSfqCwiWaPvaOyu3rstdL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+ "image/png": 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7tuPpbbu30969e22+xk6dOnHTTTexZv4sdq5PNo63uLCQneuTWb8okQERo/AP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\nGQyaJEmSMhg0SZIkZfj/hsMYhadW6SQAAAAASUVORK5CYII=\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1252,8 +1230,12 @@
"\n",
"db = DBSCAN(eps=0.2, min_samples=5, metric='euclidean')\n",
"y_db = db.fit_predict(X)\n",
- "plt.scatter(X[y_db==0,0], X[y_db==0,1], c='lightblue', marker='o', s=40, label='cluster 1')\n",
- "plt.scatter(X[y_db==1,0], X[y_db==1,1], c='red', marker='s', s=40, label='cluster 2')\n",
+ "plt.scatter(X[y_db == 0, 0], X[y_db == 0, 1],\n",
+ " c='lightblue', marker='o', s=40,\n",
+ " label='cluster 1')\n",
+ "plt.scatter(X[y_db == 1, 0], X[y_db == 1, 1],\n",
+ " c='red', marker='s', s=40,\n",
+ " label='cluster 2')\n",
"plt.legend()\n",
"plt.tight_layout()\n",
"#plt.savefig('./figures/moons_dbscan.png', dpi=300)\n",
@@ -1284,6 +1266,7 @@
}
],
"metadata": {
+ "anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
@@ -1299,9 +1282,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.4.3"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/code/ch12/ch12.ipynb b/code/ch12/ch12.ipynb
index a8925b82..979ba574 100644
--- a/code/ch12/ch12.ipynb
+++ b/code/ch12/ch12.ipynb
@@ -4,9 +4,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "[Sebastian Raschka](http://sebastianraschka.com), 2015\n",
+ "Copyright (c) 2015, 2016 [Sebastian Raschka](sebastianraschka.com)\n",
"\n",
- "https://github.com/rasbt/python-machine-learning-book"
+ "https://github.com/rasbt/python-machine-learning-book\n",
+ "\n",
+ "[MIT License](https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt)"
]
},
{
@@ -33,23 +35,21 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
- "Last updated: 08/20/2015 \n",
+ "last updated: 2017-07-29 \n",
"\n",
- "CPython 3.4.3\n",
- "IPython 3.2.1\n",
+ "CPython 3.6.1\n",
+ "IPython 6.0.0\n",
"\n",
- "numpy 1.9.2\n",
- "scipy 0.15.1\n",
- "matplotlib 1.4.3\n"
+ "numpy 1.13.1\n",
+ "scipy 0.19.1\n",
+ "matplotlib 2.0.2\n"
]
}
],
@@ -59,15 +59,12 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {
"collapsed": true
},
- "outputs": [],
"source": [
- "# to install watermark just uncomment the following line:\n",
- "#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py"
+ "*The use of `watermark` is optional. You can install this IPython extension via \"`pip install watermark`\". For more information, please see: https://github.com/rasbt/watermark.*"
]
},
{
@@ -111,13 +108,14 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
- "from IPython.display import Image"
+ "from IPython.display import Image\n",
+ "%matplotlib inline"
]
},
{
@@ -144,9 +142,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -185,19 +181,17 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 4,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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EnDOYTz75RGbMmCEzZ86UhQsXyocffijbbLON/OhHP5Ltt99eyAfj+frrr2XdunVy4IEH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f2pVWJtHuXxRtEF5XdgRvlJ7DO/UfAn8tk3WKoMzkhxhzOxbPv5KS3Yl9b2Ri4oxr8OaePJlO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\nRK5CYII=\n",
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eDj300KjzY10rOFEBERABEQgRoJzyfXx0mzZtms2YMcO+/fZbdwZk0llnnWWXXnqpde/e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\n4avK8Xt0STm5XHbTI13H+n7flR8qr/zwoo7fAeX7l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xYsqJ/cfvvttZylCZJ+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BqeVAr3hOkJ8aQyYdIxzhOZ1SwmjIGLNVBEf79RYMZqns/7QUMv5IWNkbvXQisE6bMkHxBO4i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A/xa/sRaWPeloux4iPeSUvSEJ5vbG3QniM/Af1wT71PfOO258tHAF5I+T5+MMP5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\nTYTi/0S+WR1ug083LIMdAnfw5A52Z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EM5bHC9yoIR16tcUj7yryeWc1Yw0LnxxFeXXY5b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sDH8mmLTlsqHOt771rbp7Zc18cPnBD35QnvOc59T1oKbdRShN3VL2hJ9snvbB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\nxS6ns2bNqn4R5hIgyg13K8B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9L8pfv3et8qos5TzovIFite0c2NrppQOfE6//S9n5jiPL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\n5YwfZ/3K9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Yo9mIBAQCAgEBAICNRXBMKIZ5Y1o48+QgGXRj412qnjVeCTYIDw8N///d8u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\nS22T8sw2gbaPLCYTjiYgHHpAOPSAcOihSqzbiTM2ywfURPScpvIAlNUJ4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It3T8j5szw8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\nSUVORK5CYII=\n",
"text/plain": [
""
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {
"image/png": {
"width": 400
@@ -212,19 +206,17 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 5,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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DfbKzoxqr3hrGwKj1wFiLSy8xpzPgKMw3KFpaZLMlGp7C+Pi49DcxMYbXbyr+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BIOGCwxVhXTAbyiLUJA9INAIuGEfvb111/bwIEDDbp5/PHHA+NeH+PUogIU4GXRD+MCWkZohzqt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M0S\nuh/3kQO8YOzVF/s2o+wWwzgS16WswncUx2M04jB3AWMYC5Jr2jnCSRd/m/5f+Qkm6uJA410whr/+\n9a8v47s5O895z/sNjjYGmmBytNt/I2tfD0xW9Hj44YfL8cgjjxQGNVoVWjerLhnYHBhrfs7cgKSY\nYwHZgEWAf7jJ/YQTTihMcybDpBuJjDUWCtNEK8s9y/4j8BPGRxskGN2kQcAYEoyOyTfpWP2KMAHf\nq+JX2/GzZ4khjAqwFR9DYOGO5ZuC2RNEOgRlBBgMJ0sMgUce3NKOCtR9bEidV23DId/YlGfQPQ1v\naMTYMg0+8YlPFKH9xhtvfNwjXB9PPfXUMnYRPmpAy1aJI2Cj42lgp3XWEotBoH3xVpsC2SyR0kLc\n1lCaoiRyAIqlowr6lCMWJouTGIcc2y6YqBchI3VJG3MjFkti3iH0sw458lxwkudbejQx0ASTo9nu\nG1nrTGxSWkJ+3FbtEA9BE49x5jqRFZcMaHmnW6EMcAK1aT8Fa2NC+X2fd955xVpAY2OQNCEEvHeU\nAF4xSJgLlhHuV4Q6QgXcmTAw7NkIbQhufuu3fqsIDjSofOgT0Dvk3WnPKIfJjECiLeXtAGJS6jac\nlkf3Og1lhBM04htcKXYN6j7iHBPJ4uiAQxpbB2skZoHF0aEd0YcD3uu+UZ/vEr5Y+9A+awXayFKu\ns+p4/fXXF5zef//9j3uMReCkk04qgvrv/u7vlvvGMy5i6G+IuyNmThwKYQZDr20OC1injRUO40NA\nmSJwEKAIUtMAHgh9FCPid0BNo9Pe28Xr+tGjE1fVuNPWjLp729jPtCWhJPOzvkQwF5Ol7V1H+xSJ\nLIUUSs7RhHup9zbWfRdp9DDq1FblOgyst28+BgOZlAxmdlm2JCcmGZN4xhlnFK0jJilm3qR5L2ky\nzYBWD3DOaaE8y33p29/+9t7dd99d/r/xjW8sS4DSTOad5HUUUppdVinMKQ0udyl4gnOuJH1uJvPw\nwgXMhouAK0vtnz/v3Xn3WdHknaBRDA4GbxGLSf0NDPkjE2ucMhN+MOLbDukTUsKheKKf/OQnRTjP\n8p0sAbT1mErn8AcHcY3EfBJaAQad+xsLmAOzXPezbceX8rO0shxijggliwi7aAheuEX2AbdGyhbW\nP22hr7HysYgMAQIT66DgcoHCwf2Qd1d9xv4acIMenAcw0xFGaqsSC+tdd92Vx0qKDh0s3MYU1paX\nv/zlhVH94he/WBhVDKwx+iDr9phCHtAfeDCHGb/g4b3vfW+x0P3gBz8oAjE81BaEAyrWaJ+p62c8\nQTPS7GeifuYDwojDucN1fU/dj+I8PFoD7EBGTTDZgUbc1ioYwIDUoGwtc9o2E9a73vWushKNATyH\n5/JO3hs6ieW5THwGP4Ol3WitokMjaQ+Cj3/844V5OGoDYxgfeKW54o5FUFsFaEP5nC+7LHHft7nx\nESxpazHV2pA7SKwdi1h26vwx4yxqGAVM9xhWnjr/gzhP35ByD0HbN998c7EwYRzF4GBqta36ETy7\nfSrlDP3T7rMmcfeDdziCe5pPOzgL7t6fuFSmfyVNPtuSEsa5CBofCGAEgEUBXuCYJaoP4P3qq68u\ngj9BZYi1pM4nywpj7C0EsS5AEwTSCCN1HAi8EEYoMDCVXdA/WZyMJzWENgl8FB2Y1AsvvLBYjn70\nox8dF0zClNbv7to5XJjTKISMXy95yUvKWPuFL3yhjL1h0DNXbVv96/oZTwntDgKr8cH8wmJCGJGm\nvto+dd7WcWTb2mpTy9sEk01tmR0vl8HLQZt7wQUX7P3whz8sblVcrTA604SRDFjSHH2okjeQD8j/\n8mfyk3cNhu5997vfLUGsBlDCyZvf/ObC7Ho+38y7u5R2lwBWNwyHmA4TyCoAr7TDNGYYEpaNVUB+\nf/u3f1vcj2iarcClbRILoy5DY2H6ysGFDeNNk4lpxDxuC8CNgzD46U9/urhBmvQJ+a9+9auLdh6u\n9Ic8m1QdnQdC79LugXFgzeRi6SAUYqw++MEPFneN+t3kt+kpC5KYKMCKQau/LFhMQR6sC33AIidY\n/sQTTyza4b5npl0jINgnBI7FyfUJBtPenXcdXWjLCCPRbmtvAixBRJ+bNSYQ7gl3+pGy6e9cBRP8\nzFrE7TKWAjuAv+c97yn9V7/FoB4VwQROjFdwtT+Z7y6//PIi6GPW4Th4SH+a136bdh89qaO2RksO\n19CTdnaoZ20hcQ9sa503rQ22uTxNMNnm1tvCsocBMkhde+21xYxN+3fFFVeUPSUMZu7lOVU0UBm0\nHDmX5sgzQUfelSavbuqeI/nJ2+D52c9+tmg1rSl/6623Fq18/Z18YxdSzFO9BDBmnOWIxhdzRvur\n7qsALfIjEzcpGvsXvOAFpQ2XzY/WXtkwMTT/JrUAzRyGGWOlzNyxhmzMmPeTWizBRpGYBn7RswKT\n885hpqFjPv2EA8ze/oTRscmfwGvCCdrP4fllIX0lqXxou20maNUowuxnPvOZrXLzQp8sQfCDacaA\nrwoC59Fn7fZU50lYvOOOO5bqW7FCEhIIQKsADbb+rs9YXQzNA0wjYYRlxDhQ97Np3zOWcDWTp9UQ\nWYe8V1tiKSf03QgmaJb15/vf//6eDSsjmIRBnfatbb+O1iKYUNy89KUvLbijCKkFk23FQ8ak1BNN\nOICxg9CFNtQvR+6Vh9rPkcfA/7tsAkceCw0BB4KBDFgmbIHnXKiY87/61a8WbVyEEoWpB7BoWKQG\ntKTOcxjscriW86T1NXnnSJkMop45duxY0QCzoCifQHtuK3n+QBC15o9wH8BICqaFc0y8pYMJD7Sa\n3IBokeFkVStH3DYwQHAo/2WAVYRgIj9MX1dzq+24jD3hCU8o5cdo0dbaW2GRGBmMgTJ61yG2YkgA\n9DJ1WuWdCBiYPEI9qwjtK8Haf5p5+MYQ1P3KN0PLYQr0kZwnzTPSQN1X0Ib/+oa+zHplc82PfOQj\nxdUL00woAnUeyWsTUm5qhBL4Qf8Y8TEAMw8f4tjk3QWB7wRgzPiigLHXl1g3lqFN9IKu9X0uV4QD\ndKM/cd1EN4QFfUn+6GEWoAHLKlMKAAKJ2MC8pz9S+PhvLPU82nHol1x4fVt8TuhwU+llFh4WuRcc\n6Js3TlZzIxiKM1H/WjjbVjyk3NKMJ+pmjM5c7r/DMzkWwWF7drcx0Cwmu92+G1M7g7Hj0ckKJNwY\nTMxf+9rXygRu8nYPGKQyaEkNbBnAnGcQk+b5ctL5SX51GmYqjJo0h3sB3zOBcyvDXHzsYx/bs/mV\n74N8O89vSwoXcXsyKWJy+pYAxkjQ5BFgMFirapHhkmbdN1lN6kDZIbjDiHG1ieA4T1Cg+c3GjPKf\ntjHjrG8Tzvj0ows4GBqkPCvPse5pR4cdwa10wx2SgP+Od7yj9BVlruk5fSb9J2muh56ldX/Jd5LK\n03nS1Ed+Dv3mW9/61t5lE10XZlN8i13S8508vwkp5QjLGNq0uSEt/9hgIQ+r/tVtUX+D+45xZVEg\nSBAC4ZhLl3QW6McsGg4CTdrYYgf6NoFMoH/oYFZe9T3jhGB/eRIyWJyMKV1Qf8H++q3xVv/0roPb\n7J133lloWT308dBnN59d+J/+YyxEe+LZtCGBniDviHCyaHtsGn5CZ0lTvtQraa63tGEgGGgWk2Ci\npWvDgIHJgVnk7mEVoPvuu2/vKU95ynGNokGq1qhEs+KaA9PjCBPk+SFH/XzOk0/SeiJMWd178Ytf\nXLTnF198cdECm0AymCZdG9JGzhgzY8Uhe3YouyWAuW6xEHQBvp/4xCcWNwxBwRiXaL+7zw75D5d8\nzmkG+aEv4h6FgSQgaBda+CErJfkehovlg6sOwYZLCUZs6FKrGC1MViwn8LHsql9DcDTkGTgAGL2b\nbrpp75RTTiltYwUkAh/A+OU5dA0X3X6VPtVN86zrzvuO9KGa/n1PmRxPf/rTyyISrFsWs3CPC0+e\nT1oKe0g/mOQIJawDT1pTLNHTnva0YsEjlPcB4d+3WRkWgfRFVkEujH2WHnWkBLL5qHHXs2JUCAcE\ndd9kHWHdQeuLtktc4PRPeWjjaQoHedfjTE0vLLJf+tKXitsoXPTR1yK42YZn1V8/pWy56qqrSkwY\n1zlzXvoenC3aJptW97oOOc9cu+112zRc71p5mmCyay26YfXJJMRtgFDCbUjQrAkSIwMMVhFEojWr\nB+l6MKvPM9gtkuZ9affIYJkySzEuYhVotDD1/IHzXNINQ/ljigPH3DYEjQvsNwFadQgzMav8BAnM\nBGGCRpRGGb6WBS4d8nEQDvyfBzS9YSBptfsYsFl5ECQwO9qRcMJtBcNmZSEMwDzAaHEF8x4BxXuY\nuMMAdQhdWiziwx/+8N7b3va2EqelnLVAEgEjfSppLYhE6NCmzrt9ob7efTbPS2saQmvKiHEWR0Ew\n/OhHP1oCol/2speVPg539TsHjUvMOZrC0BOi1r36mnbBvOuDfXDvvfeWWCYucYsAQTl90zkaYIVm\nPbMSHmsad0x9yH31NJZxp9IutaCwyHc96xssJSwf8qPgGNKf0u6hY7gh/AvoJzhZRAHdeS7PLlq2\nTX++rjuLGavJ+973vtIHu3PeptdlaPnSnrvapkPx0J4bjoHmyjUcV+3JBTGQQdhyo8cmsRsmsW9+\n85tlEnPPQIW56TJM3clpHQOa7zsAhsrBvF4frgHlwczYU+Xtb3978ePvlrE8uGE/3JEIhJhxzCK3\ngUWXAPY+968xlvyNC4qy2NtkFjOjHR6ZBM1j6mie0c4qgGmzMaMUAwAXQ913CCUYMeWlGR5itVml\nrN13Q6tcX8Rz3HPPPSUui0UPc4dO9RE0mf4kdbhW06rnlulPKYOy+Z7/Ut93aK+UJeXxfdp68S9P\nfvKTy75BhLtly9DFy6L/MeloioA+Bk3N+z5FhsUlCAbc2r7xjW/0vkKAtseMBTcWgSwsgS7RtPoB\n+GWJYDV0rCKE1OXRxolL8T3lpehYBEIzhBrCodQiIxdddFGhFYoE9TksGlmkLos+mz4Ejyy4BOPL\nJm6PZ599dmk/7QSv6beL5t+ebxjYFQw0wWRXWnLD6hHGhSYMM0fzLNAxbgiYJRNQfdRMlOosw0At\nigblBCbMTJomDodJE7MFlI3LzFve8paytCOf/prhKw9tyI9yc6XBGIH9ySpNJsF5vuh9xVd/S+gK\nFJYH97tVICtrYVSnubBoE24OrBzcvqyONQbI1z45tMnaGvNG2zvERcsysBYMwDg85znPGWTxGavM\nyk0oYa1j+cLICdYObeonmJn0JedhbsLgpS8lXaZs6SvSHH39JgKKb+gjj05cil75ylcWPGPA4T3l\nWqYcy7yDCUbHXAnRMFpeJ2RpX9/gZofpZEViIekDAhuan9e/4JslhLWEmyW6APAZQUQ8FDodEyg3\nuIJSLhjLxZMM6TfdMqAb9GF8VfYc8kPfFhxBx+h3FVrtfncT/td1F79ogRUKD9Zp86JjV+u+Cfhv\nZdgeDDTBZHvaamtKGqbF5GPlGRr3Bx544Dgzh1kxAJs8aw2R6+AwJqSUOcKJyRODH+HEdZOlFcQu\nvfTSsu/KcycryShzyr0JDYRhsUIORox7B8ab68YqQMPMHx4uCJmr5IcmuG7I83nPe15hcrpli5XG\nd7idjY1fzKlvcCvTplYR4uoyj+6iAcdAWBlsXhB+t16L/g9NokVB7vBGOKbtR49A+R36UZiamibV\naV69Fi2X55UtqbJ0+412ds1zvs96x1WH9UA94G5dZSsFq364yxBKMNWzBOLqlZVO1dn3WDQsLrE/\nUQwAFg00b6+PPvCc3eG7Cy3AJQWPvi31H2hzFgsCvDpaaGAd1jzfJZT7rjJSKKC5ZSA0La8IJc5v\nuOGGvU984hMlJoYyQv4HRR/L1GPRd1JvfZkFlmDMhYuiS7+tBZNdqveieGrPNwzAQBNMGh2MioF6\nALaalX0N7r///sL8uYdpikBSCyWbMBgrHwijhRE3aSZN+W2+SHto3X5aSnVS/sMETA+BxMo7ykLz\nyv1pWQaiWxdaWkurmkAxV7R8y4IyYs4wUccmLn417iylavlRcSgYLTSyLsgKZdp32gpl3W9zS+Se\nRAPOckL4WwekH6HF888/v7gBWeWJ9cg9ONO2EfAjlLgeQa7G6zrKKM/0GWmfcKL/pLxidbifcaO7\n++67Cy0p4zrLqW25YXLh258w1QT1dX4PTkLDfXuN2FHd/kCe6QO4UV79jEVEXyF4wC1Ad8Yc8Vas\nLNrafQtEEPb0zbR/X/6LXNNuLJysjPIUowKHq0LGV8IU4URKkaI/iSXj7qvf++a622rVugx9P/0D\nPZ49cd1iHdPOYtZYsrV3PR8Ozbc91zCwixhowe+72KqHWKcMwAbec889tywHaTUr1000Bl8DcT0I\nr5s5GYqOlKOb5n11cGCYLSOMGaDJrp/PsweVKg8GG6MvHgOzj/GhdRyLQVEXbhuYX4wSTbD4DPVe\nBggdykrYwWhliVGaRD75JmlMinvrBN9VD9YbZYFHTBOt/jTc5R6mMSuWoeUxIXSmLOISLpv4oVta\nG05cUzZtkX7k+/677gg9jlmmaXnV33Jel6F+R7m5AB2bCKKUFQQFLk6hoaT1O6ueE4qMQ9wQxVZh\netfxnbqc6Jq7nbZh7dMuNRDouSxZAIR7VBcIGVbxQmfOWff0PQKBlfQcBB7XUhfCMdcxNAz/q1g0\nUx6CAusNYRLzzFK6aDxJ8pqW1nSu3E+axJewmnDrYtlK/ZJOy2fTr9f1FOMknsZKZNkANv03fXjb\n67vp7dHKt/kYaBaTzW+jrSmhARgDQgsmMNLKT/Y1cN3EYwDOYRA2AOfYtEpmMok/NE0XzZ7/7gmk\n5poiqNVeBYfhesA1hUsSQQE+uSTVE/o6cMpSxLUDE0H7vCwQBh588MFCFwLhMWksMugBAx5hZdn8\nF32v6wLHBYdGehqw6tB6YxCVdxULUvcb6UeWeiVkEvDt6u46/Gjruh9FGJCP+4cJdb/RV/SbHCm/\nXc8tImHH76zEpA5jgm8TSrjrsTCIyRn7G93yGvu4POqXmOtZq8ixRGD2CWh9wFXwK1/5ShGohrgM\nGpv0J7hmNRnyTt93XeN2J/aBcEIYYaUjaI0FNY3EaqLcrp9zzjmlXxlnjAEZV8f69mHko17okdCK\nLvYnQuYtt9xS+qp+TBEDv6nrYffhw8BR+2bDQI2BZjGpsdHOl8ZAPdl88Ytf3MN8MMmbICOURMNb\na4Y2dRBOuaQ5IEc9MSCYHRrz66+/fu+Nb3xjmVzUM+8tjcgBL/p+vQQwDSoGdt4SwAOynvuIb8S9\nBFNOC74MmJDhCw4JJdxFaLhtZjiGxnfRMqFTwhZGieZZsDsBm3CCYeiCAG7l5fPvsGoZul4VQl+Y\nQvFZLF9f/vKXS7Zoyzf0I4dzZQvdHQTtzatfyiCtD+9ljBAjQxAU6HzmmWcWt708Oy//Iff1D4yt\ndkSvaAqO1g0ESfTMCkcbPg1YcNA8weXhhx8u40n3WfSnzK94xSu6t3r/owOWDVZHDPCyFs1f/OIX\nxeKDtrUT960x6LoudGgEPYDQvNQ4xjrIXfLUU099HA3V+WzDeeqGJt/0pjeVcdviFcZObZY5se7H\n21CvVsaGgXVioAkm68TuEco7AzBtGwuCPRb4k5uEMKEZgE1ym8RIzWqiTKA105R6SmkSr7vuusLA\nHvtVrET97Ky8l70HvzTBGDuaNu4pLCVwfBBgAiU4WO6SgEIoWtblikaUqwi/expFQbUY8cMCdVMf\n9WOF4koza2NGTC8BwnMOwok8lgU0Ffr69Kc/XWKzxJUQmtCVvpO+FKFk3fS2TF2UCfSVDYPm0F8w\naD//+c/37HGSZ/PuMt/1DvxxpSIgECoxuqu0ydBy6JeslyxnNgKtv6lM7otP8owUfXG71HcJJ32g\nHtqb9WQIoBMWGAKZ8XaRQHiCiJg5ZfMuYW5/otlftT1mlTt513RPuOKuJj6RO1vtfpfnZ+W5Sffq\nehG2PvvZz+59/etfL22uLto2B3pxbdvquEn4bmXZHQw0wWR32vLQapIBGHNp8LWCi4HYoGvAzeAr\n3RahJMisJ4qcqy/mCkPunMsFdxv/1zW5cHWwcZoAd1p9TAMG6KBdnuCFQCROhFCBIaedrRmx4G5e\nCoexTKAN7jbB8bx313mfNpP1RNuqn3rScGN0u9pjri7x7+c2tIpwEroS62K/kve85z3FLUddfbcW\n7jedkUk7pj/kvzo61AcNY0DFYjjPs8u2rXyNPawGmPK+GI9l8571HqaeG6I+qk/qG8ZCtEPwIoxY\n0Y2lRBlZW7MxIQGNgHvffff1fsLyyvoXy8UQQKPoBy0S8vWrecD1TPkJT8YTgtC6x5XQQ8oGLxFa\nlRt929SWssLmk7F4dd/L+5uWqg9QJzFDb3jDG/be9a53lb2wXK/7s7pmXnSvQcPAUcdAE0yOOgWs\nWH8DcCYV2rqzzjqrmKxNbgZbE2MYqk1npqahossw1XXGYFhCWN2ytG33+Wn5Dr3OOsJKgnEQ6MpP\n2fK2vnlYQDtr0uXGxHVEcPEiTAMc0girEy0ptyn1wVhtAqBd7losKBhKWuhHJ/txED5r9zV1xmgS\nXDxDE044CSM1tC6hKQyt+AurrF111VUlH3hJH9Kf/B+bxoaWc5HnQg9J827GC/FQ3J/EmhDsU6fu\n83lvXmrhBIKu9hG/MYQpn5fnkPsED3SsPtqJm6VrrG36hvZCE6wj4rIw3k94whOO919WATixxHAf\nEFrEPM3b48S7GW9ZMwXOz7NAEuKMLSx/hHFjizocBHTbOX3AuELAY/UiuGpL9cjzSQ+ijMt8Qz2A\neojVsX+NQ13cI5SkndCGsWLR8WKZcrV3Gga2BQNNMNmWltrgchpsMVTiLbglXHvttYWBywAcZsrg\nu+mTyjQ0d8utziYek7hAbib6d7zjHaMyjZhTGmCMDvzyW7eowLqWqJ1W92nXuTxhyDDkcIGRHwoY\nUox+VhHDUC6i5R36nVWfI4hg2DAQ6klIJHwQoMLAoQ3CCSbUMwSZRYQTtBR60tbvfve797hyaW99\nRv/JoU/5XpceV63nut5POesyp65ScQysrPZ1+L3f+73jDFreG1ou1kRWCdYKSpG0zdD3l30O3Woz\n9IEu0AchFd2wdKgXrb94EmWbxoBa6c+7BJou6Ft33nlnsZ6hq3lAMEOH+pOxghDUBXnqg2I54Jrg\nQ8myKN67+a76X7nSH6zapnzog8BUx80cdjmn1VPZgXpYtZGborJfc8015br2j7VEGsFkU+tTCt1+\nGgYOGANtVa4DRviufc4AjGnmxsCfGzMlqNUAnNVGMFX+T5uUtwUnYai4bqgvLb+U1pHA8J3vfKcs\nBWrCUddlJxvfwWRZuce3MO80rX0MxmHjDg6sRESI4peOQZ8H2aiQyxSGDJ0QUqx0xkJBW7qJMG9j\nRv3AEquYQvXAkAyh+dAVWrIqEUuSPQ68GyYGo70qXR0mTlNHOOKK6FBf1y0ewe0JIxclxiJ9Jyuk\nYcIPYplprnssElz8CKEBggf6J4Qs4woFN6ecckrZ4yV51qmYCziaFVif5wlHDz30UKEZy7XrYwF9\nlSafIKUP6reHObaENox1oQ3nrktZEK04ZjEVlge0kSN12oRUeXPce++9ZSl54xtFHbpW5l3pz5uA\n71aG3cVAE0x2t23XXrN6QqF9EwzOLYOAYgCOYLJLWiGCmANTVU+iVpARbyCgdxnmKo3F3xuDLiAc\nDg9iCeB8e9kUc/bIZH1+jPRzn/vcohmelhf3DMw7HJm0MXMALclDvTH0s5ZanZb3QV0nRNHkogEM\nKG1zGDvMJeaRJUkd5sXNhJHxHgGXu86VV16598pXvrJoUwkkDviCX8e2grqqZ5cBZe2wAtkDDzxQ\naALdD2U8s5khd0BCiXQdoF+yaDhYxmpgKWQVCS3X9xY9JzSwFHCv6gNWA/1niAIguOFmSXECWFEI\nJRQKhGdjNto6bOiOq/oWOgFowXLZN910U3GHsmO6fjCURg6ibunH6kExp7z2uGLtcQ+ga7jelf58\nEHht3ziaGNjeWe5ottfG1dqgazC2YRhGjLY7mqEIJJlENq7wSxQok6G6pX6unXbaaXs//OEPi183\nfGSiGvoJDNu//du/FS0n5hzTgEERSyL/TQbMOcYMI2GjxzAU3TKLQcIUoYcECecZdUyAL6Z/Wh55\n/jDT/UmgNi00eieUsRjR2mtDNEEwZ+XCxHLFC2OSMtO4/8d//Ef+lvvetWknxtoGfPAhL8xMTWfH\nX9rCE3XS9t162e2cyxOhHh70nyFgBSl45zYlDmFMoUSb6YcULoKX//Iv/7L0T0IJF0bMPkiQ/RhC\nifys6nXPPfcUFzf/u8B1jBDXFY66z/lPyCUws+yIBbMktyB3QgmFB0vJJgglyhraCPOuXM6BtvjU\npz61d+mll+59+MMfLnvfEPwXHWNLZmv4STnQi+WdCSWOK6644njfR/PqlHrt0py4BpS2LI84Bppg\ncsQJYJXqG5AxEQ6xJTTgwCCcw4SzS5AJNAyWejq3qZkAUprO4GRovU2ymFu+6rRptOwY9zEZraFl\nWfY5cRgOLiQsPl0QhwM3GE/Bvn1B7nzjrcDjWbjYZMAMs+w4WAZpp7nOaEsMFeEEUygAWlA2oK12\nX5AzpjoMTeiFYGLDQfmhqQglaAzd7UJfSv9RJ/VTT3D66aeXOAptDx/BTbnZ82PlKQKs/kIoGSPu\nyne54Wmv+++/v4xpmHlloihgGTv55JML/bL8qQOLw9jtQtBnPZoWT0JYErtAwJgFcKvMgMLAexhj\n+OIONna5Z5Vl3j1lCW2gizDxcAyMG295y1vK6mWEUYoQ/WUIrcz79rL3Q6PK8N3vfreUCe3cfffd\nxf1MmYE6pD6h+dR32W+39xoGdhkDTTDZ5dZdY90yKEu5ORiQrUqVycWk6NjVATj1NOmopyU/WTcw\nnSak4GdWE3BXEOzqHQz9/q808dMYkll5bcI9mm9MFZckDF1APbPyD01ttM25X6eCoTH9GHd0tenA\nasJ6QigTg6It9QX0gQGkScdEu2afCBpr7jqsJrTe6ARjQxATU8SFy7voKof/uwLq4tBn1C+MmtgB\nAgCrY5jNaXUm7Ok3YbKtELcs6KviRbSNuADtIwaKxU4/pCQgLFIU7E/6J0HIs+7bb2MMgaiv7PoI\n4UR/6gN0ZnNKuJoFcAxPniPwUqAsskjFrLzHvtelizDz6gDUgUDyyMTlU5973eteV1L9JuOtdN1Q\nf4uV+8QTTyzLAFPMKRthsCuUoBu0ri6ZF9ddzpZ/w8C2YqAJJtvachtQ7jBVfJ5N1LTEYTgMwGFC\nNqCooxYh9UpdM9EQzDA2NWM1baLEvAvoxLRibqwkZEIzgW0raHMWBHXALLAQwIVduAkZGLt5gbsm\nb8wHvGHmtwEwUBYn0IbaEmPL9UdwcX2NQAIfAa5e6UMsZiwsYgFCVzUTg+Z2BWoGNHVkQdPu9u2A\no7oP1fWGM4IBOtHf6qWb6+dmnYsNI9ywInCbkvoP72jUOEYYIZQQTnwrQGBm9aKI+J3f+Z1cXktK\niKd9J6j3gXi+888/v+9WucaFCz4pBtSBhQUNbjLUtKHMxpIw9e6hCwH7n/vc58r4qS3QDYGesKo/\n5Ri7nslXaj+pV7/61UU4ZT0jUIstoYhQRmXtlj+07l6DhoGGgekYaILJdNy0OzMwkEHaIMy0ztUB\nc2FyD6O+ywOwuqWuJhz//+AP/qC49MCJA466gDEgyCUAFaP+/Oc/v/itd5/dxv/czzB06q6OmEju\nMbS0iSGZVy9WCIwfpuPRSaD5toDYA21p2VtufRhegd3TLEQYmtCKVbj4/IeudpmJUce+ehII4GFa\n34EvNAU3nhXfMRS0B4GR4oBlBF2ylLAisHQKnD/ppJOKcsAiFvp2FwjXxjqCaFykus+M/Z/wxWVJ\nnfvA5q72x6gB/jDOVncD3M3QFuBm6f6mA/yrM+YevmvhxNgSixXBS1C8mC0uohbf+MY3vlEUIaGj\nvnF4aP2965AXi+iNN95Y+jhFBPdNy8Q/MrGSGNuUybNoO2WWqkP689DvtucaBo4yBtqqXEe59Zes\newZrAzEt3HnnnVcYyNtvv71MICZ7E4nB2CC9i5DJCg5oYOGBuxKXFNYC2lSTUnDgeYyRe97BVGFu\nVnFD2WS8cuXi0w7U8dixYwUfQ8uMCWB1MKm/8IUvLPQ09N1NeE6gPyZQ2gfpQwQZNGIDufe+9717\nb37zm4/3oZp++vLY5mvqz90lY4j+g8F71ateVdza9ieWC/WPkoNwq38BblUE13mAhggfrCwCkwPo\nMcv6DrW4YEzF0WnPWFKS30GkNnEVYzENvva1r5Wlpik+WCjVl/WOBTMrxqHHRyeCPmUIa8ymQ8ZY\nuEcnDtYfdBNXqVrART8333xzsV4YN7h7cV3jYmWPFjB0PvJtQPjQ7gQgiyD4vsUHXv/61xchSJki\nAHleX3ZEIFGOKLCGfls+DRoGjjIGfm2jPspYOEJ1z4CryqsMlPWk8Ytf/KJMdhmAw0zsMlrhzpE6\nO6d5Bdw99ieMFRw5EhCOWTBRcT0guKyC/03Hbe1+gjEyUS8CmCrMBA01AeegNNSLlHHWsxheDPA0\nwcS7aAPjLBVvImahS1OzvrHt9+q66kf6BSC8E9TgBXCJY2X0H6M9SyiBR4KIo45RogiIMIK2FgWx\nBNqS9eswYsAIrFbWsjJVHxBaKIPEpBDy1JUFoe53Vj6Tx89//vNShwgsffltwrXQhzTn6IRwICUU\noAlCCuFATNexiQLEeHvXXXcVQeLiiy8udGAPGJZMh9XK0AO3q9ACIdZ7xmjjNxw5WG09x3L18Y9/\nvKy65b9vUkiFRpXP2B6hRBqhJOXfBJy2MjQMbAMGmmCyDa00QhkNoI4vfOELe6997WuL25X/Bk2Q\ndOinkp8Uc0XzKw8TRvJKOjTPbXtO/XKoN1c2DDmmCF5MnDRuJjr/ubthvrZpta1l2kRsiWVyTc6s\nZ3zduXLtT4S1RQADYXlUWl7vYi62BbQ916N5oO9giADBFh2lD4W25uWxjfdTN2nqTJDTh/SXaKEx\nivaFwQiyVHT379CvPKPPwaVFBYB80ZznHZbhXRYIRphUeUR4WjavVd6zVK46XnfddY/LBn7OPffc\nspyupcsx4F0guCg/iwrrCUsCPG0ypHx1n3CufyUNrURA0U5nnHFGCY5XN+6U6ouuWHJZPwiZhBHv\nAGMVIYVCgdKIRYn1m8sWnClH8icYoTvgunIQQnLIyzWH+6lDeaH9NAw0DMzFQBNM5qJo+x8wiBq8\nHV/+8pfL5PXWt75178ILLyyTdz1w1ufTap5BWerADBjUMwgnnfb+rl2vJyB4MOFhzmn7uVaEoTkM\nTetB4xqTLbYCXdBuC1TFCAhkp6GdtspQXznhle+2uADvc8sYQp99eR30NczJvLLCERclgdfwQvAK\nQyPddcg4kTr7TziLII95REsYQXES6T/GMf0rwggLAZBPBBFKAIz4qoABJmSDTVic4uqrr9777//+\n77K0crduNPiCwu0ePw3EbzngjqXb8tybDulHdRqa0c+0UYSGzEloxDXvEDJYi5x3jywGYIzOu91U\n/rkWXMlHGXw/lpJYSFI2z3quQcNAw8BiGGiCyWL42rqnDagZpDPAEiRs/nTttdcWn3a+7dwj6kG0\nPu+rdAZqKUYcA+qdHH3v7OK11Ndk5JxgwkrCH95EhVF60mQpWS4VGArPgDqtz7v38j/PdP/nel/a\nd23o+55bFBJng5EkUAgiBlxKaGkdglNZUYYCrTf3GVYXDCsryjaA1dYwin1Q9x3ntLjqqL1yeC/t\n15fHrlxLfcPMET4wzdywBLpjLtFSmGn3uCOhMaBfwV0WTNDnxgR7pRjfnvzkJ890IRvzm7PywggL\nhrf5KktSFwhzYiC4vsFJH7AAEIgpToxPxu5Nh/SFjLOhF/QBJ9Ic5rtuHwudqWd9Lh9g7PIO6Kbl\n4uQn73nHN7tHypbn8l5LGwYaBhbDwLij+GLf3omnM4jVlem7Vt8/yHNlMVATSgy+zgMEFEscCqzk\nw9wVUAyw88BkIN9Mbnkn6bz3d+V+6kvzZjfw0ABhBBOQ+9tU35RZWp+rQ/3fufrGxQFzSDjj/pJn\nMZDZp4IrW/1+nummeUbezsUecGXBEOTeMumsd2bdS/mGPKOuaCEaWe/2gbpx+YrFMQxXvtH3zq5d\nU9cc/PcJoVmC/Ld/+7eLQC/OKGMXd0kCDMbbSmhhLsfGCwEoy3nTuG8KqL/VuKwiBlddYIEjnLA0\n9gX3e188E/cmS+yKzdgWSL8IvUgjmKAP81Gd6l+hG+cg6bw651sROKS+1U09l2fkmffm5d/uNww0\nDPRjoAkm/XgZdNUA56B5oskDQwe9QR8Y4SHlMVhjGgkQfYxSBBS+y4Io3/e+9xUXr3qw7RYldfcM\nptNynBmQk3bf2bX/6plD3ZzDAyGNVYAmF6OVZ0IbdVqfy6Pvf9+1PNu9N+16/Vx9nue76ZBn8o5n\no3GMFtG1LkMAD2iRBhrd5P2k+Wa5MeWHv/+ugPo6CDERTEIru1LHWfVIXetUvyHMG6cIuPb7QSvw\nQ7tPGBG47J11AmsXxt13uJGh600AfUrMhBX+xJw4+mKZCPEvfelLy14fBJEu7O/vFxdCSpNHfxXD\n1X1mU//Xba9d9CE0Ajf577x7pL+pl/M6TZ516ly+fUfuSet3Sqbtp2GgYWAlDDTBZEn0ZZAz+PHr\ntUzhtgMGKRYUy0/atMqgnIFX/TKgp67+Yxowm6B+Ns8chTT1hgfCCAHFhG9pTvubEN52FTBK/NUx\njDYUnMbEEV5sJIjptJEgRnMWpI95Rj97ZLIcqFWXBEFzPQwt9qV91+RT55n/SYe8M/QZWmvWnWmQ\nfNzHjGdBhNCRNOfT8tiV63Vd4UHfgR8M9ZMmbpD7EyZ6kdikMfDCkqAcVoZD15sAxmfukCyyxhju\nXPZzsWKUBQC6wGIiCNwy7sbxLgjstiQ3dzVul30CTPedTfpf94/QkHqinRzGjZx3U3VxDSSv5NNN\nMw92r9fvlozaT8NAw8DKGGiCyQooNOjRAL/4xS8uKy5lEFwhy9FfNfAqF1cuWsBvfetbx1cB6vuY\neICLLrqo7B2hbvWA3fe8axFM8uy053b1euotZX162tOeVhhvrkziImg0+ckLyN01IJA4WInsLzFN\nKFFvDLiN3ggYNrgTb4LBmgbwGdxiDAQfC6SnDYbLWd+aludBXedipN3D+PR91z3B3lajIqylrkn7\n3tnla+ptnAqDTDBAW3AjxkO80UHghlDJAs4NimJhE4AVyYaJxnBWI+M0ixLasXu9VRHhqQs/+MEP\n9t7+9rfvXXPNNd1bZdwWGM5NzoaMFqvYRghNSNPf6tR5/V8du//rPNyv/zuv/9f3nTdoGGgYGBcD\nTTBZAp8Z6KSYdxvAHTt27PjqIBn0lsh61FdSTmU0ydO43X333b2CCa3+2972trLyEWYRgxCzeAbl\naYUbwoRNe3cXrqe9BezCMQaKRh8DQTDhdsFnnl/8M5/5zOOM17bXnUWAtcTqR7S2BI95QPNNSOMm\nY8UlwsnQgGXvYlDt8iyGZZM3iYMTwlosiX14oYkXzE+wt8oUmNfX+vLZ5mth+lJv/UffwWhjxgl3\n6EwqZmd/Yj0Rd5KYtrHrjrnHpBOEuXBJDxOMLRQc3IXhSKxLdwEICoHbbrutrMZlrO+CRU5YRPr2\nQLEqlzgVghg8z7NidvPetP+hIync5X/G6JS3+z/X83zf/+69PNPShoGGgXEx0ASTFfBpcHOwSMR6\nYmKYNuit8KmlXk35lCnl62ZkvfszzzyzaLtN9p7N8331yICffPyn9cUsHnUghADMM7xguAWZ7k+Y\nKcvd8pfHbGEuXNvmiY47CQ0uxg1jhLkeCurO9URgsaVYF9HUEkbgEbNmE75ZFpeh5VnXczT/0wQT\ntMECBH8RYLaZHsbAofrDF1qCO2OTvoJW7GeDcf7Zz35WDsI/OsJIj2U5M96hR/F4FAisEYcJrCPK\nQyiDDy6M9nnpA/EkBJA3velNfbf3LrvssoKr7n04jyXSGEXJtCtup3V/ynnmtPzvRdbk4rz7095r\n1xsGGgZWx0ATTFbAocELY+HAaBj0nEszAK6Q/SivKodyEjZqzTQXhdNPP71sIGXSo+F1mJQ8px7z\nBufcx4hb2hNsSr1HQd6cTOq6Oud2ApcY5hpo+lkGCC52kOa/jtHi433YzE9dzqHn3NUsh4ymMEvL\nbHxoyVJLm9LUWi63qwWeVhb0iWkkFGGkxLRsGoilUadZgfqsRgQSlkzMoBWgGuwVJjzLTAcf8MOK\nQmCg3SfQCtp2oAf9bX8ipPStQJU8hqSUK/L0PWPaYYK+IZ5EXyOM6GfGlllgg0V09KEPfaj3MRZx\nAt3LXvayx9yHN0IgKyS3LuPSrkLmrF2tX6tXw8AuYKAJJku0YgY3zDttHYY+wgnLRM2wLpH9qK8o\nC7cs5cNIYuouuOCCoslXD2U34Qk6xShJueSY8NUtde0WKtelJjUMg8BkE98m1b9b7rH/q2sOkzrr\nEZzUh2/6j/nmzoWhxkA89NBDxYcd/sbS+o5dv25+mENCCeab+5/6LAPokaVEMDy8Ed4whEPAameY\nU+5P4gG6guCQPNbxDNwQPh2JleAq4z8BJKC88OZ5dIFmaMVZC4a4wyWfXUv1I8IBt0B46YIxya7c\nDm6TaED7e8dhA88nTYLl0YdxbRGwMSg69A3xG33fXyS/VZ59dLJohjHCXIJ+9DP9ZQhccsklxbLU\nF1Ni/H/Na15TAt4FzddAUcUSCafwN7Qv1nm084aBhoGGgTEw8BuTyeD/lqUYI7cjlEeYUakJJMcm\noTNlxCRhjGjfMD/8qE1SmGGMEN9tBwHF/1hOTIaO7iStrt7HWMkXg2j1mltuuWXv5S9/eXlf3kMn\n020lm7Q9PGDUMVSCuz/xiU8UPMLlNOsTQc5qOPDHdYUGnVC3yaDdbeqmvTGAY2hWaaitHgRXrEpZ\nnWoeHjCSVhRCq2K8MJSHBfoXxph7GVpQJkIogUM/EGOE0QTaWj31Ka466IZ7EkZRYL+4Brg4Cv0H\nPjKWwAXFBoHthhtuKFr9jEWzcOF9Ll6PTph5bpLAuCO+a39iRcFgd8ev8lD1I49HJgsycE+E/8MS\ndI2pYrYIB8YNLlbLCP7qY0XFO+64o6rlr08pAfRji3TU8Mtf/nLvr/7qrwqNivGB9wYNAw0DDQMH\njYFmMVkS45nspA6T4SYJJaqV8phglDH/MU7OTX4m/xyYO9c8n3dSzxpNqbNrzrkC8P3/67/+68JQ\n5Dv1O7t6rq4OfvACVG1SGfz04S54oJXkskJLixHBnAvq5VOvPTYRBKwTSrjVEKTGAMKYOsMD15Vj\nk0Ukhgi0YktoeTH1VukaqzyL1AkDqO2UgYCh7ygToaQWlPYnDDLXLoHdlkn2HCY0dOK+Nkc/GOOj\n1H9qfHNzVHf4C25m9SHvohXMu4PCRXuwohD8HQRd+CVssAj3ARcmQglh5rCEEkoj9M+FC21TcCwb\nPwUnVl884YQTypjcrbO6EuYtPlELPlzGWKMI0tpCv2zQMNAw0DBw0BhogskKGK8nzfp8hSxHfbXL\n4PhP4KDhBRgkDFQtkJjUhjIFnsvzdg+mwcas+U6OTcTLWEhOHaW03XCBoajxl/O+b8I7qwOBJHEn\nXHq42x0Wg9RXTtcwKhg+7jJcsMZsV4wowU68iRWRhlpiuMApE0YK80kTfBCgvcUIEUgIG9qdy43y\n9AmV7tNOs44po/drutAPuQ/RYp911lnH+06eO4g6HeY31NNBOGfhYGkCobGk88rI6gvPrLcEaEIK\nawradRCDv4qdAABAAElEQVSo0Um91DQmnWDCWnwYwq06ceu0fDarEUGBpQRNrALo8K677ioxWIT+\nLnDbIpygubrfcBszBnE/pDxZNW6n+932v2GgYaBhYB4GmivXPAxt+f1M+jS0DkIJ4cFkT0jBNCUN\nszSPEZCnPORFU+ygTT/xxBP3Hn744cK4YrrlPS+vbUZvcIChOPXUUwt+b7311uLKgzGAg+B2Xj3l\nxRUIk+Qcg4ZBX2S1q3nfWPY+JtzqQBi/P//zPy/psnlNew8tEe64aGHMMJBDAFMn5gUDFRepIe8t\n8wy6JzyxbHCJRNvKiRGGm1ngXeCdvv5zxRVXFFdIDHRoZ9f7TxcP3EAJvtddd13BQVxK9aFlxxGu\ndYRXQgpXMaBfUgZgvNE1muNK1w26Lw+v8Uf9CbcO9bOCX4SysT5LMKMsgYM+UG8KpZp+CSaWNz+I\nPtVXpnatYaBh4GhjYFhE3dHG0VbX3oTnMLnTwmF6aAcd9cSPCRrKCCVPad6zypJJ9dvf/nZhrDHX\nYca2GoFTCq9uOcRJYKoJJzVucj4li8dchkcMrt2cuTfR+GIYIqg85uED/KNuhE60g4mpGZgxiyF/\nDJRUPAZN9hDATHLB4QIjzmNdgFkTqG/1OUIJixaNM+FxCE66tJD/SeEWE6m9Q1frqsum5au+LE9w\ny/IanCRdpbyEECtsiZlwZClvK+hRohBKrCp30Lu7U2YQqAklxuLnPOc5owsl8GY55R/96EdTV85T\nBgHxlFYBFiWWG32KC2KDhoGGgYaBg8RAE0wOEtuH9K1M8ISTCCg5j2DhmUXBO/X7p5122t73v//9\nEmSP2QBJF817G55XNwIYKwnmlMWoi5NF68FCgjmLOwftvNW7Zi09u+g3hj5v5SN+74D7Fm32OoFP\nPZcmOPVdlrghwPWNQANX3KXGBAIiBpYGmcadEER4FAsyLWZh3vfT16Q51BsT+d3vfrfUH23tct8J\njlJPu5dj1gl7wYmxJbjK86uk6JcC5aSTTipud8lL37rvvvuKO5X2XjfeCd2EXMH6LKPPe97zpgoO\nKeMqqbine++9d+rCEly+LCVcgz5FccWCRxBv0DDQMNAwcFAY+H+XTeCgPta+c3gYyGTfly5bKhM4\nJtJB40Yb+ZWvfKW4AAjy9a2xmYtlyzrme2Gm1Jum9+yzz9571ateVRgMDDItrZTwp/7LADeK/Ymb\nUFY9e3Sy6hBGXYCqfNcNvmsxA99kFaiDZNf5bfu6wCsXLRpblol5zGlc5lg1BECPUVYxL/z+abTl\nKT6BcMYqyOo4BtT9R50xxQT78847r7Rx3VfH+N6m5ZH6c+P74Ac/WHB7xhlnlL6TPhTFx5hl597F\nKue72pRALPjcqlTcFrk9uSdwXjnGBC5lhG5CmNgqAu7Y3+grrz5B2Pje975X+lf3GTFu2oOrJjB+\nseRwXSSQc32b1w+7eW7if3UcArtQ1yH1bM80DGwiBlqMySa2yhaUqWYqTLIYWJP5hz/84b3777+/\naK9N7Ksw55uKhrruBDFMFWaDWxGmlabRMRZTxZ0KI0VziVmg9aW5XxdoR8uGEgxoW624dpAAv4Jy\n1VtAuYDceeAdWmhMFIvTsksve5/lhZADCIK+v8wmkrPKHBrKUtP6DzcuLj133nlnsRxgWHdRsIeX\n1B+tWbzAClB2Ln/JS15S+lDibIwfYzOJNue0YldN28qD3ggO2p6gCPRpCgLuTcsqGeRDcaMPE3ww\n/Sxk6+zDvtkHX//61/fOOeecvlvlmv1P3vrWtx6/rx+y7FBOWCJ8m0CbBpwbP7mmceMztnHjc7hH\nOHVQCFE+EBr9r2mvPk++LW0YaBgYHwNNMBkfp0cmR5O3CTfMFQGFnzwtpL08zj///DIJ7xpzlXpz\ndSIkcO355Cc/WYSwCCZjM5XwTHtvYjWRYpisIjR034+hRClvy4hizmhZY/ka+v5Yz2HUxe2wSKGn\nIUwcl5xHJvtRcIfTJoswkhgUbitWKwJWKiKQsZSsA+DZgTFX1wj2p59+evncAw88UDTpuyjYq6C6\nZ+x417veVZQZ6E7/yYGBH3vs0L5iWbh1WSyhj0aMYwmY18cBRQOrgWNRl0bWGHUj9LIIxkpTMj6E\nH2PVxRdf3Ptl+Lj99tv3XvGKV5T7yi7WDVNu+eEh8VS9GR/QRXQFpMZLY4iDMGpuAuhKOxA8uGOq\nG6HFGKC99UlAGDX+sSKhFeNBhJOk5cH20zDQMDAqBppgMio6j1ZmYS4M5LGaYDYuv/zyvRtvvLFs\nFoah3CXmSp0JJup80UUXlc3gBJDyFSeMYKqkJj+T19gTmImT2wUmHF5NljR8Y33Hcr2CyFkKbBjp\nG4cFfPEJGpgljAFGYh7ADa23hQS6G8j1vUvwwcA8OnGVAxgW7w0RhMoLK/xEwE3fIeCjJYsoiCuC\n/3Uw5ysUeZRX9SGHPsRVCL4vvfTSvbMnLpHpQwSB1H2Uj04y4ZIHr74rrkNbzwOadfTEwqJ9gED5\n/YkVheCuvLOAgM8l0LtWAWN5UK/Dhne84x17V199dW8xWGUffPDBYnn0AAuDjR/FQFmgYtMALQGp\nZY6/+c1vlkOfJkTqR8961rOK9ZW7sbYzpuS91McY6pr3smEqgdIy1uiAUHrmmWfuvfa1ry15ZcxN\nmnxa2jDQMLAaBppgshr+jvTbBnGHid7EKy7BOWbv2c9+dpkQCChhMHZhAA8zaVO/P/7jP977/+y9\nC7hVVbn/P3p++g89Bw3NS14O27S8pFRiaVYKeio0Y5viFTTxKJKaiXdRLDFF8QZEipeEELwEmVgK\nmRdSSz2lJ+GUpKBQaiZHUfAEBefhPz9j892MPZlr77XWnmutOed6x/PMNeaca84xx/iOd4zxvuN9\n3zGuuOIKv/cEDDzMFIIJ5eW6VuUFc8xfMDkCd8wPYHjC/QiqIUwGdXajR+PALCHlaXSASWBVMIQS\nhJOumDqYfFYhgg7RmpRabhlaxXQKHMGTmVMYZJjHWtVbHEu+K62BhBPoa+DAgV4TNnv2bM/41pKW\n4nmqxzXlppzQ7ogRI9xPf/pTr8WAIZa2BIYf5pEjrYDQh+8SZmOY6lQSqCeEKOgRXyAC9YJwAqOL\nIB8GyojAywE94d8BU5yVAP74xaEdSQr0KTDkmDFSlrnRBAF+V/R5lDkLgXwRiBGkmBDDLw5NB2Vr\nbW31uIM/5eU5HXrPJ7DuR+2eWIfoj34RE0sWp4AOEHYuuuii9gVPSELvh2nauSFgCFSOgAkmlWNm\nbwQI0NGHzBUMFtcMFGwWx/LBzAAziKuTD17P1SllZYBD8OrXr58vJ8wj5YJhhqnSTK8GtloWEOYa\nDQcDJYFN/mCuu2Lek/JEGvjJkH+EklIMfdK7tb4nLQhaDMxgugradwUzLBiIMECfsjOHTjFNQbvC\nbGi96VNMEkKUBBOYdfbWQDj50Y9+5BkstZ0iMD4qM9hTryyTfO211/ola6HbULinPtIqMwIoPh4I\nEEyadCddzJsQUKAztDAE2ktLJKCwWAP5xnQInxXoC5qt93LEIc2XOsd8cMCAAV7oSHqG9sZqdJQJ\nTS3aJuoHky7iRgVoiEBM/4u2jckLJiJYOAI/Lf6TMEKs5/1JmT+iEdEhMQfCD76FTIAwIfTdaP0g\nVnrT84rL/Iw9ZggYAjEETDCJAWKXlSGgAQBGA+aKA+aK+xdeeKF74IEHPMPLbGGeZ34pDwdMJLO8\nU6dO9cIXDqGUi4Gag5neepcTcxEEFAQmGCF8T5g1LDcwE4qzO4FBPWtMFIzF3GjGFtMuZnBxiO8q\nUB5WWdIMLzSJSQpCCXWIEEk6H/3oR319dZVerf6HpsK2Q/vh+rzzzvNlpl7RhIkpqlU+6pWuyksd\niIGkj4CZC8241IbSYPLwH4CpJi32Mql2mec4RpQFx3CEFPwXuCZQV9As5p3QH7SW1YDfC4IaGoGk\ngNCOAzx9Aj5YaIAQ4nHeb0QAYw58gPBNYqljhILzzz/fb1BJ25FAovpQPkVLxDrXf4qVPtdJ7/Me\n9Qt9ojW/7rrr/Bh32GGHufHjx7uWSDjtLH19x2JDwBAojYAJJqWxsX/KRIAOnAEBZoNZODFXxIce\neqgfmHFAZFaRTr3UoFDm5+r+mAYrBrx7773X2xizOzWz2pRFDJWEkkaUEewx7YL5JmBuwU7SXTmr\nMvvLPh3UW7lO5nWvgOiDCF3QEPksZ9WtcIYXHxzM1KBH6ggzHu5Vo1lKu+zQFnRF/ZE/DoQohEXK\nCdN12223+bw2gq7SLK/KSl/BTves4IdmFeYXRk8aR+oorbLyTYRa8ISZhqmuRYAu0chIe8k3mKiA\nUeWb5fiz1CJf5aTJxAY+GAhYSQG/Ela8o34Q8HASL6cNJqVV7T3qkUBbQVuBGRUaHbRt5CUukEg4\nIBYtERN07S9iP6JRbkvAUaw88J/ShW7RKjGRgLDEwgIs+qJv8ZwFQ8AQqAwBE0wqw8ueTkCADpsD\n5gqmSswVgwWdNcIJTC97NDBYa9BISCpztzQYURYYeJYzPfHEE93o0aN9XmV+Qrk410xvowqCkyZm\nDcTkB3t6tDpJAyT1NDdi2mA0EGIwBctywLYfMwqwxnm5sxXJYCZgGJjRJlAvWgaU97MUyCv0pbZD\nvXAPJvCEE05wt99+u6c50VZSXWapPKXyQpk48PXA7IbFI9jYj/JQJzrSLCd+RAjstXTcpu4wS6Ov\nQ6hiGWK0NDjM0ycSWG66JRJSmDDIgkAcr6M//elP3uwRLWNSoN/DF4h+ZW7UZ6B1QvtUj7JofOHb\nw4YN8/lAQ3LOOed42oGm1E9rbIGGEA7CQ/8pTiqnvqVYNBuPw+9JCEFjcvXVVzu0Jz/84Q+9prOz\nbyV93+4ZAoZAJPhHDaxtKsLQMAS6gQBkROcdn/klSVZ0wc/kiCOO8B12moxHN7Jc1qsqF4wHzFS/\nyLeE/Ra4TznETElbkoWBiLyhIcD0AqYJMwx2kg9nbakrnFthRDBnwvwrDwFTrPnz5/tBn7oQU6C8\nU3YYQsqONkih3jO8+m45sWgsqe2wuAK+JghkCI9qO+Wkm6VnKCO0iKDIDDxmdHfeeafPIsxt2I6o\nU9pRdwOMLFo20qbt1sKkCgEE3yxMolgBigkY+WdRXpYnRhMhhp/6Y4EFhJS098bpLl4so8wCE2gn\nkwL7nyAk05+jmWUiA5qsZYBuOGj3aA9p0+QBnMGX/wjQC3QDvjq4Fi0p1rP+pRI/SpOYflIx53yT\nQ+d6lu/zXXyLTjnlFE9zDz74oNcGZmFMKFFUu20IZBIBE0wyWS35zJQ665DBohMnwFgx+4uAwmwS\nTEKWO2wNOJSJGd7DDz/cD8LTp09vn4VDGIHpIYa5Cge/LNQgDAY+CphqgDXMILO5GkBh4PFFgVHk\n/7wEGEFMZloi5g6Bi0B9YefP7DgaIMqDCQ1CGUIlcb9IkMlqOaEzjlBrIiaIvU1gbvGbQfuVNTrr\nim7E2GFexyw7vkIwbaz8BC2GQgnX1FF36wnsEEr4JvSNxiTtAL2xFDD9HbSGcE/+kwICDHWIwzyL\nVhAQYKhPnMtZkSytgGYaB/VylteOf5PNcVnNijIlBfY/+W7k7M3eJvQvLJRB26pFEN2wGAQaGzBm\nKWB8rmgrBOiE9kD/C/Y61EaI9VwY+5ud/Kj/V8z3lB+1yzDWc3wPIRWtOpND+E/hZ6T8dPJJ+8sQ\nMATWIWCCiZFCagiEHbcYLGI6dQYQGHxW6sKemWUXGTjTYEJSK8C6hDTIkO+f/exn7vjjj/cziWhK\nZLoQzvJKKNEgmHZ+upses7bMcsIQYYIBI4FQAmOIA7LK1N3v1Ot9mCaYTgQQBBOYOgQSZsgJzEiz\nOplmrtmLACYSxhHtUBYDNMdB2dR2MOniHrPEYhYxJ2RX+7wwOuSfdoQPBswlDsM///nP25dlhvaY\npEhbuMeZG8awJRBe06p3ygS9Yf5EPbAUcLl0xbssWYyQwoQB1/SBTBCQV1aS624/wl4baBUR/mgL\nlQY0dCdFe8qUCux/gvab/hwtLGaV3c1z+C0wIUA3mI0xKYRwyYQWQqzGEwkh0JDOiSWskAbnYewv\nKvhRXhTzbc5DoYQ2q2t9izaM5oSFA+67774OGClPFWTDHjUEmgoBE0yaqrprX1g6bTrvkMFSx83g\nBWNy7LHH+p3LWUoYBpKOOiudNfnXwIMjI6Y0CCY4WXKfwEAobYkGxSyVIamWGShhVtgsjsAAzmxn\npbtYJ6XdiHsIJTjkwhAoMCvOhpOhyRr/MbOLkzX095WvfKUmJj3KQ3di0R1lQijhoN4IbKiJKQt+\nNTCcWpY2K+0mqdxqS2gtBg0a5LUL7AWBszuBtiNtCefUTxoMrnyRwAoNDW01rYCAhckT3yB9TIqq\n3T+ItNCgIKRAzwSEbDQDHNVoPEgDOkHzgbDDjH01K2jhK4EPUFKgjphYggbxq6EPV50mPV/pPeiG\nMYTFBBB6MC+7+eab25OB5um/oBkdXJOvsB9Ou22o/xddk0faKuObDq75X98+44wzfN+Dhol6UB7b\nC2MnhoAhsAECJphsAInd6C4CdMzqsGGsdKjDZsaQ2SSWnpw4caI38RJDog69u3mo5n0NOOQPzQ7m\nZzi5n3zyye0MMANgaL7FwBgOhtV8t17vYN/PLCflJFAObMRhgvIUMAVixlqO7dAOWjhmm0sFZrcR\nimGmPvOZz5R6rOH3YXY4aDMSTGB6CPgpYNYFEwvjCUOYVUYHGqMc7OWBpgQN3T333NPOwNKOJNwT\nc51GOwI3Fg1AGGUZXJbsTSsgHCKUsHcJtAYdpeW3QtoIKGg3Vd/sudISaVFYfYp+ptxAW0BLSEB4\nwvwUrUOl4dvf/rabMGFC4muUG20yWljyiwARnxBIfLGLm6IbfFj6RaaXmJ/eddddns75DzoR7Ugo\noQ2E7aDWY4j6T+UVOpdgAv0x9nGPwDOYMDMphLaT8oR57QIO+9sQaEoETDBpymqvbaHpjNVp02GL\nyeKc+wwcxFdeeaUXTNjkiwGQvU40qCiubU7bUg/zy9Ks2FFj5oTpADPwGmQ0IMLQMyhynZdBBrvn\nuZFZBIMmjAvMPSsWUTaYN8ycqp2hrUcd8Q1m3hFIMMsi4DyMaRqztpg3sZliKbqhnFrqFPO1+E7d\nPsEM/IgWqSe1GzE7ZA+zLkx1ELRuvfVWb1IDDRJKld3/WccfygDeOAIfd9xxvo0ww45ZEf+R37hQ\nklY7wh8BLQRLQrMiXVph0aJF3hyS/KMd4KgF3vSRcphHWCHQ18hhvhx/DgRWJn0UwBaNL6tYVRKo\nQ+qPuksKaFtnzJjh2yVtkXbVHUxEN9A4G2+Sb/Ypkf+N6AY8JMxyj4PQnW8nla+re+SXAE4carMa\n87hHQDOG6RsCLQJjXpfN94WxH0OgDgj8v+9GoQ7fsU80EQIaIIjDAwjozNWhY0rEYMbgNmbMGD9D\n/KlPfap9IFI6tYJO+SCGacfEbPLkyY7VZ2D6MA1isCEfGgwZEMNBsdZ5TKPsDIhof5iBx5yAJUsR\nRmB2mH3HLAUTL8oC45O1MuE4jBM/ph3kF6ER3xI2W6SOYOCYmaeuSmlNKBOMFEwre1rsVGIJ5TTw\n7k4aIfY6V5shRig+6qijvHCGAE1ZaEcIyQS90508VPtumM8bb7zRzxSzMzYmmwiOMGrkk7YUah1h\nLNPINwIrWjFm7j/72c+2M6zVlof3YDIRdlgVinaPr0MtaQcsoO+WSFNC+wQvBHK0ZdKogCPMLTgm\nhe9973t+uWL9R71gyogmmEkgMfL6v1RMneDbxLLbMgENn4XhxpzykEMO8Uw3WpRyBKcwDZ2Ldijb\nmWee6YVafDNkJgcO6nuTJobSoB/lpdyYb8aPOC1TLuoJk0L2X8HvSftf8Z1G5Lvc8tlzhkCjEDCN\nSaOQb4LvhoONZpEUazZJgySCAJuucZ+9DdjVF2ZGHbfiNGAjXwTiOXPmeM0Ngy9CEgISy2DC5PI/\n3w0HRQYZrpXvNPOVRtniaYA3QhcaE2Z5mU2NB5h1nIURXNCawExmQaOAOQ5aHRgy6oK8kX9MW0Lc\nYZBwhud5bP75v1TQjDqaMFYoy2qgvLQFtRe0JhxqN9Ag/gMjRozwwhkmkQgojaBL8kogho5gLDF5\nGjlypN9sTm2JvIVCidqR8tydusCkCDt+MMInAea+uwEhmBXgEAxID9pKa9f4SvJGncthnhicoX+E\n8pZ1DvNhe2BTVa38Ff8OPlZoQCoxu6L89I1MDiQF+stRo0b5NFmWGfOxSgNl5MDcD9OnqVOn+iWe\nSQf6CAVZ0Q1lDstd6TfTfJ464aAM0HvYbrkmYMqFthNNPGWkXGnQfprlsLQMgSwgYIJJFmqhwHmI\nd9hisOi41WEzuDDYwFiisWDVFxhpZuLowNmwitmycBAKz7uCjzwocI5ZBstOcrzyyit+ac1zzz3X\naxPIF4MLgUGDfPFtGKq8CSWUFYELZqYr3wqEElbuwiyKwCpDaCQoe70DggYCCXVDXcAMIlRRhlL1\njmkaAhh1BmNayiyNtH/xi1/4dFlStRomqh54iGYpf8jkhO2GslK3CAA4xON/wmINbCQpnBTXIs/K\nIzFaN4R62i5az+uuu84LkWrj5FVCidoS98hfGnmEzlnlCprFjr+7geWoEWLBuyVi/ikTfUGjAwKH\nHObRJBIwdSKP+IqBLYJJZwGMtIBCZ8+F/0FnmIAmaU54DnzY9JA8YIYVBjZmJF/4GiUF6Ac6R/tH\n3tiQF+d77kMj9EESTKgD0UwadJOUn2rvqT1QFuheYx0x1+T30ksv9RtEotlj8kflqfab9p4hUEQE\nTDApYq1mrEx02Bp86KAZ7MNDHTqDEAdMMkuKMnvG3g2YLbBBHgwnM3fMmsvptLPBSekyqDLzyYwV\nB34K2267rTeJwYYaRoZ8MaDoHQYMMVCKlb/Ovpkl6DF9grnHbAv/C/LfVYDBZN8PmB4whuHA9Kse\ngXrH1IGD+oDhQquB6Uw5eUezwq73CCXQCvWWFGCuKCOrFsFsZTVAixxidGgzYnbAhwAtUk5MahBQ\nwOCYY45xF110kfePEq0qTqOsaiPEMPAIIWx6hwCJedmQIUPa2xPfU1uCweTgmvokT2nkS/Welp8D\nTCOmW+QR36uWiOnPYgjNu0QP5Bl/hq4C/R8at0oWgqBdInTQRyQFNEpo8DB3wwyNcMsttzhWpho6\ndKjDfy8eQvo+66yzHHuwsEAHbRg6Ec0QQ+fqB9Kgm3he0rhW21Cbpb3SrxFzT/4zrJzGSmNhW0jj\n+5aGIVAEBEwwKUIt5qAMdNg6GEQ5QuEkFAoYdOiwORAqMLfCR4KdyvEnYHBiZg4TAgZYBjGEF2bV\nYKgxwUDjAlPOYIopAu+w3wAr9WDygqCjQTH8Ns/xXQkjxOHgkdUBMU4CWoUKbPr16+exiT9T6pq6\nQWNBGmAExggotTJjgQ5YhQdmkAGcekRYZDEEsK8kSBjDnAtGKSlQJrQrzNDWavO9pO9Wc09thhic\nqJtQOOE+AZygTZbjveGGG3z9IXShccQnBVOkkHbD867ypW/wHOdonXBKRuOI9gmTS1ZwQiCBgVR7\n4htqS2Is025LMHosakC+sOOnH6g2oI3ABA2GH00atJGGSVi1+Sn3PeiCVc8Q0DClwwy2nIBmhTos\nR5BRemwoSf8J7kmBCQG+jzYSDdrll1/uH0NQkTY2fA9agabR1rIR4fjx4/3S0tCOhBL6gzwIJWG5\noEfKRt3EhRMEQsyVEcDw+VObCN+3c0OgmREwwaSZa78BZVeHTRwXTrgOmRoGJwSF8EDQgGGGkeWA\niUAY4WBmCsaEA4Yc0x9W58G0BS0LdtWkHx6CIGSixEwRc/CfDj2f5ZhVfWCw0HjARFQrUCDQoVlA\nGAQH/DJCM6HuYkB9o71ACILZhflA2KTOOK8mULcIHZh2dWbWw2aMaBlgQLGLr/Z71eSx0ndoKwTK\nBmYcMDswPRzcJ4iGqSs0gzido3nkfZhsaAHGER8irXTEO50F3uUbaBHAFV8eJgkwu2SJWLQzmFrS\nRsmX8hoK+KFQonbU1Xc7y5P+41vkhT6AMqFZqzaQBm0G4QRBHE0CDHHeAkIa9BwPtCsmZfDDCQUE\n6uGaa67xZljxd0pdM1GEAzc0mBS0NDFCchj++Mc/epNM3aP+oF3oC98L+gHyT6A9gr+EEmiakAbd\n+IRq/KN2QJugfIxNHJwTWISAyTX8fSgr7SUvZasxdJa8IeBMMDEiqDsCdNo6xGgppuNmsOLQM3TY\n4UEnTlAc79A1KOh9xUozLDBpcDDwMUAQ6+C+0lYcvpvFc4QImDXyCyNS7So5KhvYITzAmMKIMIMM\nE6jVcvRcJTH1gJ08S5qyYhh4ox1BS5IGM0iaCB0IO2jGmNFPCtodPO2lZZO+lcY96kJtQwwP7YWD\na/4nUPeiaQQI9jzBJBK6QGjlfwntMEcI7AjzCLAw5mgcOXgWLRb1zzegJfDEnBJhBBt5tdv4t2lL\n4aF2lmY7klYQQSLu11AJ3kxwQAuUoZZLAVeSp2qfpa4xE1KAtsEcrFgpDQEVBpmlnNF2QRO0k1NP\nPdWvGkWdlRPuvPNOv9eT6r2cd/i+tDm8xwH9ILDQp2DahMADrUgoQailf6AMadJOOfnt7jNhGWk/\n4Az2tOHZs2c7Vn9EA8VeUnktY3cxsvcNgSQETDBJQsXu1RwBDWjEIbMlRod7nBOrg49nKj5Qca10\n9Wz8mvs8J0ZJQohi3ecZHUor6zFaI2bKGfzSNlGCYWVVHnwKCMzAooUql5HhHeoCZhcfH0xBwBcn\ne/xINIPPc2kE7OAx/YOxYWY/yckdZgHmDIYBM6BKVipKI4/VpKG2oPZBG6EcHGov/EeAlkNa5xpz\nHwRCmHG0j9SnzB8RYtCyyTSSpZepZw7qiEPfTWqXakPQBOeKlQ/yklbAVBPtDd9AQ1AN/YAZPknQ\nJIwwSwyXWm46rXzXOh02I0T7oIAGgmWUTzzxRC+w4OtBwIyrpaXF+58hpKAFgX4wv2JZ7XICe6Nc\ncMEF5Tzqn2ExEwQnAnQs2j399NO9gER7hVZosxJMoCPRj38xZz+UU22GfpmDCR7u94tMbNHM4Xuj\ntpJmG8kZVJZdQ6AdARNM2qGwk0YgQAdNEKNDrIOBS8yWOnhiHeXml85eDJpiDXiKGfw0AGpwUFzu\ndxr5HMz13MjUBiYTXxpMrmoR8PnBjwMmFuYG52CcyLsKMEcIJJiHEZipR7BJEhi6Sqvc/5ntZ8dl\ntDvM8lPX8QBjzsIIzP7zTB6C6F9tQu1EjF7YZngGOg4P6JwQ3ouXm/cI8W+F13qf9MCWQwyW2pWe\nIU4r0D8glCCc4JdQzeIM0CH1jmYI+sAfqZa0mFbZu0qHVdFYrpnA0sAIHDDCtDfKii8HWlXoHjoh\noFFsiYQUlh9GWNPCIv7PLn5wdh83blwXT7X9Db74dSF0qI8nTztFJnisijhs2DBPQ3yfZ0RLadJO\nWRlN+SHajNomggl9Ndd33HGHFwTxhUQTTZtRe0k5C5acIZArBGyDxVxVV/Eyq46YOBQOdJ7E7HBP\njI9iPa84fI8Bjlm48OCejjCNMD95QZtBDkdKGDX509Qq75j8wEjAWLCpIU63MHmsiAS+8fC3v/3N\nm40wO8+ADBMJE0gaSc/H3+/ONXkib+QTjQ+MVzygJYFZQsMC45QHZ+eQRtVuFIv+Fau8MEfUmWIx\nhtAOB9c6j8d6lncJ+hbtRm2KthQyk2pTyqvykUaMgAtjjTkapleVBjQkLC8MTUCHCDeVMOOVfq+e\nz+NDgtAG7mxci5kbdcGEBZssYjKJszsx9E6blKDCMuoIMWifytVAIfygcUED11UgbbRStDXaHTTD\nKlwcCDf0LaInYvJdC/rpKp+1+J9yEGhDaoPQL4IkffZee+21wcRYLfJhaRoCeUDANCZ5qKUmyqOY\nH3Xg8VjMVfhcKXg0qIUxDFt4rQGDNMLzUmlm7T44wBjAbMF4w/TXqxw4kOMcj6M5TAaDK4we38eZ\nGAaSmIBWBef5cs1E0sKZGWAYNWZmSzlIw7TB0FEGGC0Y7LyEsB2obUjQkLAhwUL/q02VW0bqU4fa\nDzGMowQQCUL6n7RrQYcw0XMjzSDaOky4KhFuKT++UgjJ5BN6QJNQpHDeeef5jWpZIQ0/EAX2eKGs\ntE+czMO6YUIDEz+c4pnRJ9BOeZbVtDprD0yI4G9EX1BOYCd5VopDGMRn6cgjj/TCEKuDQUvSllCv\nIS2Vk3aWn6HNQX8IZ6HW5KSTTvITAqxyJ2GMclswBJoZAdOYNHPtZ7DsYoAUa3Ai1iGGiBhmMrzW\nedJ9va809Q3FGYSjyyzBaOGcjDkKy8NS/noFZlVbIhMQBlS0EphrMZONkIQfA+Ze7KECE4ITbrmz\nsGnmn7rGVAVne5gzZpBhasMgxgszNZiGJM1K+HyWzkPaFV2LztUWwpj/dK1zPR/GeoZ2pIN61qF7\ninme95Uf4rQDQuZTTz3lmTt8qCrxCUI7AhMNbeLkjxM/tFC0wCpPOJOzqWGo/cNviPssgkA/gcZE\ngXYJFvgSIZCAMwIg7QFNCEI99Y6GJaxXmGkEDQT7cgP1gPCPNpPvYgp28skn++XIoSXRV0hL5aad\nl+ckpCCosFDHlClTHHu40A9RbkKIc17KZfk0BNJCoLwlONL6mqVjCFSAgDpnYjpzXZME1+WG8D3e\niV+Xm07WnkMgwY8ChgFmg4G93gEsxdCwwox8SGA6+vbtmwnmD6YMJ9Onn37aHzjDx013WBGMGWNm\njlsiYQvGKU8hpGnOdR3XkogpUvuJx5RZ74Yx5yGzyHV4hO/VCjf8I1g0AdOXUiutJX0brR3+JJgt\noblj7wgJo0nP5/ke2ks2NEzSBOF7wqa1EydO9PuMxMtJ/WJqycGkAsI8B+aaHPQzLVHbIO3p06f7\nvTjQzlUSSI88kv7vfvc7L9Sw+Su0JGE4TleVpJ/VZykTISwnAiCrySGcQJ8s5U17BQcLhkAzI2A6\nw2au/RyVPRysOGcQ1RH/r6v/c1TsklmVEzozjAxujdBGkDkYRRgMVtRhNhTmhTxxzipe+JhkIaAF\nYVUpmABM38SQK2/QEo78BJz74//ruazHaguUh3Mxe9JsEMOUxw8ENR3x/7imTpWG0oy3v1pig7YL\noREhk/1pyg0I7loSF1NCNC2Up8hh5MiRicWjn2Cj1AcffNBjmfjQupu0Y/x30G6gXcKkizaN1mX0\n6NHeUb1SoUTfo33RDudGJnkIiqzMJ1pSrGeLFqt8ivEz4YBGw0mEopXbymMIVIKAmXJVgpY9m0kE\nxIyFcSYzmlKmsOfGLAXmGWaju3uVVJMtGAvMyBBKyA+OqzA92O1jJoItNeZdaCEQXlj1qhEanbBs\nmJVhokK+YKriS8Ni4oNZCv+T17xpTcKych5vD1yLIQrPda+rWOnxnM7j36zFNZoOTLhg3KB3mOau\nAvSHBg9TJAQRNIrM9JPvIgdMOvH1KhUQMDHBApOkjRjj74EX7WL77bdvd5inXaCBRJBlggSBpZLA\nYhOHHnqoYwNGTMgwByNfHAi9osNK0szDs2BJn60DeuagD8UMdtCgQe3lLzqd5qG+LI+NQ8Cc3xuH\nvX3ZEKgYAQQCnLlhBmAOmG2rZ4BJZBYaho9BVTOrSUwfDAj7RGDeBSPERmJJJib1zD8+JGy+iCkJ\nCwXAcIUBXNnbhPDlL395A3+U8NminCdph7LEGGHmgu8Sy0uXswoX9IbZHkImQjs+TuUIM0Wpz87K\nQf8BzVO/+NvE/a06ezf8j8kINFiYk4I1+6WgIU2ipfA9zvENwqQMR332D7rkkkvatXUIJxJ84+8V\n4Rp86DcRnOlLOcaOHeseeOABL6DQTxYdgyLUo5WhtgiYxqS2+FrqhkBqCDCYYTKFBgImrVZ7lSRl\nmG+zezRmUCz3yWwppjEIR8zSJjGyMD07RSv7MAuKFgLmEnt/mEXeb0QgL2hv5AyPiVeYFzQlMAbM\nYCK8VLNHRiPK1Z1vUnfxozvppfku9QTdQWPQWhKdhd/DF4KlgGH4MBFCKIHZs9CGALRNG0Q4Bx+Z\nL1aKjxzmWdSCvggTOQR9tCu0ddpOqUDd8F2WCT766KP9hAX5ou1JKOmqnkulnYf7ocYEzS1aJwQ1\nFgKQUCIc8lAey6MhkDYCJpikjailZwjUAAFm2Zg5xhQJrQObKNYj4KDJ8qp8G38RmAdmrdmPAAa/\nKwaC/3kOG3VW90GoYZaVgElIV+/XoowITBwISuQHPGEEFGCC8WngP4QozNQs1B8BmFuEDGgEB+lQ\ngIznhvaBczzmhdQlDu74FDWCvuJ5y9o1Exo4wGNmedppp3Ure2DNSl60IfCm7WAixmISaCdpY0mB\nts+yxaeccop/l36FQ6ZcSe8U4Z7oUZoT6BbNHks7s8Qz+Eko0bN5LjflRFAlRuhSKELZVBaL00dg\n/WicftqWoiFgCKSEAA6jCAasRoQfR60DAyYbrrFzNMweAwsCyYABAzzTAQNRSYC5x4mWWW8YEJxo\nMf/QPieVpJXGsy3R6kLMGCMsYeMdBgZNYYx5SrVOvmGadl4ZAtAb9YJgjM8ETu+lAuZJLIMLvUJn\n/fv3r7uJY6m8ZfE+dI+PB30Kgl9aAW0JZlow1vibYap1++23ex+S+BLctCuEE4Qa2puY8bTykvV0\nwjKLtjFBhO6LEigL4wg0Bs1dffXV3qyXexxFKmtR6iwr5TDBJCs1YfkwBEogwMwi9twM+phLMIjX\nKjBY8C38LGAeMOFiOWAEEkw2wlmvavKATwwr/TDDilAAQ8kmjcyu1jugdUIjwqwu5kJhYOYSJ37M\n5sDfQn0RwIcJjRWCeLjnRjwXPINZEv5MML8suQqza6FzBFg6mIDmJM0Qd4SnLthQccKECX41rwMP\nPNCb1uGnxj4rCDMw6eGRZn6ymFZYVs4lmNAf0v+KYVecxTJ0lSfyjvDBpA6TC0xA4UuEtm7MmDEm\noHQFYJP/b87vTU4AVvxsI4DNPH4d2HQzE1wrJ14GEhh0dmvHtIABE/8QzDOqdZDtClmYSoQSvoeZ\nDrOs9fbpYLYdxha7dzQ64f4YCGUIaAhNmKeIgeiqXPZ/9xBg5pg6QSsH7qXoD2ESbR6BJYQxH7JQ\nHgK0d/BiEgKTrjQ2m4QJxYkbhrSzgLCPrxwasbvuussvGUz7Z9KDOqfvKXIQ007/giAH087kz4wZ\nM/yCG/hEoVXOsxZJQgllZBW4wYMHd6hStGVnn322Q0CmX6XOdXR40C6aEoHaTb02JZxWaEMgPQQY\nsBi8GaxZ7rRWQgn+FDCCzz77rBcSpNXAQbUUU5hGKVm+l1V5EH5g/vk+yyDDuNQrUD4cpBkU+X7o\ntAujhFYFRgKzFwu1RwCGhmV+iUvRH8wOK0EhlMDQHnDAASaUVFg10DsbMYLlrbfeWuHbzs+EM6GA\n/wA+Y0xo0HbLCWhKvva1r7VrZMWQEjdboMxMihDobxDuoP08H5RBmhLKpvKFdYuv5KhRozbQoNDX\nclhobgRMY9Lc9W+lzygCqPXnRhuQwbAjlLARWdoBpgKGAjMYAsuIYq6FyVi9A7PkaE8YsBDEyAez\niPViVnDwnzdvnnc+xdyEPCiw+RnanUYsz6w8NEsMPWI6h+YMgTEe3nvvPb8QA0wxZniYNtZSeI5/\nv0jXYEmbx9yK1c+YpVeAsUSbiKDOREE8jpts6b2uYjQzrOT14osvOtoZwgzarmZaJlcaE0ycwBF6\nh47pd1UH9er3uqqv7vwvAYMxjPbaWWCBFDQo3/rWt7yfGOUvAgadldn+K43A+p6o9DP2jyFgCNQR\nAWaYGLDp0DFvSlsoQRBhthlmmwCzwNK/zGQ2KjAow6hgWjJ//nx/YGLSt29fLyzUOl8IQeDC3g74\n1vBdBWbuf/nLX3rBhbrorp+N0rW4IwLgD5OG2SKYxwP0gPAK04zfCfvi1NLfKv79Il0zI4/wzaZ+\nP/rRj9z48eO95klCCEJJqYCWCqEQDS4+IsQ6R0jEdCceEDyoL/oa+jfeI0g72oxMqLQDTMoQ6APp\nW4rAlEsogc6o464EE+iG8kOTvNuM9OCJwH48AiaYGCEYAhlCAKaLVUzozGGWO3P8rTTbzJAyI43p\nFoFZKmYrsffNQmAwwq8F5p+lXxES2EwSh0kEJ80m1iqvCCNghHAE40VeCNhAY27GLC8CXRLTXKs8\nNUu6zB5jwkWgHsK9R2BuoIdXXnnFMy4sVc3y0xZKIwBmaDl0SOBQLMFDy47/8Ic/dC3RSnUEBA8W\nf5DQEcacdyUMwmQqfdJjUQImWKhT6pmgPkeTI/5mk/3AgFNPaK4J9913n58cop+DQc+rgKJyYSYI\nHTz00EPuoosuSqxd+nqWrGaBBC1aASZ5LXtiIe1mxQiYYFIxZPaCIVAbBOjQYc6YOcbEgmVS0wiY\nhbE8L4w+AaYDRn+bbbZJI/nU02DGHDMeVu7Ct4MVmnDMh7mJLzua5sdhCNgoDmEIrQkaJLAi4CjM\njD3MMQyc7qf5/WZOCy0Zs6osKxo6YsNYs4fOsmXLvIAok5dmxoqyw7zB9EnQiJtahYJBHKtQ8MCs\niraGfxVMIuehGWP83XKuEV74PrP/tFl81sSs6n2eoZ4xoSTwf7MGlrlGUAMTBDewIM6r87vqGsEE\nrT+TbfHAIiPHHnusO/zww71AAj1TZmLRAjECioXmQ8AEk+ar89yWWB2WCsA1AyCqcJgaDmblmOHm\nQDWcNLuX1c4O5gwGnNn6cna5Fg6lYpgVZvnRABDAA98NhJ48BBgXVmVCqEI4weGZvDPLWyu/AugG\n7PkWx8EHH+xnkGHWYLIwscOciGVps0pHpeo2qf3A2CK4cnAOrmo/xPEyxq9LfauS++zPg8DHHiSh\nMM59VqSDuaHe0aQ0ixldGoKHzKtgeENzq7jgcf7553uTrilTpnh/tkrqLulZvsUEAppFJhniQTSE\nNhimPE6X8eeLek25OcAA7Sy46JBQIqzyhAF5plzEKofyz4InRxxxhF99jPbOeA096rk8lldlszg9\nBEwwSQ9LSyllBMIBi3PMkJjNxqyDdfBZLrSzDfro9DADYlaQzQHZPZoVfBioFbLSETI4MXtI3nB2\njzMPym85sRwqYfbAjTQRSDB/yUp5yykHz6DFwDadWVcEAgQ3GFZM0Jhdr0V5YKow3cLfgZlklhHm\nOwhKMMjkgfqCtrIcwvbDrCX4zY0WVEAAVvvBdK1UgMHEnJD2g4aNtoNGiRl3he7ij9ChDS61+Sb5\npm3T3kkfYYU8FClI8IhrOnRdjsZDgkc8rrTvaG1t9YsNTJ8+3Y0dO7bbvmbQCnmKB+pS9EIMbVHH\n1LeO+DtFvhYW0DpCmph0Mepi1vOGAXUJfdN3c85kAtr5o446yk/oUE76ECZBJDQjwPKcyh7SSt7K\nb/ntPgK2Klf3MbQUUkaAzoyAapd9JFjrnuVsscXFvIZducUwYe6jGV46ORgdZn/RnvA8zD4HvgEw\nY3R+2Kh//etfd8cff7zvMDVAKE65OF0mh88HfiXkjb1KKE81gbJTRrQLMKJ0/AhkYMQgl/cAXSAQ\nwMxAG2iWoAXZJqdZPr6FdgQhCKZYM/kwjA8//LD/FBtFJs0Ip5mPatJS+0FAZV+Ju+++2wskCCEw\nCGh+aD8cCHy0G2gOZpJ3aDtoTxDA1H5YsYyVm6ApBGeYjKOPPtpjr3ajuJI8I/hhYoggCEMrQYU2\nAfOC6Ra+UHkLnQkeCB8cpQKMmxi2pLhSwaPUd8L7V155pbv00kvd9ddf784555zwr9TOwYR2Sx1D\nZzNnznRnnXWWX26YtizGtBo6Si2TdUiI9kn/DA6YKCKUsCs6CxFIOIGpJ+QVC8qHKRd0Tj9CoO4p\nO/UswYQ+hz6FfpSyhzSQ17L7wtpPtxAwwaRb8NnLaSEgZoqYTQVZJYYZPJwjmaX96le/6meumT3n\nmfAolQc6tvBAu8LSr48//rhn2GDAMBU6/fTTffrqCBWXSjfN+wxM7H5OeZiZr4YJYwBAGEEoofOn\n08cnAo1CLZiYNMtfTVoMdviAwLxSV9KIpV1WGAcEYr4HgywTOJh1GPVSS9pWU6buvgP9EIgx3xs3\nbpzfsA0axxzty1/+std4gFXYdvRe0vfDtsM5AgQb4z3yyCPeoRVGk/0oWOITbaTajeKkNMN72jyU\nyQYEckwy8SdBKMLmHsyzKPhRhlDwgD7IcxhzXiokCR7SeiCIpE3HpfIR3mcSB40qB/Rdbh2GaXR1\nDmZiyBFM3njjDa8NZWPBQw89tJ0pLcIkSmdYCAf6bSbeMG3CtxDs6bvFnOcVB/oUykj5mMihLXBw\nDV1B/5STtk0cCiTQPs/Ugv46qxP7L1sImGCSrfpoytyIUYK5vuaaa9ydd97pZ3ZPOukkPytLh01H\nx6FnxVApLgWcOjh1dnT2HHSSc+bMcdOmTfNLwTKLzMohzASH75RKN437dNYISQzS1aw0xCCPBgFT\nAJhoBjQYT0yMNOOWRj6zmgYzcQgo4AdDh0172g797777rq8jaAbmGT8daA6BBQ1EfLf4emMl+idm\noYCrrrrK/fSnP/WC6Te+8Q0/C4uwm9R+9G5neQ7bAhhwTQztoo2ZOnWqFyYw8xo5cqQX9MN3SqUN\nw8ISzNAwghMCOqZm5BNNDqZ6fKdRgXyETFWlgocEjXjcKMGjHBxZGYn+8MEHH/SCQjnvVPIMmGom\nnSWD6bOgGzZZpd+HQaXfamS9V1Keap8FB2mO0FLRjqQxL4pgQt9CGalnHZSbvoFxirpWfSOMhAKJ\n+o9q8bX38o+ACSb5r8PcloDOi4NB//LLL3cTJkzwpiWYEmAmQgdFZ8YzxKVCqY6M95ICz3PQGTII\nMsOMCcP999/vHWx/8IMf+FjPJaXR3XsIRnMje39miTFhwZSl3AAWiyOHdvINU045ZJpDZ99MARwx\n08OfhoBpElo1Bvi0AiZM+EFg7oRwwsDKRpDUHw6caN0awUyp/aAJRKieEjkvI2Cfe+65XgMIE6j2\no2fjmJRqOzzXWfuhvBzQHuZY1157rRfWMG/7/ve/781TOms/Tz31VLuvEO2f3cNhSnFwRxNV60DZ\nEDySBA7dK5UH2lhc4Aiv8zopQD2ipTrkkEO8RqxU+au9D+ZxwWTMmDHu3nvv9f5ctFkJJp3RZbXf\nz8J7YECblGBC3z9w4EC/CzrlDwWTvGJAGcNy0kdTXgJloo7pN3Son8hrebNAV0XLgwkmRavRHJSH\nTotAzGwRdsb4hXznO99xJ554or8vpiosjjowGCKd83+pDk0dpGIJN7pWGmKwMIWCqcOcZPjw4d7u\nlxlOPRfmpTvn5AP/BcwnWqKlZ2HGygnkmyVrEUhgnsg35loINWky4uXkJWvPsMTyc8895wU9BAeE\nE7BNK6CNQPjBnAvmjcAMPww1Cwvgy1OvAB0QoCP2n7j44ou9WQRMHqZVajuid55VGyEO24/u80w8\nqJ0oJj19W8+Slg5W0DrvvPO8WeGFF17oLrnkEs+ExNsPWj40XfgVkCZaKYQ+TDar9a9SfhSTz84E\nD/6Ll0XvFlXwUPk6i/fZZx9P1/SFaS/uAN7UdziTzncwA2RSCKGWtiv67Cyfef1PGMCss5ALJspM\ncNCHSDApgnBGOVVW6pyDoP5HdRzvG/Jar5bvdBEwwSRdPC21LhBQh4Uan2UqJ06c6I477jg3evRo\n79gedmIkFXZkYoCSOjWeU+AbBMWkGe8kS32HWRxsnmGqWNrwnnvu8Y7PaXagzL4zC4/ZEY7ElKez\nQN4xW2LZXAQ48tISMd0IJMzUWmhDgDrFPh6cOMeECef4NJhd0sMXCAEIMyN8eKBhbMRhtNCaoD2p\ndYAWONC0nXLKKd5s64wzznAXXHCBF05E1zwTbzvhteiZmKCYc95VrO+RLudKX7F/MPpR2sS33nqr\nw5maZZ1ZuAItFvc5oF/M4AjQPQwaGhKE80o0DeRFplbScIRxJYJHqO1gIqKSfPiCFOiHXeBPikxo\nR4wY4W644YbUSwbdSFuAtpdzmHMmWPg2ggl9cFd9YuoZq1OClF9aI9ov2l4W04Dm5AAuwaROWarZ\nZ9R38AHOCfQBinXub9iPIRAgYIJJAIad1hYBdVQw5fhyMFuGkzuqbM3ykgMxMRqgFDNYceh/dWyK\n47lXZ6jvipnS4BDGepa0+B6rMZ166ql+ZhfhCXt9fTv+nUqu0XbAOOOr0K9fPz8Qd/b+m2++6Veh\nYlaZgL8Ns2v1YII7y1eW/8Phmxl56pA6Q4BDkOC8OwFmF6Yam2n5lsjMCyGTmd9aBtExy2XTfign\nQsDnP//5DdoPZeVQ2yGGtrlHHB6l8qw2QUxbUax2ozbLNYfS5Fv4i5188sm+DvAZYzac/5kdxp+E\nwDWaraSZeb5VSvCQM63yF88/zC0CRlzg0HUzCx5xrOLXCAv0MQgMTIakPfEh2kEglZ8Jplxoqmmz\nmKSKZqGPIgXolQNs0bRC+2g5Bw8e7McBBJMiCmbxdlq0ei0SjWalLCaYZKUmCp4POicGJWaImCFj\niVccZ5lNhcHhfzosGCcYBwYnDVDE+k+dmmJgC89DGMMOkXMdYqQ0SPJ9HXqHfBAYOFjhCB8YtCh8\nS0f4rXLOMcNi9RUGIHwVOhv0WY2MZXHxZSCwtwb2yAg0FspDALxZPQsGCK0J2pNqVj0Lv0a9sDIV\nDARO29QhmhT8PNg1u1b+EdAl9Mq3WMWH5Ytvv/12bw4F7RKgSwkjaj9qQ3GBRGUq1Xb4X21B5/H2\nw3fVhmC2ONc7fA/mE4bzJz/5iRegMBNiMoJAG8AfBvMVBI1Q21GN4CGho9k1Hh7cbv5gGsjytbfc\ncosbNmxYN1Pr+Dr0Ad1AL3KKRvOIcI0TPP596v87o82OqebjKiz72Wef7X7+85/7TVxlwkVc1LLn\no4Ysl1lBwASTrNREQfMhRgWmhZVH2MwLe3xWf4GJ4D5BDJU6ZjFWYraI4wNV/LoUhMoD/3MeHmKu\nGCg5uOYgkD75QIDC7Oyb3/ymF1JCJs8/WMaPGFrSO/DAA0tuYsZsMgIJs/2Erbfe2psO9erVq4yv\n2CNxBGB6tAcH/+0U7bCMKVZ3FgnQcsHUCXUJU80SujAWaAag4bSCaJd2wmpbQ4YM8c7JN910k28z\n+j9sP2pD3AtpNd5e4tel8qxv8H/YdsgTh9pMqfajlcJwkOebHGGa8e8i9EnQSIr530LtEEALyN4a\naGZpO2kG0Q+0QttEOEGARas2atQorzXBrAsaFq2k+f1GpaVy01bAF20JK3KhVYSe6Y8kmBSp3I3C\n276bbwRMMMl3/WU+93TIMC84DzPDjLkL5icMPNwX88Q1HbQEkiSGig47jSCmSINFnLkSg8V9Anl6\n6KGHvGkXe55cd911HRi+rvKEPwAmLKSLTwk7iMcDzyCQsLY/gX0c0JDg52Kh+wggGOKsjvkTDAD+\nD5isVBu0MWBLS9viBWgCWbYZsyTSTiOITqFDlrZmU1CWdGU2m3v8T5uAPmk/OtSm1IbIS1pth7SU\nL2LlA4ZL7YaYQ8+RP5z0EeTQWJFPJiVKmVuZ4AHKjQ3QGg7paOhY0jfNAM1wSDAh5kCLzKQVPn7Q\nQEi/aX6/EWmprSCEsbEvZmssFS+/EvokhBO13Ubk0b5pCGQFgf/33ShkJTOWj2IhoM4Ye3M2eGOW\nCAdHOl8CsQQSOmUxVjAyGpQUp8lYkZbS07m+o1j/UwYO7Q/CLBeDCAKGnlGcVHvYbLOpIzOD7LMR\nZ4blD4HghmMwm83hCMysPoybhXQQAEu0JdQV2ijs53FkRwCsRnuCcMkGj6TFzsXMMLNhINeY3cFw\npBFg4BCC0DTCLLKstYQBaBUGTkdS+6G8ndFnNXkM0wzbC+e6Jl21HWJoSr1X9gAAQABJREFUGiaX\nVbvQKh122GG+LYAjdYCpHZjR9i00HgEmRNAUow3En6lWQUIKNEUfy6IJmFxyLrpVXKs81DpdtQPK\niqM7q0+i8WQSA3pX+w3HvVrnydI3BLKMgGlMslw7Oc4bnTEdMU7bLK+KszYzcDD1BBgYCSViqMTU\nhIxPPSAgrwQxfJr9ZXZLs7/8T/7uuOMOv5Ecqw0NGjTI3+N+UiAdmDHMs3C+RthQwI5+wYIFbnG0\nHwnfhzHDdILlaPM+EKuMWY3RTiEI4hcCMwDuMAml6rFUORAkWfITusGkCyH06aef9n4f/fr161Y9\nqv1g9oHvCoz9lGifEgXyHbYfMTUqQz1pKGw/YKH2Q9uhDXFNIG/Mg6E9YXNF/Aq4V8+8Cj+Lu0YA\n7QWTSvRR9EtpBeiFA7qAPmg3xNzDXJZlv1l6Gu0a9CGaTuv79U6HNsHBRAhtmXHgtttu83SPUIJA\nTkwbrvfYV28s7HuGQDkImMakHJTsmYoQ0MBDZ3xStPQkNvmzZs1qX7aVDpiOmJnq+GxRIxgVDQaK\nlYeQYVKZmM3DIX3s2LHeCZnZXr0XgsTzzHRjQoRDNNoSnkNzgskWSwYjsDCTj+kP6bIgQPjNMD07\nTw8BhOPevXt7TQf1g+aDA20V2o9yA+mwGIE0JZjeIayQJulU6xcE7dB2MG9BUwI9snIRgggh3n64\nL8EkiRbLLU+1z+mbYSxmUvRMeSgX5joIhZhz4i8DTnqv2u/be7VBgHrBQZs+inpLK4gmSE/9qmge\nU1+06ggmaGqS+uK08lGPdFQ+6J+VHRHy0EThO0WbDS0FVNZ65Mu+YQhkGQHTmGS5dnKaNzphZsMm\nTZrkWH1k5syZfvM0iiOmCoGk0QxVKXg1SFIOzfrCJFImlY2VxRhI2MGaGa/4oMKSrsw2IriwtCzv\nIqBxcM47zEi2RD4KvGuhMQgwW0tdvfbaaz4DaE4QMCQElJMrBE20X5i/IGDiCE+dYrIkDWE56fCM\nGBlohN3caUPslUK+CGo/Ymi4FmMfMnz+4Qb8kH+C2onaj7SP/IfG6qCDDvICOQ79lCHefnjOQmMR\nQMhGUwITzQp31Zg8lipBSOfyMYFGoBsmbTBbvOKKK/yqbiGNl0ovi/fDMk6YMMGx6ShjIRYE0Lsm\n58A1r2XMIu6Wp/wjYBqT/NdhpkpAZ8zgwswQM15nnnmmO+aYY3we6Xxh+EKmSgxJFpgqASlGj2ud\nE2ugIc+Y7tx4441eyMA5VPknRiBhvxLM19jNmjXr0Z6w0zuDEaZDn/3sZ73Qovf0bYvriwD0iEaL\nXcjRhOEjAhPGLHG5GzMikGCmQf1C4/hNsP8M2jH8TSoJaj/MGJ922mleMyfnY9KWhpGY66y1H+g5\n6RAG9A0Iawhw3/ve91xLJJjje6Z2oFjPW9w4BKgnFuPASZtJFJaoTivE61l9K/RBm6HvvOyyy7yv\nCRMFel5xWvmoVTqUh0B5EL4xUWMZZpb6JtDvqC2H7dj/aT+GQJMjYBqTJieANIsvpopZ0qOPPtqb\nLDGo0QnHmSruMcjoSDMfaaWlwZLZa8qkmT2uCazzzwpJmKbIRwGGFD8DBh0cohFKYFApLw6dbCDG\nuYXsIUAdI1Ci1aLuYZDYa6Mc8y5og80X8R1C6CQdZpwRYMvdO0Xth5ljFldgppo9QGBuoBkxMnGh\nJHtItuVI5SH/lAmMiNV+Ro4c6R588EG/JC2aRfqIvDCeWcU87XyhCUQoYYKF5d7TDtBGUv8KHSCY\nYNaFGTArOkIfhKzTCHRPoGyMf1/72tf8Slwsm622zORcfILOv2Q/hoAh4EwwMSJIBQE6Yw4GGZbW\nPfzww92Pf/xjb8bEQAIzRUdMDJPFTC8hD4OMyhVnrigrAybr7jMrBiPK5ns8T1kRSBhMEVoQSrhn\nIfsIvPfee94BFx8gaBVnVeq4K1ploQcYEWgbgQaTFLQubBwnei9VerUfhKObb77Z75uDYz20w3fV\ndoihKWlKSqWXlfuUS8xnvP2wIh0O8MwiY+qSp3JlBd965ONLX/qSN0/EKR1NV5qhFH1AM9A9y7Nj\nyoiP1YABA/w97nfVFtPMYyVpqR0Ts8ADE3SYLWKSyT3aLWMg2qi8TDBUUn571hBIAwEz5UoDRUvD\nIyAG5MQTT/TO3meddZa/H872cp6XmS8yHw6AOtfgw/8wjuwO3y9ahWnRokXts8FggcYEe2Jm3lVm\n3rGQbQTw/2mJTIwQAnBkx5wFTRjmXvxXKvAfWg78VRBSt9lmG/8+NN+V1gSagmbQuBx33HHe/PHI\nI4/0n4KB4ZBQAi2JFkvlJSv3yacO5Unth/Kw4AMbLw4ePLh98Ye8lE3lKXpMHd1zzz1e28ViDGkG\n0YbqXLSh9nDooYd6rfN3v/tdPzkQmpPpnTTz0520wrwzKXfsscc6fBEnTpzo2wBCCX0BdE975jwv\nEwzdwcXeNQQqRcC8bitFzJ7fAAENIjBWzG5h2jRixAg/QwQTRQccdsQkkLVBZYNCBTfIKwOIykJ5\nxBzi2L7PPvu4+fPne3MvXsM/AcaWGMaWA0aV2XAL+UCAOkfoZP8dBEu0J5hqsZGiTJGSSsJqX2hX\nqG80BNA9Zl0IHKWCGBraz5RoSWD8Vc444wz/eJzmoMO8hVLth3Iwo7z11lu7G264wQtmwiJvZSxy\nftlzBrq+++67vR9W2mWFPjigddoLh/pX7n//+9/3/lasaoV5F/1o1uhE+aFvGD16tF9x7j/+4z/8\nfiVqsypfOH5QPguGgCHQEQHTmHTEw66qRACmigEDh13MloYPH+6ZeTphZojy3hlrAFGsgYgYxoqN\nwViFiQBDilmPNvJjLwq0KewM/tJLLzmu2eCP/3G4xnQIxhXTLwY2vsEgZqHxCMAksSkms8bUFZoT\nlgfGRAsH3aSApgRHeARSHOFZhYqN6uKba+pd0RI+GCyhO3DgQL9MMHQgRi3OrOndPMVqO8SUmT6D\nQP+AKddJ0dLiYBo+l6fyFTWvMNb0TXPmzPELdmB+V4uQVO/QCLTSL9JI05ZYMAETRyYMtDiF3qtF\nnspJU+2XvgGzRAQ4/EmYnNMkBuNfkdpyObjYM4ZAtQiYj0m1yNl7HgF1ygglzCazEdyMGTMc69FL\nKJHqmgGu0YNId6otLCvCB4M1MffRnGBLzM7c3NP/K1eu9BuIsSytDu51pT0BK0yDOHC+1rli3QPb\nPGPanfqo97vUKUsDI2QSEDTYgyZpSWDqGA0LNAIDhQYFB2K0L2GAdiTU45sFY8PiCS2Rxo32Q9oS\n7IvQfigrzJoWkiCmXYAjyyOfc845vtx5L2tYx0U4ZzNS6B3hAPqnftIOagvQiPpQ6EPMPd/EGR9N\nBBpMJoNOOeUU3//RB9a7H9R4QDx58mS/6hb7Gt1+++1+yXHKQQiFEs6ZdDL6Tpt6LL0iIWCCSZFq\nswFloVNm4GAgGTVqlJ8twumXjheGKu7k14AspvpJDZ6Ul0FTWg5mfNk0jkEboYEBqLPBEsxKCS0S\nYIj5RmeBb0hYCWMJLrpHPdSCmegsb0X9D1Or559/3mu6mAVluVsEiXhAY8JiCNAC9IL/CY7EXENH\nbOqIFoZVj/gf3yy0aaxCBPOi9hMyM/Fv5O067C+gbTGe5513nt9PhmW1i6Adylu9lJPfoUOHuimR\nqSH0iVavFgH64GDihoN2wUF/yX36MPpNNrhlVUTMaMeNG+dXwpNgorgW+SNN8qGYBQHYqwu6PfXU\nU71wTd9rQomHyH4MgaoQMMGkKtjsJRAIBxEGj912281vjMUyoDBTRZrtDWs8adYX8x6WiUWNj4Mo\nzFUas2J8S4JKkiCj/4i7CtQHgkpcaJHwwn2eMTOyrpBsWwqUZYXxH4FpwrmdFYtkXqIUeGbevHke\nV4RYzBw5nnjiCa9FoQ2x2hAaFXxTLr/8cnfCCSd4+kEwKYq2RHjE+wxpHZ955hm/kh8xK5rRf6TR\nfvRdi7uPAEw4ggDC9cMPP9z9BEukAI2oj00SThA86KPQnlxwwQVew4hpF+MOmmsJJopLfKbi2+SL\nQPzrX//aL3oye/Zs3+9fd911foJBApTyKPOtcHIh7XxVXBB7wRDIOAImmGS8grKcPQ0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JFAG1H7QYBS+zGBvmbkkauEMZ3dcccd/UH/DF2kH95wV3xge3dZlPD0hSvd8Tv3KPGJFW7K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\nxQ6xFz+vM0poL9DRX8VZ/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+hnwaSU7pbYq+WVfj/UUbDKNpPhJZdFHu3RCRH6mzTDUjJQ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\nAABJRU5ErkJggg==\n",
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RsStIkiT5W+qB9DEZR1ke2U9y9fJxCuokiNSlmK1kqcygPKRSrOqqxqU2gkSBmwabxrDm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Of8dpEHF+qk7gcrRcinULou8hj9v9A9aembmcmQEzJs1SfyMlj85X7hwGTmQAHsk7n2\nx6js0XkHuAngHsBCJ23wcBgHH76XXHJJ3pz0gQ98IJ+ex9Zx+eWXzx37UIOKdIBoS658Yzuxz6Eg\n3NnxUOCyaTTSVXzpAuhAjTAGMs+YANx2223Z9Z2Bhl2vA0XiQBDvIM9+//vfZ/BOG29QZPvuJMAC\nUubwaCL+rcp1ABKy3SxfnlnVIdfkiSyTaReATT6Hkmu8A4TZxpt08nXt0m4222yzPLldZpllcroB\nzjwLLWp1EiodF3MJANseEhsCn3rqqSzXTGRGA5q0v6oWXpsOsloFuIcm3gR3uHLHtyUcmgMhiw8+\n+GCWq1122SWbHAY4biULUc/dqgN5inxpE81tRInIx0c/+tHcJmyOlqfI19AlLk8LByYfBwpgn3x1\nPi4ljo7bABIdt9AVIMPAobMGmtmXAyUADpCiY+cK0qmS4bkivLh4BkwDHoAz8P3P//zP6UMf+lBa\ns2EisMIKK2QwUwVSkU6AmQDr7lcHMCCJ3S8gQ5suLaf5OSinmZSRfbD3gBbxAPdTG1p3WqRCE48D\nAUrIVhWUkGv/o6pcM6O6+eab80TSZPKZZ57JsuGId8Ddag4gC6DTQNJgkycmWNIyYaRNd6222mq5\nvUin2oa0n6pck+0AQSHbJpbaFu9HjpV3sq90uC/VXnxTB+FDaOCF2mnwRRpVAK+N1JVuHXnvlzhC\nBskK00KKDu4TA6yThVhlIRvRx4UsdLuc8icvLvIQq1F+I32mFQF7P/bff//c/49V3rpd9hJ/4UCd\nHCiAvU5ulriG5EAMLDruADc6bVeADu8EwDGwxEUDCDgDLgZ92m8XDTaAA3jQagP0zFZ8V41TvEjc\nBi3AwEAWvwcbxMQBWPneQMhMRro0o0PZrBs8TSCYH/jWJOPtDbODdrSpQzKxPOw5DqhfVxWUkOnQ\ntrsfFPIcIXMwcv3444/nSR6Q7iKb5NplczNTMfJDbiMdoXRRyHUVrAc4C9mOPEQIoNPIhg07eQXc\ntaX3v//92UQh3q0rlN+qGQ0Qz3QoSNphRiOMDYnxvIQv5wA50E/t0HDjqT6vvfba3K+p98HA+stj\n6e6dahvRNoB2l9/orrvuyuYxN95448BEtID27tZJib3/OFAAe//VWd/nuApuDDQB2COsAhyF1XE3\nX3FfGINBc+gZim8NYABPXFVAE+/lD5r+cGkHqNOUsz9mIwpkADVOCxyK+OsGjGwMNEABIIAXECYf\nhSYOB0L+AkCR55Dp6uQxShxyWQ098z8SX4R+VwG6/70XwJ8sxeRT6GqON0dW+SMOG1BNEGjsySZP\nMzZb+02+w81j5bPaf2pLzWY0UXYT8iqAtwKhzIXmcACfyBZTQhtMf/SjH+W+BY9o1Vtp1seLd/Lq\nIsfaRTNoP/HEE3M57K8wUVWGaAvjleeSbuFAL3GgAPZeqo1JlJfovKMDN+g0X/FM2CkFWNHpBzAP\nQBP34p12BoV/+7d/y15hHK9Na0UjZMBhegNQDEfKBgwB7wASgGUlwKpAOVlyOO71z/OQVaAkrma5\nDuAtRL5pRwa9W5XZAObVkGwH0GknTiZeTM6YwgDoyASVW1PxujfW5lz41WxGA+AheWJGEyDeb+1x\nMlL0j7Eh3imm3CWqfzyxiVOorwm56QU+ybc6DtBOqeF/7YGXsI033jh96UtfGui3eyHPJQ+FA73A\ngQLYe6EWJnEedN5xBcARRgce9+KdCJtZFuBEGKAlQoN8/BbG4BXfNMfV6n9aQBtZLdmzE7W5jqZd\nPtkAdwJqACKmPbxzIPbwtO7tAP9WeSv3eo8D5BSRX79byTTZCXmOsLkkIaMh1xGS45DrCD2Lqzme\nof5nkmOvhn0aVn4QEGhVSR7ZtA9l/jVU3HU8w5vYpxKaeO0viNY9ALxwskyAQ6bYfdvvc/31188D\n1mnXq7IR/OqFUJ2SLWZjFB8B2skcd4/33HNPWmqppXL+yXShwoHCgYbCptFwOldfFs4VDtTMgRBD\noasKdKr/x/Pm5AOoRBjAvBr6Jp43f9/O/7TjbEQd684DDE05Tbs8AfGAQyfke8Cd5l15gX7AHYCX\n70L9zwGygUJu65JrchyyHTItHAnJkw2owBPTGEAP2asBQAHHQx2wNJI0R/sNgFfVwptgBK/lPzzR\nAPAm2ROtPSkrwGuPDGB74YUX5j02ADrNepjChIyMlt/d+J7cBWhXn4C7cu288855FeXss8/uudWB\nbvChxFk40C4HCmBvl1PlvTHjQAy8wrgk3vw7MhRAJYCL+82/4158M5JQ+swHaNt5oDEo2kB39913\n5/SAdraXnZKBygmZjg83cBlwpzbs5ZnMBHjqNM7yfu9xgPygZjlu/j9yXpVr90KmW92Pb0YaAr9W\njLg95QUpiDwC7eQ8NqfGs14KAT+gvQriTUAQ0GoyDMSHOU0/t6uQFyYlDuW644470lVXXZXLqVxh\nCgO8h6z0Ul1FXqrlCC27OuOWd4MNNkgPPPBA3jOk/ibahCt4UMLCgU44UAB7J9wq7445B3TqVWr+\nv/rM7+oAVf3d/N5I/w9g43Ak/rQRrzX8WrMVBdpHuiRP40Rj9kRDkw98GKSYKADvI5kIjLSM5bvu\nc6BZjpv/b85BVZarv5vfG83/Dz30UF7xYbdeNYEBDNm0j/SApdHkaTTfWsGqAngrBkE201bNaPqp\nfZEVExSTKCtyp512Wpo+ffqA3TrQri/qB5CrLPo9QD207OTNAWMmiE7BjrJ0S+5DJkpYONDrHCiA\nvddrqOSv5zhAA0QjDpzT2iHAAGg3WLo/Wnd0bHWZy/BRjAAog7PNgYUKB7rBAUCJC1PENAZQCgKq\neI+p64CliHcsQ1rcKoA3KVYuZHNmFcDTyNNQ9xoBuC51xQzm0EMPzSsg6opmPbTrwHq/AFx1EKYx\nVi/VE3v8Aw88MLs81Zf2+mpBr8lJyc/E5EAB7BOzXkupusgBAwpgY3DkNSY0WXxqMx8wwADtno+W\nHHDzREPj7kAogzRNIODuQKZeBBSjLW/5fnw54CCnn//853mPhr0aVQIUaeHJY90HLFXTGavfgGKz\nGY22jYBdoL0K4utoz6MtmzoIcDtjxoyshWYWY8LBBSaFgX4h+qTRpjcW3yuTutC/0bK77JtwSNhZ\nZ52VT0JVvn4q01jwraQx+ThQAPvkq/NS4ho4YKMof8E2fPHNHgTw8HBgyR1oN4DWQQYzvtzZuQPx\n4rXxlVvIXgASdZSxxNEbHCDX5NtBYQBrM43FAUvNaY7V/8xmmLiFNxpmNUFM3aoA3uR5rLXYAWzl\nkbncDTfckDfB6wNcAWzHOl/Bo5GGMRExYQot+7777ptXeZj86O/6adVgpHwo3xUODMWBAtiH4k55\nVjgwBAds0mNHGofOxKuOn+e3ndcYLh8NonWRgY2ZDHMZy/sGZtp2WnfeMAoVDoyWA0ATF6YA4Gqr\nrdZSswnQj/UBS6Mt10i+Z1sdAF7oouFGzFCqAJ5GvmpGNJL0hvsmtOs/+MEP0i677JI3aPazdj3K\nW9WyA+y07FdeeWU67rjjspyRRbztt4lIlK+EhQN1cKAA9jq4WOKYlByIQ2fYl6+00krz8CA08LxS\nOFypGwO55XzA3UZVAx57esB9kUUWKQPbPLVR/umUA2TKpNPqkVWkVjTeByy1ylO372lnJulVW3gA\nEwGTJs0B4rX9ke5lYQJnY3sVoEobYDehYt/9RMM0ickIMMscpl+161FnYcuufExiKD6WW2659Ktf\n/SrLYZQv3i9h4cBk48Arj2jQZCt0KW/hQB0cMFAaZAyutGvVY9wN3AZRm1MN7tzl1W2DKX4+27l/\nFDcAJS82BiJL9nWnWQffShy9zwGrQ7TJJp5kjKw3E3k3WSVvzLUA1JF6SGqOu1f/B6C1O2XVpk2Q\nmab4H48ATStgwOYTDUCNL/hIY6wtMu2ogvBW5QTKrd6ZkJt8V9twmMSccMIJef8MZYA4gdl+s11v\nVXb3lNGFz5deemk2+Vl66aUHNp4Ox7/B4i33Cwf6nQMFsPd7DZb8jysHaNOAGmCZTXl1cAXiDaRA\nu8HXAN+NwYb23ibAqQ33jzR60pIn9u60VYC7fBQqHOiEA1ZsYsJpUthKdoGqN7/5zRmkkjcgvtMD\nxDrJUy++q20pM3CtDwDi3/SmN+XJC+DJHh6Ix0srYjan24fiGbDdvHkcwDfxZk/vO5MiaYSG3X6W\no48+Om255ZZpySWXzM/0AeJpVUe9yLNWeYq8RzlNXG655Zbcf6266qoDE5J4r1UcvXRPOdD3v//9\nXI/aUFC/lCHyW8Le4EAB7L1RDyUXfcoBAJ1W0WCsg252u0jzZiD1nAkNbWW3Omt5MUmwEVW6lupp\nP2n6pA1cjXSJvk+rp2R7FBwAEgFKWmKA0OS0FXnHZJSfdqB9qHdbfT/R7kWfALQza3nHO96R2z1Q\nry/QFm1O1zZt4KWNd4+9PN6Z/AP1yITb5Fu71n6BWBPyo446Ku23334DYF680u1W3zJWdaQPdZnM\nKCuzLJOWDTfcsK8Ae7Uc3/3ud9MOO+yQPYvpmwtwHytpmnjpvORod+KVrZSocGBMOEDD6KI9szxO\no10lA7bB59FHH82eZbgr6/bASivn4uVCvgz6gIGBnwbQxMEAX6hwYCgOABdkh+ySmcFMXpiD8IrE\nQxLXjyaLzBi6LedD5b1XnuEBEzkXwIaYyIQnGiZzJkUm9ai5XQLy3MXaS2BiBNADhDH5F39cOYI+\n/qMcyh+XMt511125vMrcD1QF6+rOigi69dZb87Xmmmumww47LDsrUM6JUnf9UDf9nscC2Pu9Bkv+\ne4IDyyyzTB5IHaoEuDSTpWsdN/BMG2YzlY6622TyIC2DPUBAA3rfffdlbR3wYAm/mMt0uxb6N34y\nSn5uv/327JGEzfRgRDvslFRuIck5UDpt2rSXAdDBvp9M901wgG8XCs25/qPqSjJ4AgQ+/PDDecMr\nbTygZ/IUwHYs+pLIy1iEAWStCOKHvlO/qZzxbCzyMdI0rBDIs4mrVZIqFeBe5Ub53QkHCmDvhFvl\n3cKBQThgYHnXu96VB1X2pzTtzeSobR050GzQaT6Ypvn9Ov9ntvDOd74zL8/zAAJQ/frXv86aU3md\n2rB/b14ZqDP9Elf/coBcmHCSF5rg6pJ+c6nItRUkgPSJhikWsLLCCit0xUtSc9r9/D8was8AU7ZW\ngD3KFhvKw4MMADvRwHqUR0j2rNg4E6AfgHrUk9AkS38f5k3VZ35XgfuRRx6ZXai6H+X3u1DhQJUD\nBbBXuVF+9zwHhloWHe+ODugF1mnCmMi00lzTxNOm0XYbpIH4sSSDnsHeZUkecJcXl01zytBshz+W\n+ZusaQ0l13gy3rLNjApYJNvkBCAfjOSVnLO59v7dd9+d3Z4O9c1gcU22+ybW7VCY1YVcRNjOt738\nTrUcwK7Lyk3VhK/6Tq+WRXt26et5DhoMtBsDjBUma96NVYReLVfJ1/hyoAD28eV/SX0IDgSI0ZHR\n1hn8H3/88ewm0bIwTwtAgc1cbLOBTXazNMnVgW+sOvjQmrO5lFemBM0kL+4biIBl3wzm57r527r/\nN0i48BF/TTZshsNPvHQgkwGkUL0cCLkWsg+nuX7kkUfyoE6uXeRXPbiYLZERKzhV16FjJddKT07J\n7Z133plNY1ZcccVhmWLvBpDugCVuCpnTDGYDP2xkk+SF8Ok+XHFNghAZGEs5GC5fdTyvlkdbIEdn\nnHFGBu7k0FV9p440645D/85+Xd/6zW9+M5199tnzJKFfXXfdddOee+6Z3vOe9+R27htl0y/0evnm\nKUz5Z8w4UAD7mLG6JDQcB6pAhh2sY7dnzpyZQYKOj+mGg1yATEulwIsNW7TDDjMB5v3W6bGlXWut\ntdI666yTN/dEBxjhcHkZ6XN5k88wHbDE3UzywLbXRESeaZCUa7wIiKIRBQjlG3h/4IEHMpAEFtm6\nF+3o6GonZNsk7brrrsvu6pwmapXD5mAmJ0Jy7WIWwXQJYGFCRU4AAKCZXE+fPj0P+FEv3ZZrpSfL\nwBPPJjx30AwOR1ZyTD7uvffe3I61S5PrQi9xAEi3kdRkWdgOeY/bx4ns9Umb0Q70T/pK/+srAdux\nkPd26mGwd+SRDbt8+x1kbHIy9nbbbZfbvI3I8U71vXi/hIUDVQ4UwF7lRvk9LhwIMAMEnHvuuenC\nCy/MttUf/OAH04c+9KF00EEHZUBpcPJuvC+MjlvoAmpoK++4447sRuuUU07JQGPbbbdNn/rUpzIw\njne7VVjaUGWxgUwZIo/V9HTc73vf+7JXDRpWGpepDa32eJLBkOmDfMg/cPnYY49lgGZznGcGmELt\ncSDkFLC66KKL0ne+852saaadXrPhKWLHHXdMyy+/fAawVbmuxl6VHfVh9QbQ33nnnbOsb7755jke\n2utuy7V8mViYSJBtk9NWZl/V/PttIrLyyiunn//85zn/yj+Zza7UNdeMAdD9RnjJFST3mCE7+UHT\nH++pa/UwUfkYsqyMYV6of9Jv9oPZSEwqYrOsPAPq2qs9IMYyq8Mm3OrTc2UrVDgwFAde0egY+sNX\n0lClKM/6kgNEz0Wr+9WvfjWdc845eSPm1ltvnTbddNMMDnV8rni3WtAQ3SqoiY5e5+fiqeL6669P\nF198cdZqfvzjH88TAJrKeLcaZ12/lYmWmo06oDsY0a4AMmwcbUKl0e4lAiYARQMnfgMUAD075irf\neynP452XkEu8+8Y3vpGqk0YDNlOjkOkIq3mO76v8DVkVkmvhrQ03cZdcckm6+uqr86a8Qw45JGve\n491qnHX+Bih/+tOf5pUkK0XtkomL72iUTVTwYbIQRQK+hSY9PIdYTdGWXPzcq7tf/epXeVVlMN5Y\nodhmm23Sl7/85bTJJptk0AfM+nYikD4Rf9h+z5gxIyttdt1117xSE6C9l8uq/WrXymBl2OFPVglM\nXLVdIN3qcFyeAe8B3Hu5bBNBvvq5DAWw93Pt9XHedWo6s2OOOSZ9/etfzzvk991336yJiyVC7wR4\nqRZ1sA6t1bsB3IW0xY70vuKKK9L222+fvvKVr+ROVNyDxVlNt5Pf8sJu15IuzYoOeTCihQFknHAI\nyDAj6DUCsphmMDkCPgwyUxvAnfmPQbTQHA6EzJ555pkJgKZN22efffLBL56R7XinyrPh5M83VQrQ\nTjMHBLKTtTrlREiTBJpwcQ4XbzXOTn6bjJqUcmFK094umUDzKc6EbbjJbLtx9up72n4AdKZP6lC9\nmfQGSG9l0qIf0Hc013mUk8bZoUmrrbZa2n///ScUYFdmYFcfA7Cz79ZPr7feegPa6JD94Eevhcrg\nCsBODkxWtX19pbFAvbuAdyZj1YlIt9psr/Gp5KdzDhTA3jnPyhej4EB0ZjfffHOiNdF5fe1rX8sD\nvw7NFRSAQxiddNyLdyKMeCMMzaX/g8QB4LAHNjmgyZL2Jz/5ya6AG6CE/2oeDri2G4oMUEA7rax3\nfdOLpH5slKR1NwgZaIBS4L0V+OjFMnQjTyF3XNAxdTGx+dKXvpSs6JiQhTxG2lV5rv6O59VQ3PF9\nhJFetA1ybZXm8MMPT1dddVU64IAD0qGHHprrxzt1E3llmkMrCDhKv13CDwcsyS+beCZk3chju/mp\n6z11A5gzdXFRSCB9HIBOw8qEpR1ePfjgg+mJxl6SVmRPiU2MZM0pmuIPwNfq/X66F7IO7FKwWMEx\nObQao5zkLdpLr5Yr2iY5N0E18RC6r56A9DCFiXpTJjQR2kGv1stEyFcB7BOhFvukDDosnRifs4Dy\ngQcemHbfffec+wAiAUB0YAY2YVyexdWqyNHZRwhcitdVnQhE3N/73veypuqjH/1oOu200/JmP/HW\n2WkaVIFbm+0M2EORQYr3BxoZdr4G+V4lPLbEr2xCPDPJmNoA7oMdYd+rZRltvkLe2Kh/7nOfy3aq\nRxxxRF6FCLmWRshxyF/8HzI9mNyJP9IIeQ6ZrsbvewCAnftnP/vZPJGyH8QqSKQx2rJWv2cm5eh4\nJl+duieVbxvLxQGM9esBS9UNo9pB9DPagPauDXe67wPAo2HXH+gDTY6qxBOWw89MDIF6q10BZKvv\n9eNvco6Hyn7eeedlc7If//jHGeBWAXsvl00ZkHIY79RfyIX61EYLUO/lGuzdvBXA3rt1M2FyFh0Y\n7fFmm22WN4WyV2f+ER0ZQBFAJjqzKmAPMBNhK+ZEOsJmYCOduOI98dMW24xq4OW9g/aqTnCjw6aJ\nlNbqq68+rHaNJgbgMmivtNJKfbGpjCcTwAEv8Z2NLc2ppXt1OpGJLJGrPfbYI28sZa9uAugeXgQo\nF5JrchByHXIW4VB8kk5cIdtkK2RaGHItLdpdLuPI0pVXXplt3NtJZ6g8tHrG+4sNyjZXdwpM5deE\nluzQPPfDAUvyrB8LLbpVNAQwK0No0qtuZVvxbbB7+iF1JtR+TGj0SVZson5tVlf3vDpdc801uV8J\nwK6O+5nINlnWD5qQ6EuOPfbYrF2nlY6xodfLqK7iUqaoO/UTfYIy9Ht99Xo9TLT8FcA+0Wq0x8oT\nnZZBfcMNN8z2ejxm2HATnRgAE2AmwujUokOLsJ3iRbzCADcBbAx0AXTEJV7v0YzSagHtdW9Iffrp\np7NbO/7hDbLDkcE6NGw0853YCA8Xdzef04oBFmzdDbg0YlMbGncmMwDFRCIy41JXPBABrpdddlku\nL5lDAdJDpgNskLnq1QlfIl1hgBuyXZVrz8SvXZ144onp5JNPzpuuN9hgg5wnz+oi5acBpeUdzCPS\ncGkxUeMpCTgzSQXMeoloSGnPw6sLOUetNoyOJt/VyTozIZMZ3pkAdOZD+hEA3uqbPH3605/OqxPq\n2ARBfddZt6Mpy0i/JdNkmWmRfRhWrSg69CUB2PuljNohitDvyHuE7hUqHGiXAwWwt8up8l7HHNBR\nuYB17hlpXc8666w8uLgP0BhkgLkANf53H+nURtOxRUcprAJ3A4IBTxjgSvqHHXZYuvTSS7Pv97pB\nO/t0g5DBx2RlOKIhpWmTRy77WvlzHy6O8XqOp4AFcxkaSHXKLGNqA7y3U/bxyncn6Soj4LbRRhvl\nE0DJDQ0rWSOz5Kl6VSegIdMRdpKud4eSa/LiAuIR3jOLYX7GU9LGG29cO2gPj0gmoyalIyGrMw5Y\nsg+iFw5YYpYWAL2TDaMjKbtv9Efau3StPJrAkDH9RWjr/W/lTWiixPvVF77whWzrrV1V+86R5mM8\nv4t+Gi8AdXtA9JvKD7BPlEnJePK4pN3fHCiAvb/rr2dzr/N1AWxrr7121hLrhAOkGFwCqAtjsInn\nEdZVwMiPwa6qkQzgLh15sGnPMvNtt92WAaZ81JEXGzTFSVsOkLRDvglvEfxYd2py0E4a3X4H2AHc\nTdoQk4GpDeAO3PYrkSGXw0/YcF977bX5pELlAczJc8h2yHV1ElpnuatyLU/AOpl2kXP35MGEwkbU\nH/zgB3mTqPzUIdfKIg/2XvBu0u6EtBUPeFSxUiG/2shYyjtekdXw6tK8YZTc8u4ib3WTOsM/pja0\n6SYKJjBWG6QbhM/eNVEE2IF3ZkTHH3984grXBLHOeo10xypUPvWgfA4Gs2ITXnCq9ut1ye1Ylauk\nUzhQFwcKYK+LkyWeeTgAKOh8afQsJ19++eUDS90GllagRgTd7owNCvLWDG4MhJ4Z8GzYczAMsAw0\nyFMd+eL1wKFO733ve9v2Qc0+nOZN+lzoWYbvRwKA2CrTxpKLKVOmpLc3Nita8u8GCOoWjwJUOMyL\naRcAzNYYKQctYBWsh+zUIT9DlUm+QraroB1wd1/63/rWt7L7xzvvvDObZkXehoq33WcxIbUSRE5H\nSkC/cwm0T2C0mxM7oDcAevOG0bBF7/akQVugRXZis423ZIcHHXbrXBpWKeo3ALvQngl++EMTTQa7\nLWvVPNX5O/pkfss/9rGP5TJpW1VzmJj41pluiatwoF84UAB7v9RUH+XTwGIgslno1FNPzSYmcShI\ngHXApqoRGstBRv5i8JNPA19oJSPvTB3YkVoVMAjWMVBIi5tHgIpvdoNzO0TzRgMnHx/4wAeyvXA7\n3/XiO/gMtLNzpyEkB8CJA6N6zXa5mX8hMzTqW221VdasB6giy4OB9eZ4uvW//KEAPqGNDW07Gf78\n5z+fJ43kEBCqE7T/5je/SQ8//HAGmup0pAT8A6AAdZ0HLOHPYBtGeXSJSz2OBekPgHOTBXLEy5JV\nOLLEVaawmdSt+mTvjj9WMGnizzjjjAxyo09t/q7X/4+2pS+214L9+lFHHZXblH4hxouxHCd6nWcl\nf5OPAwWwT74672qJo+M18DjswlJ8mIAAqFVQA0DUCRg6LVjkNTSSAdzd//3vf5/WWWedvNzMi0xd\nmiuDMzDSSoM2VP5p4HyHh0B7v/s8x2Ob6GjdlY0scO83tWEu022t5lB8HuxZyIrJBoBkqf4Tn/hE\nfj3AeoCKXpFrgBC4C7kG9oC8j3zkI+nDH/5w3pBa12QUI/DojjvuyB5qhjssbDA+x32AlLxbYRrN\nAUvKr82FPTpeICs84XYxlAmR9liE6oL5j3xx02h/D/DOdEz7Hsw1Kh7HRExdKp+Dspx4K76wZe83\nYIsf5JWr3Z122mng/IrQruv3ol2NRf2UNAoHepEDBbD3Yq30aZ4MJi6DLU8GgAG7WQTUVDUlvdL5\nyq/BohVot9TM9MHBHbRfdeWZD+Wnnnoq22jyjNEu8RThlEiDmEG91zXS7ZaLGQTgbqOq+mArzFwG\noOoF4BFyTUYcCU+ref755+e89hpYD55X8xygHVh1n/cRk2mHlzFfqWsyKm0abCY3TEr0AaMh+QZC\nyf0SSyyRV7zaiS82jALDJoPKrO0Od8JoO3HX8Y782PtgwhobdWPj7nCepHyrv8Ib/WxcbL633HLL\nvAen37TsUSbtirkgBYlD9ZQjAHudE8s66rDEUTgwHhwogH08uD5B0wwtiUORTj/99OzuDahsBjV1\nAoQ6WBkDRmiuQiMpboMgcw0nCypHHSsCBlm+2WnDHCXfCShlc0sTx40esDVWy/d18Hm4OGgMAXfg\nBSDBHxr3xRZbLPN+uO+79Zx80P7dcMMNaYsttsh1x+tNVa5pAHtVruWdTIdcKw9zA3sjgOu6tZc8\nnHDV2MlejcHqTp8SByyRA96bgO8qKZ8NowC6i6kVAvZMHFzd2jBazUc7v/GeNxybSmMSIr9WJLVp\n/UFz+ZrjjX42wLq2YmXDJuhbb701T5R6TRaby1D9X3n0vVzrMtW66aabctsydqjDuuWzmnb5XTjQ\nTxwogL2faquH8xqgxvIzrdE3v/nNtO6662YQA1SGKUyvDiTyb+Aw+AE2BkNAAPBYa6218oBKY2gw\nHW5Abaea2HD/6le/GpG9r2VzmkcbUIF2A9pEInxnkgS8s2cGjE2amBGNtSlQyAVAof6dJbD33nvn\nSRZAERPSmMz1Wj1U8x9aWaFNwMAh13mf/OQnczutQ66VX/0BoHjGneto5VMZ4oAlqy5xcBBwbgKr\nz5EmYkoSIJ3ZS6+RzezOKgiTOGULDzu8orST56jTqE99lb7LJAzYFZ/Nv+qzE2XAePBKWdTdd7/7\n3bTLLrtkV5XGD+0JWA8zszoUJeNRvpJm4UCdHCiAvU5uTtK4dLouA/SRRx6ZDx+ijdTJ6nCBGoM2\nsF4XKOgGq2PwiIEQcDcQ7rXXXhnE82NtIKljIJRW2PsCNXjUCTGpYVrDpIbLR/maaIRHANkTDeAO\nlJEnpknMZcbKL736ByicFmqZngcTmn/yHICiH+S61WT0zDPPzD7ayZF2WodchwwyYwEcacVtHB0t\nkQVg1+qLfCoPUg9hiy4c7eRgtPkc6nuTDi5OHSS27LLLZnmOjbqd2umHXIZyQaj/3WSTTfJeEF65\noq8aKk/j+UydKocVFEqRo48+Oq9gqd+YDKvPOuVyPMtb0i4cGC0HCmAfLQfL9xmsAzVskWlHTjjh\nhGwjq7PV8faTlqTVQMgdowEFWFtmmWVqm3jgF5MEmy2ZD3RKtNCW12kVbewFHCcq2XwIuDMlUEcA\nO+AOwHdLixiAwgSO9pOd8D777JP5HIAiQFG38lBXfSqLNloFeLTsTtI96aSTsulX3RMPK0hWksjm\nSNwz4nurDaN4om8BertZ/3XxXjzcuepHtHUTGPJCpk3aybJJdycyFLIJpIdpjPq16sDDldXN0047\nLctqJ/HWWeah4or8m8DYoDxjxoz0xS9+MX8SSh5h3TI5VJ7Ks8KBXufAK49oUK9nsuSvdzmg4w0w\nwC+1TZFf+cpXslZEhxtgvR+1JDGoAMS0kMAizzFRltEOhDS0ABTg7UAlNqydEG8q+GuJ3Wa/2Bjb\nSRz98i6AzCcz0xiDOCCnPvBOPTEPqnvCEvXP/Oi4447LrvPUUUxEgXVpjlYOxqIOmvNo0iPvQOON\nN96YD96pS66jPNqNOrJKot7EPxzZMKpO2cE/+OCDeSMysyjtY2pjPwOQDvzboEwGaNXJRi8ToP7o\no49m+WXOoy7wnwIA4DahIVOdkDiiTslpyCr55AHIAXDAu/6q+m4naXTr3civyZzNz8rv8CekTeFF\nFaxHObuVnxJv4UC/cKBo2Pulpno0nzpfmh3aMJ0vjd2+++6bO91+MRloZm2UqaqNdPopDZCBF0Co\nC6jhmw2oBimnRLYDaprzG8vqbHcdNjOSOJrj7PX/AR5mQU80tO68S6gPG0GBOiYro6UAQOpnv/32\ny/V+3nnnZUARch3a9dGmNVbfR5lCrmlmgUkmWfyns6vGxzrlx34Lm6TVi9WpZtJ3jGTDqNUpygFl\nsrfAptJeJPJp4mFiEXtg5BO/tVvadmZDIyFldwH95FR9Ct1jfuOsAH3yWWedleW2F4B75JnnLftB\nrCxwS0nmXPpWYF1/WFcfOxLelm8KB3qRAwWw92Kt9FGeACcDBttS5jCWePkU1vG6dLw64n7SksSg\nYvADbngvYT7gJMILL7wwa7CiXHVUVdij4x+3biMhwMuyOy17aPFGEk8/fsNe2tI6jSIycWEuMxoQ\nF0BI/asTmzM/+tGPziPX/Qgoor2GXGu7gNNmm22WN9OGXNfZXrkwpBG3yZX5h/akrlzqDmhHnW4Y\npY0H2sXHpMypub1E4arR6gBFBnlBXE06RdmKERA/GiKn1TpVr/otZKWCVyPeaLgh1S7GE7RHXr//\n/e9n142bb7553igr/3hD9mLM8H+dE8fR8Lh8WzjQKxwYfo2yV3Ja8tFzHAhgq8OdOXNmBuo0aTrb\nqrlAnYP/WDAhBjXliMtAQgPOd7XyRtnryA+7VuAS6DYxGAkBlQZmvp3ZtcvfZCG8c5gRTTEtcWx4\n5KkEaFFfQxENbzO//O87mkCmF2uuueaALASY6De5xgN5DpnWRv3PbKIq10PxaiTPbKiUJhMQdaKv\nsIEUcAVagW1mHM4WIMPteEqRD2ZQJgFCk4InGtrsXiGmQGTHBIVsKj8yQbLJkhaZec9oSf0BtuoS\n4HX5jaw4XX/99VmOTQys5NXZb7Wb90jTZMKBY1tvvXU65JBD8iZTbSzyjyfy3m8Knnb5UN4rHBgt\nB4oN+2g5OMm/1+EahLhxpNU08Bo0JlLnq4wuQNAAWHWDVxdoM7Cz6QTYAfiRENte2jWaPcvjwNBk\nIjJHiwi0+w2IA05s/GlxAbsAM8EXYII/cvxitoACYJBr7uaYX6hz3/a7XFfllUzjC4B0yimnpD33\n3DO33ToAEzmkQWf2wTwDL0OTblLvdE8mMrTiAHoA2qiXdkN1or2oI/WsTCPZ4Npueu28Z9IMlNtj\n0myfbiOufsQkpZND04ZKV51WL++SYbxgvkWTjf+f+cxnct9gkuM+qspDvlHzn2hLtzb8w2+88cZZ\nFpx+vfbaaw/IXowX2hY5qEP+ai5Gia5woCc4UDTsPVEN/ZmJGBQMxJZ4ach0ttVOt9sDQrc4FwNg\ntTwGOgMxUB0DUV3pA5O0i0AO84GREhDEbRzQznZ2MhIAwCyLZx/mQTbi2Xtwyy235NUHGy2DgCsm\nFUxqaOODgB1Xs1yHbMd7/RiS7ZBr5cEjbZhsK/NIZZuJCoBuAvSjH/0oa73JMlMX2mQh4Mhm2++6\n+gb1DRgzB3NuQpRjPOpG+6Xtt4+iGazbfKtdap91T6ajTk1gAgDH5FSd7rHHHnllQ59gxYMnL/U1\n0roejrcxNlhlcPjcBhtskD3B0PKbrMkTGYy8FrA+HEfL88KBlApgn4RSoDOlkdJpA58jHaSjswd4\nLEfriHXCrgC8/czeKEOAG4Dab7biI+XZUPwQv4OBQiM51LtDPQOOaB3VCW8bk5XUHw2uiZYLSKJx\nZ5YBVAKTgHpQmGmEXKtjdQ3ghAwIkbj7meRfWVxAE9kjdyHX7ZQNyAdCaY1Nhn784x/nzZRO7uQV\nhimIFTeh/8MfOxCHx3WScph4WF1Rxza6hka/znSGiovmnEchE0SbKYHQIOYgTNW0b/LUDarWp7TD\nHlxdq1dmczbPH3vssenb3/52ntQ6N8PKXtT7aOol2g2+m7Btuumm2UZf+vY2cYkqj94TRh4LWO+G\nNJQ4JyIHCmCfiLU6RJl0ljpng6oOlCYScOc6rZNOOzp237C91imLKwYNYb+TMrlCqwrYAARVwF5n\nGaXznve8J2t8aYRHSvIMHIXGUf1MdmJyBNCtueaaWU65waQJNXENIsuAXqygMOsA6GOiRqZDJuKb\nfgyjDNW2qoxkLvqAwcplcs70hD06UGbTp/+ZWCy11FJ5n4eVDXLMPIlMBwGyJvVs14HEukm5pGvz\ntomESRmgPBakTHgCJNOsC6tkMsjsSrsMzXf1eV2/8UC9SiMAsX7LPX02mWaaAkB//etfzyejqnsm\nKqeeemqecIUMeL96RR6r9+K38YQ8sFGf2jB5sonZ3pKf/vSnOV729IC8/Mkb/sSEIvpXzwoVDhQO\nDM6BObtTBn9enkwgDuhcdcaWpQ0eiNs1buu++tWvZneMu+22W9YQ6eDRUJ1odNZADY2uASJAgO+G\n+jZH3gd/ohzK5ffUxmD0RENzHYNa3UUAcgBtaRjk2t2A15wPeWUnS+NngmFQtMdgshMNJ9Bo4kUr\n3EwAHiAP3PPeo53gW1Wum7/p1//JSLNcR5sWei40qWHq4QpzIiDQigV57eSE0dCAW/nxvfqom2iS\nTSBo8pk08dBistAtMvkzcQFEadaby0Tjz/SKQoPHmG6SOkPau99x+R9YB5r1Xa71118/ez5ipsOu\n/Nxzz837GPQ/NqmagJn8qGMme/oicZABrlRNutSjlRkTXeV2CJITS7mTxI8Yb+SJrLnnIj/Caj69\nU6hwoHBgcA4UwD44b2p5EgOgyPweT5K+DhQooRGpksGYduRrX/vaAHAPf9YxCFTf9zvi04HbQOW9\nAADN7/br/zHgRaicyhv1KhyMPyMtMzt0GkJauVVWWWXE8asLwJPmz6BqcGSaUChl4DEYH9QvW1/t\nBA9tHoz6j3Cwb/vlfpRD+VzkGgiL/oH8xQWkIYCNNhaAs2IxErn3Dfeot99+ezalYS7TDTLZpUAw\nYQ3Qrh7rJrJCi4yHwHrzxEA/y2QI75Zccsm6k28ZX9SLPCH/a/v+p6xxAe5xMRvbe++9c7+vPPhF\n/q24sDnnJYm9uwvQDvCOx8pEm87MZrnllsvyQ4Zc5Eba0nUFSA+g7p7nkd+WhSk3CwcKBwY4UAD7\nACvq/xGAlp3gZZddVn8CI4gxgKZOuxVVgTvN++677541J82dasQj1MnrxKPzjbBV/P14T3li0FFO\nG+asTMQA1MybOspI+0jDS0s4Wq8XNvkxZTABAMq6reWro/zdjqO6wbRVWkAKAsCi/rtRz63SHqt7\nyhMXuXboFnnTnrVrMk/2QoverDkeaT6BV1pwgJD2eaQHBw2Xvnyb8NJ+M4+hNR6Nb/7m9JgR3n33\n3fk2Mxg8rBIeslsHjJnCAM1jSVW5jd/y4ArQHuBa6J6VienTp89zQqpvUYTKhYQu3wpNcOOZ5+RH\nWqFVrwL1AOveK1Q4UDjQHgcKYG+PTx2/FR2Zzppmx6DkXrVD6zjSGj6IfAFwPDoMRgY2A54OV4c8\nWAcrPlok9og69LgGi7cf78dAJe+0UQZ+ZiZjQYDlcOCyk3ywN3YVGp4D5J42GIVcV2Vh+Bh6/40o\nF6DGyxOwrr9iBqT9A1ndIJp6E1IrP0xqaMO7QVYClAtodwHO2vBoyT4HYJ2MAOuttPfM2mxEZYLV\n6vlo89DO91G/Qn14FUQbm4B0ZfA7xgbx+h8NJe+txrJIx7ghrQDrkW41PzmB8qdwoHCgbQ50pzdu\nO/mJ/aIOTce30047pe23335Aq9GqoxsLTkR+gHV2i2wYm4mdJTv2j3/843kZV4eu821FUQ5ayDCx\nGaqDbxVHv9yLgQbvgmjYAY86XdRF3ELacFpxwIlGcrSkLsUHbAARk1XTTm6BqeHcZwIZJmZR96Pl\nfy9939xOyQTvOUxUgGjmD9wPsjkP07g684+3JkPML5iMMN3qFtF8B2i3P8G+hKmNvSgjJX0AsM7k\ng328SUEz4R/7bn2Dyc94U9R3AOlQxADUAdiNVX5rHxFGvqOvj/+jTVRDdRrAvJqOe9X3Io4SFg4U\nDnTGgQLYO+NXx29H56czDG2GznA8qJqX0KBEPtg2b7XVVmndddcdAOrRaTd31vGN0DMDugEqBoXq\n84n220Yrfp5t6MTDhx9+OG8O5aoNgK+TAAEb2kyuYmIw2vgBF+YBgKiVgjgsaLTx9tv3TJqGI0CD\nhjTkfyLLN/OOO++8M1144YXZtOHMM8/MkxoTGxNGoLNOcxK8B2YBZ2mwh+6mLFpBYB5jcyT7bKDd\npspOyXfAutDkptWkV7+pjyAvNPq9IjfVfFQBtfxWrxgnhNUreBXxCF0ByIXVK55HGN+XsHCgcGBk\nHCiAfWR8a/srHZjOEZjTcQUIbjuCGl/U+YYWJcAlDdoWW2yRPvjBD2b7RaYtBjfPo1OX76HIhrWq\ne7yh3u33Z8rJVpOJE/tbZWeywvafS7k6lturPAIqeJigGVdH5Gk0pH5tjgPagRfL+a1Ax2jS6Idv\npzaA4lAmYcpgIqrNWOlo3kzYD2VsJ48BptiukwdKBbTvvvtmN31knZyTb5pqfCP7+oY6iBcS8i19\nHka6ZYIjr/o0GnFg2gFLQLczC9ptU3hhg6nVCJPdwfaWcKNqom0TZi/KTfTnQvKt/FVg3up3q7oO\n2Rks9E2k1er7cq9woHCgMw4UwN4Zvzp6O8A6+0wDHLCsMxwvMlmQB/mi3frSl76UwZs8yaNNZUCK\ngdlAA7gbQIcb0GjfmBcwi/HNRCYg7xOf+ET2tmLgBuBp19mFW24HboCAuvigXsRv8xpNJJOl0ZK8\nAe3MEdj1+t1qWX+06fTy94BpAJbB8mnypR0Ad1zdTUTS9q+//vp09dVXz9M3kWdmcQ5DMmm0ymN1\nycTRqpIVOddoAan+RXvhyUi8fneT9MPMb2jZlQdot9l2uAkI8xdthY2/7606tCL9AXmxWsCkqNcp\nAHW1LcQYFeFQZah+H+/Fvfi/hIUDhQP1cGB06rp68jAhY9FpuQwENDs0mwDxeF4GVxdQbkBxgAbg\nbqmbtsjltw1S3omJRpSluaLiPi29gddANZHJ4B7mKcpOo65eldvyODDNFR6QA/C0M+C1wy8aTfXE\nq0bsFWjnu6HeIYeAunqjNaRFnkzEDpl8D0UmruoU39VlXfU5VJpj9axaFvsZWk3Kab032WSTPMk3\nKf/Qhz6UNctWlUxcb7311uw28U9/+tOosg38knFtxgFE3aZou+0esETJYUJBaz7UplXv0d5rU7FZ\nudtlqTP+6M/JgsvYFb+HC+NbYaHCgcKB7nCgAPbu8DXHGp2Yjk8n7gLex/MCUmhYgRFaVeYQAdSB\nQi7XgHrvya+OerhO2HuAjc1jEw3YqMgok814UVY8AdbZstPWGqgBAGYrJjs0kcxO2PaPlqRF82iF\nhGawLpJPoF0ds8ulPZzoxKzBaoVJCgJWW5FJq7pWpyHXrd7r93tkmwtRMmYS10z8cG+77bZZ9rzj\nXTLDfIUG2QSVnNuwajILtI6EYg+ISQI5HwuykZvZismq1aZWk+EA6yYS2qCJxWCkf7AXwHv6holA\n6nyoayKUsZShcKBfOPDKIxrUL5ntx3wO1dmN5zMgLTT/ADoQb5BxL4D6UGA9QGyY2VjOpnXbaKON\n8vdRtn6ss+Y8KytQfskll+RBfZttthmYdOEdvtEOAoNAjAsPua1zHy9MjoQjJWkAD+zlaTiB7TrI\nxMyETbzMmgCy4TTPdaQ7HnEoH00p8KWO2CGbpKqnsN2OfOGDySt/7N///vfTpz71qYF2MZp6jPh7\nIdR2Xbc2NOXMYs4555x01VVXvQwwA6L4sOGGGw5km4zgkdU1v/GUORh5x0vyqS9plyg1TBh8r72R\nybEgbcllskEOpBtgG2+YuSm7CcXUhv3+YOQdk2mAvg6PToOlU+4XDhQOTF4OFA37JKr7ANHApEHJ\nAAlwCv1vgI1l0OFAScQV4aqrrppPLzTIGXAnCsXEREiLqJzKXJ3MsOUFAIENwNdzKw60kAAh9253\n3HHHqDfmAgImVjS+I9VktqoX2mSbTwEtmvZWmsZW3/XLPeVhfwx8kXHeQmhWtQP3PMcDAD1IvanH\n1VZbLfMbICXbE42UiQkXuWbycvrpp7cs4qmnnpocANdM+EnW11xzzWwLjodMxEwC8Bbf2qVFF100\nm+qZ+I/lag+THDKBF1YLwjPQfffdlzfbWmVhEjQYsW+3aqNtOqW4UOFA4UDhQDc4UDTs3eBqD8cJ\nhCCA0wWgB0ivgtB2igDEGuSAR7bvX/3qV9N6662XPaVE/O3E08vvKKPyAR5Ofj3wwAOzCzpAJVYi\n8JRmjpYNYLc/wODtHe7q/OYJwyY3cQGD+NMp+cYEqxtaSHlkHiVuLg9tslS+fiZ1h+eOp2eqwDWm\nDYN4iGhEaVUBMhpU2mLyzETCRkvfA6CXXnpp1iYzf9JW1He0o37lT8i1Sdr++++fNt100wy42V5r\nyzfddNPLigaEm5japNlM+MHMjryTHXJO5mmuyRO+0boPJ/chgzZvimus+Ez+rRhYhdEGtGWXSfKS\nSy7ZXNx5/gfW5RdfqpO+eV4q/xQOFA4UDoySAwWwj5KB/fh5AI7mcCRliYEfODVwWRb/8Ic/PCGA\njbJF+S6++OLseu7QQw/NQJYZQEx08BEQAdQBdgDF0rjnyJI78EGTG0vvBvYAjp3wHSiy+Q2feTGp\n03zFSgvTnQDt4u9X0A50c1OoPpSJOz8a3ACAtLg0weqJVjQmX+pQfeFFTEZN1gBYplDq2RXxdFJ3\nvfRuyLWVh2984xvpxBNPHDBjsQ8DkL/99ttfluXrrrsuT3qG8mFutQ74tfKErzalkleyH+Yyg8mV\nfsTlXe+Y3I4VaUtknsyY4Jk82GQ6FAH4NiXTwJvwFSocKBwoHOgWBzpX83UrJyXevuJAgP0qgNls\ns83SFVdckW25A+z2VaFaZDaAjQNllE95q1rWKnAD8mhwAXPL6b4NAmJo4NhNA4I2PZrgWE7vlHin\nka6NrXWTlQJl4LJSHkeSv7rz1El8NLthggR0AePMHUx0gmh+7bmgSWYa00x4gL8h2+odeI2VjWq9\nNn/bD//LPxl0XXTRRXmCHas+MRnh8tXpzM2Ev1tuuWXepNn8rPl/AJiGeq211soyRcPOR/ktt9yS\n2wetdCsC9OUHECaHY0kmcmReezXRIEuDEU9D2qByWaUpVDhQOFA40E0OFA17N7k7CeKOwd9ATsNk\n45rBFuiLwb8KavuJJcqmXOxamfucfPLJWfsaWsAA7tUy0ZorNw2h74G/KgGOzAqAApo8F6DfyVK6\n9PE0vmV/XSfJo4s5CXteWsdYLagznbrjYsJgU6lDftgl06oH+I60gETvAFns9gfT9Ho/NOzqhiYa\nmPvIRz4yIeRa2awE7bbbbumLX/ximtrYUAlgky11TYZtIFduILtKtORXXnlldgtrRWI4Iqt4SO5p\n3rUpkyYTIHWlDtRH9BNCfYjnbNmH8swyXNqdPAfOf/vb3+aVGLJhwqcdm4ArZ+Qv4mSjzwsUOdOG\nCxUOFA4UDnSTAwWwd5O7kyTuAO1Cg+9pp52Wdt1114GBv3mg6we2KIsLOPn85z+fwR07X4AGsFHO\nmJA0l8dSOpMMgz0wXdXuehcgAlxoeIFMwISZi++GApDVdJh5AD1AFyBUN6AOkx2gnUkI0K68vUi8\n89iIy5uJPDJjoPFs5iVtrU213qF1p0VtReQ16j800cxpDj/88PTJT34yg8/B6r5VfL10L8oFNB9/\n/PFZTg866KAsP80TUWX82Mc+ln74wx9mW/9qOYBY3nOcktzJhBHP2bjTopNZE8Iwl8FrwF29aWNI\nG2Jfzkypm2RS4pK32Kvgt4m1PGjP2iyeIO3iicZBZuSMbBQqHCgcKBzoNgcKYO82hydw/AHEgYDQ\nRtq8961vfSvbDYeWHQvi3X5hR5SJd5ejjjoqe8+g9WsGNYOVi4YXoAZGDPwBQKrlp5UDtqUVoEX8\nANBg8cb3ngPVgANQAUzUTfIBYJlQ0Ez3Imhv5aqxFbjDI2BdyI948ySqFe+q4JaWlykHu3dadvyP\nq9W3vXxPW7XZdqeddsqyzWylOhGtlovcAu3cPTYfkGSSGbb9QHUnBKybsE5taPaBdJpqeQKCTay0\nDe3GhFQ7sgekeQLWSXpDvUurTrtOi85sLUA5PmjHQm3A5EI7YwpjI7P2YXLoeaHCgcKBwoFuc+AV\njUHpJUPbbqdW4p9wHCA+tNC0nAYygIhXDSCXjbYBz+DcT7I7hkoAAEAASURBVIOaMgE1Tjb9wAc+\nkF37HXbYYRkwACZVDftQFQqEAPzAB7d5QwEOGjyHxgBBJgZsq9sBlQ5sAvbFT+veDQJoaK8BGjb4\nAWi6kVa7cdLwsh92cA/Ah19WKFqRumSPD3DKfzuTGzJAC02eyTX5xmtmIg4TEo/67Ee51l6tFADD\nbNiVw8SMbPvdqn7JABkDnptp9dVXz8C9U9DeHI9JIcBuEob/2gGZe+SRRzJ4b+WdpjmOTv8HxMmR\nyYPTigdbqaJl9562jEfaNpef7bTRTvPUi++rj6B+kvnIcwkLByYCBwpgnwi1OI5lCHAL2AC4LgCJ\nVs5y8VlnnTUAbPqlo5d/YO2EE05Ip5xySva/DhQCNS7ayHYnIcAHW1faaSsOQxFeAkYACuJOjveJ\nVgAq4sFvAJLtPO8e3eIxc4EATsrRrXSiXIOFeGRVgUZUPXHVSEM8FI9sAKa9tQF1akOj2w5Jx1WV\na0CXlyATUb7LTdykO168aKcc1XdCrpm4MO+aOXNm9qGuHDERHUquTVicLWBy2Uza++WXXz5kPTR/\nM9j/ZFodA9MmSvKkPdJm12nPbqKrTCYG7NAB8aHIBIdmnVyQu6E85QwVTy8/U7YgygNt3qTYBMWl\n7zNJsbpgdZAP/irf+qUtRBlLWDjQTxwogL2faqtH8xpAoKplB/D4ZHcQy1ZbbZUH8qFAVa8UzYAF\nHNhsN3369LyJlpcLg1KAGr8NTO0OTnEK7NJLL50HuOHKyiSAtt0SPLMX2uOhtOehJWSONNQBL8Ol\nO9xzg7d6ZbM7HqYAI1mFCN4DFvjfCYUshFwLbYJcZ511sqeUL3/5yxlM9otca6cmkDZUfvrTn067\n7757zj+5NhEl18NNQJgFadd40Uz2rQx28FLzu+38L79hJhOTBICdjAOMo6GYSDOfwg9AdDgCYO+8\n8878mkmEVRaa+X6mAOj6PJuxTeLUsf7HBMWKgo3bQLq+yARWG3DFhmGTF2Zm+ktXTKra7R/7mX8l\n74UDY8mBAtjHktsTNC2dvsG1qo30P43bIYcckl3A0bYPpb3rBdZEOZhO2JS4wQYb5PwbeEaiXY8y\niTdMMoCDdgd5y/BMUWh2gZTgYcQbofh/8pOfZA0YDehoTRMi3lah/FgFoF1bdtll2560tIqr3XvA\nhIkCLy1kCB94JBoOEOAf4MEEhjnFcO835yfkoWryJS+0sjNmzEiXXXZZWn/99ftCrpVF+9xwww2z\nhpibUtSudr3KGyZvW2+9dW7z1ft+Mx1jDlc3MZVx0FUQ8yerJeq203oFNPnoB0IBTTwYjtQ78zbm\nUYA6GTBpMXG1etZvRB5cVp/OP//85JwJkyImgE68tYpm5So21Ho3KPhtJcSeDhN5ExkrfX6L4xOf\n+ETekByKhvgm4ihh4UDhQOccKIC9c56VL1pwAEA3qBnEdOTAgU7e6aDA5K233jqwabEXO+8YwNhG\nA+ryD5ABiLRvAdj9PxKNKp4Y8PGJ7Wu7oNp3PKCwHabtom1vdhWpOgy2fIXbqNcNW99qlbPlpdUH\nmvmE7ybxoiM9qw72Q5gktMM731kloUEFytTbSIhckGvyELbs7p133nnp2GOPTT/60Y8GNir2olwr\nc7RNGvUbbrgh3XjjjQMeiUKuO101ctjSnnvu2ZKlZ5xxRtpll11aPhvNTZMvkzAgUv3qa8gC4G4C\n2Y6W3Hc0ydrSUJ6CmvNpsmDSwIOM9MkC+aJp1gb65dCk6OeYRVkh4rLWJmoTMAfekQPy4j0h8ruZ\nQtaFLn2iy4qIftMEgNtZcrDPPvsMAP/4rjm+8n/hQOHA8BwoXmKG51F5o00OVDtjnb2L+QB7329/\n+9tZ4xJgq/pum9F37bUYxIAyA5eB5rvf/e6AmQANHDDQjsnAYJn0LVtZQJcG37JxOzzwHQ0eEwBL\n1L43qaBhrIJQwIsmWN5ptdjcd4tsBJQH4AmYbTWBGG3awFg7rhpbpQNEAVN4Aqy3A+RaxRP31JMr\n5IRcA25seq0gbbzxxrk+4r34rhfCAF5HHHFEuuCCC7K3F5O6mIiS7ZHINb7ig7bdTNdff33WPHdq\ngtQcT/P/ZJ58a6c2uoZ3GbbowDQQDYir91ak3QHrngPr0Re1erd6j2kaWQTU7StBeOZ/G2W1SXxu\nd+WsGvdY/Q7ZZYP/8Y9/PHvy+uhHP5onnk7wZTLmHe3ZpW7jG3kM+Y8wngm9G9/FqsXOO++cTY2+\n973vpf333z9PbGjeYzVDPIUKBwoHOuNA0bB3xq/y9hAciA7fgBqadh253+zYaYuvvfbarAXuFXAT\nAw8ACqzT4vEvbfClMTK4G2RcQM5oBxqDO40xP9S0xZ0QvrLJBpTlh2avuhwPsFuWlk+ApgroO0mn\nnXfxzXI6W2AgJoBMO98O9w4tHY0muaE5BfzaBd1Am+V5cgcgABCjpQAl+EuGXeInC/vuu2+6+eab\n03XXXTdg1z9aGRltfuP7aI/8rJ955pnZRM0KjfzhZ8g2ORnJqpF0dtxxxwz6Is0IgWFaXKtJdVLY\nnlf3JADUTzQAuwktMoGk8Q6XjO6xP+fWE9AG1m3Sboe0udtuuy2DV+ZmzXJIDpjHhOvJ4G87cY/V\nO+TAChw5OPvss9OnPvWpdMABB2SbdPkPcE4uhrqq+RVnXPG9/4PEQ6bIlskz0O48h5NOOiltsskm\nA+nE+yUsHCgcGJ4DRcM+PI/KG21yQCeNItSB68x13LxI2NDE84ol2NBGxbttJlHrazHgGPBpmwy6\nNEIGfHk2OAPGQgOPvI42v0w0gEqgmzawk81z8sBmF+8s7QP/gAjNY2hJxSlu+Qwe18q0uZGJX14A\nAenJmxWE0ZBJk0kAW3WAj63w1Ia5g7jbIYDaXgHx8PrRyh97O/E0v9Nc7yHXZNsGTEARcLc/IWzr\nRysnzXno5P+QaxMLmk4ybT+JCZ58kRVgPeSarI80v9oNre2jjz46TxbVBd/tngPOdZGNj2SOVp38\nKQfwTdvNV7uysFGnifcOwg+adc+sDHSy+mQCD2gyM5N2M4nTioXJpXYgb/Ll/nhTyAEvVeuuu27u\nM7jxdNiVNgWse0fdyy+5cJGLat8X9+J5cygulzhCjsQbQF7dcCMqTofQqRsrr+JB8c1486ukXzjQ\n6xwogL3Xa6gP89fcAeu8deg0K9zxATc0ZNzsebf5/bEocgxm7MoNZsw82F0CefJjMAmw7nd1MBpt\n/kwIAG4Dl3TbXZqPdIFymmdlAEoABYMh8A9UABi01LTvytAtwifgxKTB5EFascmskzSVgxs/wM9x\n8Da72fTWrhZUWuLwvbLbCFgnSBR/yKjQFfIDlPAipOy77bZblvNwrxnf+H6sKPJl0gMs2xQIsPPk\ngUKu5bcOudYutGuTcfJcJRPTa665Jm2++ea1TZ7Eb4JK5pm4aAfBZ21A25ramORpI0yjtI/IF7nw\nbbtkAm8TpUmYOAcj6Ye8yRcFgHahzxsvCjk4r7HXYrPNNst1YJUFfwB1JN9kIMA5mfDb5X5cyuG3\nsHrFvQjjGZmI/lI+tBGXSY9zDLjKlS97hUJhEXU4Xvwq6RYO9AMHCmDvh1rqozxGxyuMKwYPxaBd\np3HZY489srcRS+YGVxTf5n+69EdeEDDxpS99KYMs7ui+/vWv50HJs+oAZjCKwcezOkh8gDogQTvL\nnt1g1wmJw+ALGACpARRouYEHAJqNdbhY6yTuTt6VDxpGeZCmyUcnmm0aSR478ALYpxk30ehUFtgY\nM5dgPsPcqFsU+YowtIjy7WAhXlKc/skcJ1Yc4t1u5SniDXB07rnnZpAmTzyABEgly0BZgHUyV0fe\ntBcraFdffXUGq5Efofq1yZWddLTz6vOR/I5ykHlliPJFXGSSDGpjAHv0P+SDzbnymwwOVXarE7Ty\n5BnQFOdwZEXL+9qBdq1t4s1YU8jBV77ylWwG4ywMbjxDVpU7eBjyIJ8u9/FUeSP02zfVy73my/vx\nTYTeQZEndbXtttvmza7631CWeGeo+vC8UOHAZOdAAeyTXQK6UP5qxx6dcAyaBg222zbq2QR39NFH\n5wE3/HrH+13I1sDAzVMGt3xAHptOGihaJ2kbtAxi1cGrG3kSP1BBswzUmMSMJB0mAbSMBlpadWDB\nPWARCLb832opv07+GpSBdppF6beTJn4zo3AIEfMJPuSZbMh7pxQuH8PevdPv230/6kcYV1WumWTY\nq8FbD88Yygg0q2sU37ebXrvvRR6YPjB34HbxmGOOyStZAZgCoMmLK8BWu2kM9x4ATHsqbZrtKjl4\nhx040B68qD4fyW+aWRr2wVaSmEWxWSdbzGCs2qAwlwHekT0OwaN8Y+4fplnapfrrZKVHm5Y3kwl5\nM2kYiUxX89LJ7wDGBx54YPrmN7+Z68N+Fv0uAqTVgTyFLAgDYJNR/Aj57iSM74QRn9+uIPnzv/5f\n3yefzGP0H6hbbSTSL2HhQD9zoGw67efa6/G8x+Bh0IyNqEL3dcw6dQP8kUcembWrNkJtv/32A5ru\nOjpvaSGhjYGADJAIUHFzJ43IT4AagF3eXHXkYahqYrLATKiOkxObD1wKXvOrrGzdJukBSQAbk5YY\nhJvTZQ40EleNzfH4PzYhWm1wtHwVHLR6v4575AUAAsirch2gCK+ZWtlox1xor732Sp/5zGdqX/6X\nDxdNMLkm3/xfO43VpKman5iEAmfNIKoOnkQcbL4BROVuJiCNeY52VQcxnzIRsDJjI2kQ7Th3hbGX\nobqXQ19E6/5EY5Oq79WViRazl7BtN+kkn0A+n/8jIStO6kX9kMvmVYCRxDncN9Iik1/72tcS7brN\n8/Lvvn5MWQOkV/s3z+rs56SHhNFOYgzQXuQRyQOf/fZX8DakD6w7Lzmh8qdwYIJwoAD2CVKRvVqM\n6LR12C6bs3TaASaAB//Ttp988sm5GDRxgHvV40InA0p1wKDFEbeLTSq/wGyNacFi4JAHg1mAGr/d\n6yTN0fCfSYgldJssLaOPlmj34sAlcdE84+VYkPoFloAhQIWGMcgzXm5o/mn4aNQHA/XxzVAhUGSC\nAGgxQVFvY0Uh12SoKtdVmSJDzEROPPHErHG1krPddtvlg2mqslX9PVT+q3Kt7PZcMHmhDebhyIY+\ndS0/SLyDyXW7aQ6Vn8Ge8VTE9A1wbqaddtopMdGoi9jqkymrdkyhyBiZYA6mPYVteav0aP4BdyHy\nLnMsYJ1MMXFShyMlebAJWp64AR2NrA+Xh5BHB2Lp3yhCrA64rwwB1Ju16d2Ug5BXfb2LXOrrXX5H\n3sgtTzLO6zC5kqdu5ms4XpbnhQO9yoEC2Hu1ZiZQvmIwCXATnbb/PdM5G1T8z/6XD3Qh8GFDn2va\ntGlZWwRUD9aZGwScwknLBzQ4Ypt7QGCOqQINn+XtSBeLaXkGG8zGqgrkm0YWwLFhMTR9o0lfXEx+\nTFIQcEyLOBYkbYOvfQJAg0GYecBIXTW2yrMJAfeN5Aawqss+ulVag90ju65muVafAAo5DU2m+r3k\nkkvyJkwyuOaaaybHuLOPXmqppbJpxmByLQ2TMPyjUSbXtLe0p4C6iUBsJvQuki6wHrLtf7ySxmDp\nDFbOkdy/4oorsnlOTMyrcRx88MH50J7qvZH+Fj85sLpEDrhYpN3H13YBMlkC3E0k1SWiXafxxbfR\nkDYAtAPv3WqD6hwftHc84DqReZL78k8G9JvkoSoHoylXJ9/KR+RR24j+XxjywZSLOdGVV16Z8zhW\nctpJOcq7hQPjzYEC2Me7BiZJ+tFpGxBdoWXRgccgiRUGFBf7UcukLjbB7JTF8f+zd2dP1xXV/cDP\nr/IPpCpVqUpufE1uYsyFyUWMqUp8UZBBkElAEAUEVERRJkUUmRQFkXkUR2RSRplHgQpxKFOx9Cax\nKmW4SVWqvEjlP/g9nzbf12Zz5unZ53l6Ve2z99lD7+7Vq7u/a/XqtYF4AJBfNpBmQDQYWkzGmi49\nljYDl7jJNgO3dxgcpIGAFwNYQI29cwE15aY1/igDUKdM8i4/yyAghAsQMvXPV9wAvmrijsDSDryb\nzVA/FBGW/kXdA1gsgTRpU8akv10UuSZbZC8b+a7lmlyR6ygzgDfZBsKds4bBbAS5xifPkwntgIsG\nQGr2RehIMs3NCaj0Dlst196jjiPf/m+HXN9yyy2DM888c2jVuMZNaBkEoGs7yoxv1sPMs9jamgpb\n0sE//Q1FdxY/9m6Z5ImCZVZkETebbrr+R/7IirbAX98XeJ1X75k1VCb/twsIRz7TTvBEO7Z3znqE\nvVtKrC9jn3POOfvyOqzM7VzjwG7lQAPsu7Xmt6HcGVx00Om4A3DsnQsFYNjbgBJWMBZ0IIafNBAj\nKgOQY/HYnq3Qa2984xvLubwj+zpdA1fAjH0NaNZhfUxeuvv4Y5uW5wO+LIqfvPQM4Cx93rFKUtfA\nD0ULAT7eqy4XIXLAYkkB6LrcLJLuIs/WYCQAGhAh0/mf9MlXZNref4CcXFu0C3jFtzrgnaIF6Pkf\nec4+6SbNWq4d5x3bJdeXXHJJWaOSfGYvXz5hf9RRR+XU3Hs8pvxQEsnZPO5f+hRKoMXaZoWss9Df\n2CPKkv6l9oefJcPyyHWJ65s8cuFZRp2QA2lbt8Bn3YJ6bVyfBqQHsG8nWK/5pK3Is7ZRg3bnzYr6\nEJfwrD7EFtmtn2/HjQO7mQMNsO/m2t+msuuc03EbbAJsstehux7KwGafzbWcT3rO5Tj73N8FNAaw\nAPU+DQz8cfnlCk8oVv0yCC9YIAFBAzhgw18XaKDwLJuAH25JLJ+s396HLAwEOucl5RAJhZtPfJbn\nTWsVz0XmAqK6st2Va3mIfGafc/bSyz5p51y5sPVDdiPLAeu1XLsv7STPrHvPb100pi5Zx8D1zYzB\nvISn1oDwQwdQldXsg+NpSRraBzm1YLZ2r6JAAe4WqqpP8gu4mxHB51lI3XFboaBpf5TyWdOo3yc9\neTJLY+aFAkTZIBPaed/AevIu33iuv2dlj6XddbMuZk2t/SDPdbvI823fOLBbOdAA+26t+W0ud4CH\njjubwafenE/nLrt5ps56wIhrOU4nbx+gHlCTfUB67q3T3M5j5WBBNkVsEJ7XotctA4s0CyLLOkDC\noosXFAMWv/Cu+9ws/9VdQixKm2829ySzIRYCegfQbjZkHrKQVr6XEVFnnvdP80xkNLJby7Nj5+3d\nly3p5ln/R9WH85HdyHL2zjt2T7akvZ175RWnnQW4S/yWuQfNYxXHryhwZE20GHLGHYZbzLQUuRrn\nSsMabB0BtzvAnkJAtm01wJ/mnXG9kV+zRID1rBTZkS8haqXFd129yxtlSLq1PMz6jlXeL//aAtDO\nJQxodyzspnU8ggS8+93v3td/rzIvLe3GgU3hQAPsm1JTOzSfASkBOPYBNTmur+X+7A1QoYCUgPR6\nb+AK0Ml99bNJow97AxiLn3L7sNSyrOCsewAHP1eDeqzg3AAApnmBNJ5NCtVIYaCIsJoB7bP6BCfU\nHoVDxI2+1l3kJ/JpX8ux4wD3yLV76i1ppIyR18ivfeS5lvHcl+eSTh/2QK5425TGLrFWW6QM/E5L\n+GWBqcXMtV+4CC9khbJrIe4kohhba2Gdi4Wqk8h7gUqKo2fxOu4ys6zNiDxbryCvs4L+yJQ2tXfL\n9xv/KCral/5C+3YcmZlUru24jpfaQlxj9HvKdeWVV5bycJHpexm2g2/tnbuXAw2w796671XJdd7I\nPlsATfY5n737A05qsOI4A1X3uH7GcV8JwAUkuJQAuMqzKBkYDYIGc1P/eAN4+Pw64jf6xq01ALO8\ni2Vs2lCNFt0BGCx/yjQtSAGQLNqjWAA3wOqmEFlFkVn7Wp7r49yTsnVlOv+HybZnXO8zAbgUUBbt\nLpnpsbh82hklyiaLN3m1kDpExvmzkxEyPk5WWHRZ94FGbjmzWrrNHMVdRj1qq3u23GUolePem7zy\nZzdDoD2S62ndxSJD8i+6CtCfLzUrA+s6oCsPfZcJfMN//QjArv4o//ghPjslTzlm6ZPC37ZvHNhp\nHGiAfafV6A4ojwEJ1fsazOR8XdQMTAE1rnWP6/s34ThWOJZHPtvLIBZJi9+EBGSZRN0PLgmhyVVh\nEs0TqtFgDHwD60A7cDGO+MOzHrpP9JxZQdW4tNd9LXJb74cd1/maJNfuzT31c309BrJFM+ET3iXy\n8MILL0ycfeGz/eqWX/modgEI82sHnv/yL/+y+5p9/0VPEkWJW8q4eO37HhhxAGzGXYb/NRmVN9uk\n2TFKrPZADuRjGiu9e4F1s2UUHWE+tWfAP9b1TQG5ymKrrezK9rnPfa4swhYKVbmipI6ogna6cWBX\ncKAB9l1RzZtdSB16Td3/uVYDl/o41zdxH0siEC1ayDKIny+gwKpYu6YAHfngEsslEDDMUsi9geuB\nhX7zhGpkMQeoPAukjQLh3sOFghUOWHf/TqKuHHf/p6xdWe7+z32bsueaxdJuFqlL/JYfeeSRYiHu\nXvPfV4FFPeL+oU2M4gXLtahL5IZ/d5cyazMK9Hfvn+a/+rMgmjKhfckbazvFYVgekqaFrWaeAP9R\nH1iStvTstQeg9qqrriqLMx9//PHSToF1bQnAde8o3uS9fdkrU1xjKDz4wKjAN19dU6bMGGxKefrC\n15aPnceBP7hki3ZesVqJdhIHMvjMst8p5eeHyyrNEihO9ySL3TTlBh5Y51jW+Q+HWNUpBYCy97Gg\nm6YPqDewAiPCrok4w0Iv0kWuJ51Je8BbumYQlG2YCwGLGxBjADc9vp2x1ieVZ97rs8hzfe+87+vL\ncwAYwO5LrYBnTRZkkj1grUsJE8rffNI6BpZqCig3nO6iaoAwrlm+hrosdwt1RK69j187YE1pIOcU\nBMqvdSLuqwnI1gbco12aTapnuDIDlrYawO4LoT6cpQ0C6dk2zRpd8yMKCRl56KGHCr/U0U6S/7ru\n23HjwCwcaIB9Fm61exsH1swBgy+gDsSY6mdZHGb1niVbAIKBEaABMOrFpixZwAOA7H3ABgAPDLBa\nygfAL3yc++rBdpY8eCegL32gSloBTgAJpYAFFjBT/kY7iwOxbPNTJos1sa5S1Pgvh6y1YF0H5ADU\nyEqud/fkGPAl42S09o3P11C5oKxq1sa7gXbllBcx9rkByQ9rMvl3PuQYIGeZ1ybwxD3uNSOmLVBU\ntEPn3HPxxRcX33Vt1XntWt8QcJu0N22v/dvMVpiJO/roo0u5Nk0R2TS+t/z2nwMNsPe/jloOdzkH\nDMYsbqxvfLoN7PMC5bAS6GZB59bCItgFQMCC8xaCARqAOqs3n2AfQAJIFiVAw4wB8AGoBLRzAzJY\nW0woD412Jgf4XwO1XDq6JEqS2SWzK+SD3zrQzdo6rcJKviiD5BzYBWjJMTcLoUHXIVvyytq/Z8st\nRpsyMyU/r27NVLGcW8uRWTP3attcZAB77c5GYUbaPJ4A7NyGKDCf+MQn9lnW9RObDGqVLxb2gPab\nb755YCYBbza5bF35bv8bB+bhQAPs83CtPdM4sGYOsEYbsAzkBrVpQtaNy6K0WBeBIYPjMCt2rH2A\nugHTfQZVwKm2Do57z6RrFBFAQz58aAmgAWZYJsXWbrSzOcDKTbYsnOzSM888U4A2pTFfIHXvLAQs\nU3TJFrnN2olJLjWzvGOae7WbuMtoa0B33GW4hWlP2qPyUVwp5rbaZUjbeMMb3lCeveWWWwZ7tpSA\nAw44YJ/fume9x7ZplDzr22z4o4+79tprBwcddFBRZPRZtkaNA7uVAw2w79aab+XeOA4AHwZxoJ31\nkMVuEQIQhKZjxWOBjNWcj6+FgRagGiB9UEacdgOpe70fyJaHDLSL5IO1H9gA2llEAZp1A6pF8t+e\nXYwDvkwKvALTNZG3p59+uljZ+bTPoySSU7JFZvmIUz65c8WqXb9vXcferb3FXcYsl3bF+g+oatdm\nAWqwLm+uUdw9f/XVVw/e8Y53vMZ/XTl3AqBV7zEOPProo2U2xAJjZds0hURZalpGf1mn1453Fwea\nurq76ruVdsM5ADwD2vxwWdwWJW4nBkJRXwwupuvFsQYeuAyIJANcAANcGHyF0PvdL0686ftlENCe\nwWwngI5l8GQ3pcFifMQRR7yuyECrEH8UyHmJJRrI5YIi+hFFsw9EQfbtA8Bbu/bfNxGEtowbTJ1P\n7ZPiAcz6ojC3Hm0lQD3tp35mk44Dxu0Dzi1st9hYmbvgt+9lk1+bGPnnnXdeWROUcmxaWfrO692S\nvwbYd0tNt3LuCA6wMvLj1eGzSHatcLMWkg+t8I3cX3xExoI/VknhFlnVHdcE7AiVB7xzM/CxG9ZA\nA9G8BPQrC8BCSbDYlR97G9Tm5ejmPQd0ihojekyXzAIdfPDBg1e3XKXmIYottxpkhqpvBJxaTE4Z\nBsKHUdpCIkZpe8BsgO2mg/W6zMqUjYKV/gUPwof6/j4eJ69mRcjvNddcU+r23HPPLbNJDbj3sdb6\nn6cG2PtfRy2HjQOv4YApc1PEgC5guwhlYJGG9AyQvhDJ/WYUAQd/9md/VqzvfIstfrNIEIiYlQCp\nn//852Ug5qpASeCny8K/iFV11ny0+7efA6zgd955Z3EV6eaGy8yBBx5YwoB2r437DxhRQgFAchX3\nk3HPbOc1Fveukpz8pK1qb0jbC7CNdTr3buo+5VAux/ohEXIA3xrkhhd93ssz90JuWMgMz3XXXVdm\nVRpw31QJ3d58/z6u1Pbmo729caBxYAYOWJgGIP/mN78pEWQA6FmJtRHgl44FcSxBBheD5TTEn/at\nb31rmabn7w60A/yjPrjUTdOA5iuPQvgB6/KARKJxjU87y2v96fluGu3/zuEAYMb9wQeBABp+3TVx\nF/FhpR/96EfFLau+NupYeuScgqvNsFCTVeskRI3pG5mxCsAbljcAVZuXd1sA7rB7N/VcymSvj7Gu\nRT0qrxnGgPm+li9KhNlPfZsZw5oA9+uvv37w9a9/ffDhD394cP755xfZdI8yN2ocGMWBBthHcaad\nbxzoOQdEUQG2Wdz4gI+zitdFAYb5wBr4AWJhGi2A45fOR5ZbCuvdtOR+8bFZxMXLFpKRpXxcJBuD\nGsun/HfvNWg5x6ImPXmkBDTauRwAqs20AGOHHnpoAdjcY4C1mnzw6JhjjhlYjDhpESoFgDsF2cxX\nggH3n/zkJyVMpIXNfaNxYF27UGazUlzZ/K+3vpVlGfmxXgZYF95zp5F1CoD7HXfcMbjxxhsHJ510\nUu+VkZ1WB5tWngbYN63GWn4bB/6PAwZroINl24eGABxuBeOIhREwZ+URAxtYzzMUAGDbdf6001ra\nvY//+d/8zd8Ua5LnASsgiXV82BQ/KyfLExcAgL9LygZcRbkA2kf593afbf83iwNcscgLMmMDpJGb\nxx57rHw8qbsA86mnnhqceuqpg+9+97sjC0puLMxmlaX8hSiiXGOEeuQ3Piycae5d954Sa22IfA8j\nCixAry1wtQhYH3bvpp5TphB+pJ/60Ic+VPqjWNfr+3J/X/byjVjY1ZOQpdYHDSNy/ulPf3rw3ve+\ntxgodmKdDit3OzcfBxpgn49v7ak1cSCdX15nipHrRtw3uFHY+HXXnXh9nGd34j5AWcQWoN1i0WFA\n28Dx7//+78U3PM+I/lITcGMA+dWvflUW+M3jZkMJYOn3roTSoxRwRwixmltA6CMxAPsoUg5ftbQg\n1SwCoLJnK+JHo53DAYDMlzyBUWA9blFKaHHz97///cGRRx5ZFLe61HzdWc6/+tWv1qf3HVMILTal\nRJL3miimWdgsCtIkS3397CqP9Vn4YN/t9+r3ajOUmNy70/q6lAcP9PP6lKOOOqrUkz7A9dxT86Uv\nx/Jto1yRb+txuoBd33raaacVFy+zo+6Nu49n+1y+vvB5N+ajAfbdWOs9LrPOCtmbzuavakAH2Gw+\n8T2MWIlFTeA6IUTafvvtVwBArLs7uQPU4fP7Ztn2Rci/+qu/eg2LhGp0HmhnzWbFC19ec+PWH1ZH\nQNs0NJBt6n1WkjarprT4yP/iF78o/sjyxQUGmALqWdAnEdAOdPF1Vwb/ue/sZEob2Mkyq/6ATm2b\nJZLLg49odemwww4b3HbbbYPTTz+9e6nEIjeL40uYNVlYav2Da12l1H2AEVkkU/oUCuV2knYpRrzZ\nLXlP/Q/Lk7ZlhgABs2YjdjIpY5Q4CsowY0Tfyq/+snXzxuBw3HHHDXx7gJEp942r824a7f/u5UAD\n7Lu37ntV8nRcrLt33XXX4P777y+WCaDubW972+DYY48t1liDcCzqBl4dOiuGgQ7I5Jv94x//uFje\npPmud71rcOKJJxa/2IDUnQiEgFg+u4AKAI9PABEQjzcGdsB3kp873gAzFr8ByEJIzkvexU1HvbCq\nv/zyy8UqKC/SnXbwZVVzP7cJ5fGfMrDJVA/QLMF4RBnly+0/WTWgC6Opbm213NbHm8gHftjq096C\n43FrJlgiAVrx2Lt09tlnFwvs+973vnKJpVIfQoE3WzSKWOfN8HCNsR/3/lFpLHJe22Tlt8UIoc7l\nxSxiznXfAaxTpJVPmxqmkHSf2cT/5NumP2NkMPuHP9q+fqPv8k+5MD7ZyzOLOqBuBlRZLKbVD9qb\nAYp1ve/l2kRZ2kl5/n9bA8drP8W1k0rXytJ7DhA/ndoDDzxQokMYbIVvA9BZIVjdXHdfvdUFSydn\nb9Ohe4Zl95FHHingn0/rGWecUaxxPlGO8lyd1iYfKyPXGKBvz5Ylx2CHD3y/E7N52vJxabEo1afj\ngZtFCeDi2qIOgVC+9wDpLASMAXms9JSPTQIryo0il/xabWYgREMBRig4eGJjdQXc1aUY+WY6uEIY\n8H1oxwxS/K83TY6VjZwqH/lKOSbJwplnnjnwgaUuAUBPPvnk4J3vfGdp80JAcq9JO+/en//yQYkE\nmCiW0yqQeX7WPV99VnQgnQwjwJvLh01+1SUZ8YXXmsiPa4wXZiT0jWeddVYxRgTwbZoc1OWrj/Vj\n6oZSc8ABB5RFxh/84AdLG1HXytn3sqrDtGGKlXzrv4B39RWwDrBr267rAzZBGanrqh2vlwMNsK+X\n3+1t/8eBgO+HHnpocNFFF5Vwax/72MfKSnnWLp22Ti/3eSyD1jAmdq+l49NBuvbss8+Wj1cAogb+\nCy+8cN9Ua987/2HlHXWONd1UvzKztJuhmBUYSxso8MVTvOHni4/zkoFKZA4zIaxlXG4QACoM5CxA\nySDIjQL4YXVfhjIxb7mmeU492MwMfO973xvcc889xXrKBQToorhQplhOI4fuR/lvFol7mLCGZo9e\neumloozt3bt3AMjw700d55lp8rYd95AF9adM1ifMonTpDyjyDz744OuybtbNrJx+g0yNs67XD3MX\nE60o7nT1tUWP1SNgDqAD6hQUBKwB6MqujdbkmcwUds/v2VLCyQkLvEWYrLaXX355Af0stH2v+7o8\no46VXz2TE/2FMluvYBE85WZTQG3KQOnQV9mTTfWkHNkC1PWvGbNG8aadbxxogL3JwFo5oEO2cV1h\nIQLkrJI/5ZRTipVBR2eryUDU3errSdO5PJtzeU5nqFO0+Ofiiy8uoMkno63Ozz11mpt2bDCI64my\n+s9iBxgq3zwEaLCKs9BbqDcPqQ/h+kzxA9iAioGYZdk5QItSMcx/edT7uFGw0BoEWWjHhY8clcaq\nz0f+rMG44oorityxFh5//PHFTctAjTe5L/tuvlJ39hnQ7VntfvCDHwzuu+++YoGnhPLljrU6z3XT\n287/lEAzJFy3KCrcP2YldW8GjmW8S8DvrbfeWiyysyiY5FP0JFb2+Et30572vzo1IwKg2+QXmVUC\n0G1RrrppUkbNCmoXjBbkOl/5VJ8UZwSw8+v/4Q9/OHj++ef3AXZysemkHeAhXpAVdc3Ion/YNMCu\nD1b/NuVBADulQ/t3XAP1PrbZTZennZb/Bth3Wo32uDwBJd/+9rcLWPcRlMsuu6wMTDo311HASQ1Q\n6uPcY59nkrbOvrvlHvfrIKUvD1/84hfLJ89vv/32MqC6vomd5rBQjdwsLKhbBGzjB2s9q72vn44C\nGu4bRULUyQuLJ8tnTSztFqACcq5N+8ElaQAtlD0DIR/oSb759XtXeRw5ZLWlkAJgrKEf//jHC5iO\nbOY+eXHclTv/a7mt8+ya9mAjz88991xZgCkOvg8OXXDBBQXcdNOs01j3sfYNGIurbkHysFCe0+aJ\n1Zo8Uvq6xOpMmYvi0r0+7D/Fz2wSsM7lZFa+KZs2SMG1sQ5LA+gOSAc2xxElRpQnck05TghTEUa4\nCbKsO0/e3aPs+++/f/HBp5jH8jzuHZtwjczjp3JeffXVg2eeeWbw8MMPFzeSGrD3vSzKoa0ri/7N\nMdJeA9Izps0qb30ve8vf6jjQAPvqeNtS/j8OBHgYGD/ykY+Uj57ccMMNJaSVDi3XaxBSd2w6tHRu\nkhzWwUkjm87RsbTrLZ2mNKTP11U8ZwDAdDogIe1h6Xumb2RQY30yqPOLFO0CQAixjgMQsWzn/Cx7\n1nBghvWS//QsxIXDTMqerWltUWyGEesToMkayZ/TgtdpLebyBpwZEM0kdN0Lhr1vlefIHFcPiyN9\nxfDkk08efOYznynWQXJYy598RKYjc9knj9JDkWv7yHbu8Yx0yLN6AtYBxptuuqlYJ7tp5rl17uWZ\nLFL8yAF5WJS4sgj7aNFol8y6WB8wi4IpHXI4TLHspu8/HisPubU+QxnVA9mNTzor6jRkrYl3A93c\nhLq+965RRKTnvQC7dmNmCqg1S+ha5Gmad/b1HnzUVpTv4IMPLq4wlFBgPb76yrkJVLdXx2mL9X4T\nytHy2B8ONMDen7rYkTkJ2GBBOvzww8t0MV9eUT50ziiAA+jINKHjumNzjLIvfzo/eZfT6SwzABgE\nALsAePckrUsuuWRw7733FksOv+K81z19pTpUowgirG8G/JqU10eVDH58QPnOzkOm5VnrhcucNjpL\nLIN8zCkM4fWo91Ms+HnLqwg3gFO3PMOe5RfM0q6+LTTkerBuityZTRCthOJx3XXXFeWjBup4QNa7\nW87Ld5dPSbuWZzJdb655TptxzF3iy1/+cllkbQ/MddNdF4/k0yyD+q0tx8t4P5kE2rtfQ5W26FCP\nP/74VDLkfnwjRxQurifDwpmSTeUA0rmteEZ/xZpPUbb3f1oiGyzl2jJrPLA+zhKPlzVgt/bH7JVY\n9cBs+sxp39/H+1JGC94p79ZsmG3Iwkz83S5ZnodfZKRLm5T/bt7b/+3lQAPs28v/Hf12nZXNIMcX\nEZgSslHnq2MOUNcJZzPoOK9Tqzu2+ngS09JJ5v3eZQtoB2RtziHvDMi5++67B+95z3v25WHSu9Z9\n3SxFHarRrMA4dxALnoB2PAduZgEUKRs+8f0HFig0k4A09wDuD+qbVR5/pyHpmzHgKgNkdj+4NCoN\nIIulnYx43yyW1VFpTns+Muarm/zIT96yqlsjIS+RL8d4UG8B7a5l807HNY2S5chzFFB79yZdrkZc\ncVh8LewGKOv31O9Y1bH8UGIA0lUt6jSTwXcfmO7S+9///rLQt8vT7n35r60IZ6o9mbFBFEL9l41v\nOiKbsaLjL57PStLlAkN2ubtQUCelo871W8pqowgdeuihZTGyUI/a9qQ0Zs3nOu8nL8qoH6Bo+qLt\no48+WvgdwK5809bnOvPe3tU4sA4O/MElW7SOF7V37C4O6Hxt3E2AdZZWYN1gh4AXx/WWAUennI55\nHpCRZ7JPevU+nX4GCX7QBj2hHwHbRO3Ifdtde/L56tbXQQ3yBnsAiEWONXcc4S/Luml3LiTKOCvh\nAf9eaQAM4yKzACDAOosfi/ckcF/nhUwAQlwCgH7vE9YQgBqnaHgX4MSqDxwCp7O8t87DLMeRnSuv\nvLK4olgL8eEPf7iADtfwTb5rGZcv52zKSyaz78q852uZzbH780z27rUBPN6NHwCrRa++BnrIIYfs\nU+zct2qSB9ZjFuA3bq1P8LGuZRN3K/K4dytazhNPPLFPQcp7vB8IZ22fhtQTojCSu0TmIYt4b3ZJ\nObj1kFPtah5eAv/aCGBq1ooFeZZ08JaCpo75eGdWoJafacrbt3uUi/wySoi9L2oYg4Q2Y4us9y3f\nLT+NA+viQAPs6+L0LnuPzpcViBuMwQVYD4gCVhwDWgbJGrxk0FkWuwyEGQyTtn02+cxmcGDJ+dSn\nPjU46KCDyqBcP7+sPM2aDvDABxgg5acd5SLlmpQesK0OABGDnun3WYliAPDLg6n/YVP36pul26DL\n0j1JmRiVB3VgUaJ6Afjkm7yw2I8qs/wA9u61NmHVoF3elPOzn/3s4MYbbywRWyyExGeEz5Fxe1sN\n1JUj8hgZm3af5+wDYrIPT+XN+4444ojyASzuE2Q6izFH8THPL7r30S11wV1rFV8SZQhgvVcei9fJ\ni6gpXeLmAlhzCRtF6pLL3qtbCjGFjxJAKVZnFHcAnTuPd5HNeXnnPdZ18EmXzjSx4oflWTrkzKYt\ns0b7Eqw0IxvDnuvzOWVKuQQE4Aojipc6MEbYb2rZ+sz3lrfN4kAD7JtVXxuRWx0vwCBcoygjpuQD\n3oCIWBwDYnTEq+6Mu2Co+87k2YI1/qmsph/4wAf2AdN5B+lFKsyAbIAXJQKIMG0O/FB0ZiVWa1P6\nALdjg/usBBwAYdIBkGqeyB+rIVBPoVh0Aaj6YUFkyeSj7L320o0ltJt/ZUoeWTHjJtC9b9H/ARYA\nxfXXX1+m7Sl7zuNJQIZ6cmwLoI6c17ybJT+eq7fIcfa5Jk35QRbv4Z+ITEcfffS+EJrz5qEkOuaH\nWxPwyyKNL8t+j3Yh5J/ykTV9itkm9S/MYZecMyNlkWZI/8RyLjwmS7xFp0A7dyoyRyHQTvhRz9Pe\n8p7sKbOUbgooZVK+hym9uX/YPnxUbvnHB+US3pNVuv74U+4dlk5fzymT/uOkk04q1nUfR0v7Ucdp\nO33Nf8tX48CqOdB82FfN4V2WvsHEQML30JS8LxACmgaQgHWdcN0Bb8fgUg96pqZFXLGXd8QyCZRa\njBqwtc6qBCb4qhvAAAhAfdYBvptfoIE/u4HR4D5PetxU5KuO9oGXwIhoGbMsTO3mb9R/6QNWlBc0\n6YNLonfIDyWRpX8UwB/1vnHnIzfWOnz0ox8tC/74OzsPUARg1CCdfK9Sxr0bBcSR4ci0Y+flTX4t\n1CQD4lqvIl++BWCjLIm1vopys1AD2EB618VLKE0zHl1SH/ok8kmZI6uUTPkDzLUxQDrg3BoAMrdI\nhKXkgYLLlU0fw1Iv4su8pK7VqbREi7F/4YUXSvQt75C2/moVfJ83z5OeUyab+rj00kuLAsKVS13o\no6L4kuFGjQO7mQMNsO/m2l9y2XW6wAHAxLLGws5KjQyYmdoMWN/uDjj5rQGOAVAZRILw+XfhJ084\n4YS1DYLeDyywxBmoAHVAYlnEishtJQtCZ60DPDNdzWVANA0DagCUOOosfqsiygtr6DQfXALKABju\nQLP60o/Kf+RFea1zEAnmsMMOK2ADSIqMk+8oeesETvKXPAI/QHs258n5kUceWdw8fHE1eRxV3lnP\ni+zBum6NA+vorLI1zfsosqzrPrpEIeiStitSjzCtXSKrX/nKV4ofutkb7Upe1VuX8EqYTHzj6jTs\nnu4zw/6badCe9X0UjHELxIc93z1X1y8FHGiXVz7f+P3AAw+UvJK7dcpeN5+z/I9skh0zD9wnuTDh\nuTrDO21qU8ozS9nbvY0Ds3CgucTMwq1270gO6HQzmIibCzDwrUQ6W50uAFqDmZGJrelCBjUDXY5T\nDn6vBlduBKJtGDhyz6qyx3+WCxFQzfeXdW+WL4BOk68MgCzllINxC0iHpYcH3FIAEWDBVLzFeQnF\nOOyZZZ0zgHuPMvBTZ2UlZ+qpCw65NqhD9wD4AF73nlnyFbmgqPAFt5DaojjnAV/ybZNHMh6ZmuUd\ni96rbpB35/05B8giSqgoNhS2hNvMPeWGOX/wGTAFhIF1PFk2UT64XeEv17Vh71AWi0xfeuml4v5V\n54GsaF/nnHNOMSjgwbA0PIN/lD3l8l4W+FnIu7iyUWK4aVEapbcoKZ8t8mivbvHj8ssvL22BYpD7\nFn3fqp9P/vUlZjUpwhZu43/dnjalPKvmV0t/d3OgAfbdXf9LLb2BIwOiMHemmoeBdZ1vXyh5qQeE\nDCJmCR577LEyNQ4E1PcsM/9Ary9jGtyBUUDKYrdRYGLRd1MCvJNfM79fwGUWongBMfzhWTzVc0DC\nLOnMe6/8A+7KIA/AO4CedRJJF0BSPsoJ//dFQDuZYMm8ZCuoFpePb3zjG6V+1BGQnml7/1clJynX\nuH3e3d17RhnwiUsR0GrmaBmuMeqAmxRgykK6Krk1u8K9hELQBb+iwZBnSoP6kQ9uUe6vySwNNz1W\neLwYR+QpMkbGydI0JC9mASiKe/bsKTMBZGQVlL6Kcqpehbi0CJeCERlYxXuXlWbyf/7555eZuu98\n5zulPeFXAHsU0GW9s6XTOLCpHGguMZtacz3KdzpdViWL2gzcQskZMACZLpjpUdb3ZaUug6lmG4D2\ns5/9bHD88ccXIGARXQDZvgcXOPBOlmq+2ZQd4d24lBigVk3KxjVGSDifY5/Vkh/XBHXMakvR2A7i\n+gIsqq9Y+bvgiJWUGwvrL2VoVkCpnvBLGiyl3A5YNGMFDLCIbOBJH6iWabMpNooWMmukXfroDqV6\nXnBHWRIPnPzgjbRWQXkPAGz9BLIwlOuazcwHAlzjj85qS7a1sS4B/azwXeDfvQ+/Xn755QIirfuY\nJDvywrKO9xa5dn3su+nP+19/YVOnZN/eOymUQj0qm/ZARvsij92ypl3dcccdg/POO6+E5hQ2E48z\nbpCnPpehW6b2v3FglRxogH2V3N0laRs4ABpWYlOaFrVx6YjlUeerE14HEF2E5RlADNIZBJWN36/F\niyLHLAJu6rwJ1chiCHRQcFjzJ1n86ueXccx66EMxysRnFPCchlgp+bHjDSWNZc+2XaS+Jn1waV7/\najJh8w4f1OJ+c+211xYQFPnGt76B9dSFvKsn+Q9oV2es4tYgiOC0//77l7Y5a/vMOgFyq310FaXk\nYdG9tsifHJ+BdQtGvRsgR2aI+KPbum2IMky2KZhdUm7x2yfJfZQFCzpHxZPHZwt6yZk8UAi6eem+\nf5H/kUt1iT82x87zZzfbYOEmeZ1XGVskf5OejVySP+uchHKk+Mur+jBm2JPJWeVy0rvb9caBTeVA\nA+ybWnM9yXc6XoCA7yFL180337yxVpKAUOUBCOxffPHFEmFD1Ij4vc5rtaLYmLKXFpAnagTlZt70\nFhWDWMpZn7kRTMoH0Ce2Nd4AaUAKdxML81g3t5PkgxJEBllZu5F1fGgHgAPspnXhIQ82My3veMc7\nyqxEwkVmPUCU0Um82y7eaKPkLqAduPPf+gzx0p999tmitAFG05YhkXi4iZADAGsVJJ9mgii2+Ow/\nAkRjSZ/kqsI9Rd3FCl/nk2uMBbiTys3VT5mB/64LGX6aZSB/ZIPyvaqZhjrvdd8rD9pm6lm5nHv8\n8cfLOhXlm1TGOu1VHiffZqpO3voysPCoRx11VHllrQQvyziyyrK0tBsH1smBBtjXye0d+C6dL8sO\ni7EvGrKUmDqurSSxPm5C8TOYxHIFmDoGZr/0pS8Vv995BxLgeNmhGpfBU4tGAW8uORSIURTgyi+Y\nS4gFq0DQP/3TPxUAlc+5j3p+HecBFsCcpZPcsYjWMeOVU3m5N3FZGAdiall473vfW9w+uHqp/8g3\ngDEL0F0HD4a9Q93hDVAXcMdKzY3FJ+Dj7jGNNRMwBYIpLMD6st2hKBbAMfcSeVQP6imRXQB1/J+F\nnn766RLRR1vukg+lmTUZR/oBVn5+7UB75AYvgHV8JWv6wHVS6hXPUq/4Jb8s7dZv8Nk3OyDPyfc6\n81i/K23q1ltvLW4wIi2ZwXS+bleON6Fd1WVrx40Dq+ZAW3S6ag7v8PR1tICAMGpcYUQqAJQMqMDM\nvOB2u9hWD2gZXOwtIGNpP+644/ZN09b3jsuvwRxQ57ZhEBILWghEvOkDsVZSuEyjsx4Om8rHA765\ngBTLNcCL1LNrXCw8N8kneNXlxV/ADqgDppTJntuRvLoG3DgP4LivJmUB6sgwItuvbvlAA3VARhZS\nsygHVEwDcut3bPexMgJ6wKd6M3sgQkcA0ji5FsEIWMdLYH2SdXvasqoLUZLMgGgrwDqXLXmVz7e/\n/e1lJoqvfOpm2rTdZ23Ini3/92FfQ2XBVw5KyyhS1/ozcuP92gy54AboP4V+Vf7qo/KU86kv+9St\n/KpTbfbCCy8srkR44J7cn+fXsZcvG3e6j3/84+WDYwITiLjkPB6SKZu8+78d+VwHL9o7Ggfm5UAD\n7PNyrj23b3AAcK666qriZsAPUadrcNsU6+O4qgRsbEIZXnHFFWWwAdYCbsY969o6QjVOysM011nL\ngaR8iVEd1sQyzVrHUteNtY43/Hz5FnPv6QOAVUcs6wZ/ZZJ3AEBelZUFEvgC3n1yngyrZwCHguU+\n/wH22267bSDyhzCOgIS0N00ZrYFawJPyUWSEX/VRJZbycXJNqQPu8RRYB6QXIeCNwkCR5ZqTjxmJ\n5mNNhNkbdbEsxYCizG3rueeee122fXyIbLtnFFFmKX/kieJiMTPgbpZimJI7Kp1lng+oretX+uoW\niRgjb2TXzFgdUz7PlhtX+BN5o9z4boF29+CDDxZeu0bmAta1K22sD33IClnSkm4cmIsDqw9HMVe2\n2kObwIF0xAYHbhEGAx2tDneTLSQZ/JQl1h6fKDfY50uhyj6OWAfFjDZIGYSADmk47iMppwgqyiUc\nXu06YIDlYsL3e5jLjLpWNlZSFtK+kHoEwiyuBEwpHeqPP7T8AoavbllJnQcaLaSleHDDQHgBsN93\n330DLjHSizxEvtcFepbF08i0cjiOa9MjjzxSQN4ouU6oQuXl+jTvegWg3xoO/cVLW5FMhGGkPO3Z\nsn5rI+985zuLD7joRUAxN5NlgmHRSMyWdEm5Rc7hOjOKlJ3bmHvNNHF/AdYpcNtJ8pV+V/8SK7Vz\n+uYPfvCDxe1J2cyO+eKrMoyq62WVJe9Q52effXaJ2KOO5UPYWte1oy5Y37Q2tSx+tXQaByZxoB9z\n8pNy2a73lgMGBNYxLiM6Yx2wzWCh493UzrceBJUHgBUBR4g3MdkzGHXL5zwQCLjiDUuhQR4/+k6A\nEb9uPrkWb1qYCZhwUWBxZn3sljdl4mrCJUDZhZPrLszLfduxZwkGrCgeACLQDmwB7eqIT3tNLJGU\nD3WmPMD8IYccUv4DugG79TObcJy6Uy4yrRwUkoMPPrgsPBWtw3n35V7lYulmWccrbXwWtyftAfCO\nPzqrOiJrZmq4JHVDigbUkynAbtl0zTXXlPxQxGrSxilmXN+4uHSJMmcGBv/wgtJS86l7/zr/y4d8\nqdOQc2aQ5PVNb3pTKZfvB5x44omlbXOVWcX3JdQ5ohjfdNNN5WvR+gRrJSzIJXOIrAHrlAzbMNkr\nN7afxoHGgcKB37fuxpDGgRk4EMBqMPjlL39ZBl+DsEEjW18GsxmKte9Wec8gmPIAsCKkKHMGpX0P\nbB0AGtsdqrHOzzzHQLeBlkWd5ZDLAv9ellgD6jgCCgB8PKDc9K3+uchwhxFPXZQelnRrCYDJmtQv\n9wz3sgKz8gKPym8jD5GP+rlNOZb3uiwWUbKAAlJp16k7M0XAOjBL6ZlGEcM/Cjz+4i23FkTp4zIF\npI+y0MuD/kT+gLvkY5m8lSb/abLKFaYmygnlzGxLQpUqj1kYMqOP0w/II0WOjCx70W2dn1mOlYts\njgLt0hLJi1LCzeuYY44pZTzllFPKtya494Tf2U/7/vSH9mYW77zzzsHdd99dFC4LtbnmqFtyJG15\nrGcD1Lfzs7532vy1+xpH9GCjAABAAElEQVQHdgIHWpSYnVCL21AGHbPO12B86aWXFiv7t771rTJ4\nxcfX4LHJVJfRtL2vnl5yySXFylaX0UDUp1CNi/JcuS3EY2k2sAJ0owBW910s0mZcTL2vwjrafd+8\n/4FJMwcs6V1SfgCdBV6kDRZ6ft7qvK73TQUXACiZ1XbJdVxPzDyYRQGktF28SQhPFmcLbkeRvkAU\nJAA9vuj44xkA3TYNsDUDAhi/5S1vKS5Lo963jPPcbixmNaPUJTMwQDslw3VtgQsVJQK4pNS6DrBz\nJesTkd/Ucfpoe5tr6lYZuDndddddxeXLjODevXtL+EshMPGfHEwi6fHrF0HHzIQQoXz7KT0s+dL0\nXvlBZEK62fQv8uP8pranSTxq1xsHlsWBZmFfFic3LB0dbU2zdpaet+mIgdW4feh8bTuF8CVlUkZu\nFUCOqVzlZ6VjsTXVD5R0Y39vIh+AOVPpIYP7tASks8qzSPJ5B3D7SCzFdRm7eVSvrgOQJ2/Fiq7l\nwPGs7aWb/nb+r8tCtlmN92z5kJNjgJRcA+ss62QdIB0G1gF+4BxIB9b1BdKziFfdA7PTgL7wAvAD\n1j0LHK+auPZw0zAbZEapJvnwYSWhXLV1H2zCoxA3HqDec1xltitCTPJT71O/kVN7bVjdBDzbmzmz\n2PjMM88sSrYvpPIvZ4BxXfnMQKlHvCInzlN0zCZSzgF9bUXUKMrPOeecU1ysKPhRCsmTd8tDLOv2\n/qdvkcdGjQONA+M50AD7eP7suKs6T2SgZQH5whe+sM99IR38tIWWlkHa9DeXCc/rmGdNZ9r3rfu+\nlCN7gFx5gRNAlCXZYM1y6MuGgMamkzplUWR9M1iLaf6v//qvxXdZ3U4ivGKZZn0Edlnq+kgAqboc\nRngAbAAi/NujjAb0KOMmU+Q5IMp/ZaR44wneWHjMPYRcA+AhAB5AN0MBYOMVUA6wah/uDQjLM9Ps\nAUFuJtoV+VkXyTOgahZJn1gTBeaSrRk1INZ9XeIygxf6ATMysygn3bSW/T8yqi4cq+vUN16Tb3Wd\nY2WxoJxrFEWVwkIeyL/+HUjX1ykj4K6vo8hZh+BZ/6UnXTIRZdi75QFAr7fkJ/lcdvlbeo0DO5ED\nDbDvxFodUSYdKdKp6qiff/75sonMEOCuI9WJjutIpVNvwJ1OfJpnR2Stt6fDC2XLYjtWNQOaAWrP\nltWNj3OfButFmMlNBFA1eANxrIvOASXTAqn4KgulyG98mHV2kTwu41lK1zBKG7EH2Mh2AHtkYdhz\nm3guoMleGck1q7mFlcrNVxtQdRx/dK4gCLBWt4Aa32dpLELki7881xsyt04COn1caO+W+4ay1iTK\n01lnnTW49957X1dGQNSMhJkIyqkF232iyKt96lqebRkDArD1ZQHw7hddiUwkjW656v7fMSUvbce9\nnsu77APWk49R6Xbf0/43DjQO/J4DDbD/nhcrPao7s5W+aEzi8pBOWQcbsvDKBrhfdNFFxdqkY53U\nqUrPxhIHsE+6P+/btH3KZdBhTQdoLLDk2wz48f11TyjH0+49l3fkeNG0kk7ykP/j9sAYqyl3EQod\ntxbPU1SAbzKTiB5Jd9QebwzUAA9A5L5R9046nzznvvyfZ+8ZFOD5u3+v/Y1c88VFFI6dBjRSH2nn\nwl6yqMayTmFjVeWbHBDLzQGQA9LJQV0fr+XgbP8oA9yoLEitrfmzpbLY3azF3/ve98qCTCC2ph/8\n4AdFcbnhhhvq0+WYbFBcuMpx42Fp7xulnrRH9W2vjPoz+2zGhsh+lwejyuT+yJJ92ol3dLfIWvIz\nKs12vnGgcWA4BxpgH86XpZzVmSEgiM9f/i8l8TkS8X4dMSua6c0uBbhbdMTi7st/Olk0rpN1T7eD\nH3d/9719/q8c2eRTOVkAgVpANgMU3tb1O+y/50edN1jm+jr2eUed55KBrR+yausSYGWbhfCLpXrT\nCF9YfPn57lSgEblWPmBcn2BxJWWLK5TrgDnXKJb2zDAtsy4pgaIKeaeZqu0iHzhT1+eee275CFw3\nHzfeeGNRVIRC7JJ8c6cxE+VbFIBq3yh1Ta4dq3P51O/YtNMcZ9/tq7plSppJL31h0q7/uwdl302r\n/W8caByYzIEG2CfzaKE7dH4XXHDBQPzbTaEf/ehHA5uv0t1zzz1DI4SkM7dnXWdlrzvwTSnrLPnk\nl2lTXpE1gAyLTKeNoDLLu9Z5L4XS5+aVI7H0vV/dZg+8ihjCKie8HxegXM89w/6zsAP/XB3MTrin\ne1+eX+eeNZk/9jBKHgGa1O1Ole2US91YOK3s3F0sHraQkIyvkoBcBgQWbrK1btI/C8/46tYCSgoJ\nQ4VFlj6w1KXPfe5zxYp+8tYi5Jq0Be5iZicswjT71ldKfatnxwHteI8Xznf3znUp6WQPnDsOSM95\ne5R9N532v3GgcWB6Dqy/h5w+bxt9Zzo+rgVHHHFEmUpOR7hdBUuegE6A8+abbx6aFYP3SSedVAat\ndOTpkIc9AMCaPt+plMEnZbRADchglfPFRhY2AGcTByWKFqBh1gCoHuWLD8xYgAjYs5i7d5ryChOI\nRwCRhcl9IXI9LJxf8ocf6pUFOPWfazth3607/RQLMwDHBUZ9sbpy94jSsuxycyMxY8NXmkvOuonC\nlpCNlBOgW/lZ2S0mvfrqq1+XpdNPP7247YgrXpMZCK5C1rZwjYnrWH1Pn45rmXZsbFB2+2zyWx93\n8580sne9e9x9pv1vHGgcmJ8DDbDPz7uJT+rsDHrChgE7gPJ2gvbkx0DFZ7QL2AH1ww8/vMSe3rNn\nT3H7kH8dOcA+igxU//Vf/zXq8safxzeEZ6yPBmfgBo8sNrNgTvktQJvmwzJ9YQilw0dOyCSrufof\nR/xz+TbzbWdJdDyJKHP8nkWbAILISh/IQslxpC75sFNoUA1Exj23SdeUCdkrJ7nm0sG/H5BWZxai\n4lUWmOoLlkFmbLQd7UWUkXWTyCfAuj552DcDrrrqquIi5OM/NVFsjj322LLmx2xUTdKxpsUaF0r9\nuD6zfm67j2vZTl8nT/Vx/T9yk3zX/+vjXG/7xoHGgeVwYDQKW076LZUtDtQdn+Pt3lRKnScg9D3v\nec/gjjvuKF/CYx2q8ziqEtM5W1TI53UnE34AMG/cik2s3DZg421ve1uJ0QzwvPLKK2V6nZLTd5LH\nf/mXfyl+2pTJaRUN1lDADi+m9WcnHxQcig3A0wcCGEeROgZSzSpQZgLaR92/yefThrktUa7MPPiM\nPDD6j//4j0XelV/IRWtcuLCYnVuEtCXp4a0ZmHUDW0qIyC7eq/0O+8AXvnz7298evOtd73pdUbkO\nHXrooUVxrS/qR8VrNxPnHZtI6dvs8afeKGsx3tTn62c2scwtz40Dm8KBZmFfYU3pyHRw8fd13AcL\nu3wZXFhUDz744OKyI9qB/6a/bY7lW57d36WcswfIfOp7GpDfTWdT/isby3JCnSXfBq49W5Z2lmOu\nIgZqi/dY2/jC9pECmAAvbgCzRuYAskTGAbqm+QoqGcIPCgLf8e308TW7RLnkjpF2UPuyx+LL8iq2\nuDbw6pZ7SB+jfyxDtsgCMpvAil4TAK+uzKQI9YlnNhGDXBPVhfvHrGEYuY1YNyHtVSxkrctQH6tT\n1m+Kpvr0jYBxeVf3Dz744GC//fYrslunxUJ/4IEHlu8N1B95ouyYbSNj+gR8atQ40DjQOLAMDjTA\nvgwuDkkDGLABKwaFgHW3ZpAc8thKT3lvXFyAyYcffnifG4Q8WmAGrNvbgHoWN6B0GKWMLLQGcj7d\nrJPIu1zfdFKObPy39+7dW8qVsqd8FBwL54ABwB04NZADJfjYJ7LIjnsK5QPompXIhLIC7fzfuXw5\nN47IGwAD/AI16wRq8sX9x6wAYEppBsZFP9EezIwgZRB7XFsF7vxnfQW+lJcc7BSKTCuPY7zhDpM2\nm73r2r9ZFRuffq5hNjMm3FqcB/aB4Po5z3aJ9Zk7lRmMPVuK7rrIe30AzGwBuVf3k/IqbwD3E088\nURTT7iwiWTrooIPKGo3aZ507lXCYot+YqZjmPeviQ3tP40DjwOZyYPwou7nl6kXOddQGfwOePdrO\nQd+7ART5AlpYkAAZ+QMquS0Ano5dA1hcc3896OQ45w3Y/FANUnu2BuHtLOMqKl55WGH5e3/+859/\nHT/qd+KF2QrWeAO6cG9AO5AavtX3r/tYnlg4A1jnfT8g40Mx/IABE0B3EuEDSy23inUBGcBbeW1k\nn+LAYlwrDBQJCoxZAIpq2oj6MntEwdlpMl3XlfICo/ElHyen+gaA18ZKTlHHO5u+g5zb8LFL+hxR\ng/QpZmnGvaf77CL/WbzJqPdSvLTRWYjM+BoqxbTrBiZd635cj2Ku7GSMMmM2YpjLzSzvb/c2DjQO\nNA7gQAPsK5SDekAKYF/h6yYmDXQYtJBjeTJY27Ow2wB1/92Xe+ty5CXOZXNOzHahID/wgQ+UtKU/\n7Lk8vwl7ZbABGqywAEl8buuyd8tC0QH+ABcDuo1FkuVtO6fIKQ+s/yJyANuL1g/Qz60GGPZ108yu\ndPmR/xRCoBAAxo+uC0buW8aeXFNOWI6BdkoUECWfXWJtVWcihURuU79/+7d/W3y3Iwu53k1jU/8r\nD9cm/JpVJljJbWTd7Jo6Bfxt+aAQcJy+jzsUS7f3kIVVk3YLNJMDChqwPkyRmCYfFk4/9dRTg7e/\n/e2vi4j18ssvD97//vcPfGApfSbDBZ5Q3AH+dZR3mnK0exoHGgc2lwP/b6vD3jnzvJtbD2vJuaq2\nGciAGIO0/wYZg+o0QD0Z9ZwFhKbIWZ+F7vvgBz9YABJQFOt87t/EvTLikVmIU089tQDc6667rljS\ngHdlDBgZVT5pALTACr6z2Br8Jz03Kr15z4v88dOf/rQoHRbaUc6WQcrHVYi1VaQZAG4c4QFXGjID\n/CwrH3mn9Fk1gUb1RjkByAHIcaQcQHpd5/LItcmCbKCPXFNoA8rGpdf3a3U5r7zyyqJs89dmJSbb\n85ZTSEhWd1Zt/I8iZG0AxRWAB5xXTRYVm/2hUFKcKRXLaHMW3h5yyCGlbN0yfOxjH3tN5C0Lecm6\nNSJ9CmnazXf73zjQOLAZHBjunLwZeW+5nJEDsRoCHIBSBmd7/wOyc9+k5N0nLRsQCNA88sgjBZgC\nBLZNJvkHAFkFH3/88cExxxxTQJ3yzsIjAF3EDT6+gDtrvUVr6yLgBfAEWMRPXyZIxgeL98gQH2Eg\ndxzhnYWuFEaW9mWRumLhfemll4pvNdAJGJLLSWBdHpQjlLqVV9Zg6zqkSxZ2glwrZ8qhTM8//3yR\nT+WObIcXs+7NIHF98rVka1soTJQdYF36fL0B+VWS0IqAMsBM1tThMsC6PL/zne8sC+xreUlZbrnl\nlsHll1+evyXyEtchM1us7Y0aBxoHGgcW4cAfXLJFiyTQnt0sDmSgyeBsgM7mXK5PWyoDPyu0jTXr\nhz/8YfnokgFy0cF/2jys4r4AGuUS3g3g0FQoNayPsUBOyy/3c7kA/vjBsgIDtwDNssDEMD4Axizg\n3gWsTxu+cVhao87hiXIoE0u7co7jC/cA+QGwKTGLuAuoJ/7TlAXpURyANOH15nE/ku+67gFaFmOL\na4866qhSV5ss16nDtFuzP77g6SNBZkfIqfokk+PqMOmM2nsW/8mChZ6UXooi8Prq1sJj/71L3S/y\nnvr9yiRKk0gw0ubONKu/ep3eqGPyxTjx9NNPv+6WF198sbh6UVaQ+8inNs/Sv8q2/rrMtBONA40D\nO4oDDbDvqOqcrjAGyO423ZO/vyuDbAZ+wIbrwZe+9KXiGsGfedOBjTJx+eHqY7qbJRkQADwCaMKH\n33Nm/BFfWr7bgDQgCGSyBteLIMenMP1V+QdkKVLyPmv4xunfNChlwBegnQWV3+44AvCVH8DHj1n5\nKG3gj9sDCy6QybJrnQBezpNenV9yjX8UNqCLXH/4wx8uPtCbLtfKZuPSxiqsvpSNbNvwEv8W5WHq\niB+3OjbbQSnAVwCW7Nvkg2+5d89L2hNZIH+UQGCdcrwqsmia0smS3yVRZSzCtmaCrFCSyaj7LXDu\nO5GNmvzXDsxY2JOPLi1DVrpptv+NA40Dr+VA82F/LT/avxk4kEHfgA/Y2n/hC18org6iJgBwmwhu\nlCtl++Y3vzm47LLLyodWWAOBa1Zcg5ayLULAqogp/H6BDJa7eRfFDcsHSyNABMhOWhA67Pl5zuWd\n3BBYFMcRwG7m4i/+4i+KX/+4e+tr3Im40/DLVxfcDoSnXLQ+8o4AFPLMnQgYFIv7hBNOGJx//vkF\nWG6iXKd8UURYvs1EfPaznx0cd9xxhZeL+K8n/ezxz9oWvBIysgZ6eJqFquoRaQOAPWVvFks0IEkx\n9UEja0QsbF4HgCQnJ5988uDOO+9Mkfft9RVcjUSWQRZ7A+1muVapOO/LwAwHyoHsueyZJdCOKVpC\ncGpv9YfG1GcMD4w0FBMKjDUpmcFbB/9nKGK7tXFgR3CgAfYdUY3bUwgdvMHf4JvFp6yePqZz6623\nFp/vZQDbdZdOuViSDFSiwgBpokBQQACa2sK+aN7wTyQTGzIA7tmKMLEo+DTw2oSUs+BuXYRvFrdy\neWBRreNTd/OAz+4FuPj4T3KN+Z//+Z8CIig6rLHWBuDVLOCum4dh/2u5ZhUl2w899NDgki2XKFFH\ngJLMsAx7vs/nUjZW7dtuu23wta99bfCTn/xkH1inACnbovKHB0A0dxBgbtxiZLJCebNQVV+ibn3D\nAHgPABzFUwoppVeetdVJMzuj0pn3PD5alCyCTJeU2XoVCrP7hL1FZL1WXrrPres/WbCZJbjrrrtK\nvHkffTMbx6WH4kMBomCYreDipH4YGNSZ2QyLuwH7H//4xyVkq3UjlD/KbdyRGnhfV4229+x0DjTA\nvtNreMXli7UugN3AxCoNsAsXx53A4L8pnXYGMeX45Cc/Wfy/H3vssTLAAjMAe5SQZZaJtRPwoCSw\nXrG2cxuZhwAfvAdeDKDLzOc0+WGNA1TwifJGwRlFBn73Jq/D7gPoWfoog9IE0oH1VYIeck0GIteA\nitClp59++uDcc88t78bXdfN2GH9mOUe+KVX4Dphx9TrppJMKYCffy1JGI4PqyQzKNITnXGWAdwtH\nEYXPTA0AX7vMuNeHm4BGoB7AXObs1DT5zT3arkW21op0idJBIeLLT34t/l63El3nSf0jbfTrX//6\n4KabbipfZj7ssMPKF6+1V/0P/qYvzDP2kffs07fbMxCIovP973+/WOh9Rfszn/lMmWXYxLZS860d\nNw70gQMNsPehFjY4DzpxnXvcYgAcgEDoM4DAoBCAuwnFVBbbc889NzjyyCNLdBjgGViIdV15MmAt\nu0zACncPANHAzuJeA5VJ7wP4AQeDLsvmKkHtuLwAXPLBzYEbwDh+sVpb/Cj0XW0hZckDAlj9AAL8\n4P4yTgEYl6dZrpFrckyuWdntn3zyycHZZ59dfKW54GyalV2ZbBSRS7ZmCx544IECsKKI2pM1vF6E\ngEGuMGZMAMB50pNGfNwdS4P/NwAsXf7qXGmWGbJxkTKTd2WlWHbJDBellPHCh6O4Apl9mlch76Y/\n7X91r18RmtbMCkWHUUI/h6fp+yIn0nU8jOr2nGN1lI3l/Y477ijuQvqhK664Yt/H0nL/sHTbucaB\nxoHRHGiAfTRv2pUpOJDOHQgIuHEMgL3rXe8a3HDDDeVjSpsAbpTFoGVABTI/8pGPDM4444wyCAEz\nNaBZ5aCDj6yH8uGdFIZML4+rEgDX1DSQDjx4djuJm4/pciCbn+soIi/cBfCUu4DyA+oAm3NAmun5\ndZYnsiBvsbKTjRNPPLG4d3zve98r4Fb+VikLo3g2z3llooRQCMk3NwhyQgHCW/tFlVHvoKhxW5L2\nJJeWSeWQHiWULHCvUQcIz8kUC35f6NWt6Dd81imYXTI7wwgA0PrQEn475/+qCQ9tPmz3iU98orSv\niy66aMCq7nyAep2PyHVkO/vckzT9r4/9z7P6fHV3/fXXD77xjW+UceDLX/5yUeJzn32jxoHGgek4\n0AD7dHxqd43hQDp91hvgxmYQEOKR+4CBStQGg1O34x+T7FovZdAxvW0q1/S6xWTyy+oYQGMQWscg\nq/CsdtxkLKZjeWapG+XnjefAujpgvWNh7wMJh8jNgWvOOKXDPe4F8LhrqA9uBGYYRpV51eWTh1oR\nxVsLB/fff/+iiALv65SHRcqbNspavXfv3qIAXXvttUWWybYt1vVF2ijQStkEpilqyyL5Z72mBMqf\n/4h/NSs72VpXuxxXJq5olE5uXF064ogjyqwGxcN9/MPJ9yoJn8wQ6YeFpzVDdNZZZxW51UeHj3iK\nf9ny3z5b8pln7JNGQH/+5x7paSMs7uedd16pQ/3qAQcc8Lp0k37bNw40DgznQAvrOJwv7ewMHNCh\nh3TUNpY8vquApCgUrDk+YNPt/PPcdu/lGSB73/veVyxkLKixONoDMwFndXlXmW9KA+uyvPEJ5q/L\nAsqvt84DXrNqUja4lax7qn0cDygaAIr8c2nAyy6xqLNKWlRKXrjRKAe3E3zvAwWQUCjk65xzzik+\nv32W6ZpvZEgZLKAGqL/zne8U3uJv5DtgrX5ulmMzPBaacv0QXrOW0VnS6d6rXXKBYWUH0Fmx+bRL\n/7e//W2RLYoU2eG2RvnYLiLjXEDuu+++0gfW+TCzQTG1IFOo1bSJVeRXfdsoOAwQZqwefvjhwbvf\n/e5yniwgda5PIQe1LDh2ftimH3Q++xynf5SmukkevMsCXOWW7kc/+tHiQ793S3F037LkpOZ1O24c\n2IkcaIB9J9ZqD8oUgGDal3uMONYGDh13nzrpDCpAwSmnnDL4+c9/Xga2LJbNIJZBad2Di8EPgAUE\nDPJ83IEU+TPQyz8wY+pZKEX39YmSf8qGfLOaO4dYrwEKfr3yTzZYA1nU++DqkLrOHvDAb4oosMUP\n2NdvRdDok0x36z9tkVvCVVddVcDkn/zJnxTABazbArZS1m4ak/7jjQWV2pHZtGGK2aQ0hl1nqf7Z\nz35W/NW5RXEPk1eyTxncs7UA2WwSsE7pI2cWdyIKr3sXJa5ps8xYCaFqhuHBBx8s8lK/3ywS8hEu\nedWmzRDMy/c67RynT9OXsWRTfrk/6Uco90gb1Lelf4vSln4O3yIT2Xsmm/w6zrXcn33u867kh4zI\ni6/FXnrppaVeGXK8Ey2TByXB9tM4sMM40FxidliFbmdxdMwGBIM2q6nNf+dFCxCbXcQVnbbOebs7\n6AwkLNPHHntsma79wQ9+UEClvBnMAIMAmu3Os/yyJHINwFfAwN45YMbWVwKmKBaAIsVCnoF1sgKo\nAzhmBpzj9y5EH3C/3RQZiUwDhhQNfPdBLQsfxdtmed9u+RjGK/kHlFhXufB861vfKhFN5JVcR75r\ngDUsnUnnyCS3B5Z1s0LLIMqp+OVA4DQf/uLu4xmWeIqfMpE3+SFj89LFF19clAEfmQq4nCYtEVj4\njA+j22+/vazxMduxzO8kpL75qx999NHlg1j6XvKKArKVI1vqviu//k9D3onSVpIH77RpL9qPY9e8\nj/JO2VU/jzzySAkZ6XyjxoHGgdEcaIB9NG/alTk4ABzomHXQwE06aoPulVdeWaLGCPl4/PHHl457\n2kFhjqyMfSSDC5DBDcb/e+65p4BGeTKYBcw47g5mYxNf8UVgBJAxvY64Zbz1rW9d8VsXTx44eXXL\nxxk/DeJcewB1lr8Q+RFhhNz4EAulabuJbESma0VUPbz3ve8tcmK9Rh9nj/CTpVfoxquvvrqAOPys\nldFF5ZuVWOhC7iqU8UUJr8kK8E1GhGycZR2D+rL+A3DXRvAg7mWUQG4zs5BIKhbPs1bff//9JU/T\nPv+5z32uREjp3q8/lBbfe2s2pvkOQTeN7v/0aWYkLPjniigMKX4i71Tv6tvmf8C668voi+UB2eO7\nLW1Hm4+y673KLWa7/uvRRx8tCmQD7YV97adxYCgHmko7lC3t5Lwc0OkbCAwImWbVCeu4WXqABj6M\nNpbtDDLzvm+e5/JO8YJN3wONAFd8v+u8K0ufwLryAhym0RHecifhVkJB6iPhNxeSKBgGbYvtRBGp\nwXrKw+0BMGZp7wOpf3wmFwE8zqkHMzKu7d3yxwUQI1vbnW/50OZ8HMlMwDXXXFOUC/lKOZRlUfkG\nxiyglCbr+qLESu5jWsA6yzh/8FnAuverG8rDX//1Xxf3C19zVU7yxPLMdaeOODMpz9ZWIIvnySyl\nc1riCsjVrkv4xqeb37968sXfRSj1LUSqDzmJr3/aaacVsIwfUdBihIgck13XbcugpCXdjAPdd5MV\n+fUhpnvvvbe4TOIRPtgaNQ40DgznQLOwD+dLO7sABzJ4xLISNwKdsQ7dgMfywz9VpIrDDz98qYPG\nqKzLl41P/ac+9anBSy+9VKz+pmblFRlMKBo2xwE0o9LcjvMsmkANIEPh+M1vflNcTOTX9PqyfWLn\nLSNeA0bcJShnfL33bPkcm9VwzfqGUdZOIBDIt8CQv/52U2SHshEru1kA58nOmWeeWXxyRcDgoxvg\nsu58J5+s/yKC3H333SU6CAuua+Q58l0D9nnzaaaHexMr+KLrJ7hJUDzxk9IWpXTevHWf475EEeCT\nrh7xgcWdYgA8jiIAmCtfiFWcgj/trJZ3iRDzxBNPJIl9e7KtjhBXsXnKnDpnsRYhyizHV7/61VLf\n5FA9K2vqe9kgfV9hRhzIX8C4NpMNX5A+4sADDyzyKpJMH/vcEUVrpxsH1sqBtuh0rezeHS8LWMk+\npc7AwpLN6ue6cGPiEu/ZAnLxfXV+mZT3svBefvnlxeLFwmugFI86ikQsQX0G60I8mvI26LI+muq3\n+M7GEgiQsLjjsXJsFwFffNaBOQOwhZoACYDCdcSCO7G6AaZh9S3/yqJM5GLYPessW95vny1y5T+l\nE7FsUlBZYtcNPJIf1tpDDz20KMbcYfh/u0ZmyEQXvM3LR3XMdUUdClE4L8kbJU6+KXCUUBbyZZO0\ns1AVQKfUWFtBRpUFUSrxqSa+6+Q1RPnUd5iZY72fRNID2Fn3zcLUJA/6P0oewA2wU7xnIfyj5Ji1\nVA7rFMhkrZx1+zTX10VpL/Z4kf/yre9VF9as8Pffb7/99hkc1pnHdfGivadxYBEONMC+CPfasyM5\nkE55VCftQWDZtLBwZ8LN+ay1AVP8ZoMNmrfTNhgge6HmvvjFLw4+9KEPlYGNVZ9FlIXadYNIrFDd\nga0k0pMflilgnYUXqKkjVwAjgK3BPiBEtgHkLgBZZXEoC6zjFo/iLcUIYJSP1KW84jMQpCxAVJeU\nQ50A7e71/HZTLdMpizLaAA/yvHfLNYZ1kysKJcXCYJT7V1EG70cA34UXXlgWGorI9M1vfrP4B7te\ng7dYWlOeefJEFkUhUU8summvs6al/il26pkc4CEldJWkPVgkDBxTNuSdYsjybvaPUoxHccXRXyTy\nTPLFOuxLsWTZTNEkkp4vivLV5l9fk3rTZijg5MhCzJrwhi//MIrsUczInTxRdpVRu8mmjM4tUufD\n3j/tuci/ffLh2eRf34WnPqzElQe/UJ4rf9pP48Au50BzidnlArDq4qdDNhBlKtSeRcg1HbLBBND7\n+te/Xnwa+bGKcGCR1zve8Y598dunzSsQwGWERcsAxnoHwLBAAQTeLT/IuwMODRKO6wFl2neu+j75\nzRckJ32ECP98cIm1jQLEvcDCrlUSNx2uL8AIPgKqNvwdRb/61a+KxXGUKwD58DEoFk0LUPnf9oEi\n0wAGWSZv9pFndXXzzTcXv3FgzsLDf/iHf1g6WPI+xLXMQm6fnOeWItwknpJzpA4AN/Vi81+7WwQM\nAdkUQ24h88oWFxXKNHml2FHUF8lTKeycP3hJdoFjM3H+azuApLoD5kfRqaeeWviPt5OINZ+b17D0\nKLZf+MIXyvV8ZIz1HdAXR73LZ3kka+rf+gGzO4wSeCgvw8D6pPyt47o827QfbceMFFm1mWlQ3ssu\nu6zIqb64UeNA48DvONAAe5OElXPAwJLBJSAHwHGs40YGmQAJn6kX6stgxQrLUmn62f4Nb3hDsSyb\nRjUosU5ZuGWQBRgtLAMEdfTAEt9I09Gs0TVQ9z7gPGDd3vv7CNbxh2+vQd4U/J4t96FpyP1cFgyK\ngAceGsSXSfiP7/iPf/Imhvo04EV9iC6i/vjeDrMiAnX//M//XKyOFhH2hYbJcxTRyDKeCO3Hrx0Y\ns7BO+FCy6B7brOS9yB5o9oEv6bPK+pgTf+vkzX2Rb/UeGZ/33dJDCdGprqdxCfndU6/9BYz5v8sT\n3nQXH7/27vX+016sn+C+Qr7FTJ+0oBvQZByYZiaI6w8lgGx3STsQo5xriD5QKE7vtjhTNKua9J3a\n0AUXXDB49tlnS9hc/Ze2R7m1x98+9mlkVN6j8Aa0m0FUToocJS5jQl3udtw4sFs50AD7bq35NZdb\nBx0goaMG1rP5XwN3A4xNZ21g97ERFiabQRTAM5B6jhUMADLg86O1sSjziQRMkrZ3I+eka8uAVg9q\n84CoVbMSIDZLMA9AMiByOcJHoA3A8pXIRYnVW31QCtSVr3/i/awKAevqK6+8UoAFv+9hzyccJDeg\nPgG7Wp7xORuZQ5E17hZAta9fkt/MHAFlZBX/JpF3saQKefniiy8WcIb/Bx10UAF1+++/f5H1vFua\n5JqMR86dk6dFZJzPtTwAhPHTn5T3+rr8Aer4ME/IxjqtdRybpapdtvBUv2UGkMJkwXfIgm8LS80s\nTSJGCaEXhykCznMz+spXvrKvXzz55JPL4uGkSx7w0oJNijj5Sn3UkWBS53muT/soHNoNPlCUlItL\njHUuPvSF34vKbJ/K3PLSOLAIBxpgX4R77dmZORCQk87a4GfgyT7AXcLpqDPo5H8XcEizu0nHuZBn\nAtQNArb8lz7qpptnt3MP2JgxMEUuEse8ebTAk5sMZQfoBRTn8RUGsCkP8oX4AftgE1/eeYkrAncf\n+eKy1C2jAR3AUWdmTdRbXyhyFxmW1yiikeVa9oA81lCzR8ILKhNFhzURMDRzRAEFXiilQPqrW37V\nlDbKETkQ8YWLkM/M88X2vuQj75JugHrkHM+6vJ2Vj+qJ+xq3jmEzIuPS4xvOcqpMFDzKY9reuOe2\n81oXsLP+CuVp5u6iiy4qvFePQj6+tBV1Sv3xU+ePPokeeuih8vGgyMm4+ynZrP4oda3euVuRJe9U\nt8B6rOvqfdH6HpenRa/V5SDvQLv2YwaCbOuvzNb1vRyL8qE93zgwLQcaYJ+WU+2+pXEgHbW9wQrY\nCeCxzznXuzRqABp1L0Bg0+l3Qbrz0huVZvfd6/4PyFrYB5QBAMqwCOGrxaA2BCTu2XJrmAY0GUw9\nx0VJOgCE5+cB/cPKIG3uTPyYWQy7FDeMPn7RNfIcuQ1gt488pzy1PLJWs5JTgGyUKgqV2QuyCrgD\ngFGKAHuuTd5Tb9Imw9L2XL3lfcuQcf7XrOPqXV5mIYs2uXXJN39riz03gShKkUfKEjn10TcL5Lmp\npE/RHiiclGsA2geBzPJNImsP+J5PQ9LGO/JGrih02q/1EmZYKGixrsvXNO16mveu8h5lIRO1lV27\n4Qr05je/uSykTVmWIcOrLEtLu3Fg1RyYLX7UqnPT0t8VHNDx2nTW9gaWAGoDUUCOfTp0e9soSprZ\nB6hIN+nnnL37UPaj0t2u8wZj1kiW60WicNT5V26AF7hgxeIqw2oHBIzyvTWQmvZn5VUfQAvABkwu\nkwB1C1cBInnxnpr4aHPrcV3+uUL1hSJz2UfeyF5Ae2Q6QNt517lRsDTn2W6ZIvfZU5zqduA57wtw\nzD5yPyrd7nsm/adEkBd1o66mJXmNUkK5M0tEAd0UIpOhiy++uMidbziIy65diHJFLs042VOwuHRM\nq5CcccYZZU2AcLOTyMwM5YEMqXNWde3AwvzIQOpfvW8CRT7JK4VDO7Hha74S61qjxoHGgS28stWh\njkZBjUONA2vgABGsNwOS/wE3+V/fU2crg5O9gSt7x/X/DA6ezTN1On05BsostATquB4AAasgAAMI\nA8ot5gUGDPjIu4F0oMQx66Hrs7pBzJJv71Fu5eeP2wXlQCP/af6t/Nn7SLWMktsAEHvli0znvnnK\nUMs3MNPdIvPLknF5tTiYK4vFkt16GVUGbg6s6maKKGAi1wBlm0TPPPNMWSdASc0iWfmnZKlT7Qef\nyWwWqpolcY6LkxkRMeXH1YWZFYpbN3Rkl09coUSRca/QjRbTyxdgi68Jl0oeyMCmEPnSLrQPfLRx\nn7IYWWhS3xTQL21SmTaF9y2fm8WBZmHfrPrakbk1mNl03EjHHEBT713Lf8ehDIZJZ9jevbkvz/Vx\nb9DiBmPQAkpXBdaVHZgApCzq5O5gARsgwlWDJRvgAgwAdSB51WRQFrISaLfQmLISBcK7AUXWXRZb\nLjLdeNWrzt806Uf2yKljsgxAASTKEsBe77sy7X8o6dX7pFnvHdvch7JPOovsyQJLMxeFacG6+80Q\nkSXyM4tVfpG8LvvZWNivuOKK18jixz/+8fItB1Zv/uxcUfhb2ywyZnG35kCkINe4Ndm6/ONiZuHw\nJLCuXFGarCMhSxYf+36Fevc/QH2Zdb9sfg5LT34jy8qhD8SzQw45pMxkCMmrbGlTw9Jo5xoHdgMH\nmoV9N9TyBpaxBi05zn5UcTJQZe+++njUc305r3yAqsGb/+u00+rLyL/FdT7eAqQjLi/CaK7ii5OT\n8ht/dYC8G8qRVZOV3d7CSwN8n0mddjdg3bmA9vp6tyzkt94Cyrv7yHn23XTm/S/0oFj4YoBPO6sB\nhFIC1Y3668YPnzcv2/GcD2B95zvfKd91qN/PZU37JIPcY4YR4EmWgXcgHlF8Kcri5f/bv/1bAetZ\nTDosje45IR9FqOFbf/rpp5eFmZtsXU/50h7wjJLHYOFjUFdffXXplwB48rRs+c77275xYBM4sDnz\nZpvAzZbHpXGgC1JigbGftNXPLi1Da0gIyAHWTXOvC6wbKAEG0/3AelwWTElzQXF93QSov3ErNB6w\n85//+Z+veT1LG0uvAR1o6TuRxcirvNvw2CaEJSBi484wbMv17PMs8GJL2pH5ZfKDUkSJ8x7uLJMo\n94vuwU9dRJ9NBuvKy8J+1VVXva7oFNqTTz558OSTTw5e3XIdG0b4BpyLrc6dhfWduwyefve73y0z\nSLOAde8QpxygFZHGDJR6j1yRBf83kSK/yqA89tziuORx3YuSu4lla3luHFgWB/7gki1aVmItncaB\nVXMgHfuw/arfvcr0DUxcD0yb81FdB5mu57ZgQAQEucOw7APMgIrzLO8WGgKM6yQ+83x7WSeBvnwm\nXh64FQA+rnHpWXfe5uHDMHkN2M4+QKX+3z3nmrSyd7wq4p9NgQTWuUaNI8qdkI/81a2HYFknUzuB\n+O0PI0qlCC2Audjp44hyRqb37NlT1oGQZ6FVPUsxpYBOQ+4VWvKee+4pioAvzUobryMr06TT13sy\n2wSgU2Dvv//+oqCb7VO+tKO+5r/lq3FglRxoLjGr5G5Lu3FgCg4YhMXnNqCLCAOMrZKAcNZp7g4G\nRSH6KAr1ew2c/Nrdx3IKnIgwY9BcFwEx/NkN3qy18hpiZRSbnY8/C+YqgWveuZv2Yq2z5g5zS+ry\ngeLHakxmRBwSxWe3EKDOjY3yWCuV05SffHvOLNLzzz9f4vPrByaRD3Cde+655cNK/Lu1C6Ad+N/0\ndqCtWwQftxjKidkJvvoU84D2STx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CaWHg2972trGgmxUWYDXLoKzAKovSKkHlsDxv9zkK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q0ibd3ZiY9LrxqVZ7eIsYvxWH0Jatft0O5ldPLqD8ooySuy0dOYiEczt0oedlXJ4nt1btlT\n2L9N2eq7SXTbWZj2GErSa8VXHL1LXpy9zDNHBn+3Sz12JsExcSWOVpfgm4VHUV5nF19D9MYqUrCm\n5+Mbjz8m2qt4eE5HCO37AEufaQQ8e9ip/IMtZaI3R3tb3V0lJkPQfdKae2i89whp9Ly9WXs7RrG9\npS+leHsL93ZMS1IIUpMqiR52MSGGew+R6+1flYvWxsx//uQq+aa1ufffzeZRj1OzW3/T311l9FD4\nKr9JrGvTa34GTBOmjC6Pq24GNT+zVH/CTy8Fo+fNyE9w+Qx2VJt82Gp5s+6uGGX/6iqgv62J9bAP\ndIkJL+rr/S52mhRmPGG4x1mx29WzaB7XMR4xHJcJuAgMO4cHxeI64NoSn9CGvX1GoxjDg84+MXFQ\nj1j6Bga9X69LGhzo0+O5UqE03YOffJgjuqXrKUNF9CPLX5nU5b7Wfq71ydBNlq98DYsJkzSWAqUW\nZFpuF+s7Hc5c/VmYXmb3FsF1zI2V/zpyXTRFW3re+vq83wMH+7Q20yPWvmrZX061NtfnNHjKyL74\ni5N+6nY8bd0zT8ODA/rvhPIykZJ4Spzc/jS9JkzHqwmnOV0E4lOEjfnuw7AdsePIvkKkbH3fT1bc\ne1Ipol+vBH4kBX5KpOnR7T7Sd1F3UyV62IW9vPSwUCXetMnDwr/djJFBlwWjlk4s5i1cGHiSppiB\njbAXfc76CPvRhEzCAtxsP/kVbNyr9QjZckuw07YKc2fHoflYOraKenIF3btB+V7xwUF4N3j8E769\nGwSJT/zCDcKH7QD21P4ETz2+FaUCe92RrZiXMAfDBzdMgRtLB34kxg5kinT9BVuZHae2jaMbPnYu\nthwqw/NHVkob13f+SvaRwahNf7nkc9FNIBaxnp20qsDiE5rPB7y4KHmurwuVAG0dn5iMeI9mGkuO\npd2Cn3yY4wWUrh9Z/spkTsfbtp9rR5fHqwAfrMl931z3X7KPtPrPPYdDUrQFWz+71FsirmMBsXJF\nn9KtMfIWnyzazCQy5K3NCZdCPviLhHzwllkYI6/+shkrGn6yZ+P3d0GIz3n+6kKcHIufmQRisSmv\nAJYjG8XnpVLsMOuDCohpMMubYryKa4KNXvyyRlMsVdTgr8vx8oVirDCZ0HS+/HO3ZK6YnaTt/9mB\nOfMXut+c3WKOfyd+kUU316nCC7/vxWoxqYYr9OLFo2TII6x81qYEKd0hvPLzBinTml+DUy5ns4hL\nEd9MXR87ZZy5N25CFvYKA6FyZN4mPhHKo7txx0qXyVHw+SRiWeoWHEu9F188/jDWZIpP8Bfeki7B\nPHQHmZvA/gR6u0uE9WtlYqDyRVxxuffvr5cuXcLixVcGlqw51uB7GND34y8LND9mAbzNBJhAZBIY\nQvVTouODgu0h3Dyff/8aDP4bKAH2EhMoKY43OQJzN6C0QPdh6k1SEL0SeBPv65gay75j5a04drZT\nRhvqrsXX7yEbTxGE94LlQkOc1KhzTZLvv0EaYe87AW9nNLX77b4+w2tD97njuM+bC4gp9G4w1HlG\n+JU/jtZeNTo/1vXxY+CS4Ue//WQeYsRI/tSck0b+vZXSfOwd4QFoyNGP/n7X4hjy/FKiXUGTZhzc\ntw979uzxuuwT57bcMt8s3m27+XgGYtIOoLa508jfiKMdxTvX6V9ugCTD643bpbzDBJhAVBIYxmVX\nWWGxWFH05TuD1PkSlaC4UD4IsMLuAwwfDj6B1Tv3y+mylWTV0yj345chM5d6d0Uo3Yo5McsRk7Bo\n3C7ENAGB/3X11tqxY90ixIhJlhIWrDdmriv76p3y017y6kyUpWtySzPXICGGJmlKExMOxWDOAtFD\nfs9etE94RohkZPxfMVBJhnKsW5QkJnpKxfKYJKzfpX1dECPskT5qgJJ+ybhX8Vh1hxjrL4L9yD1I\nSM0QkxQtx4I1mR596y7Bq+75kmtHbH1h0yq3/WDxGX7bjkP7M7F0XoKc7CotNQYrqXddBFvaKn1m\nUAdeLNc+LNe98bacIdstMx47ar6W/RuXICFpDubMcS1rixs9Ygdnd3igS4zu2o/1lkWirSyXE3LF\nJS2BXp3Q6tPV+oKTKkthAkwgfAkkYvPhWpw/X4udfl72wzf/nLPpJsAK+3TXQLSmr1uQuBUvfgUK\n6ouMQxs+8SFjmzakV4IsXWnXVUfySlBVkqXF82ad4O0YxfaWvibF7a/sYbcVoV74XpduZHX/uEI9\nRFlDF7YZbhO0UedVBXpepIeFKohB5CKQh4UDbh4W3BIJYEcbYV8B3e06aIS91gdOI+zrce6gD9tt\nX+UfI82Fm/INH/I0X3Y5uQ6w5qKkyDtr5d1AivXj3WCyfBKv34SiLM2hr72uCmQWTyGrqBrP7Fyh\n7Yi+qZs26m9PXl1V6NFU7KtVm3I/TnvXxnsMXhgdZUJHLBlFKMlNN/z512kNRciyIr/CT31OKDW+\niAkwASbABKKdQAyNWY32QnL5IosAmS2QqUJsfDKSEzU7vxG5b7b5G8EIWU2IwU/mo1TSkRFh5uBl\nEIo8TgNXTBe4HRMC+/sdwuwiVgzISh4l16BoxBPJiAEpiYnxvuMaFwW+MSRMNhx6GZLF4B1Tdk1C\ntPIHNljKdJnH5siQA/00MwUNzElOlGmNZk0XdSIvZpEcMCW8G+AZf4Mtg8CH8uUQ+aK6SKQBTF4g\nDA0NiWwHl70HniDsjsAh2tSQqM9YwThRZxwEwSyCCTABJsAEZhABVthnUGVzUZnARAn0nyvEnDU0\nYMqC6q5GbOABUxNFydcxASbABJgAExg3ATaJGTcyvoAJzDQC7N1gptU4l5cJMAEmwATCi4CXD83h\nlUHODRNgAtNNQHk3AB5k7wbTXRmcPhNgAkyACcxAAmwSMwMrnYvMBJgAE2ACTCD0BBxoPvsyejEX\nq25Zxq4MQw+cU4hiAqywR3HlctGYABNgAkyACUyEQOvpYhz+6R+w+PZsPLp5hY/B72NIdpxDatIa\n4VPLioaBWqyOVE+mI/1obXwFr158C++++zdcNuejWPGpVUiZH6kFGqPe+HRYEmCTmLCsFs4UE2AC\nTIAJMIHpItCPF/btQin5ln3/M9hpKOwOtDd34P24JFyfsnDs6efFhHgLZBGSXBOgTVeRJphu77lj\nuG3NDq9zVORWtuDg5pQJSubLmMD4CPCg0/Hx4thMgAkwASbABKKcQCI+sVmbmdr6kTmusjouYLvF\nAsvSTJxX00S7zkbhVj+ezdKVdUs6iioqUVagTTpHhT10z0M42x+FxeYihSUB7mEPy2rhTDEBJsAE\nmAATmC4CsUjddwrOfR7pJ8zSe8wXYFaCx7mo3E3E2r0FKJmzCQ9uSNHNgjbjs5+6BgvWk5vbOrzy\nWj9uWZ0claXnQoUXAe5hD6/64NwwASbABJgAEwiIQPvpQqSlZaDwTLt7/KF2FGZnICO7GO00wRyF\nETqWhuzCWjExXSdOHMhG6vLlWL48FRk5xWjqFRPOmULnmWJkpKWh8DTJHkJt8QHkPFaIchmnHLse\nzsOBvBzkFdeKs+MLjs4mnCjOE/JTtTykpiI7rxjnupWkIZwpzEFGRgaOne0eJbz77DF5Lsec9kgv\nao8XSpnLqVypacg5cByt5h5wE4Pe7nPIE+lT3NScEzBHcyUYixVb9iDbUNa1M/PXbYSaazlibX1c\nheStSCFAM51yYAJMgAkwASbABCKLgL3ERjOVO61Fje4ZH2h0CoMWcc7mbBjQTxnH6Li3JcvZMugS\n0+gmu89Z5PUaklPg7HNd5r412OgUiq3MR6Oej8G2Sh/pa/EapLBBZ0W6nkdbiVMVQRM+4Cyxaecs\nuTXaoeE2Z75Fjz8qnzZnfY+eLZ8Mcp0d7jn3u9dSkaWXweqsUbL9XsEnmcDkCXAPe6S8WXE+mQAT\nYAJMgAm4EdC8lCS5HRM7cTBcKIpNLZiO0QFbfiXaenrQUJGrRyjFsRc69W0SYZadjIyuDrQ0Vgp/\nLxQsKGtoQVuLWDq2Y1wGIe+/KyVkFVTC3tGDgcEBtNWI1wEZqvBUVbPYisfGrAL90FH8ulfblH97\nX8bRKm0/5/Or5Ma5J7djPw2QFbmraOjAsNOJvpZqvRe8Co8cPqNd4MHAmluGhoZqlFVshMlSX4tr\n+uvobsW5piacO1uLY3lpWLq1VJ61FXwN6+aaIvImEwghAbZhDyFcFs0EmAATYAJMINwIZJU04lj2\nCpmtxVtyUVZ9CJnC1uV8x9vi2EKv2U2evxDJcwZ0G3Yx8HR5ChbHe43q92D8sm0YHt6GWJP2kZi6\nE/X5p7Bufx0cA8Py+uQbNyELe1Eq/LNU/LQVG7Zp3ljaf/6c7rElH9Zl4qViqBlP7a2T1+RWl2PL\n6vna9Skb8HRDAcrX7IX9UD068ze4lyyrAlUHt8jXktWr/WXZgRMbl2KHfCFwj/flzHUTc3fpLob3\nmEBABEw/mYDicyQmwASYABNgAkwgYgnYkP2ApqxrRUjEyvXCcKW8HKN66keVUVOmAeEihjYnoLCT\nSFLWRxy9aHm9FV097wGXAa+9Ri8LphCbgvQCK0qFMl5++DSeFAr7XGFp/uJ/6L3bJXfoCrjKE9BQ\n/xOceOty/E0Xc+ki9dZTuIC3BsWriPG5wYr6JzRlXTvv728iNhwuQ/6ve3D55ZfQ/tJJlFZp2vu6\neXGoaBnAlhTta4Q/KXyOCUyWACvskyXI1zMBJsAEmAATiCQCnsr2panMvEMMKH0IG/eWj5noqru/\nBFDvuX0vftm5B5tnncNR2ZluwRc3WOT1jtdf1gfCCp8th3YIvy3ewoDH2NAkfGAc2s/C1G3Yl6rk\n7sPjTSdw28qtsqd/67/+CHee2maYIKlYvGYCwSbANuzBJsrymAATYAJMgAlMJYHLPRMzupI9T0z7\nfvvJrxjKui23BDUNjbC3CLOX3ZoCbs5g/OLbUaAfrqprRvtvfqaZw1gfxC0LNY078UPXG3b1FY0d\n6OroQFtbm9vS0VWFFUHsBJ+7YgvKisSwXgoDPeP2kqNdyH+ZwPgIjOMdc3yCOTYTYAJMgAkwASYQ\negJvdPa4JdJ//iWj19ntRNB2hPY7oXeCIbzy8waZC2t+DU65uq0Rl0Kauaeh+Fxs2iss2cUgz/LM\ndHFWO5/10B2uga6JV+imPHZhqDMH8xcGUTP3w+u9oQH9bDzbsfvhxKeCR4B72IPHkiUxASbABJgA\nE5gyAnF6z7r90F4cb9I8iXcKH+Vz1uwITR6EKY2wXhfBjtbXKb0h9PZqR+ThgP5oSvfbfX1Gz3T3\nueO4b0e516tTNt6n96Ardd2K9PWLXXHjr0Wq5roGO1bux7le5ctdRBnpR+vZ0zjTNNqXu0uAny0x\noDUjJgZ5x2vR2a/kjqC9thjr9IGuWHAVZsQcUn4w8ampIcAK+9Rw5lSYABNgAkyACQSVQModW4WD\nRQp2ZK6cIyYBisGidWZlPVGODQ1aogmzca2e3lbLHMTEJGDe9h8ZivfY6cRj1R27NQlH7kFCqpjc\nKW05FqzJ1PvOvUhIvglfMmYpEufTv4RVbn4kk5FZVKZfeARr5iUgVUz4lCYmRYqJm4Ol62zY+NOL\nXgQHcGj4fXSJaIcy12PRnAQ5ydTymDgsWb9Lv9iCqsfvnejY2wAywFGYgIsAK+wuFrzFBJgAE2AC\nTCByCMzdgOr6El1pF2q77LxOR2VNlXCJSMExPsuVUbbwQoT5WOxiPGZKTyZx9QflKtA/Czflo7pA\n18DrylFOHlesuSgp0nLslp4UGo/blU92sZ+feesoBTlx2Tb02UWZ9Z72uqoqVFVpw0+ttixUpl0X\naPbc4yWuxNHqEqRb9dcie53xYmFNz0d9xzls0m3p3S/kPSYQfAIxNPdS8MWyRCbABJgAE2ACTGBq\nCAyhX5imjAhr6uS5yZpN9ciI3Df7O4c8Rm4VRw9fGxkaEW4aY0322OJ6sgJxO6aXRpzo7xemMLHx\nSE6emM34yJAD/Q6RgC6DckR5iBXpeYbO03lYZDskDgs79sFnsMyPO8khRz8cVBZRxsTERJH90fI8\n5QeyT/l1iPwS43ghN9FLPgORw3GYwEQJsMI+UXJ8HRNgAkyACTABJhBiAr0oTp2HXaLD3JJbjfMH\nN4Q4PRbPBMKTAJvEhGe9cK6YABNgAkyACcx4AkPtP5fKOoF4aPPNM54HA5i5BILzrWjm8uOSMwEm\nwASYABNgAqEiIDzTWMlKP/1B3BlMZ+qhyi/LZQIhIsAmMSECy2KZABNgAkyACTABJsAEmEAwCLBJ\nTDAosgwmwASYABNgAkyACTABJhAiAqywhwgsi2UCTIAJMAEmwASYABNgAsEgwAp7MCiyDCbABJgA\nE2ACTIAJMAEmECICrLCHCCyLZQJMgAkwASbABJgAE2ACwSDACnswKLIMJsAEmAATYAJMgAkwASYQ\nIgKssIcILItlAkyACTABJsAEmAATYALBIMAKezAosgwmwASYABNgAkyACTABJhAiAqywhwgsi2UC\nTIAJMAEmwASYABNgAsEgwAp7MCiyDCbABJgAE2ACTIAJMAEmECICrLCHCCyLZQJMgAkwASbABJgA\nE2ACwSDw/wH5BwaEpb29cAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {
"image/png": {
"width": 500
@@ -254,10 +246,8 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 6,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -266,7 +256,7 @@
""
]
},
- "execution_count": 9,
+ "execution_count": 6,
"metadata": {
"image/png": {
"width": 500
@@ -328,7 +318,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 7,
"metadata": {
"collapsed": true
},
@@ -341,11 +331,9 @@
"def load_mnist(path, kind='train'):\n",
" \"\"\"Load MNIST data from `path`\"\"\"\n",
" labels_path = os.path.join(path, \n",
- " '%s-labels-idx1-ubyte' \n",
- " % kind)\n",
+ " '%s-labels-idx1-ubyte' % kind)\n",
" images_path = os.path.join(path, \n",
- " '%s-images-idx3-ubyte' \n",
- " % kind)\n",
+ " '%s-images-idx3-ubyte' % kind)\n",
" \n",
" with open(labels_path, 'rb') as lbpath:\n",
" magic, n = struct.unpack('>II', \n",
@@ -362,12 +350,54 @@
" return images, labels"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Important Note**\n",
+ "\n",
+ "Some readers experienced issues with the `load_mnist` function above as certain decompression tools renamed the files from *-labels-idx1-ubyte* to *-labels.idx1-ubyte*. To avoid this problem altogether, you the modified function above will directly load the dataset from the `gz` archives using Python's `gzip` module."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 8,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import struct\n",
+ "import numpy as np\n",
+ "import gzip\n",
+ " \n",
+ "def load_mnist(path, kind='train'):\n",
+ " \"\"\"Load MNIST data from `path`\"\"\"\n",
+ " labels_path = os.path.join(path, \n",
+ " '%s-labels-idx1-ubyte.gz' % kind)\n",
+ " images_path = os.path.join(path, \n",
+ " '%s-images-idx3-ubyte.gz' % kind)\n",
+ " \n",
+ " with gzip.open(labels_path, 'rb') as lbpath:\n",
+ " lbpath.read(8)\n",
+ " buffer = lbpath.read()\n",
+ " labels = np.frombuffer(buffer, dtype=np.uint8)\n",
+ "\n",
+ " with gzip.open(images_path, 'rb') as imgpath:\n",
+ " imgpath.read(16)\n",
+ " buffer = imgpath.read()\n",
+ " images = np.frombuffer(buffer, \n",
+ " dtype=np.uint8).reshape(\n",
+ " len(labels), 784).astype(np.float64)\n",
+ " \n",
+ " return images, labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -378,16 +408,14 @@
}
],
"source": [
- "X_train, y_train = load_mnist('mnist', kind='train')\n",
+ "X_train, y_train = load_mnist('mnist/', kind='train')\n",
"print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))"
]
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 10,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -398,7 +426,7 @@
}
],
"source": [
- "X_test, y_test = load_mnist('mnist', kind='t10k')\n",
+ "X_test, y_test = load_mnist('mnist/', kind='t10k')\n",
"print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))"
]
},
@@ -411,16 +439,14 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 11,
+ "metadata": {},
"outputs": [
{
"data": {
"image/png": 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ASgoJCgAQJRIUACBKJCgAQJRIUACAKJGgAABRIkEBAKJEggIARIkEBQCIEgkKABAlEhQA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\n/wHTK4KPiGYqugAAAABJRU5ErkJggg==\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -429,7 +455,6 @@
],
"source": [
"import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
"\n",
"fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True,)\n",
"ax = ax.flatten()\n",
@@ -453,16 +478,14 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 12,
+ "metadata": {},
"outputs": [
{
"data": {
"image/png": 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KHeGiShIiIhJVwnoPasyYMfTs2RPwpuoG4+px79699r6WGVv95ptvqFq16lm/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\nxEn/D4pZX87LFMIKAAAAAElFTkSuQmCC\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -498,21 +521,21 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 13,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
- "#np.savetxt('train_img.csv', X_train, fmt='%i', delimiter=',')\n",
- "#np.savetxt('train_labels.csv', y_train, fmt='%i', delimiter=',')\n",
- "X_train = np.genfromtxt('train_img.csv', dtype=int, delimiter=',')\n",
- "y_train = np.genfromtxt('train_labels.csv', dtype=int, delimiter=',')\n",
+ "# np.savetxt('train_img.csv', X_train, fmt='%i', delimiter=',')\n",
+ "# np.savetxt('train_labels.csv', y_train, fmt='%i', delimiter=',')\n",
+ "# X_train = np.genfromtxt('train_img.csv', dtype=int, delimiter=',')\n",
+ "# y_train = np.genfromtxt('train_labels.csv', dtype=int, delimiter=',')\n",
"\n",
- "#np.savetxt('test_img.csv', X_test, fmt='%i', delimiter=',')\n",
- "#np.savetxt('test_labels.csv', y_test, fmt='%i', delimiter=',')\n",
- "X_test = np.genfromtxt('test_img.csv', dtype=int, delimiter=',')\n",
- "y_test = np.genfromtxt('test_labels.csv', dtype=int, delimiter=',')\n"
+ "# np.savetxt('test_img.csv', X_test, fmt='%i', delimiter=',')\n",
+ "# np.savetxt('test_labels.csv', y_test, fmt='%i', delimiter=',')\n",
+ "# X_test = np.genfromtxt('test_img.csv', dtype=int, delimiter=',')\n",
+ "# y_test = np.genfromtxt('test_labels.csv', dtype=int, delimiter=',')\n"
]
},
{
@@ -527,18 +550,16 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"source": [
"## Implementing a multi-layer perceptron"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 8,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -553,49 +574,38 @@
" Parameters\n",
" ------------\n",
" n_output : int\n",
- " Number of output units, should be equal to the\n",
- " number of unique class labels.\n",
- "\n",
+ " Number of output units, should be equal to the\n",
+ " number of unique class labels.\n",
" n_features : int\n",
- " Number of features (dimensions) in the target dataset.\n",
- " Should be equal to the number of columns in the X array.\n",
- "\n",
+ " Number of features (dimensions) in the target dataset.\n",
+ " Should be equal to the number of columns in the X array.\n",
" n_hidden : int (default: 30)\n",
- " Number of hidden units.\n",
- "\n",
+ " Number of hidden units.\n",
" l1 : float (default: 0.0)\n",
- " Lambda value for L1-regularization.\n",
- " No regularization if l1=0.0 (default)\n",
- "\n",
+ " Lambda value for L1-regularization.\n",
+ " No regularization if l1=0.0 (default)\n",
" l2 : float (default: 0.0)\n",
- " Lambda value for L2-regularization.\n",
- " No regularization if l2=0.0 (default)\n",
- "\n",
+ " Lambda value for L2-regularization.\n",
+ " No regularization if l2=0.0 (default)\n",
" epochs : int (default: 500)\n",
- " Number of passes over the training set.\n",
- "\n",
+ " Number of passes over the training set.\n",
" eta : float (default: 0.001)\n",
- " Learning rate.\n",
- "\n",
+ " Learning rate.\n",
" alpha : float (default: 0.0)\n",
- " Momentum constant. Factor multiplied with the\n",
- " gradient of the previous epoch t-1 to improve\n",
- " learning speed\n",
- " w(t) := w(t) - (grad(t) + alpha*grad(t-1))\n",
- " \n",
+ " Momentum constant. Factor multiplied with the\n",
+ " gradient of the previous epoch t-1 to improve\n",
+ " learning speed\n",
+ " w(t) := w(t) - (grad(t) + alpha*grad(t-1))\n",
" decrease_const : float (default: 0.0)\n",
- " Decrease constant. Shrinks the learning rate\n",
- " after each epoch via eta / (1 + epoch*decrease_const)\n",
- "\n",
- " shuffle : bool (default: False)\n",
- " Shuffles training data every epoch if True to prevent circles.\n",
- "\n",
+ " Decrease constant. Shrinks the learning rate\n",
+ " after each epoch via eta / (1 + epoch*decrease_const)\n",
+ " shuffle : bool (default: True)\n",
+ " Shuffles training data every epoch if True to prevent circles.\n",
" minibatches : int (default: 1)\n",
- " Divides training data into k minibatches for efficiency.\n",
- " Normal gradient descent learning if k=1 (default).\n",
- "\n",
+ " Divides training data into k minibatches for efficiency.\n",
+ " Normal gradient descent learning if k=1 (default).\n",
" random_state : int (default: None)\n",
- " Set random state for shuffling and initializing the weights.\n",
+ " Set random state for shuffling and initializing the weights.\n",
"\n",
" Attributes\n",
" -----------\n",
@@ -604,8 +614,8 @@
"\n",
" \"\"\"\n",
" def __init__(self, n_output, n_features, n_hidden=30,\n",
- " l1=0.0, l2=0.0, epochs=500, eta=0.001, \n",
- " alpha=0.0, decrease_const=0.0, shuffle=True, \n",
+ " l1=0.0, l2=0.0, epochs=500, eta=0.001,\n",
+ " alpha=0.0, decrease_const=0.0, shuffle=True,\n",
" minibatches=1, random_state=None):\n",
"\n",
" np.random.seed(random_state)\n",
@@ -642,9 +652,11 @@
"\n",
" def _initialize_weights(self):\n",
" \"\"\"Initialize weights with small random numbers.\"\"\"\n",
- " w1 = np.random.uniform(-1.0, 1.0, size=self.n_hidden*(self.n_features + 1))\n",
+ " w1 = np.random.uniform(-1.0, 1.0,\n",
+ " size=self.n_hidden*(self.n_features + 1))\n",
" w1 = w1.reshape(self.n_hidden, self.n_features + 1)\n",
- " w2 = np.random.uniform(-1.0, 1.0, size=self.n_output*(self.n_hidden + 1))\n",
+ " w2 = np.random.uniform(-1.0, 1.0,\n",
+ " size=self.n_output*(self.n_hidden + 1))\n",
" w2 = w2.reshape(self.n_output, self.n_hidden + 1)\n",
" return w1, w2\n",
"\n",
@@ -661,15 +673,15 @@
" def _sigmoid_gradient(self, z):\n",
" \"\"\"Compute gradient of the logistic function\"\"\"\n",
" sg = self._sigmoid(z)\n",
- " return sg * (1 - sg)\n",
+ " return sg * (1.0 - sg)\n",
"\n",
" def _add_bias_unit(self, X, how='column'):\n",
" \"\"\"Add bias unit (column or row of 1s) to array at index 0\"\"\"\n",
" if how == 'column':\n",
- " X_new = np.ones((X.shape[0], X.shape[1]+1))\n",
+ " X_new = np.ones((X.shape[0], X.shape[1] + 1))\n",
" X_new[:, 1:] = X\n",
" elif how == 'row':\n",
- " X_new = np.ones((X.shape[0]+1, X.shape[1]))\n",
+ " X_new = np.ones((X.shape[0] + 1, X.shape[1]))\n",
" X_new[1:, :] = X\n",
" else:\n",
" raise AttributeError('`how` must be `column` or `row`')\n",
@@ -681,30 +693,24 @@
" Parameters\n",
" -----------\n",
" X : array, shape = [n_samples, n_features]\n",
- " Input layer with original features.\n",
- "\n",
+ " Input layer with original features.\n",
" w1 : array, shape = [n_hidden_units, n_features]\n",
- " Weight matrix for input layer -> hidden layer.\n",
- "\n",
+ " Weight matrix for input layer -> hidden layer.\n",
" w2 : array, shape = [n_output_units, n_hidden_units]\n",
- " Weight matrix for hidden layer -> output layer.\n",
+ " Weight matrix for hidden layer -> output layer.\n",
"\n",
" Returns\n",
" ----------\n",
" a1 : array, shape = [n_samples, n_features+1]\n",
- " Input values with bias unit.\n",
- "\n",
+ " Input values with bias unit.\n",
" z2 : array, shape = [n_hidden, n_samples]\n",
- " Net input of hidden layer.\n",
- "\n",
+ " Net input of hidden layer.\n",
" a2 : array, shape = [n_hidden+1, n_samples]\n",
- " Activation of hidden layer.\n",
- "\n",
+ " Activation of hidden layer.\n",
" z3 : array, shape = [n_output_units, n_samples]\n",
- " Net input of output layer.\n",
- "\n",
+ " Net input of output layer.\n",
" a3 : array, shape = [n_output_units, n_samples]\n",
- " Activation of output layer.\n",
+ " Activation of output layer.\n",
"\n",
" \"\"\"\n",
" a1 = self._add_bias_unit(X, how='column')\n",
@@ -717,35 +723,36 @@
"\n",
" def _L2_reg(self, lambda_, w1, w2):\n",
" \"\"\"Compute L2-regularization cost\"\"\"\n",
- " return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) + np.sum(w2[:, 1:] ** 2))\n",
+ " return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) +\n",
+ " np.sum(w2[:, 1:] ** 2))\n",
"\n",
" def _L1_reg(self, lambda_, w1, w2):\n",
" \"\"\"Compute L1-regularization cost\"\"\"\n",
- " return (lambda_/2.0) * (np.abs(w1[:, 1:]).sum() + np.abs(w2[:, 1:]).sum())\n",
+ " return (lambda_/2.0) * (np.abs(w1[:, 1:]).sum() +\n",
+ " np.abs(w2[:, 1:]).sum())\n",
"\n",
" def _get_cost(self, y_enc, output, w1, w2):\n",
" \"\"\"Compute cost function.\n",
"\n",
+ " Parameters\n",
+ " ----------\n",
" y_enc : array, shape = (n_labels, n_samples)\n",
- " one-hot encoded class labels.\n",
- "\n",
+ " one-hot encoded class labels.\n",
" output : array, shape = [n_output_units, n_samples]\n",
- " Activation of the output layer (feedforward)\n",
- "\n",
+ " Activation of the output layer (feedforward)\n",
" w1 : array, shape = [n_hidden_units, n_features]\n",
- " Weight matrix for input layer -> hidden layer.\n",
- "\n",
+ " Weight matrix for input layer -> hidden layer.\n",
" w2 : array, shape = [n_output_units, n_hidden_units]\n",
- " Weight matrix for hidden layer -> output layer.\n",
+ " Weight matrix for hidden layer -> output layer.\n",
"\n",
" Returns\n",
" ---------\n",
" cost : float\n",
- " Regularized cost.\n",
+ " Regularized cost.\n",
"\n",
" \"\"\"\n",
" term1 = -y_enc * (np.log(output))\n",
- " term2 = (1 - y_enc) * np.log(1 - output)\n",
+ " term2 = (1.0 - y_enc) * np.log(1.0 - output)\n",
" cost = np.sum(term1 - term2)\n",
" L1_term = self._L1_reg(self.l1, w1, w2)\n",
" L2_term = self._L2_reg(self.l2, w1, w2)\n",
@@ -758,32 +765,24 @@
" Parameters\n",
" ------------\n",
" a1 : array, shape = [n_samples, n_features+1]\n",
- " Input values with bias unit.\n",
- "\n",
+ " Input values with bias unit.\n",
" a2 : array, shape = [n_hidden+1, n_samples]\n",
- " Activation of hidden layer.\n",
- "\n",
+ " Activation of hidden layer.\n",
" a3 : array, shape = [n_output_units, n_samples]\n",
- " Activation of output layer.\n",
- "\n",
+ " Activation of output layer.\n",
" z2 : array, shape = [n_hidden, n_samples]\n",
- " Net input of hidden layer.\n",
- "\n",
+ " Net input of hidden layer.\n",
" y_enc : array, shape = (n_labels, n_samples)\n",
- " one-hot encoded class labels.\n",
- "\n",
+ " one-hot encoded class labels.\n",
" w1 : array, shape = [n_hidden_units, n_features]\n",
- " Weight matrix for input layer -> hidden layer.\n",
- "\n",
+ " Weight matrix for input layer -> hidden layer.\n",
" w2 : array, shape = [n_output_units, n_hidden_units]\n",
- " Weight matrix for hidden layer -> output layer.\n",
+ " Weight matrix for hidden layer -> output layer.\n",
"\n",
" Returns\n",
" ---------\n",
- "\n",
" grad1 : array, shape = [n_hidden_units, n_features]\n",
- " Gradient of the weight matrix w1.\n",
- "\n",
+ " Gradient of the weight matrix w1.\n",
" grad2 : array, shape = [n_output_units, n_hidden_units]\n",
" Gradient of the weight matrix w2.\n",
"\n",
@@ -797,8 +796,10 @@
" grad2 = sigma3.dot(a2.T)\n",
"\n",
" # regularize\n",
- " grad1[:, 1:] += (w1[:, 1:] * (self.l1 + self.l2))\n",
- " grad2[:, 1:] += (w2[:, 1:] * (self.l1 + self.l2))\n",
+ " grad1[:, 1:] += self.l2 * w1[:, 1:]\n",
+ " grad1[:, 1:] += self.l1 * np.sign(w1[:, 1:])\n",
+ " grad2[:, 1:] += self.l2 * w2[:, 1:]\n",
+ " grad2[:, 1:] += self.l1 * np.sign(w2[:, 1:])\n",
"\n",
" return grad1, grad2\n",
"\n",
@@ -808,12 +809,12 @@
" Parameters\n",
" -----------\n",
" X : array, shape = [n_samples, n_features]\n",
- " Input layer with original features.\n",
+ " Input layer with original features.\n",
"\n",
" Returns:\n",
" ----------\n",
" y_pred : array, shape = [n_samples]\n",
- " Predicted class labels.\n",
+ " Predicted class labels.\n",
"\n",
" \"\"\"\n",
" if len(X.shape) != 2:\n",
@@ -831,14 +832,12 @@
" Parameters\n",
" -----------\n",
" X : array, shape = [n_samples, n_features]\n",
- " Input layer with original features.\n",
- "\n",
+ " Input layer with original features.\n",
" y : array, shape = [n_samples]\n",
- " Target class labels.\n",
- "\n",
+ " Target class labels.\n",
" print_progress : bool (default: False)\n",
- " Prints progress as the number of epochs\n",
- " to stderr.\n",
+ " Prints progress as the number of epochs\n",
+ " to stderr.\n",
"\n",
" Returns:\n",
" ----------\n",
@@ -853,7 +852,7 @@
" delta_w2_prev = np.zeros(self.w2.shape)\n",
"\n",
" for i in range(self.epochs):\n",
- " \n",
+ "\n",
" # adaptive learning rate\n",
" self.eta /= (1 + self.decrease_const*i)\n",
"\n",
@@ -863,13 +862,15 @@
"\n",
" if self.shuffle:\n",
" idx = np.random.permutation(y_data.shape[0])\n",
- " X_data, y_data = X_data[idx], y_data[idx]\n",
+ " X_data, y_enc = X_data[idx], y_enc[:, idx]\n",
"\n",
" mini = np.array_split(range(y_data.shape[0]), self.minibatches)\n",
" for idx in mini:\n",
"\n",
" # feedforward\n",
- " a1, z2, a2, z3, a3 = self._feedforward(X[idx], self.w1, self.w2)\n",
+ " a1, z2, a2, z3, a3 = self._feedforward(X_data[idx],\n",
+ " self.w1,\n",
+ " self.w2)\n",
" cost = self._get_cost(y_enc=y_enc[:, idx],\n",
" output=a3,\n",
" w1=self.w1,\n",
@@ -891,11 +892,87 @@
" return self"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "**Note**\n",
+ "\n",
+ "In the fit method of the MLP example above,\n",
+ "\n",
+ "```python\n",
+ "\n",
+ "for idx in mini:\n",
+ "...\n",
+ " # compute gradient via backpropagation\n",
+ " grad1, grad2 = self._get_gradient(a1=a1, a2=a2,\n",
+ " a3=a3, z2=z2,\n",
+ " y_enc=y_enc[:, idx],\n",
+ " w1=self.w1,\n",
+ " w2=self.w2)\n",
+ "\n",
+ " delta_w1, delta_w2 = self.eta * grad1, self.eta * grad2\n",
+ " self.w1 -= (delta_w1 + (self.alpha * delta_w1_prev))\n",
+ " self.w2 -= (delta_w2 + (self.alpha * delta_w2_prev))\n",
+ " delta_w1_prev, delta_w2_prev = delta_w1, delta_w2\n",
+ "```\n",
+ "\n",
+ "`delta_w1_prev` (same applies to `delta_w2_prev`) is a memory view on `delta_w1` via \n",
+ "\n",
+ "```python\n",
+ "delta_w1_prev = delta_w1\n",
+ "```\n",
+ "on the last line. This could be problematic, since updating `delta_w1 = self.eta * grad1` would change `delta_w1_prev` as well when we iterate over the for loop. Note that this is not the case here, because we assign a new array to `delta_w1` in each iteration -- the gradient array times the learning rate:\n",
+ "\n",
+ "```python\n",
+ "delta_w1 = self.eta * grad1\n",
+ "```\n",
+ "\n",
+ "The assignment shown above leaves the `delta_w1_prev` pointing to the \"old\" `delta_w1` array. To illustrates this with a simple snippet, consider the following example:\n",
+ "\n"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a & b True\n",
+ "a & b False\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "a = np.arange(5)\n",
+ "b = a\n",
+ "print('a & b', np.may_share_memory(a, b))\n",
+ "\n",
+ "\n",
+ "a = np.arange(5)\n",
+ "print('a & b', np.may_share_memory(a, b))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "(End of note.)\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -909,15 +986,14 @@
" alpha=0.001,\n",
" decrease_const=0.00001,\n",
" minibatches=50, \n",
+ " shuffle=True,\n",
" random_state=1)"
]
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 16,
+ "metadata": {},
"outputs": [
{
"name": "stderr",
@@ -929,10 +1005,10 @@
{
"data": {
"text/plain": [
- "<__main__.NeuralNetMLP at 0x109d527b8>"
+ "<__main__.NeuralNetMLP at 0x113abb780>"
]
},
- "execution_count": 20,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -943,16 +1019,14 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 17,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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dc024dJhrdY0fH0ZU2G23ZOOULFDHiWYpSYm0XG1tGKFgzhzYa68waeS99xbf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\nRERSS0lKRERS6/8D1eIjaZLpzHsAAAAASUVORK5CYII=\n",
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9TMSbWofryCPD86/UYGQzuPDC7M/BMi1fHp6j9euXe75GjgzzTW65ZXi/YkUY\np1dq4uzdF0vkS/KFalJS5BYuDH+5X3NNqA3Mnev+7rvuRxyRvTax/fbJ12ia82vWLPcnn6yd7u7+\n29+G/VNOcV+61H327Lr/3U47LX1dLiZNCufvuWc6Ddw/+ijsr1uX/X7g/pOfhOMpS5eG2uIll7gf\nfXTueYjT11+79+sX9omxJqVpkUQKrGvXMM/g0KFhPa3NNw/raN1xR/bzt9qqoNkrOQMHwo9/nP1Y\nqvbkHnoZbrFFeH/OOTB7dvVzlywJ28WLQ+0rVRP78Y9D7Tiz5yKEmlc2ixaF7ZgxYbt6NYwaVb1D\nziOPVB+X17kz3Hdf6C3ZlF6kn38OM2ZUT1u8GH7729rnZq47lmKWbgFYsKBAYwPjin5JvVBNSpqx\nVM0q9dd+u3buf/iDe48eyddISvm1557Z02fODJ1hMtOOOSZsx4xJ14a+852wffTRsP3xj92vuirs\nb7dd+hkYuL/5Znof3G+6Kb1fUZHeX7o0/XNRM18rVoRt377ul12WPm/VqlArX7HC/YUXQlrqGd7k\nyenr33gjfc1TT4W09993r6ys/pmff+4+f374nm+9lb7ePX2/E0/0WGtSsdw0yZeClDR3a9emf0Gm\nZPZwy3wtXNj4X8x6Nf114IG5n7tkSdhus026KbC+13nnhSa++fNrH7vrrurvJ00KQe2MM8L7c88N\n22efzX7vMWPcDz7Yfeed3bfYovqxV19Nd0qZPj1shwypfk7fvjXvibvH8ztdHSdEmoFZs8LErz17\nhvkFu3cP6e7pZqJXXgkdByCM57rmmmTyKqXjoIPClGLrp0UPc6YgJeUgFZhSQWqHHcJM5IccEgYT\nu8NJJ4WlSSBMC/XZZ6G34RtvJJZtKVmlOXefiDTB/Pnp/dQMF5ndmx94IMyZt/fe8PDDoSZ27LH1\n3/M738l/PkWaQjUpkWbo/vvh5JPDFEHXXQdHHx0CzBtvhF5jV1xR97WpWti224YA1qVLupdbVVW4\nJ8C998Jpp6Wv69w5rMwrUpua+3KmICVSv5kzoW/fsNDjlCnwxRehGzyEZsJRo+CII6LH4YRZ5M3g\nsMNCoBKpLb4gVccUjSJSqvr0CavtbrddeN+jBzzzTDooHX54aBpMOfHE9P4774QZy6+8Mlw/dWrB\nsi1lSs+kRMrQV1+lO1WYwaGHwo9+lD5e1wDiwYOhffuw37dv7YGhNZXrOluSPwpSImWoU6fGr7F1\n/vlh26IfozUvAAAKvElEQVRFmMfOHTp2hP33Ty8rD9CrF7z/fu3re/asewYIkZrU3CciDdK2bdi2\nyPgT94svwvpOZ58dmgMzex5CmGR1+PDQSWOjjcJzsU8+gU02CR0+7rqrYNmXZkYdJ0Skwd58MzQJ\n9uhRPb2qKrwyFyQ0g+9/PywKWZff/S501mjXDu68E26/PZ58S1zUuy9nClIixcUsdHV/7bXczp8/\nPyxtMn166GkIobv9tdeG2ltqtg2Ap56Co47Kf56loRSkcqYgJVJcrrkmzPI+ZEjDr/3oI2jdOnTS\nSJk3LzxP69gxHFu5MtTArrsOLr64+vWZ00ZJnNQFXUSaqWHDGn/t1lvXTqvZxNi2bbr7/Pz58Ktf\nheB1440h7euvQ49EM5g7NwS0rl3T13fsWPeyGpI89e4TkZJxww3Qv38YC3brrSEt1WUeQnDq0iUE\nMgizc7zwQtj/5pv0PoTa36pV6QB46aVhRo+//jV9zg9+kHvettkmbDfdNGz33DN97Jhjcr9PQ1x7\nbfb03/ym4fd6882m5aXR4ppePalX+EoiIrkbPz6937u3++DBYcmUujz8sPtjj7n/5z/uhxxSfdmK\niy5y/8Uv3BcscB80KKQNGpS+dsmS8HlVVeHYX/4S0i+9NH1ux45hBd4pU9wnTkzf+7nn3HffPfvy\nG+C+777hvqtWuS9fHtaD6tbN/Wc/cz/5ZPezzgrnjR8f8jdiRPrabCtAX365+w03hH139802C/tt\n2qQ/T0t1NJCeSYlIoT3wQHhuts8+1dNTXe333z/7dW++Cd/73vqfm335ZVhBd+ut0+eawYsvhg4p\nV1wRxqR161a7ObSmX/4SbroJOnQI79u3D6sKV1aG2fInTIDTTw9b97Ai8dVXh2d+EAZw9+8f9h95\nBE44AdRxogEUpESknKxcCXfcARdc0LjrZ80KvSZbtw6Dsd3Dsi4LFoQmz/q4h2d+nTopSOVMQUpE\npHEyZ8FvCDOtJyUiIjFrTICKWxFmSUREJFCQEhGRoqUgJSIiRUtBSkREipaClIiIFC0FKRERKVoK\nUiIiUrQUpEREpGgpSImISNFSkBIRkaKlICUiIkVLQUpERIqWgpSIiBQtBSkRESlaClIiIlK0FKRE\nRKRoKUiJiEjRUpASEZGipSAlIiJFS0FKRESKloKUiIgULQUpEREpWgpSIiJStBSkRESkaClIiYhI\n0VKQEhGRoqUgJSIiRatZBSkzO8jMPjSzj8zs4qTzIyIi8Wo2QcrMWgB/Ag4Etgd+YmbbJpur5qOy\nsjLpLBQllUt2KpfsVC6F12yCFDAYmOHus9x9DTASOCLhPDUb+s+VncolO5VLdiqXwmtOQaonMDvj\n/ZwoTURESlRzClIiIlJmzN2TzkNOzGwPYIS7HxS9Hwq4u19X47zm8YVEREqIu1sc921OQaolMB3Y\nH/gCGAf8xN2nJZoxERGJTaukM5Ard19nZmcDYwjNlPcqQImIlLZmU5MSEZHyUzIdJ8phoK+Z3Wtm\n881sUkZaFzMbY2bTzewFM9sw49gwM5thZtPMbEhG+i5mNikqq5sz0jcws5HRNW+Z2ZaF+3aNZ2a9\nzOwlM/vAzCab2TlRelmXjZm1MbN3zGxiVC7Do/SyLpcUM2thZhPMbFT0vuzLxcxmmtn70c/MuCgt\n2XJx92b/IgTbj4HeQGvgPWDbpPMVw/fcG9gJmJSRdh1wUbR/MXBttL8dMJHQpNsnKp9UzfkdYLdo\nfzRwYLR/JnBHtH8cMDLp75xjufQAdor2OxKeXW6rsnGA9tG2JfA2Ybxh2ZdLlN/zgb8Bo6L3ZV8u\nwH+BLjXSEi2XxAslTwW7B/BcxvuhwMVJ5yum79qb6kHqQ6B7tN8D+DBbGQDPAbtH50zNSD8e+HO0\n/zywe7TfEliQ9PdtZBn9EzhAZVOtTNoD7wK7qVwcoBcwFqggHaRULvApsHGNtETLpVSa+8p5oG83\nd58P4O7zgG5Res0ymRul9SSUT0pmWX17jbuvA/5nZl3jy3r+mVkfQm3zbcJ/rLIum6hJayIwDxjr\n7uNRuQDcBPwWyHwor3IJ5THWzMab2elRWqLl0mx690nO8tkTJpZxD3Exs47AE8C57r48y5i5sisb\nd68CdjazzsBTZrY9tcuhrMrFzA4F5rv7e2ZWUc+pZVUukb3c/Qsz2xQYY2bTSfjnpVRqUnOBzAdw\nvaK0cjDfzLoDmFkP4MsofS6wRcZ5qTKpK73aNRbGpXV290XxZT1/zKwVIUA95O7/ipJVNhF3XwpU\nAgehctkLONzM/gs8AuxnZg8B88q8XHD3L6LtAkKz+WAS/nkplSA1HuhnZr3NbANCG+iohPMUF6P6\nXx+jgFOi/ZOBf2WkHx/1pukL9APGRdX1JWY22MwMOKnGNSdH+8cAL8X2LfLvPkI7+C0ZaWVdNma2\nSaonlpm1A34ITKPMy8XdL3H3Ld19K8Lvipfc/WfA05RxuZhZ+6g1AjPrAAwBJpP0z0vSD+ry+MDv\nIEKvrhnA0KTzE9N3fBj4HFgFfAacCnQBXoy++xhgo4zzhxF63EwDhmSkD4p++GYAt2SktwEei9Lf\nBvok/Z1zLJe9gHWEXp0TgQnRz0PXci4bYMeoLN4DJgGXRullXS41ymhf0h0nyrpcgL4Z/4cmp36P\nJl0uGswrIiJFq1Sa+0REpAQpSImISNFSkBIRkaKlICUiIkVLQUpERIqWgpSIiBQtBSmRLMxsXbSM\nw8Roe1Ee793bzCY38R7D6zmWmfd/ZqT3MbO3o+UTHolm6RApahonJZKFmS11984x3bs38LS7D2zE\ntVcRlkE4AKgC7nP3STXOyZp3M3sUeMLdHzezPwPvuftfGvUlRApENSmR7LJOfGlmn5rZddGCbm+b\n2VZRem8z+7eZvWdmY82sV5TezcyejNInmtke0a1amdldZjbFzJ43szbR+edYWLzxPTN7uObnu/ul\nhNk0TgRurxmg6ss7sB/wj2j/AeCoHMtCJDEKUiLZtavR3HdMxrHFUS3odiA1V+BtwP3uvhNh+qrb\novRbgcoofRfggyi9P3Cbu+8ALAH+X5R+MWEBx52AX9XMlJn9gbBuz9+Bs8xsxyx5b2Nm75rZm2Z2\nRHTdxlG+q6Jz5gCbN6hERBKg5j6RLOppMvsU+IG7z4ye6Xzh7pua2QKgh7uvi9I/d/duZvYl0NPd\n12Tcozcwxt23id5fBLRy96vNbDTwNWEG6n+6+9d15O9yd/99Hcc287DcQl/CBJ77AUuBt929f3RO\nL2B0Y5ocRQpJNSmRhvM69htiVcb+OtJrux0K/IlQ6xpvZln/j9YVoKJjqeUWPiUsz7Gzuy8ENsy4\nXzktZyPNmIKUSHb1LcZ2XLQ9Hngr2n8D+Em0/1PgtWj/ReDX8O0quanaWV3339LdXyEszd0Z6Nig\nTJttFC1Xg5ltQpghfmp0+GXC8ghQfckFkaKlLqgi2bU1swmEYOLA8+5+SXSsi5m9D6wkHZjOAe43\nswuBBYRlVADOA+4ys9OAtcCZhKXca9XAombCv0WBzAhLHCxtYL4HAH8xs3WEP0KvdvcPo2NDgZHR\nc62JwL0NvLdIwemZlEgDRM+kBnkzWGVVpBSouU+kYfRXnUgBqSYlIiJFSzUpEREpWgpSIiJStBSk\nRESkaClIiYhI0VKQEhGRoqUgJSIiRev/A8HcazOgjkmHAAAAAElFTkSuQmCC\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -960,8 +1034,8 @@
}
],
"source": [
- "%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
+ "\n",
"plt.plot(range(len(nn.cost_)), nn.cost_)\n",
"plt.ylim([0, 2000])\n",
"plt.ylabel('Cost')\n",
@@ -973,9 +1047,9 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 18,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -986,16 +1060,14 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 19,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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Jc+ZES6xVq7RrXpMUUgUUUiI1xD1aT2vWwNy58Mor8MEHMa5rypQYz9WlS8xr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\n",
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Y7O6X1fCdhduS2tmXX8YM6s2awebNcNppEVjDhsVs6mZxverSS6v/jO3bI/j6989fvUVE\natCQLanaXpNqslP33qfUohXm7mdW89Jx1Rx/LXBtFeVvAoNqUc/i0qJFdr9Vq7i3atKkylMsrVpV\n82fcey+cdx6sXw9tdzngUkSkqNQ2pJ41s+eA+5PnPwQmN06Vyljmxt9ly+APf4j9zz+v+T2bNsX2\nqKOie1BEpITsau6+vmY23N1/BfwRGJw8XgX+lIf6lZctW2K7eDFclIwLufXW6M5bujRW/c01Y0Z2\n2qX33stfPUVE8mRXLanfk8wI4e6PAY8BmNmg5LXvNWrtyk3//tC+Paxbly374gs44IDs8wUL4Pnn\nYfbs7P1VENemRERKTI0DJ8zsdXc/vJrX3nH3grtOVFQDJ6ozciQ8+2zMULHzXH//8z9w0klVz05R\n7L9bREpCPoegd6jhtVYNUQGpwjPPwCuvwN//XnlNKoCPP9Z9VCJSNnbVkrofeNHdb9+p/EJiSPoP\nG7l+dVYSLamd9e4dy4Bs2RJrVi1fXvn18eNhwoQYRGEN8o8XEZF6a8iW1K5Cai/gceBL4M2k+DCg\nBfDPyY22BaUkQyrj0Ufh1OQe6iFDsqP5Jk2KLsB334VbboHzz4fDDkuvniJS1vLW3efuK9z9SOBq\nYFHyuNrdv1mIAVXyRo2KGdQhggiiBXXssbF/8MGxAvDtScP35Zdh27bY/+STuM4Fce3qrbfyV28R\nkXqq9dx9xaKkW1IZGzfGdaklS6B79yi75JIYrg5wyinw8MNxs/CkSTBiBPz0p3DnnRFQM2fC178e\nAZZ747CISAPI+9x9UmAyAycyAQVw002x/dGPYon6WcliyDffHNMuNUn+Uy9enL2navbs6r9j82YN\naxeR1CmkSkUmhLp3h4ULI4jMYqQgxLpVAFddlZ3MdskSeO217HWuXHvtBf/6r41fbxGRGiikSs1h\nh8Vs6+PHZ69bQQy6APjf/82WffopvPpqvJY72/rvfhdLhrz0Ul6qLCJSHV2TKkVt28Z1q7lzo0V0\n110wZ060sKZMgccei8EVI0bENaoxY2Do0Owj04Lq3TtaYv36QevW8K1vwfTp2VaZiEgV8jYEvRgp\npICnnoLOneGb36xc/pvfwL//O6xZA//xHzFwYscOuOaar37GwoVw4IFxberooyu3wMr9/IpIjRRS\nNVBI7cLWrdC8ebSucrsDcz38cFynOuUUmDjxq68//TR06ADDhzduXUWkKGl0n9Rf8+axPe+8ql9/\n4YXsQIrBg2N78MGVj/nud6sebCEi0sAUUuXsoIPiPqmXXoI//zmGth9zTPb1c8+Fiy+OLr+dNdEf\nHRFpfOruK2dr18Z6VH37xkCL+++Hf/mXrx53+eUx4m/sWNh//5h+6d574c0349rX2rWV79kSkbKm\na1I1UEjlyUUXxSjC66+P5zrnIpJQSNVAIZUn8+dHCyzj1VejNVVVi2revFgj68MP81c/EUmNBk5I\n+vbfP7bXXw99+sRw9/794x4siKHtmW7CV16JUBMRqSOFlNTfK6/ExLaZ1tPnn8MPfgDr1sHZZ0dA\n3XRTdhVh95gPcP369OosIkVFISX1d+SR0KoVdOoUzzMT0nboAPfdB3ffHTcOZya03bwZ/vY3aN8+\nFmjUBLYisgsKKdl9F1wQiy42aQI33JAtP+usmEswM2/gp5/GVE0Qw90vvDBC7JNP8l9nESkKGjgh\njWPHjgitX/8arr228ms9e8JHH8WaVvvsA5Mna3SgSAnRwAkpfJmbfQ8/PLZnnhnbXr2yo/2aNYsH\nZNe/yli/XsElImpJSSP78kuYNi0mqd2xI+YO3GOPmEl90KDo9ps7N1pUf/0r/NM/xaKNAwZEN+H3\nv5/2LxCROlJLSopHixYRUBCtqz32iP0994wpl+bOhVGj4rrUBRfEa5mJb//rvyrPvi4iZUctKUnP\n88/DCSfEvVXf/36sfTVkSKx5NXJkdlXhMWOgR48YFXjqqdCuXbr1FpEaacaJGiikisimTbGQ4uOP\nQ9euMZz9a1+L+62eegq+973ssXvuGV2EEydmy91h2bKYJHfvvdP5DSLyFeruk9LQunVMUtuzJ7Rs\nGcuHPPhg3D910knwyCMxiwVk76maMSO2GzZE92H37tFdKCIlSS0pKWzLlsWs6z/9KSxaBCefHNex\nOnWKGS8y1q+PCW9FJHVqSUn52HffuG41bRpMnx4tqfffj4AaMCCOadoUfv7zWDIEajeTxbPPwsqV\njVdvEWkQaklJ8XCP61ZbtsTzxx6L1tUjj8Df/x7Xtz79FJYsiUebNjFv4Nq1cc3KPYJuyJBodV14\nIdx+e6o/SaQUqSUl5cksrltlZmA/+ugY+XfTTfCjH8UKw6NHxwS3bdtGUN19d4wMbN48hrQfdVS2\nW/COO+IzN2xI7zeJSI3UkpLi9NprcMQR2efbt8Nzz8F3vhPBA3DccfDWW9G6glhN+Mc//upnPfQQ\nnHZa49dZpExoCHoNFFLCmWfGqL/MqsEZ//zPMdw9Y9KkGEUIcW9W377Qu3f+6ilSohRSNVBIyT9k\nWlTuMQDjk09i2ZDNm2H1ahgxIl675BK47bY4dvXq7NIjIlIvCqkaKKTkH26+GVatgquvjvuwevbM\nLhWyszVroqU1fz784Q8xorB58/zWV6REKKRqoJCSKs2bF4sxdulS/TETJsDYsXDQQbBgAbzwQrwn\nM/x9/PiYuklEaqSQqoFCSurNPR5r12a7/AYNgnfeyR7z5ZdVt7CWLoXrrouRhiJlTkPQRRqDWUy1\ntOeecMstcMwxEVB9+sTrxx8fS4lceSUsX175vS+9FNe1Bg+OkYci0iAUUiJVufjiGB3Yt2+sLHzZ\nZdHdt2MH/Pa3sf7VU09Fl+CXX8ZQ9+3bI9SuvTbK16xJ+1eIFD1194nU1VNPwe9+B1OnxvNLL4XZ\ns2NW9z33jKHtGfqzKGVI16RqoJCSvPj88xgF+OST0dXXvn2sKNy5M/zsZzGbBcQEufvsk25dRfJM\nIVUDhZTk1YYNsQjjM8/AiSdWfu2qq6Lb7/nnY52sjKuvhgMOiJuORUqQQqoGCinJu7VrY6j6zrZv\nj3uv1q2LARe//GVc27rzTvjJT+CPf8x/XUXyQCFVA4WUFJQFCyKcVqyIQRW5k9leeWXMyH766dnZ\nMURKgIagixSLPn1iSZEnn4wZ2CFGCE6dCk8/HS2rZ56JARb33Reztu/YUfkzvvgiBmaIlKHUWlJm\ntghYB+wAtrr7MDPrCDwI9AIWAaPdfV1y/DjgfGAbcJm7T6nmc9WSksK0dWt0/XXunC37059i1eGh\nQ2HmzGz59dfDL34R0zl17BjD2e+6K1pdLVvmv+4iddCQLalmDfEh9bQDqHD33JtJxgIvuPtvzexK\nYBww1swOBEYDA4HuwAtmdoDSSIpK8+aVAwpifaytW+Gjj2Jhxh/8AHr1gssvhyeeiGPWrImlRM47\nL+YevOaayp/hru5CKVlptqQWAoe5+6c5Ze8D33b3FWa2NzDV3QeY2VjA3X1CctwzwHh3n1bF5yq7\npPitWgVdu8JvfgP/9m/R5ff667H68F//GmtnXXNNrJU1dGjMjvHcc5oUVwpCqbSkHHjezLYDf3T3\nO4C93H0FgLsvN7OuybHdgFdz3rs0KRMpTV26wMaN0KpVPG/ZMq5pnX9+rEK8xx4xY/uqVXDYYfDm\nm9CiBRx+ONx4Y+UFIUWKWJohNdzdPzGzLsAUM/uACK5cahJJ+WrduvJzsxi+3qRJtJxefjnmC9y6\nNdbB+sUv4p6t0aNjccdZs2DkyOhi3L4dFi+O+7Z+9rOYyknXtqQIpBZS7v5Jsl1lZk8Aw4AVZrZX\nTnffyuTwpUCPnLd3T8qqNH78+H/sV1RUUFFR0bCVF0nT7bfH9owzojuwWbO4nvXww9EteNJJ0brK\n1aFDBNPnn0dY/fa3sGVLtL5EdtPUqVOZmpkmrIGlck3KzL4GNHH3jWbWGpgCXA0cC3zm7hOSgRMd\n3T0zcOI+4BtEN9/zQJUDJ3RNSgT47LNodV1xRYwe3HffmAHju9+Noe9f+1rMMdilC/zwhzEo49JL\ndU1LGkTR38xrZr2Bx4nuvGbAfe5+nZntCTxEtJoWE0PQ1ybvGQdcAGxFQ9BF6m7HDti8OeYTnDQJ\nxoyp/PpPfgLnnAOPPgoPPRTTOQ0YkE5dpagVfUg1JoWUSC0tWABt2kRr6sMPY2b3yZNjODzExLhn\nnx2zZDz9NPz973F9q2nT6GLM2LgxPkckoZCqgUJKZDdl7rt64gl45JHoOvz881gza/36aF1NnBjX\ntObPh1GjYNo0GDYs7ZpLgVBI1UAhJdJIZsyA6dMjwMaMia5DiNGGffvCdddB27YwYUK0ygYNSre+\nkhqFVA0UUiJ5sGBBhNS6dXDkkXDLLfCrX0VZp04RZN/5TrTALr885h488cQYwCElTyFVA4WUSIrm\nz49JdT/8MLoEb7kFFi6Mm4+7d4cDD4R+/WLI/NNPR9mNN8b+6NG6d6tEKKRqoJASKTCbNsX9WNdc\nA//937ByZaxivP/+ccPye+/FDck9e0JFBQweDD/+cUwLJUVJIVUDhZRIgXOP4fBNm8bzxYtjhOFH\nH8Ww97ffjnu8vv3tuMY1aFBMB9W3b7r1llpTSNVAISVSAtavjwlzH3sspnp6+OEYTWgWS5ece25s\n338/Zo0/6yzYb7+0ay0JhVQNFFIiJWjTJti2LeYgXLgQ/vKXGJQxZAi89FJ0I7ZtCwMHRsura1fo\n3TtaX3vsEa23gQPT/hVlQyFVA4WUSBnasiW6Dd9/P65xzZ0b174WLozXFi2C/v3jZuR9943uxqOO\nitZY//5w6KHZ7kfZbQqpGiikROQrvvwS3nkHli+P8Fq2DJYsgbVr4ZVXYr9ly2iZbdsWra9Ro+CA\nA2Keww4dItDat49BIC1bxjFSJYVUDRRSIlJn7nEdbNasCKGlS6MLcdGiuPdr7drsY+vWKGvZMlpm\nnTvHwI/OnWOC3syMHV26ZMMss808mjePlluTJultmzSJeuY+GohCqgYKKRFpdJlQW706HqtWxXbr\n1vjLfseOeL5lSyyfsmVL5f2tW+OY7dvT3brHI2Pn0DKrOsyqK2/SBIYNwyZPVkhVRyElIlIPmcDK\nDa+ayqorb9oU69SpJJaPFxGRQpHbGioghVUbERGRHAopEREpWAopEREpWAopEREpWAopEREpWAop\nEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREp\nWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAop\nEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREpWAopEREp\nWAopEREpWAopEREpWEUVUmZ2opm9b2ZzzezKtOsjIiKNq2hCysyaADcDI4CDgDPMbEC6tSoeU6dO\nTbsKBUvnpno6N9XTucmPogkpYBgwz90Xu/tW4AHglJTrVDT0P1T1dG6qp3NTPZ2b/CimkOoGfJzz\nfElSJiIiJaqYQkpERMqMuXvadagVMzsCGO/uJybPxwLu7hN2Oq44fpCISAlzd2uIzymmkGoKfAAc\nC3wCTAfOcPc5qVZMREQaTbO0K1Bb7r7dzH4OTCG6Ke9UQImIlLaiaUmJiEj5KZmBE+V+o6+ZdTez\nF83sPTN7x8wuTco7mtkUM/vAzJ4zs/Y57xlnZvPMbI6ZnZBe7RufmTUxsxlmNjF5rvMCmFl7M3s4\n+a3vmdk3dG6CmY0xs3fNbJaZ3WdmLcr13JjZnWa2wsxm5ZTV+VyY2SHJ+ZxrZr+v1Ze7e9E/iLD9\nEOgFNAdmAgPSrleez8HewNBkvw1x/W4AMAG4Iim/Ergu2T8QeIvo8t0vOX+W9u9oxPMzBvgrMDF5\nrvMSv/cvwHnJfjOgvc6NA+wLLABaJM8fBM4p13MDHAUMBWbllNX5XADTgMOT/cnAiF19d6m0pMr+\nRl93X+7uM5P9jcAcoDtxHu5ODrsbGJXsnww84O7b3H0RMI84jyXHzLoD3wHuyCnWeTFrBxzt7ncB\nJL95HTrzLLRhAAAENklEQVQ3GU2B1mbWDGgFLKVMz427vwys2am4TufCzPYG2rr768lx9+S8p1ql\nElK60TeHme1H/KvnNWAvd18BEWRA1+Swnc/ZUkr3nP0/4FdA7gVYnRfoDaw2s7uSrtA/mdnX0LnB\n3ZcB/wl8RPzOde7+Ajo3ubrW8Vx0I/5uzqjV39OlElKSMLM2wCPAZUmLaueRMWU1UsbMTgJWJK3M\nmu7bKKvzkmgGHALc4u6HAJuAsZT5nxkAM+tAtBR6EV1/rc3sLHRuatIo56JUQmop0DPnefekrKwk\n3RKPAPe6+5NJ8Qoz2yt5fW9gZVK+FOiR8/ZSPWfDgZPNbAFwP/BPZnYvsLzMzwvEv2Q/dvc3kueP\nEqFV7n9mAI4DFrj7Z+6+HXgcOBKdm1x1PRf1OkelElKvA33NrJeZtQBOByamXKc0/BmY7e435JRN\nBM5N9s8BnswpPz0ZsdQb6EvcIF1S3P3X7t7T3fsQfy5edPcfA09RxucFIOmq+djM+iVFxwLvUeZ/\nZhIfAUeYWUszM+LczKa8z41RuTeiTuci6RJcZ2bDknN6ds57qpf2qJEGHH1yIjGibR4wNu36pPD7\nhwPbiZGNbwEzknOyJ/BCcm6mAB1y3jOOGHkzBzgh7d+Qh3P0bbKj+3Re4rcOIf6RNxN4jBjdp3MT\nv/Wq5HfOIgYGNC/XcwP8DVgGbCEC/DygY13PBXAo8E7y9/QNtflu3cwrIiIFq1S6+0REpAQppERE\npGAppEREpGAppEREpGAppEREpGAppEREpGAppEQakJltT+bBeyvZXtGAn93LzN5pqM8TKQZFszKv\nSJHY5DEPXmPRjY1SVtSSEmlYVU5ia2YLzWxCsuDba2bWJynvZWb/ZWYzzez5ZFkRzKyrmT2WlL9l\nZkckH9Usma38XTN71sz2SI6/NFm0cKaZ/S0vv1QkDxRSIg2r1U7dfaflvLbG3QcDtwCZ+RVvAu5y\n96HE1DM3JeU3AlOT8kOIOfUADgBucveDgXXAD5LyK4lFL4cCP2usHyeSb5oWSaQBmdl6d29XRflC\n4Bh3X5TMVv+Ju3cxs1XA3u6+PSlf5u5dzWwl0M1jEc/MZ/QCprh7/+T5FUAzd7/GzCYTS208ATzh\n7psa/9eKND61pETyx6vZr4stOfvbyV5XPgm4mWh1vW5m+n9bSoL+IIs0rJoWVvxhsj0deDXZfwU4\nI9n/EfC/yf4LwMUAZtYkWeq9ps/v6e7/Qyxa2A5oU/eqixQeje4TaVgtzWwGESYOPOvuv05e62hm\nbwNfkA2mS4G7zOyXwCpiCQSAfwX+ZGYXANuAi4DlVNECS7oJ/5oEmRFLIKxvlF8nkme6JiWSB8k1\nqUPd/bO06yJSTNTdJ5If+tegSD2oJSUiIgVLLSkRESlYCikRESlYCikRESlYCikRESlYCikRESlY\nCikRESlY/x8Zue88KbAoRQAAAABJRU5ErkJggg==\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1008,64 +1080,105 @@
"plt.ylabel('Cost')\n",
"plt.xlabel('Epochs')\n",
"plt.tight_layout()\n",
- "plt.savefig('./figures/cost2.png', dpi=300)\n",
+ "#plt.savefig('./figures/cost2.png', dpi=300)\n",
"plt.show()"
]
},
{
"cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 20,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training accuracy: 97.74%\n"
+ "Training accuracy: 97.59%\n"
]
}
],
"source": [
"y_train_pred = nn.predict(X_train)\n",
- "acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]\n",
+ "\n",
+ "if sys.version_info < (3, 0):\n",
+ " acc = ((np.sum(y_train == y_train_pred, axis=0)).astype('float') /\n",
+ " X_train.shape[0])\n",
+ "else:\n",
+ " acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]\n",
+ "\n",
"print('Training accuracy: %.2f%%' % (acc * 100))"
]
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 21,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training accuracy: 96.18%\n"
+ "Test accuracy: 95.62%\n"
]
}
],
"source": [
"y_test_pred = nn.predict(X_test)\n",
- "acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]\n",
- "print('Training accuracy: %.2f%%' % (acc * 100))"
+ "\n",
+ "if sys.version_info < (3, 0):\n",
+ " acc = ((np.sum(y_test == y_test_pred, axis=0)).astype('float') /\n",
+ " X_test.shape[0])\n",
+ "else:\n",
+ " acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]\n",
+ "\n",
+ "print('Test accuracy: %.2f%%' % (acc * 100))"
]
},
{
"cell_type": "code",
- "execution_count": 48,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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9bUYCq1V1ZwRlIkZVjwI7gbtEJMkOwLdgjdl7cVrPc0Ukza6fdbFGEH7CWkkV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\n0Wg0QcX/BxwZsXUhaiL1AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "miscl_img = X_test[y_test != y_test_pred][:25]\n",
+ "correct_lab = y_test[y_test != y_test_pred][:25]\n",
+ "miscl_lab = y_test_pred[y_test != y_test_pred][:25]\n",
+ "\n",
+ "fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)\n",
+ "ax = ax.flatten()\n",
+ "for i in range(25):\n",
+ " img = miscl_img[i].reshape(28, 28)\n",
+ " ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
+ " ax[i].set_title('%d) t: %d p: %d' % (i+1, correct_lab[i], miscl_lab[i]))\n",
+ "\n",
+ "ax[0].set_xticks([])\n",
+ "ax[0].set_yticks([])\n",
+ "plt.tight_layout()\n",
+ "# plt.savefig('./figures/mnist_miscl.png', dpi=300)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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TgZ5hwl+O1aI6DPzLPjeD8GOa/bC61JlY3chnwvh9B3gdq9WahXVjbOTnHjat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\nVeTgwYPmBAhN08wp9grfuPrqq/nlL39JWVmZuZWWlpb7HkrYoqUEP8+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+ "image/png": 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9bUYCq1V1ZwRlIkZVjwI7gbtEJMkOwLdgjdl7cVrPc0Ukza6fdbFGEH7CWkkV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\npVQ7t2P2A6OBrKqJReR74CQw2Mv5FSCOf31CKTXKoc+2xf5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OtJyww302XtXXXZw/f97f\nckIa99lOZ8+e5dZbbw2gmtDHudqDxlo++ugj1/fExMSgnUQWnKocOGugw4YNC1oDQ5mEhASgchBy\ndu8NGDAgIJrCAf225eunsLDQL48uaIKToJ6V16BBA+699146deoUaClhydy5c5k7dy5gThlv3749\nsbGxALz33nuBlBZSNG/enFmzZrl+P/roo9x3332UlJQEUFVos3XrVpRSro/Gelq0aBFoCV4J6sB0\n+PBh5s6dS2RkZKClhCUjR45k5MiRbNiwATBbTvPmzSMnJ0e/ydYHDMOo9FxTQUEBPXr0cC2WqdEE\nIw8//HCgJXglqAOTRqPRaG48gnrgZsCAAXz11Vf07Nkz0FLCGmfLSVN/oqKiKi0yrLGO6dOn614T\ni3COHXfr1i2oh0hCYq28QBOua5EFCu2ntWg/rSWU18oLVnz1VHflaTQajSaoqHdXnp4pYy3aT2vR\nflqL9tN6tKfeqVdXnkaj0Wg0dqG78jQajUYTVOjApNFoNJqgQgcmjUaj0QQVOjBpNBqNJqjQgUmj\n0Wg0QcX/BxwZsXUhaiL1AAAAAElFTkSuQmCC\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1124,10 +1237,8 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 24,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1136,7 +1247,7 @@
""
]
},
- "execution_count": 13,
+ "execution_count": 24,
"metadata": {
"image/png": {
"width": 300
@@ -1166,10 +1277,8 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 25,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1178,7 +1287,7 @@
""
]
},
- "execution_count": 15,
+ "execution_count": 25,
"metadata": {
"image/png": {
"width": 400
@@ -1193,10 +1302,8 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 26,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1205,7 +1312,7 @@
""
]
},
- "execution_count": 17,
+ "execution_count": 26,
"metadata": {
"image/png": {
"width": 500
@@ -1251,10 +1358,8 @@
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 27,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1263,7 +1368,7 @@
""
]
},
- "execution_count": 23,
+ "execution_count": 27,
"metadata": {
"image/png": {
"width": 500
@@ -1278,7 +1383,29 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The following code requires NumPy >= 1.9.1. Your NumPy version is 1.11.0.\n",
+ "The following code requires SciPy >= 0.14.0. Your SciPy version is 0.17.0. \n"
+ ]
+ }
+ ],
+ "source": [
+ "from scipy import __version__ as scipy_ver\n",
+ "from numpy import __version__ as numpy_ver\n",
+ "\n",
+ "print('The following code requires NumPy >= 1.9.1. Your NumPy version is %s.' % (numpy_ver))\n",
+ "print('The following code requires SciPy >= 0.14.0. Your SciPy version is %s. ' % (scipy_ver))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
"metadata": {
"collapsed": true
},
@@ -1295,59 +1422,48 @@
" Parameters\n",
" ------------\n",
" n_output : int\n",
- " Number of output units, should be equal to the\n",
- " number of unique class labels.\n",
- "\n",
+ " Number of output units, should be equal to the\n",
+ " number of unique class labels.\n",
" n_features : int\n",
- " Number of features (dimensions) in the target dataset.\n",
- " Should be equal to the number of columns in the X array.\n",
- "\n",
+ " Number of features (dimensions) in the target dataset.\n",
+ " Should be equal to the number of columns in the X array.\n",
" n_hidden : int (default: 30)\n",
- " Number of hidden units.\n",
- "\n",
+ " Number of hidden units.\n",
" l1 : float (default: 0.0)\n",
- " Lambda value for L1-regularization.\n",
- " No regularization if l1=0.0 (default)\n",
- "\n",
+ " Lambda value for L1-regularization.\n",
+ " No regularization if l1=0.0 (default)\n",
" l2 : float (default: 0.0)\n",
- " Lambda value for L2-regularization.\n",
- " No regularization if l2=0.0 (default)\n",
- "\n",
+ " Lambda value for L2-regularization.\n",
+ " No regularization if l2=0.0 (default)\n",
" epochs : int (default: 500)\n",
- " Number of passes over the training set.\n",
- "\n",
+ " Number of passes over the training set.\n",
" eta : float (default: 0.001)\n",
- " Learning rate.\n",
- "\n",
+ " Learning rate.\n",
" alpha : float (default: 0.0)\n",
- " Momentum constant. Factor multiplied with the\n",
- " gradient of the previous epoch t-1 to improve\n",
- " learning speed\n",
- " w(t) := w(t) - (grad(t) + alpha*grad(t-1))\n",
- " \n",
+ " Momentum constant. Factor multiplied with the\n",
+ " gradient of the previous epoch t-1 to improve\n",
+ " learning speed\n",
+ " w(t) := w(t) - (grad(t) + alpha*grad(t-1))\n",
" decrease_const : float (default: 0.0)\n",
- " Decrease constant. Shrinks the learning rate\n",
- " after each epoch via eta / (1 + epoch*decrease_const)\n",
- "\n",
+ " Decrease constant. Shrinks the learning rate\n",
+ " after each epoch via eta / (1 + epoch*decrease_const)\n",
" shuffle : bool (default: False)\n",
- " Shuffles training data every epoch if True to prevent circles.\n",
- "\n",
+ " Shuffles training data every epoch if True to prevent circles.\n",
" minibatches : int (default: 1)\n",
- " Divides training data into k minibatches for efficiency.\n",
- " Normal gradient descent learning if k=1 (default).\n",
- "\n",
+ " Divides training data into k minibatches for efficiency.\n",
+ " Normal gradient descent learning if k=1 (default).\n",
" random_state : int (default: None)\n",
- " Set random state for shuffling and initializing the weights.\n",
+ " Set random state for shuffling and initializing the weights.\n",
"\n",
" Attributes\n",
" -----------\n",
" cost_ : list\n",
- " Sum of squared errors after each epoch.\n",
+ " Sum of squared errors after each epoch.\n",
"\n",
" \"\"\"\n",
" def __init__(self, n_output, n_features, n_hidden=30,\n",
- " l1=0.0, l2=0.0, epochs=500, eta=0.001, \n",
- " alpha=0.0, decrease_const=0.0, shuffle=True, \n",
+ " l1=0.0, l2=0.0, epochs=500, eta=0.001,\n",
+ " alpha=0.0, decrease_const=0.0, shuffle=True,\n",
" minibatches=1, random_state=None):\n",
"\n",
" np.random.seed(random_state)\n",
@@ -1384,9 +1500,11 @@
"\n",
" def _initialize_weights(self):\n",
" \"\"\"Initialize weights with small random numbers.\"\"\"\n",
- " w1 = np.random.uniform(-1.0, 1.0, size=self.n_hidden*(self.n_features + 1))\n",
+ " w1 = np.random.uniform(-1.0, 1.0,\n",
+ " size=self.n_hidden*(self.n_features + 1))\n",
" w1 = w1.reshape(self.n_hidden, self.n_features + 1)\n",
- " w2 = np.random.uniform(-1.0, 1.0, size=self.n_output*(self.n_hidden + 1))\n",
+ " w2 = np.random.uniform(-1.0, 1.0,\n",
+ " size=self.n_output*(self.n_hidden + 1))\n",
" w2 = w2.reshape(self.n_output, self.n_hidden + 1)\n",
" return w1, w2\n",
"\n",
@@ -1403,12 +1521,12 @@
" def _sigmoid_gradient(self, z):\n",
" \"\"\"Compute gradient of the logistic function\"\"\"\n",
" sg = self._sigmoid(z)\n",
- " return sg * (1 - sg)\n",
+ " return sg * (1.0 - sg)\n",
"\n",
" def _add_bias_unit(self, X, how='column'):\n",
" \"\"\"Add bias unit (column or row of 1s) to array at index 0\"\"\"\n",
" if how == 'column':\n",
- " X_new = np.ones((X.shape[0], X.shape[1]+1))\n",
+ " X_new = np.ones((X.shape[0], X.shape[1] + 1))\n",
" X_new[:, 1:] = X\n",
" elif how == 'row':\n",
" X_new = np.ones((X.shape[0]+1, X.shape[1]))\n",
@@ -1423,30 +1541,24 @@
" Parameters\n",
" -----------\n",
" X : array, shape = [n_samples, n_features]\n",
- " Input layer with original features.\n",
- "\n",
+ " Input layer with original features.\n",
" w1 : array, shape = [n_hidden_units, n_features]\n",
- " Weight matrix for input layer -> hidden layer.\n",
- "\n",
+ " Weight matrix for input layer -> hidden layer.\n",
" w2 : array, shape = [n_output_units, n_hidden_units]\n",
- " Weight matrix for hidden layer -> output layer.\n",
+ " Weight matrix for hidden layer -> output layer.\n",
"\n",
" Returns\n",
" ----------\n",
" a1 : array, shape = [n_samples, n_features+1]\n",
- " Input values with bias unit.\n",
- "\n",
+ " Input values with bias unit.\n",
" z2 : array, shape = [n_hidden, n_samples]\n",
- " Net input of hidden layer.\n",
- "\n",
+ " Net input of hidden layer.\n",
" a2 : array, shape = [n_hidden+1, n_samples]\n",
- " Activation of hidden layer.\n",
- "\n",
+ " Activation of hidden layer.\n",
" z3 : array, shape = [n_output_units, n_samples]\n",
- " Net input of output layer.\n",
- "\n",
+ " Net input of output layer.\n",
" a3 : array, shape = [n_output_units, n_samples]\n",
- " Activation of output layer.\n",
+ " Activation of output layer.\n",
"\n",
" \"\"\"\n",
" a1 = self._add_bias_unit(X, how='column')\n",
@@ -1459,35 +1571,36 @@
"\n",
" def _L2_reg(self, lambda_, w1, w2):\n",
" \"\"\"Compute L2-regularization cost\"\"\"\n",
- " return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) + np.sum(w2[:, 1:] ** 2))\n",
+ " return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) +\n",
+ " np.sum(w2[:, 1:] ** 2))\n",
"\n",
" def _L1_reg(self, lambda_, w1, w2):\n",
" \"\"\"Compute L1-regularization cost\"\"\"\n",
- " return (lambda_/2.0) * (np.abs(w1[:, 1:]).sum() + np.abs(w2[:, 1:]).sum())\n",
+ " return (lambda_/2.0) * (np.abs(w1[:, 1:]).sum() +\n",
+ " np.abs(w2[:, 1:]).sum())\n",
"\n",
" def _get_cost(self, y_enc, output, w1, w2):\n",
" \"\"\"Compute cost function.\n",
"\n",
+ " Parameters\n",
+ " ----------\n",
" y_enc : array, shape = (n_labels, n_samples)\n",
- " one-hot encoded class labels.\n",
- "\n",
+ " one-hot encoded class labels.\n",
" output : array, shape = [n_output_units, n_samples]\n",
- " Activation of the output layer (feedforward)\n",
- "\n",
+ " Activation of the output layer (feedforward)\n",
" w1 : array, shape = [n_hidden_units, n_features]\n",
- " Weight matrix for input layer -> hidden layer.\n",
- "\n",
+ " Weight matrix for input layer -> hidden layer.\n",
" w2 : array, shape = [n_output_units, n_hidden_units]\n",
- " Weight matrix for hidden layer -> output layer.\n",
+ " Weight matrix for hidden layer -> output layer.\n",
"\n",
" Returns\n",
" ---------\n",
" cost : float\n",
- " Regularized cost.\n",
+ " Regularized cost.\n",
"\n",
" \"\"\"\n",
" term1 = -y_enc * (np.log(output))\n",
- " term2 = (1 - y_enc) * np.log(1 - output)\n",
+ " term2 = (1.0 - y_enc) * np.log(1.0 - output)\n",
" cost = np.sum(term1 - term2)\n",
" L1_term = self._L1_reg(self.l1, w1, w2)\n",
" L2_term = self._L2_reg(self.l2, w1, w2)\n",
@@ -1500,32 +1613,24 @@
" Parameters\n",
" ------------\n",
" a1 : array, shape = [n_samples, n_features+1]\n",
- " Input values with bias unit.\n",
- "\n",
+ " Input values with bias unit.\n",
" a2 : array, shape = [n_hidden+1, n_samples]\n",
- " Activation of hidden layer.\n",
- "\n",
+ " Activation of hidden layer.\n",
" a3 : array, shape = [n_output_units, n_samples]\n",
- " Activation of output layer.\n",
- "\n",
+ " Activation of output layer.\n",
" z2 : array, shape = [n_hidden, n_samples]\n",
- " Net input of hidden layer.\n",
- "\n",
+ " Net input of hidden layer.\n",
" y_enc : array, shape = (n_labels, n_samples)\n",
- " one-hot encoded class labels.\n",
- "\n",
+ " one-hot encoded class labels.\n",
" w1 : array, shape = [n_hidden_units, n_features]\n",
- " Weight matrix for input layer -> hidden layer.\n",
- "\n",
+ " Weight matrix for input layer -> hidden layer.\n",
" w2 : array, shape = [n_output_units, n_hidden_units]\n",
- " Weight matrix for hidden layer -> output layer.\n",
+ " Weight matrix for hidden layer -> output layer.\n",
"\n",
" Returns\n",
" ---------\n",
- "\n",
" grad1 : array, shape = [n_hidden_units, n_features]\n",
- " Gradient of the weight matrix w1.\n",
- "\n",
+ " Gradient of the weight matrix w1.\n",
" grad2 : array, shape = [n_output_units, n_hidden_units]\n",
" Gradient of the weight matrix w2.\n",
"\n",
@@ -1539,8 +1644,10 @@
" grad2 = sigma3.dot(a2.T)\n",
"\n",
" # regularize\n",
- " grad1[:, 1:] += (w1[:, 1:] * (self.l1 + self.l2))\n",
- " grad2[:, 1:] += (w2[:, 1:] * (self.l1 + self.l2))\n",
+ " grad1[:, 1:] += self.l2 * w1[:, 1:]\n",
+ " grad1[:, 1:] += self.l1 * np.sign(w1[:, 1:])\n",
+ " grad2[:, 1:] += self.l2 * w2[:, 1:]\n",
+ " grad2[:, 1:] += self.l1 * np.sign(w2[:, 1:])\n",
"\n",
" return grad1, grad2\n",
"\n",
@@ -1559,11 +1666,13 @@
" for i in range(w1.shape[0]):\n",
" for j in range(w1.shape[1]):\n",
" epsilon_ary1[i, j] = epsilon\n",
- " a1, z2, a2, z3, a3 = self._feedforward(X, w1 - epsilon_ary1, w2)\n",
+ " a1, z2, a2, z3, a3 = self._feedforward(X,\n",
+ " w1 - epsilon_ary1, w2)\n",
" cost1 = self._get_cost(y_enc, a3, w1-epsilon_ary1, w2)\n",
- " a1, z2, a2, z3, a3 = self._feedforward(X, w1 + epsilon_ary1, w2)\n",
+ " a1, z2, a2, z3, a3 = self._feedforward(X,\n",
+ " w1 + epsilon_ary1, w2)\n",
" cost2 = self._get_cost(y_enc, a3, w1 + epsilon_ary1, w2)\n",
- " num_grad1[i, j] = (cost2 - cost1) / (2 * epsilon)\n",
+ " num_grad1[i, j] = (cost2 - cost1) / (2.0 * epsilon)\n",
" epsilon_ary1[i, j] = 0\n",
"\n",
" num_grad2 = np.zeros(np.shape(w2))\n",
@@ -1571,11 +1680,13 @@
" for i in range(w2.shape[0]):\n",
" for j in range(w2.shape[1]):\n",
" epsilon_ary2[i, j] = epsilon\n",
- " a1, z2, a2, z3, a3 = self._feedforward(X, w1, w2 - epsilon_ary2)\n",
+ " a1, z2, a2, z3, a3 = self._feedforward(X, w1,\n",
+ " w2 - epsilon_ary2)\n",
" cost1 = self._get_cost(y_enc, a3, w1, w2 - epsilon_ary2)\n",
- " a1, z2, a2, z3, a3 = self._feedforward(X, w1, w2 + epsilon_ary2)\n",
+ " a1, z2, a2, z3, a3 = self._feedforward(X, w1,\n",
+ " w2 + epsilon_ary2)\n",
" cost2 = self._get_cost(y_enc, a3, w1, w2 + epsilon_ary2)\n",
- " num_grad2[i, j] = (cost2 - cost1) / (2 * epsilon)\n",
+ " num_grad2[i, j] = (cost2 - cost1) / (2.0 * epsilon)\n",
" epsilon_ary2[i, j] = 0\n",
"\n",
" num_grad = np.hstack((num_grad1.flatten(), num_grad2.flatten()))\n",
@@ -1592,12 +1703,12 @@
" Parameters\n",
" -----------\n",
" X : array, shape = [n_samples, n_features]\n",
- " Input layer with original features.\n",
+ " Input layer with original features.\n",
"\n",
" Returns:\n",
" ----------\n",
" y_pred : array, shape = [n_samples]\n",
- " Predicted class labels.\n",
+ " Predicted class labels.\n",
"\n",
" \"\"\"\n",
" if len(X.shape) != 2:\n",
@@ -1615,14 +1726,12 @@
" Parameters\n",
" -----------\n",
" X : array, shape = [n_samples, n_features]\n",
- " Input layer with original features.\n",
- "\n",
+ " Input layer with original features.\n",
" y : array, shape = [n_samples]\n",
- " Target class labels.\n",
- "\n",
+ " Target class labels.\n",
" print_progress : bool (default: False)\n",
- " Prints progress as the number of epochs\n",
- " to stderr.\n",
+ " Prints progress as the number of epochs\n",
+ " to stderr.\n",
"\n",
" Returns:\n",
" ----------\n",
@@ -1637,7 +1746,7 @@
" delta_w2_prev = np.zeros(self.w2.shape)\n",
"\n",
" for i in range(self.epochs):\n",
- " \n",
+ "\n",
" # adaptive learning rate\n",
" self.eta /= (1 + self.decrease_const*i)\n",
"\n",
@@ -1647,13 +1756,15 @@
"\n",
" if self.shuffle:\n",
" idx = np.random.permutation(y_data.shape[0])\n",
- " X_data, y_data = X_data[idx], y_data[idx]\n",
+ " X_data, y_enc = X_data[idx], y_enc[idx]\n",
"\n",
" mini = np.array_split(range(y_data.shape[0]), self.minibatches)\n",
" for idx in mini:\n",
"\n",
" # feedforward\n",
- " a1, z2, a2, z3, a3 = self._feedforward(X[idx], self.w1, self.w2)\n",
+ " a1, z2, a2, z3, a3 = self._feedforward(X[idx],\n",
+ " self.w1,\n",
+ " self.w2)\n",
" cost = self._get_cost(y_enc=y_enc[:, idx],\n",
" output=a3,\n",
" w1=self.w1,\n",
@@ -1666,13 +1777,17 @@
" y_enc=y_enc[:, idx],\n",
" w1=self.w1,\n",
" w2=self.w2)\n",
- " \n",
- " ## start gradient checking\n",
- " grad_diff = self._gradient_checking(X=X[idx], y_enc=y_enc[:, idx],\n",
- " w1=self.w1, w2=self.w2,\n",
- " epsilon=1e-5,\n",
- " grad1=grad1, grad2=grad2)\n",
- " \n",
+ "\n",
+ " # start gradient checking\n",
+ " grad_diff = self._gradient_checking(X=X_data[idx],\n",
+ " y_enc=y_enc[:, idx],\n",
+ " w1=self.w1,\n",
+ " w2=self.w2,\n",
+ " epsilon=1e-5,\n",
+ " grad1=grad1,\n",
+ " grad2=grad2)\n",
+ "\n",
+ "\n",
" if grad_diff <= 1e-7:\n",
" print('Ok: %s' % grad_diff)\n",
" elif grad_diff <= 1e-4:\n",
@@ -1691,9 +1806,9 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 29,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1707,39 +1822,38 @@
" alpha=0.0,\n",
" decrease_const=0.0,\n",
" minibatches=1, \n",
+ " shuffle=False,\n",
" random_state=1)"
]
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 30,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Ok: 2.56712936241e-10\n",
- "Ok: 2.94603251069e-10\n",
- "Ok: 2.37615620231e-10\n",
- "Ok: 2.43469423226e-10\n",
- "Ok: 3.37872073158e-10\n",
- "Ok: 3.63466384861e-10\n",
- "Ok: 2.22472120785e-10\n",
- "Ok: 2.33163708438e-10\n",
- "Ok: 3.44653686551e-10\n",
- "Ok: 2.17161707211e-10\n"
+ "Ok: 2.55068505986e-10\n",
+ "Ok: 2.93547837023e-10\n",
+ "Ok: 2.37449571314e-10\n",
+ "Ok: 3.08194323691e-10\n",
+ "Ok: 3.38249440642e-10\n",
+ "Ok: 3.57890221135e-10\n",
+ "Ok: 2.19231256383e-10\n",
+ "Ok: 2.36583740198e-10\n",
+ "Ok: 3.43584860701e-10\n",
+ "Ok: 2.13345208113e-10\n"
]
},
{
"data": {
"text/plain": [
- "<__main__.MLPGradientCheck at 0x10a13ab70>"
+ "<__main__.MLPGradientCheck at 0x1134f7ef0>"
]
},
- "execution_count": 20,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -1765,10 +1879,8 @@
},
{
"cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 31,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1777,7 +1889,7 @@
""
]
},
- "execution_count": 24,
+ "execution_count": 31,
"metadata": {
"image/png": {
"width": 500
@@ -1821,10 +1933,8 @@
},
{
"cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 32,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1833,7 +1943,7 @@
""
]
},
- "execution_count": 26,
+ "execution_count": 32,
"metadata": {
"image/png": {
"width": 400
@@ -1848,10 +1958,8 @@
},
{
"cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 33,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1860,7 +1968,7 @@
""
]
},
- "execution_count": 29,
+ "execution_count": 33,
"metadata": {
"image/png": {
"width": 700
@@ -1890,10 +1998,8 @@
},
{
"cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 34,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1902,7 +2008,7 @@
""
]
},
- "execution_count": 32,
+ "execution_count": 34,
"metadata": {
"image/png": {
"width": 400
@@ -1959,6 +2065,7 @@
}
],
"metadata": {
+ "anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
@@ -1974,9 +2081,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.4.3"
+ "version": "3.6.1"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/code/ch12/images/12_02.png b/code/ch12/images/12_02.png
index e76215ed..9169dd13 100644
Binary files a/code/ch12/images/12_02.png and b/code/ch12/images/12_02.png differ
diff --git a/code/ch12/images/12_03.png b/code/ch12/images/12_03.png
index e6161443..48dad383 100644
Binary files a/code/ch12/images/12_03.png and b/code/ch12/images/12_03.png differ
diff --git a/code/ch12/neuralnet.py b/code/ch12/neuralnet.py
new file mode 100644
index 00000000..fc2b4a25
--- /dev/null
+++ b/code/ch12/neuralnet.py
@@ -0,0 +1,331 @@
+# Python Machine Learning by Sebastian Raschka, Packt Publishing Ltd. 2015
+# Code Repository: https://github.com/rasbt/python-machine-learning-book
+# Code License: MIT License
+
+import numpy as np
+from scipy.special import expit
+import sys
+
+
+class NeuralNetMLP(object):
+ """ Feedforward neural network / Multi-layer perceptron classifier.
+
+ Parameters
+ ------------
+ n_output : int
+ Number of output units, should be equal to the
+ number of unique class labels.
+ n_features : int
+ Number of features (dimensions) in the target dataset.
+ Should be equal to the number of columns in the X array.
+ n_hidden : int (default: 30)
+ Number of hidden units.
+ l1 : float (default: 0.0)
+ Lambda value for L1-regularization.
+ No regularization if l1=0.0 (default)
+ l2 : float (default: 0.0)
+ Lambda value for L2-regularization.
+ No regularization if l2=0.0 (default)
+ epochs : int (default: 500)
+ Number of passes over the training set.
+ eta : float (default: 0.001)
+ Learning rate.
+ alpha : float (default: 0.0)
+ Momentum constant. Factor multiplied with the
+ gradient of the previous epoch t-1 to improve
+ learning speed
+ w(t) := w(t) - (grad(t) + alpha*grad(t-1))
+ decrease_const : float (default: 0.0)
+ Decrease constant. Shrinks the learning rate
+ after each epoch via eta / (1 + epoch*decrease_const)
+ shuffle : bool (default: True)
+ Shuffles training data every epoch if True to prevent circles.
+ minibatches : int (default: 1)
+ Divides training data into k minibatches for efficiency.
+ Normal gradient descent learning if k=1 (default).
+ random_state : int (default: None)
+ Set random state for shuffling and initializing the weights.
+
+ Attributes
+ -----------
+ cost_ : list
+ Sum of squared errors after each epoch.
+
+ """
+ def __init__(self, n_output, n_features, n_hidden=30,
+ l1=0.0, l2=0.0, epochs=500, eta=0.001,
+ alpha=0.0, decrease_const=0.0, shuffle=True,
+ minibatches=1, random_state=None):
+
+ np.random.seed(random_state)
+ self.n_output = n_output
+ self.n_features = n_features
+ self.n_hidden = n_hidden
+ self.w1, self.w2 = self._initialize_weights()
+ self.l1 = l1
+ self.l2 = l2
+ self.epochs = epochs
+ self.eta = eta
+ self.alpha = alpha
+ self.decrease_const = decrease_const
+ self.shuffle = shuffle
+ self.minibatches = minibatches
+
+ def _encode_labels(self, y, k):
+ """Encode labels into one-hot representation
+
+ Parameters
+ ------------
+ y : array, shape = [n_samples]
+ Target values.
+
+ Returns
+ -----------
+ onehot : array, shape = (n_labels, n_samples)
+
+ """
+ onehot = np.zeros((k, y.shape[0]))
+ for idx, val in enumerate(y):
+ onehot[val, idx] = 1.0
+ return onehot
+
+ def _initialize_weights(self):
+ """Initialize weights with small random numbers."""
+ w1 = np.random.uniform(-1.0, 1.0,
+ size=self.n_hidden*(self.n_features + 1))
+ w1 = w1.reshape(self.n_hidden, self.n_features + 1)
+ w2 = np.random.uniform(-1.0, 1.0,
+ size=self.n_output*(self.n_hidden + 1))
+ w2 = w2.reshape(self.n_output, self.n_hidden + 1)
+ return w1, w2
+
+ def _sigmoid(self, z):
+ """Compute logistic function (sigmoid)
+
+ Uses scipy.special.expit to avoid overflow
+ error for very small input values z.
+
+ """
+ # return 1.0 / (1.0 + np.exp(-z))
+ return expit(z)
+
+ def _sigmoid_gradient(self, z):
+ """Compute gradient of the logistic function"""
+ sg = self._sigmoid(z)
+ return sg * (1.0 - sg)
+
+ def _add_bias_unit(self, X, how='column'):
+ """Add bias unit (column or row of 1s) to array at index 0"""
+ if how == 'column':
+ X_new = np.ones((X.shape[0], X.shape[1] + 1))
+ X_new[:, 1:] = X
+ elif how == 'row':
+ X_new = np.ones((X.shape[0] + 1, X.shape[1]))
+ X_new[1:, :] = X
+ else:
+ raise AttributeError('`how` must be `column` or `row`')
+ return X_new
+
+ def _feedforward(self, X, w1, w2):
+ """Compute feedforward step
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ----------
+ a1 : array, shape = [n_samples, n_features+1]
+ Input values with bias unit.
+ z2 : array, shape = [n_hidden, n_samples]
+ Net input of hidden layer.
+ a2 : array, shape = [n_hidden+1, n_samples]
+ Activation of hidden layer.
+ z3 : array, shape = [n_output_units, n_samples]
+ Net input of output layer.
+ a3 : array, shape = [n_output_units, n_samples]
+ Activation of output layer.
+
+ """
+ a1 = self._add_bias_unit(X, how='column')
+ z2 = w1.dot(a1.T)
+ a2 = self._sigmoid(z2)
+ a2 = self._add_bias_unit(a2, how='row')
+ z3 = w2.dot(a2)
+ a3 = self._sigmoid(z3)
+ return a1, z2, a2, z3, a3
+
+ def _L2_reg(self, lambda_, w1, w2):
+ """Compute L2-regularization cost"""
+ return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) +
+ np.sum(w2[:, 1:] ** 2))
+
+ def _L1_reg(self, lambda_, w1, w2):
+ """Compute L1-regularization cost"""
+ return (lambda_/2.0) * (np.abs(w1[:, 1:]).sum() +
+ np.abs(w2[:, 1:]).sum())
+
+ def _get_cost(self, y_enc, output, w1, w2):
+ """Compute cost function.
+
+ Parameters
+ ----------
+ y_enc : array, shape = (n_labels, n_samples)
+ one-hot encoded class labels.
+ output : array, shape = [n_output_units, n_samples]
+ Activation of the output layer (feedforward)
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ---------
+ cost : float
+ Regularized cost.
+
+ """
+ term1 = -y_enc * (np.log(output))
+ term2 = (1.0 - y_enc) * np.log(1.0 - output)
+ cost = np.sum(term1 - term2)
+ L1_term = self._L1_reg(self.l1, w1, w2)
+ L2_term = self._L2_reg(self.l2, w1, w2)
+ cost = cost + L1_term + L2_term
+ return cost
+
+ def _get_gradient(self, a1, a2, a3, z2, y_enc, w1, w2):
+ """ Compute gradient step using backpropagation.
+
+ Parameters
+ ------------
+ a1 : array, shape = [n_samples, n_features+1]
+ Input values with bias unit.
+ a2 : array, shape = [n_hidden+1, n_samples]
+ Activation of hidden layer.
+ a3 : array, shape = [n_output_units, n_samples]
+ Activation of output layer.
+ z2 : array, shape = [n_hidden, n_samples]
+ Net input of hidden layer.
+ y_enc : array, shape = (n_labels, n_samples)
+ one-hot encoded class labels.
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ---------
+ grad1 : array, shape = [n_hidden_units, n_features]
+ Gradient of the weight matrix w1.
+ grad2 : array, shape = [n_output_units, n_hidden_units]
+ Gradient of the weight matrix w2.
+
+ """
+ # backpropagation
+ sigma3 = a3 - y_enc
+ z2 = self._add_bias_unit(z2, how='row')
+ sigma2 = w2.T.dot(sigma3) * self._sigmoid_gradient(z2)
+ sigma2 = sigma2[1:, :]
+ grad1 = sigma2.dot(a1)
+ grad2 = sigma3.dot(a2.T)
+
+ # regularize
+ grad1[:, 1:] += self.l2 * w1[:, 1:]
+ grad1[:, 1:] += self.l1 * np.sign(w1[:, 1:])
+ grad2[:, 1:] += self.l2 * w2[:, 1:]
+ grad2[:, 1:] += self.l1 * np.sign(w2[:, 1:])
+
+ return grad1, grad2
+
+ def predict(self, X):
+ """Predict class labels
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+
+ Returns:
+ ----------
+ y_pred : array, shape = [n_samples]
+ Predicted class labels.
+
+ """
+ if len(X.shape) != 2:
+ raise AttributeError('X must be a [n_samples, n_features] array.\n'
+ 'Use X[:,None] for 1-feature classification,'
+ '\nor X[[i]] for 1-sample classification')
+
+ a1, z2, a2, z3, a3 = self._feedforward(X, self.w1, self.w2)
+ y_pred = np.argmax(z3, axis=0)
+ return y_pred
+
+ def fit(self, X, y, print_progress=False):
+ """ Learn weights from training data.
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+ y : array, shape = [n_samples]
+ Target class labels.
+ print_progress : bool (default: False)
+ Prints progress as the number of epochs
+ to stderr.
+
+ Returns:
+ ----------
+ self
+
+ """
+ self.cost_ = []
+ X_data, y_data = X.copy(), y.copy()
+ y_enc = self._encode_labels(y, self.n_output)
+
+ delta_w1_prev = np.zeros(self.w1.shape)
+ delta_w2_prev = np.zeros(self.w2.shape)
+
+ for i in range(self.epochs):
+
+ # adaptive learning rate
+ self.eta /= (1 + self.decrease_const*i)
+
+ if print_progress:
+ sys.stderr.write('\rEpoch: %d/%d' % (i+1, self.epochs))
+ sys.stderr.flush()
+
+ if self.shuffle:
+ idx = np.random.permutation(y_data.shape[0])
+ X_data, y_enc = X_data[idx], y_enc[:, idx]
+
+ mini = np.array_split(range(y_data.shape[0]), self.minibatches)
+ for idx in mini:
+
+ # feedforward
+ a1, z2, a2, z3, a3 = self._feedforward(X_data[idx],
+ self.w1,
+ self.w2)
+ cost = self._get_cost(y_enc=y_enc[:, idx],
+ output=a3,
+ w1=self.w1,
+ w2=self.w2)
+ self.cost_.append(cost)
+
+ # compute gradient via backpropagation
+ grad1, grad2 = self._get_gradient(a1=a1, a2=a2,
+ a3=a3, z2=z2,
+ y_enc=y_enc[:, idx],
+ w1=self.w1,
+ w2=self.w2)
+
+ delta_w1, delta_w2 = self.eta * grad1, self.eta * grad2
+ self.w1 -= (delta_w1 + (self.alpha * delta_w1_prev))
+ self.w2 -= (delta_w2 + (self.alpha * delta_w2_prev))
+ delta_w1_prev, delta_w2_prev = delta_w1, delta_w2
+
+ return self
\ No newline at end of file
diff --git a/code/ch12/optional-streamlined-neuralnet.py b/code/ch12/optional-streamlined-neuralnet.py
new file mode 100644
index 00000000..4af365ca
--- /dev/null
+++ b/code/ch12/optional-streamlined-neuralnet.py
@@ -0,0 +1,256 @@
+# Python Machine Learning by Sebastian Raschka, Packt Publishing Ltd. 2015
+# Code Repository: https://github.com/rasbt/python-machine-learning-book
+# Code License: MIT License
+
+import numpy as np
+import sys
+
+
+class NeuralNetMLP(object):
+ """ Feedforward neural network / Multi-layer perceptron classifier.
+
+ Parameters
+ ------------
+ n_hidden : int (default: 30)
+ Number of hidden units.
+ l2 : float (default: 0.)
+ Lambda value for L2-regularization.
+ No regularization if l2=0. (default)
+ epochs : int (default: 100)
+ Number of passes over the training set.
+ eta : float (default: 0.001)
+ Learning rate.
+ shuffle : bool (default: True)
+ Shuffles training data every epoch if True to prevent circles.
+ minibatche_size : int (default: 1)
+ Number of training samples per minibatch.
+ seed : int (default: None)
+ Random seed for initializing weights and shuffling.
+
+ Attributes
+ -----------
+ eval_ : dict
+ Dictionary collecting the cost, training accuracy,
+ and validation accuracy for each epoch during training.
+
+ """
+ def __init__(self, n_hidden=30,
+ l2=0., epochs=100, eta=0.001,
+ shuffle=True, minibatch_size=1, seed=None):
+
+ self.random = np.random.RandomState(seed)
+ self.n_hidden = n_hidden
+ self.l2 = l2
+ self.epochs = epochs
+ self.eta = eta
+ self.shuffle = shuffle
+ self.minibatch_size = minibatch_size
+
+ def _onehot(self, y, n_classes):
+ """Encode labels into one-hot representation
+
+ Parameters
+ ------------
+ y : array, shape = [n_samples]
+ Target values.
+
+ Returns
+ -----------
+ onehot : array, shape = (n_samples, n_labels)
+
+ """
+ onehot = np.zeros((n_classes, y.shape[0].astype(int)))
+ for idx, val in enumerate(y):
+ onehot[val, idx] = 1.
+ return onehot.T
+
+ def _sigmoid(self, z):
+ """Compute logistic function (sigmoid)"""
+ return 1. / (1. + np.exp(-np.clip(z, -250, 250)))
+
+ def _forward(self, X):
+ """Compute forward propagation step"""
+
+ # step 1: net input of hidden layer
+ # [n_samples, n_features] dot [n_features, n_hidden]
+ # -> [n_samples, n_hidden]
+ z_h = np.dot(X, self.w_h) + self.b_h
+
+ # step 2: activation of hidden layer
+ a_h = self._sigmoid(z_h)
+
+ # step 3: net input of output layer
+ # [n_samples, n_hidden] dot [n_hidden, n_classlabels]
+ # -> [n_samples, n_classlabels]
+
+ z_out = np.dot(a_h, self.w_out) + self.b_out
+
+ # step 4: activation output layer
+ a_out = self._sigmoid(z_out)
+
+ return z_h, a_h, z_out, a_out
+
+ def _compute_cost(self, y_enc, output):
+ """Compute cost function.
+
+ Parameters
+ ----------
+ y_enc : array, shape = (n_samples, n_labels)
+ one-hot encoded class labels.
+ output : array, shape = [n_samples, n_output_units]
+ Activation of the output layer (forward propagation)
+
+ Returns
+ ---------
+ cost : float
+ Regularized cost
+
+ """
+ L2_term = (self.l2 *
+ (np.sum(self.w_h ** 2.) +
+ np.sum(self.w_out ** 2.)))
+
+ term1 = -y_enc * (np.log(output))
+ term2 = (1. - y_enc) * np.log(1. - output)
+ cost = np.sum(term1 - term2) + L2_term
+ return cost
+
+ def predict(self, X):
+ """Predict class labels
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+
+ Returns:
+ ----------
+ y_pred : array, shape = [n_samples]
+ Predicted class labels.
+
+ """
+ z_h, a_h, z_out, a_out = self._forward(X)
+ y_pred = np.argmax(z_out, axis=1)
+ return y_pred
+
+ def fit(self, X_train, y_train, X_valid, y_valid):
+ """ Learn weights from training data.
+
+ Parameters
+ -----------
+ X_train : array, shape = [n_samples, n_features]
+ Input layer with original features.
+ y_train : array, shape = [n_samples]
+ Target class labels.
+ X_valid : array, shape = [n_samples, n_features]
+ Sample features for validation during training
+ y_valid : array, shape = [n_samples]
+ Sample labels for validation during training
+
+ Returns:
+ ----------
+ self
+
+ """
+ n_output = np.unique(y_train).shape[0] # number of class labels
+ n_features = X_train.shape[1]
+
+ ########################
+ # Weight initialization
+ ########################
+
+ # weights for input -> hidden
+ self.b_h = np.zeros(self.n_hidden)
+ self.w_h = self.random.normal(loc=0.0, scale=0.1,
+ size=(n_features, self.n_hidden))
+
+ # weights for hidden -> output
+ self.b_out = np.zeros(n_output)
+ self.w_out = self.random.normal(loc=0.0, scale=0.1,
+ size=(self.n_hidden, n_output))
+
+ epoch_strlen = len(str(self.epochs)) # for progress formatting
+ self.eval_ = {'cost': [], 'train_acc': [], 'valid_acc': []}
+
+ y_train_enc = self._onehot(y_train, n_output)
+
+ # iterate over training epochs
+ for i in range(self.epochs):
+
+ # iterate over minibatches
+ indices = np.arange(X_train.shape[0])
+
+ if self.shuffle:
+ self.random.shuffle(indices)
+
+ for start_idx in range(0, indices.shape[0] - self.minibatch_size +
+ 1, self.minibatch_size):
+ batch_idx = indices[start_idx:start_idx + self.minibatch_size]
+
+ # forward propagation
+ z_h, a_h, z_out, a_out = self._forward(X_train[batch_idx])
+
+ ##################
+ # Backpropagation
+ ##################
+
+ # [n_samples, n_classlabels]
+ sigma_out = a_out - y_train_enc[batch_idx]
+
+ # [n_samples, n_hidden]
+ sigmoid_derivative_h = a_h * (1. - a_h)
+
+ # [n_samples, n_classlabels] dot [n_classlabels, n_hidden]
+ # -> [n_samples, n_hidden]
+ sigma_h = (np.dot(sigma_out, self.w_out.T) *
+ sigmoid_derivative_h)
+
+ # [n_features, n_samples] dot [n_samples, n_hidden]
+ # -> [n_features, n_hidden]
+ grad_w_h = np.dot(X_train[batch_idx].T, sigma_h)
+ grad_b_h = np.sum(sigma_h, axis=0)
+
+ # [n_hidden, n_samples] dot [n_samples, n_classlabels]
+ # -> [n_hidden, n_classlabels]
+ grad_w_out = np.dot(a_h.T, sigma_out)
+ grad_b_out = np.sum(sigma_out, axis=0)
+
+ # Regularization and weight updates
+ delta_w_h = (grad_w_h + self.l2*self.w_h)
+ delta_b_h = grad_b_h # bias is not regularized
+ self.w_h -= self.eta * delta_w_h
+ self.b_h -= self.eta * delta_b_h
+
+ delta_w_out = (grad_w_out + self.l2*self.w_out)
+ delta_b_out = grad_b_out # bias is not regularized
+ self.w_out -= self.eta * delta_w_out
+ self.b_out -= self.eta * delta_b_out
+
+ #############
+ # Evaluation
+ #############
+
+ # Evaluation after each epoch during training
+ z_h, a_h, z_out, a_out = self._forward(X_train)
+ cost = self._compute_cost(y_enc=y_train_enc,
+ output=a_out)
+
+ y_train_pred = self.predict(X_train)
+ y_valid_pred = self.predict(X_valid)
+
+ train_acc = ((np.sum(y_train == y_train_pred)).astype(np.float) /
+ X_train.shape[0])
+ valid_acc = ((np.sum(y_valid == y_valid_pred)).astype(np.float) /
+ X_valid.shape[0])
+
+ sys.stderr.write('\r%0*d/%d | Cost: %.2f '
+ '| Train/Valid Acc.: %.2f%%/%.2f%% ' %
+ (epoch_strlen, i+1, self.epochs, cost,
+ train_acc*100, valid_acc*100))
+ sys.stderr.flush()
+
+ self.eval_['cost'].append(cost)
+ self.eval_['train_acc'].append(train_acc)
+ self.eval_['valid_acc'].append(valid_acc)
+
+ return self
\ No newline at end of file
diff --git a/code/ch13/ch13.ipynb b/code/ch13/ch13.ipynb
index 3885e1cc..93492a53 100644
--- a/code/ch13/ch13.ipynb
+++ b/code/ch13/ch13.ipynb
@@ -4,9 +4,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "[Sebastian Raschka](http://sebastianraschka.com), 2015\n",
+ "Copyright (c) 2015, 2016 [Sebastian Raschka](sebastianraschka.com)\n",
"\n",
- "https://github.com/rasbt/python-machine-learning-book"
+ "https://github.com/rasbt/python-machine-learning-book\n",
+ "\n",
+ "[MIT License](https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt)"
]
},
{
@@ -33,24 +35,29 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using Theano backend.\n"
+ ]
+ },
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
- "Last updated: 08/27/2015 \n",
+ "last updated: 2017-07-04 \n",
"\n",
- "CPython 3.4.3\n",
- "IPython 4.0.0\n",
+ "CPython 3.6.1\n",
+ "IPython 6.0.0\n",
"\n",
- "numpy 1.9.2\n",
- "matplotlib 1.4.3\n",
- "theano 0.7.0\n",
- "keras 0.1.2\n"
+ "numpy 1.13.0\n",
+ "matplotlib 2.0.2\n",
+ "theano 0.9.0\n",
+ "keras 2.0.5\n"
]
}
],
@@ -60,15 +67,12 @@
]
},
{
- "cell_type": "code",
- "execution_count": 2,
+ "cell_type": "markdown",
"metadata": {
"collapsed": true
},
- "outputs": [],
"source": [
- "# to install watermark just uncomment the following line:\n",
- "#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py"
+ "*The use of `watermark` is optional. You can install this IPython extension via \"`pip install watermark`\". For more information, please see: https://github.com/rasbt/watermark.*"
]
},
{
@@ -87,7 +91,7 @@
" - [First steps with Theano](#First-steps-with-Theano)\n",
" - [Configuring Theano](#Configuring-Theano)\n",
" - [Working with array structures](#Working-with-array-structures)\n",
- " - [Wrapping things up – a linear regression example](#Wrapping-things-up-–-a-linear-regression-example)\n",
+ " - [Wrapping things up – a linear regression example](#Wrapping-things-up:-A--linear-regression-example)\n",
"- [Choosing activation functions for feedforward neural networks](#Choosing-activation-functions-for-feedforward-neural-networks)\n",
" - [Logistic function recap](#Logistic-function-recap)\n",
" - [Estimating probabilities in multi-class classification via the softmax function](#Estimating-probabilities-in-multi-class-classification-via-the-softmax-function)\n",
@@ -106,7 +110,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {
"collapsed": true
},
@@ -115,6 +119,17 @@
"from IPython.display import Image"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -135,10 +150,8 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 4,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -147,7 +160,7 @@
""
]
},
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {
"image/png": {
"width": 500
@@ -198,9 +211,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 5,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -210,18 +223,16 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 6,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array(2.5)"
+ "array(2.5, dtype=float32)"
]
},
- "execution_count": 4,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -266,16 +277,14 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 7,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "float64\n"
+ "float32\n"
]
}
],
@@ -285,7 +294,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 8,
"metadata": {
"collapsed": true
},
@@ -318,10 +327,8 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 9,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -380,10 +387,8 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 10,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -398,6 +403,8 @@
"import numpy as np\n",
"\n",
"# initialize\n",
+ "# if you are running Theano on 64 bit mode, \n",
+ "# you need to use dmatrix instead of fmatrix\n",
"x = T.fmatrix(name='x')\n",
"x_sum = T.sum(x, axis=0)\n",
"\n",
@@ -423,10 +430,8 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 11,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -468,10 +473,8 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 12,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -530,7 +533,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 13,
"metadata": {
"collapsed": true
},
@@ -555,9 +558,9 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 14,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -573,8 +576,8 @@
" y = T.fvector(name='y') \n",
" X = T.fmatrix(name='X') \n",
" w = theano.shared(np.zeros(\n",
- " shape=(X_train.shape[1] + 1),\n",
- " dtype=theano.config.floatX),\n",
+ " shape=(X_train.shape[1] + 1),\n",
+ " dtype=theano.config.floatX),\n",
" name='w')\n",
" \n",
" # calculate cost\n",
@@ -591,7 +594,7 @@
" outputs=cost,\n",
" updates=update,\n",
" givens={X: X_train,\n",
- " y: y_train,}) \n",
+ " y: y_train}) \n",
" \n",
" for _ in range(epochs):\n",
" costs.append(train(eta))\n",
@@ -608,16 +611,14 @@
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 15,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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G9eRCRPwb8MKw4RXA6mR5NXBDWj/fAZVTkpYBVwGPZltJLv0l8D+BwawLybkL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\nQJmZWS45oMzMLJf+PxNd6XAisdvzAAAAAElFTkSuQmCC\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -625,7 +626,6 @@
}
],
"source": [
- "%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"costs, w = train_linreg(X_train, y_train, eta=0.001, epochs=10)\n",
@@ -649,16 +649,14 @@
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 16,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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fMmUKFRUVLF68WIc0EhHPUUglgY6ODqqrq3nvvfci6suXL6esrIysrCxHnYmI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\n",
+ "image/png": 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fkvTcvQ5jx47F5/Mxd+5c98KJiHiQCirKckwPvrSLZJl+nr8Jb8AaijdtYOPG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\nKigREfEkFZSIiHiSCkpERDzp/wMACZgWUrI/SQAAAABJRU5ErkJggg==\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -735,10 +733,8 @@
},
{
"cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 17,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -777,10 +773,8 @@
},
{
"cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 18,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -796,7 +790,7 @@
"source": [
"# W : array, shape = [n_output_units, n_hidden_units+1]\n",
"# Weight matrix for hidden layer -> output layer.\n",
- "# note that first column (A[:][0] = 1) are the bias units\n",
+ "# note that first column (W[:][0]) contains the bias units\n",
"W = np.array([[1.1, 1.2, 1.3, 0.5],\n",
" [0.1, 0.2, 0.4, 0.1],\n",
" [0.2, 0.5, 2.1, 1.9]])\n",
@@ -820,10 +814,8 @@
},
{
"cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 19,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -868,7 +860,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 20,
"metadata": {
"collapsed": true
},
@@ -879,15 +871,13 @@
"\n",
"def softmax_activation(X, w):\n",
" z = net_input(X, w)\n",
- " return sigmoid(z)"
+ " return softmax(z)"
]
},
{
"cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 21,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -907,10 +897,8 @@
},
{
"cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 22,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -918,7 +906,7 @@
"1.0"
]
},
- "execution_count": 34,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -929,10 +917,8 @@
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 23,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -940,7 +926,7 @@
"array([2])"
]
},
- "execution_count": 35,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -950,6 +936,39 @@
"y_class"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "***Note*** \n",
+ "Below is an additional figure to illustrate the difference between logistic regression and softmax:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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JSUhOTkZ6ejp69OiBnj17Iisrq0Fxguk2uKETERABERCBMAFpWCPnUVBdR05dq6QiIAIi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\n3aP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Zy7Tcdsp29TkeGdcSxMF0+OJRmupvDjzEv+kweZ24++E4TAB3wORnImw7ZjAhn6Ty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lXynCyVTcu3qb5lkY4J1VJRXq28AsQAc2TLrn1ScqhKsrqEmWrYNyjW9qxftECk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\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TpRISLC62HuXl5di6dSveffddrFixwtw/ElV0X6mNao/6kFJhIckgpisoyI0bN84IJLKs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UrrL9LQo99UZ1u/2rttMoPC0T+pfOicAfVbh4G1G/Zoss2dMCNv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IqO+bJh7z/+8Q9MmjTJuCjiRs2sl/sX6Kp3bSfbxnZUV1u0b9UZa/v0WTl9cXg7caZwnnyd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\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gdKxIwjSJB/0dNCGCjDYSMx577DHQPA+l4W+99ValaUBmAcvCs9Y6GA5MA92HyTCheTOO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\nQQtvbW2Bk5g2cnH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ajqgJCcmi8iqJiyQ3ifevvPiMOhjhuz/5E779wD0I9/MQqXAxKUPV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7Ft9IGhdn7aBIsLPMzN7du369VXX9Vvf/tbfyjyl7/8ZW+eCO1qGKLmg9Cg5bqiHRuesetT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oLGA9cKUxyUPeR8JNiWkSbzyvkb/7xkDDp7/yqxixPtieeaztmuQnhTCtbWK+qNXrA4Ha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\nTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {
+ "image/png": {
+ "width": 800
+ }
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename='./images/bonus_softmax_1.png', width=800) "
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -978,7 +997,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 25,
"metadata": {
"collapsed": true
},
@@ -992,16 +1011,14 @@
},
{
"cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 26,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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Jb5aZLQBygbnxDkqkI3viiScoLS1l4sSJjB6dko8oirSqpnamuAJ43N1L3H1B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\nIiIp7f8BNM/UAollYmAAAAAASUVORK5CYII=\n",
+ "image/png": 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x47399tskJycze/Zspk6dWiw+d+5cEhMTmTlzJjNnziwWX7hwIVFRUbz66qvM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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1010,7 +1027,6 @@
],
"source": [
"import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
"\n",
"z = np.arange(-5, 5, 0.005)\n",
"log_act = logistic(z)\n",
@@ -1046,10 +1062,8 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 27,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1058,7 +1072,7 @@
""
]
},
- "execution_count": 6,
+ "execution_count": 27,
"metadata": {
"image/png": {
"width": 700
@@ -1086,6 +1100,24 @@
"# Training neural networks efficiently using Keras"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "--- \n",
+ "**Note about installing Keras**\n",
+ "\n",
+ "As a [kind reader pointed out](http://www.mostafaelzoghbi.com/2017/04/how-to-install-keras-on-windows-10-64.html), Keras can now be installed from conda-forge, which is a community-driven effort to make packages available to the conda manager -- if you have troubles installing Keras via `pip` (`pip install keras`). To install Keras from conda-forge, you need to specify the respective conda channel as shown below:\n",
+ "\n",
+ "```bash\n",
+ "conda install -c conda-forge keras=2.0.2\n",
+ "```\n",
+ "\n",
+ "(End of note.)\n",
+ "\n",
+ "---"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -1111,7 +1143,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 28,
"metadata": {
"collapsed": true
},
@@ -1124,11 +1156,9 @@
"def load_mnist(path, kind='train'):\n",
" \"\"\"Load MNIST data from `path`\"\"\"\n",
" labels_path = os.path.join(path, \n",
- " '%s-labels-idx1-ubyte' \n",
- " % kind)\n",
+ " '%s-labels-idx1-ubyte' % kind)\n",
" images_path = os.path.join(path, \n",
- " '%s-images-idx3-ubyte' \n",
- " % kind)\n",
+ " '%s-images-idx3-ubyte' % kind)\n",
" \n",
" with open(labels_path, 'rb') as lbpath:\n",
" magic, n = struct.unpack('>II', \n",
@@ -1147,10 +1177,8 @@
},
{
"cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 29,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1161,16 +1189,14 @@
}
],
"source": [
- "X_train, y_train = load_mnist('mnist', kind='train')\n",
+ "X_train, y_train = load_mnist('./mnist', kind='train')\n",
"print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))"
]
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 30,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1203,9 +1229,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"source": [
"In order to run the following code via GPU, you can execute the Python script that was placed in this directory via\n",
"\n",
@@ -1214,9 +1238,9 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 31,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1236,10 +1260,8 @@
},
{
"cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 32,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1265,125 +1287,123 @@
},
{
"cell_type": "code",
- "execution_count": 49,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 33,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 54000 samples, validate on 6000 samples\n",
- "Epoch 0\n",
- "54000/54000 [==============================] - 1s - loss: 2.2290 - acc: 0.3592 - val_loss: 2.1094 - val_acc: 0.5342\n",
- "Epoch 1\n",
- "54000/54000 [==============================] - 1s - loss: 1.8850 - acc: 0.5279 - val_loss: 1.6098 - val_acc: 0.5617\n",
- "Epoch 2\n",
- "54000/54000 [==============================] - 1s - loss: 1.3903 - acc: 0.5884 - val_loss: 1.1666 - val_acc: 0.6707\n",
- "Epoch 3\n",
- "54000/54000 [==============================] - 1s - loss: 1.0592 - acc: 0.6936 - val_loss: 0.8961 - val_acc: 0.7615\n",
- "Epoch 4\n",
- "54000/54000 [==============================] - 1s - loss: 0.8528 - acc: 0.7666 - val_loss: 0.7288 - val_acc: 0.8290\n",
- "Epoch 5\n",
- "54000/54000 [==============================] - 1s - loss: 0.7187 - acc: 0.8191 - val_loss: 0.6122 - val_acc: 0.8603\n",
- "Epoch 6\n",
- "54000/54000 [==============================] - 1s - loss: 0.6278 - acc: 0.8426 - val_loss: 0.5347 - val_acc: 0.8762\n",
- "Epoch 7\n",
- "54000/54000 [==============================] - 1s - loss: 0.5592 - acc: 0.8621 - val_loss: 0.4707 - val_acc: 0.8920\n",
- "Epoch 8\n",
- "54000/54000 [==============================] - 1s - loss: 0.4978 - acc: 0.8751 - val_loss: 0.4288 - val_acc: 0.9033\n",
- "Epoch 9\n",
- "54000/54000 [==============================] - 1s - loss: 0.4583 - acc: 0.8847 - val_loss: 0.3935 - val_acc: 0.9035\n",
- "Epoch 10\n",
- "54000/54000 [==============================] - 1s - loss: 0.4213 - acc: 0.8911 - val_loss: 0.3553 - val_acc: 0.9088\n",
- "Epoch 11\n",
- "54000/54000 [==============================] - 1s - loss: 0.3972 - acc: 0.8955 - val_loss: 0.3405 - val_acc: 0.9083\n",
- "Epoch 12\n",
- "54000/54000 [==============================] - 1s - loss: 0.3740 - acc: 0.9022 - val_loss: 0.3251 - val_acc: 0.9170\n",
- "Epoch 13\n",
- "54000/54000 [==============================] - 1s - loss: 0.3611 - acc: 0.9030 - val_loss: 0.3032 - val_acc: 0.9183\n",
- "Epoch 14\n",
- "54000/54000 [==============================] - 1s - loss: 0.3479 - acc: 0.9064 - val_loss: 0.2972 - val_acc: 0.9248\n",
- "Epoch 15\n",
- "54000/54000 [==============================] - 1s - loss: 0.3309 - acc: 0.9099 - val_loss: 0.2778 - val_acc: 0.9250\n",
- "Epoch 16\n",
- "54000/54000 [==============================] - 1s - loss: 0.3264 - acc: 0.9103 - val_loss: 0.2838 - val_acc: 0.9208\n",
- "Epoch 17\n",
- "54000/54000 [==============================] - 1s - loss: 0.3136 - acc: 0.9136 - val_loss: 0.2689 - val_acc: 0.9223\n",
- "Epoch 18\n",
- "54000/54000 [==============================] - 1s - loss: 0.3031 - acc: 0.9156 - val_loss: 0.2634 - val_acc: 0.9313\n",
- "Epoch 19\n",
- "54000/54000 [==============================] - 1s - loss: 0.2988 - acc: 0.9169 - val_loss: 0.2579 - val_acc: 0.9288\n",
- "Epoch 20\n",
- "54000/54000 [==============================] - 1s - loss: 0.2909 - acc: 0.9180 - val_loss: 0.2494 - val_acc: 0.9310\n",
- "Epoch 21\n",
- "54000/54000 [==============================] - 1s - loss: 0.2848 - acc: 0.9202 - val_loss: 0.2478 - val_acc: 0.9307\n",
- "Epoch 22\n",
- "54000/54000 [==============================] - 1s - loss: 0.2804 - acc: 0.9194 - val_loss: 0.2423 - val_acc: 0.9343\n",
- "Epoch 23\n",
- "54000/54000 [==============================] - 1s - loss: 0.2728 - acc: 0.9235 - val_loss: 0.2387 - val_acc: 0.9327\n",
- "Epoch 24\n",
- "54000/54000 [==============================] - 1s - loss: 0.2673 - acc: 0.9241 - val_loss: 0.2265 - val_acc: 0.9385\n",
- "Epoch 25\n",
- "54000/54000 [==============================] - 1s - loss: 0.2611 - acc: 0.9253 - val_loss: 0.2270 - val_acc: 0.9347\n",
- "Epoch 26\n",
- "54000/54000 [==============================] - 1s - loss: 0.2676 - acc: 0.9225 - val_loss: 0.2210 - val_acc: 0.9367\n",
- "Epoch 27\n",
- "54000/54000 [==============================] - 1s - loss: 0.2528 - acc: 0.9261 - val_loss: 0.2241 - val_acc: 0.9373\n",
- "Epoch 28\n",
- "54000/54000 [==============================] - 1s - loss: 0.2511 - acc: 0.9264 - val_loss: 0.2170 - val_acc: 0.9403\n",
- "Epoch 29\n",
- "54000/54000 [==============================] - 1s - loss: 0.2433 - acc: 0.9293 - val_loss: 0.2165 - val_acc: 0.9412\n",
- "Epoch 30\n",
- "54000/54000 [==============================] - 1s - loss: 0.2465 - acc: 0.9279 - val_loss: 0.2135 - val_acc: 0.9367\n",
- "Epoch 31\n",
- "54000/54000 [==============================] - 1s - loss: 0.2383 - acc: 0.9306 - val_loss: 0.2138 - val_acc: 0.9427\n",
- "Epoch 32\n",
- "54000/54000 [==============================] - 1s - loss: 0.2349 - acc: 0.9310 - val_loss: 0.2066 - val_acc: 0.9423\n",
- "Epoch 33\n",
- "54000/54000 [==============================] - 1s - loss: 0.2301 - acc: 0.9334 - val_loss: 0.2054 - val_acc: 0.9440\n",
- "Epoch 34\n",
- "54000/54000 [==============================] - 1s - loss: 0.2371 - acc: 0.9317 - val_loss: 0.1991 - val_acc: 0.9480\n",
- "Epoch 35\n",
- "54000/54000 [==============================] - 1s - loss: 0.2256 - acc: 0.9352 - val_loss: 0.1982 - val_acc: 0.9450\n",
- "Epoch 36\n",
- "54000/54000 [==============================] - 1s - loss: 0.2313 - acc: 0.9323 - val_loss: 0.2092 - val_acc: 0.9403\n",
- "Epoch 37\n",
- "54000/54000 [==============================] - 1s - loss: 0.2230 - acc: 0.9341 - val_loss: 0.1993 - val_acc: 0.9445\n",
- "Epoch 38\n",
- "54000/54000 [==============================] - 1s - loss: 0.2261 - acc: 0.9336 - val_loss: 0.1891 - val_acc: 0.9463\n",
- "Epoch 39\n",
- "54000/54000 [==============================] - 1s - loss: 0.2166 - acc: 0.9369 - val_loss: 0.1943 - val_acc: 0.9452\n",
- "Epoch 40\n",
- "54000/54000 [==============================] - 1s - loss: 0.2128 - acc: 0.9370 - val_loss: 0.1952 - val_acc: 0.9435\n",
- "Epoch 41\n",
- "54000/54000 [==============================] - 1s - loss: 0.2200 - acc: 0.9351 - val_loss: 0.1918 - val_acc: 0.9468\n",
- "Epoch 42\n",
- "54000/54000 [==============================] - 2s - loss: 0.2107 - acc: 0.9383 - val_loss: 0.1831 - val_acc: 0.9483\n",
- "Epoch 43\n",
- "54000/54000 [==============================] - 1s - loss: 0.2020 - acc: 0.9411 - val_loss: 0.1906 - val_acc: 0.9443\n",
- "Epoch 44\n",
- "54000/54000 [==============================] - 1s - loss: 0.2082 - acc: 0.9388 - val_loss: 0.1838 - val_acc: 0.9457\n",
- "Epoch 45\n",
- "54000/54000 [==============================] - 1s - loss: 0.2048 - acc: 0.9402 - val_loss: 0.1817 - val_acc: 0.9488\n",
- "Epoch 46\n",
- "54000/54000 [==============================] - 1s - loss: 0.2012 - acc: 0.9417 - val_loss: 0.1876 - val_acc: 0.9480\n",
- "Epoch 47\n",
- "54000/54000 [==============================] - 1s - loss: 0.1996 - acc: 0.9423 - val_loss: 0.1792 - val_acc: 0.9502\n",
- "Epoch 48\n",
- "54000/54000 [==============================] - 1s - loss: 0.1921 - acc: 0.9430 - val_loss: 0.1791 - val_acc: 0.9505\n",
- "Epoch 49\n",
- "54000/54000 [==============================] - 1s - loss: 0.1907 - acc: 0.9432 - val_loss: 0.1749 - val_acc: 0.9482\n"
+ "Epoch 1/50\n",
+ "54000/54000 [==============================] - 0s - loss: 2.2244 - acc: 0.4249 - val_loss: 2.1019 - val_acc: 0.5712\n",
+ "Epoch 2/50\n",
+ "54000/54000 [==============================] - 0s - loss: 1.8746 - acc: 0.5297 - val_loss: 1.5900 - val_acc: 0.5878\n",
+ "Epoch 3/50\n",
+ "54000/54000 [==============================] - 0s - loss: 1.3850 - acc: 0.6134 - val_loss: 1.1486 - val_acc: 0.7050\n",
+ "Epoch 4/50\n",
+ "54000/54000 [==============================] - 0s - loss: 1.0424 - acc: 0.7195 - val_loss: 0.8696 - val_acc: 0.7933\n",
+ "Epoch 5/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.8236 - acc: 0.7928 - val_loss: 0.6872 - val_acc: 0.8498\n",
+ "Epoch 6/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.6819 - acc: 0.8328 - val_loss: 0.5700 - val_acc: 0.8740\n",
+ "Epoch 7/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.5867 - acc: 0.8578 - val_loss: 0.5052 - val_acc: 0.8860\n",
+ "Epoch 8/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.5203 - acc: 0.8702 - val_loss: 0.4494 - val_acc: 0.8937\n",
+ "Epoch 9/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.4751 - acc: 0.8822 - val_loss: 0.4112 - val_acc: 0.9020\n",
+ "Epoch 10/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.4382 - acc: 0.8884 - val_loss: 0.3725 - val_acc: 0.9122\n",
+ "Epoch 11/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.4086 - acc: 0.8953 - val_loss: 0.3575 - val_acc: 0.9130\n",
+ "Epoch 12/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3889 - acc: 0.8981 - val_loss: 0.3393 - val_acc: 0.9118\n",
+ "Epoch 13/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3724 - acc: 0.9022 - val_loss: 0.3082 - val_acc: 0.9207\n",
+ "Epoch 14/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3490 - acc: 0.9082 - val_loss: 0.3083 - val_acc: 0.9202\n",
+ "Epoch 15/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3361 - acc: 0.9095 - val_loss: 0.2950 - val_acc: 0.9200\n",
+ "Epoch 16/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3279 - acc: 0.9108 - val_loss: 0.2829 - val_acc: 0.9262\n",
+ "Epoch 17/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3210 - acc: 0.9125 - val_loss: 0.2850 - val_acc: 0.9290\n",
+ "Epoch 18/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3067 - acc: 0.9166 - val_loss: 0.2699 - val_acc: 0.9275\n",
+ "Epoch 19/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.3006 - acc: 0.9173 - val_loss: 0.2502 - val_acc: 0.9380\n",
+ "Epoch 20/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2932 - acc: 0.9198 - val_loss: 0.2603 - val_acc: 0.9313\n",
+ "Epoch 21/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2859 - acc: 0.9201 - val_loss: 0.2457 - val_acc: 0.9325\n",
+ "Epoch 22/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2804 - acc: 0.9217 - val_loss: 0.2551 - val_acc: 0.9348\n",
+ "Epoch 23/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2735 - acc: 0.9233 - val_loss: 0.2457 - val_acc: 0.9342\n",
+ "Epoch 24/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2790 - acc: 0.9220 - val_loss: 0.2364 - val_acc: 0.9380\n",
+ "Epoch 25/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2747 - acc: 0.9227 - val_loss: 0.2361 - val_acc: 0.9345\n",
+ "Epoch 26/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2648 - acc: 0.9254 - val_loss: 0.2311 - val_acc: 0.9357\n",
+ "Epoch 27/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2627 - acc: 0.9249 - val_loss: 0.2319 - val_acc: 0.9343\n",
+ "Epoch 28/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2556 - acc: 0.9280 - val_loss: 0.2322 - val_acc: 0.9352\n",
+ "Epoch 29/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2599 - acc: 0.9264 - val_loss: 0.2249 - val_acc: 0.9410\n",
+ "Epoch 30/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2500 - acc: 0.9290 - val_loss: 0.2164 - val_acc: 0.9398\n",
+ "Epoch 31/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2460 - acc: 0.9291 - val_loss: 0.2132 - val_acc: 0.9425\n",
+ "Epoch 32/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2438 - acc: 0.9301 - val_loss: 0.2085 - val_acc: 0.9440\n",
+ "Epoch 33/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2366 - acc: 0.9323 - val_loss: 0.2104 - val_acc: 0.9437\n",
+ "Epoch 34/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2348 - acc: 0.9331 - val_loss: 0.2157 - val_acc: 0.9437\n",
+ "Epoch 35/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2333 - acc: 0.9323 - val_loss: 0.2109 - val_acc: 0.9405\n",
+ "Epoch 36/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2286 - acc: 0.9341 - val_loss: 0.2056 - val_acc: 0.9422\n",
+ "Epoch 37/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2261 - acc: 0.9353 - val_loss: 0.2144 - val_acc: 0.9448\n",
+ "Epoch 38/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2226 - acc: 0.9358 - val_loss: 0.2005 - val_acc: 0.9442\n",
+ "Epoch 39/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2192 - acc: 0.9365 - val_loss: 0.1990 - val_acc: 0.9450\n",
+ "Epoch 40/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2211 - acc: 0.9356 - val_loss: 0.2066 - val_acc: 0.9413\n",
+ "Epoch 41/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2237 - acc: 0.9356 - val_loss: 0.2165 - val_acc: 0.9380\n",
+ "Epoch 42/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2166 - acc: 0.9365 - val_loss: 0.1952 - val_acc: 0.9465\n",
+ "Epoch 43/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2109 - acc: 0.9392 - val_loss: 0.1905 - val_acc: 0.9492\n",
+ "Epoch 44/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2058 - acc: 0.9413 - val_loss: 0.1939 - val_acc: 0.9448\n",
+ "Epoch 45/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2075 - acc: 0.9398 - val_loss: 0.1934 - val_acc: 0.9448\n",
+ "Epoch 46/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2110 - acc: 0.9384 - val_loss: 0.1901 - val_acc: 0.9453\n",
+ "Epoch 47/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2048 - acc: 0.9406 - val_loss: 0.1833 - val_acc: 0.9493\n",
+ "Epoch 48/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2013 - acc: 0.9411 - val_loss: 0.1846 - val_acc: 0.9493\n",
+ "Epoch 49/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.2014 - acc: 0.9414 - val_loss: 0.1854 - val_acc: 0.9487\n",
+ "Epoch 50/50\n",
+ "54000/54000 [==============================] - 0s - loss: 0.1942 - acc: 0.9436 - val_loss: 0.1822 - val_acc: 0.9482\n"
]
},
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 49,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -1397,87 +1417,57 @@
"\n",
"model = Sequential()\n",
"model.add(Dense(input_dim=X_train.shape[1], \n",
- " output_dim=50, \n",
- " init='uniform', \n",
+ " units=50, # formerly output_dim=50 in Keras < 2\n",
+ " kernel_initializer='uniform', # formerly init='uniform' in Keras < 2\n",
" activation='tanh'))\n",
"\n",
"model.add(Dense(input_dim=50, \n",
- " output_dim=50, \n",
- " init='uniform', \n",
+ " units=50, \n",
+ " kernel_initializer='uniform', \n",
" activation='tanh'))\n",
"\n",
"model.add(Dense(input_dim=50, \n",
- " output_dim=y_train_ohe.shape[1], \n",
- " init='uniform', \n",
+ " units=y_train_ohe.shape[1],\n",
+ " kernel_initializer='uniform', \n",
" activation='softmax'))\n",
"\n",
"sgd = SGD(lr=0.001, decay=1e-7, momentum=.9)\n",
- "model.compile(loss='categorical_crossentropy', optimizer=sgd)\n",
+ "model.compile(loss='categorical_crossentropy', \n",
+ " optimizer=sgd, \n",
+ " metrics=['accuracy'])\n",
"\n",
"model.fit(X_train, y_train_ohe, \n",
- " nb_epoch=50, \n",
+ " epochs=50, # Keras 2: former nb_epoch has been renamed to epochs\n",
" batch_size=300, \n",
" verbose=1, \n",
- " validation_split=0.1, \n",
- " show_accuracy=True)"
+ " validation_split=0.1) \n",
+ " # removed former show_accuracy=True \n",
+ " # and added `metrics=['accuracy']` to the\n",
+ " # model.compile call for Keras >= 2"
]
},
{
"cell_type": "code",
- "execution_count": 50,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 34,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "First 3 predictions: [5 0 4]\n"
+ "First 3 predictions: [5 0 4]\n",
+ "Training accuracy: 94.43%\n",
+ "Test accuracy: 93.90%\n"
]
}
],
"source": [
"y_train_pred = model.predict_classes(X_train, verbose=0)\n",
- "print('First 3 predictions: ', y_train_pred[:3])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Training accuracy: 94.51%\n"
- ]
- }
- ],
- "source": [
+ "print('First 3 predictions: ', y_train_pred[:3])\n",
+ "\n",
"train_acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]\n",
- "print('Training accuracy: %.2f%%' % (train_acc * 100))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Test accuracy: 94.39%\n"
- ]
- }
- ],
- "source": [
+ "print('Training accuracy: %.2f%%' % (train_acc * 100))\n",
+ "\n",
"y_test_pred = model.predict_classes(X_test, verbose=0)\n",
"test_acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]\n",
"print('Test accuracy: %.2f%%' % (test_acc * 100))"
@@ -1509,6 +1499,7 @@
}
],
"metadata": {
+ "anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
@@ -1524,9 +1515,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.4.3"
+ "version": "3.6.1"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/code/ch13/images/bonus_softmax_1.png b/code/ch13/images/bonus_softmax_1.png
new file mode 100644
index 00000000..280c3045
Binary files /dev/null and b/code/ch13/images/bonus_softmax_1.png differ
diff --git a/code/check_environment.ipynb b/code/check_environment.ipynb
new file mode 100644
index 00000000..d5138da8
--- /dev/null
+++ b/code/check_environment.ipynb
@@ -0,0 +1,74 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Jupyter Notebook for Checking Python Package Requirements"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from distutils.version import LooseVersion as Version\n",
+ "\n",
+ "\n",
+ "def get_packages(pkgs):\n",
+ " versions = []\n",
+ " for p in packages:\n",
+ " try:\n",
+ " imported = __import__(p)\n",
+ " try:\n",
+ " versions.append(imported.__version__)\n",
+ " except AttributeError:\n",
+ " try:\n",
+ " versions.append(imported.version)\n",
+ " except AttributeError:\n",
+ " try:\n",
+ " versions.append(imported.version_info)\n",
+ " except AttributeError:\n",
+ " versions.append('0.0')\n",
+ " except ImportError:\n",
+ " print('[FAIL]: %s is not installed' % p)\n",
+ " return versions\n",
+ " \n",
+ "packages = ['numpy', 'scipy', 'matplotlib', 'sklearn', 'pandas']\n",
+ "suggested_v = ['1.10', '0.17', '1.5.1', '0.17.1', '0.17.1']\n",
+ "versions = get_packages(packages)\n",
+ "\n",
+ "for p, v, s in zip(packages, versions, suggested_v):\n",
+ " if Version(v) < Version(s):\n",
+ " print('[FAIL] %s %s, please upgrade to >= %s' % (p, v, s))\n",
+ " else:\n",
+ " print('[OK] %s %s' % (p, v))"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/code/datasets/README.md b/code/datasets/README.md
index e7d2e383..390e2c80 100644
--- a/code/datasets/README.md
+++ b/code/datasets/README.md
@@ -4,12 +4,12 @@ Sebastian Raschka, 2015
### iris
-- used in chapters 1, 2, 3
+- used in chapters 1, 2, and 3
- source: [https://archive.ics.uci.edu/ml/datasets/Iris](https://archive.ics.uci.edu/ml/datasets/Iris)
### wine
-- used in chapters 4, 5
+- used in chapters 4 and 5
- source: [https://archive.ics.uci.edu/ml/datasets/Wine](https://archive.ics.uci.edu/ml/datasets/Wine)
### wdbc
@@ -19,7 +19,7 @@ Sebastian Raschka, 2015
### movie
-- used in chapter 8, 9
+- used in chapters 8 and 9
- movie dataset converted into a 2-column CSV format: The first column (`review`) contains the text, and the second column (`sentiment`) denotes the polarity, where 0=negative and 1=positive. The first 25,000 are the training samples and the remaining 25,000 rows are the test samples from the "Large Movie Review Dataset v1.0," respectively.
- source: [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
@@ -30,5 +30,5 @@ Sebastian Raschka, 2015
### mnist
-- used in chapter 12, 13
-- source: [http://yann.lecun.com/exdb/mnist/]
\ No newline at end of file
+- used in chapters 12 and 13
+- source: [http://yann.lecun.com/exdb/mnist/]
diff --git a/code/datasets/housing/README.md b/code/datasets/housing/README.md
new file mode 100644
index 00000000..9dc5df1a
--- /dev/null
+++ b/code/datasets/housing/README.md
@@ -0,0 +1,36 @@
+Sebastian Raschka, 2015
+
+# Python Machine Learning - Supplementary Datasets
+
+## Boston Housing Data
+
+- Used in chapter 10
+
+The Boston Housing dataset for regression analysis.
+
+**Features**
+
+1. CRIM: per capita crime rate by town
+2. ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
+3. INDUS: proportion of non-retail business acres per town
+4. CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
+5. NOX: nitric oxides concentration (parts per 10 million)
+6. RM: average number of rooms per dwelling
+7. AGE: proportion of owner-occupied units built prior to 1940
+8. DIS: weighted distances to five Boston employment centres
+9. RAD: index of accessibility to radial highways
+10. TAX: full-value property-tax rate per $10,000
+11. PTRATIO: pupil-teacher ratio by town
+12. B: 1000(Bk - 0.63)^2 where Bk is the proportion of b. by town
+13. LSTAT: % lower status of the population
+
+
+- Number of samples: 506
+
+- Target variable (continuous): MEDV, Median value of owner-occupied homes in $1000's
+
+### References
+
+- Source: [https://archive.ics.uci.edu/ml/datasets/Wine](https://archive.ics.uci.edu/ml/datasets/Wine)
+- Harrison, D. and Rubinfeld, D.L.
+'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.
diff --git a/code/datasets/iris/README.md b/code/datasets/iris/README.md
new file mode 100644
index 00000000..f4dd524e
--- /dev/null
+++ b/code/datasets/iris/README.md
@@ -0,0 +1,25 @@
+Sebastian Raschka, 2015
+
+# Python Machine Learning - Supplementary Datasets
+
+## Iris Flower Dataset
+
+- Used in chapters 1, 2, and 3
+
+The Iris dataset for classification.
+
+**Features**
+
+1. Sepal length
+2. Sepal width
+3. Petal length
+4. Petal width
+
+- Number of samples: 150
+
+- Target variable (discrete): {50x Setosa, 50x Versicolor, 50x Virginica}
+
+### References
+
+- Source: [https://archive.ics.uci.edu/ml/datasets/Iris](https://archive.ics.uci.edu/ml/datasets/Iris)
+- Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
diff --git a/code/datasets/iris/iris.data b/code/datasets/iris/iris.data
index 5c4316cd..396653cc 100644
--- a/code/datasets/iris/iris.data
+++ b/code/datasets/iris/iris.data
@@ -147,5 +147,4 @@
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
-5.9,3.0,5.1,1.8,Iris-virginica
-
+5.9,3.0,5.1,1.8,Iris-virginica
\ No newline at end of file
diff --git a/code/datasets/iris/iris.names.txt b/code/datasets/iris/iris.names.txt
deleted file mode 100644
index 062b486d..00000000
--- a/code/datasets/iris/iris.names.txt
+++ /dev/null
@@ -1,69 +0,0 @@
-1. Title: Iris Plants Database
- Updated Sept 21 by C.Blake - Added discrepency information
-
-2. Sources:
- (a) Creator: R.A. Fisher
- (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
- (c) Date: July, 1988
-
-3. Past Usage:
- - Publications: too many to mention!!! Here are a few.
- 1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
- Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
- to Mathematical Statistics" (John Wiley, NY, 1950).
- 2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
- (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
- 3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
- Structure and Classification Rule for Recognition in Partially Exposed
- Environments". IEEE Transactions on Pattern Analysis and Machine
- Intelligence, Vol. PAMI-2, No. 1, 67-71.
- -- Results:
- -- very low misclassification rates (0% for the setosa class)
- 4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
- Transactions on Information Theory, May 1972, 431-433.
- -- Results:
- -- very low misclassification rates again
- 5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
- conceptual clustering system finds 3 classes in the data.
-
-4. Relevant Information:
- --- This is perhaps the best known database to be found in the pattern
- recognition literature. Fisher's paper is a classic in the field
- and is referenced frequently to this day. (See Duda & Hart, for
- example.) The data set contains 3 classes of 50 instances each,
- where each class refers to a type of iris plant. One class is
- linearly separable from the other 2; the latter are NOT linearly
- separable from each other.
- --- Predicted attribute: class of iris plant.
- --- This is an exceedingly simple domain.
- --- This data differs from the data presented in Fishers article
- (identified by Steve Chadwick, spchadwick@espeedaz.net )
- The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
- where the error is in the fourth feature.
- The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
- where the errors are in the second and third features.
-
-5. Number of Instances: 150 (50 in each of three classes)
-
-6. Number of Attributes: 4 numeric, predictive attributes and the class
-
-7. Attribute Information:
- 1. sepal length in cm
- 2. sepal width in cm
- 3. petal length in cm
- 4. petal width in cm
- 5. class:
- -- Iris Setosa
- -- Iris Versicolour
- -- Iris Virginica
-
-8. Missing Attribute Values: None
-
-Summary Statistics:
- Min Max Mean SD Class Correlation
- sepal length: 4.3 7.9 5.84 0.83 0.7826
- sepal width: 2.0 4.4 3.05 0.43 -0.4194
- petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
- petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
-
-9. Class Distribution: 33.3% for each of 3 classes.
diff --git a/code/datasets/mnist/README.md b/code/datasets/mnist/README.md
new file mode 100644
index 00000000..b9a292a0
--- /dev/null
+++ b/code/datasets/mnist/README.md
@@ -0,0 +1,34 @@
+Sebastian Raschka, 2015
+
+# Python Machine Learning - Supplementary Datasets
+
+## MNIST Dataset
+
+- Used in chapters 12 and 13
+
+
+The MNIST dataset was constructed from two datasets of the US National Institute of Standards and Technology (NIST). The training set consists of handwritten digits from 250 different people, 50 percent high school students, and 50 percent employees from the Census Bureau. Note that the test set contains handwritten digits from different people following the same split.
+
+**Features**
+
+Each feature vector (row in the feature matrix) consists of 784 pixels (intensities) -- unrolled from the original 28x28 pixels images.
+
+- Number of samples: A subset of 5000 images (the first 500 digits of each class)
+
+- Target variable (discrete): {500x 0, ..., 500x 9}
+
+
+### References
+
+- Source: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)
+- Y. LeCun and C. Cortes. Mnist handwritten digit database. AT&T Labs [Online]. Available: http://yann. lecun. com/exdb/mnist, 2010.
+
+
+### Loading MNIST
+
+- The description and code from [chapter 12](http://nbviewer.jupyter.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb#Obtaining-the-MNIST-dataset)
+
+In addition, I added to convenience function to one of my external machine learning packages
+
+- [A function that loads the MNIST dataset into NumPy arrays](http://rasbt.github.io/mlxtend/user_guide/data/load_mnist/)
+- [A utility function that loads the MNIST dataset from byte-form into NumPy arrays](http://rasbt.github.io/mlxtend/user_guide/data/mnist_data/)
diff --git a/code/datasets/movie/README.md b/code/datasets/movie/README.md
new file mode 100644
index 00000000..f3b70666
--- /dev/null
+++ b/code/datasets/movie/README.md
@@ -0,0 +1,11 @@
+Sebastian Raschka, 2015
+
+# Python Machine Learning - Supplementary Datasets
+
+## The Large Movie Review Dataset
+
+- Used in chapters 8 and 9
+
+The movie dataset converted into a 2-column CSV format: The first column (`review`) contains the text, and the second column (`sentiment`) denotes the polarity, where 0=negative and 1=positive. The first 25,000 are the training samples and the remaining 25,000 rows are the test samples from the "Large Movie Review Dataset v1.0," respectively.
+
+- Source: [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
diff --git a/code/datasets/wdbc/README.md b/code/datasets/wdbc/README.md
new file mode 100644
index 00000000..59e55501
--- /dev/null
+++ b/code/datasets/wdbc/README.md
@@ -0,0 +1,8 @@
+Sebastian Raschka, 2015
+
+# Python Machine Learning - Supplementary Datasets
+
+## Breast Cancer Wisconsin (Diagnostic) Data Set
+
+- Used in chapter 6
+- Source: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
diff --git a/code/datasets/wine/README.md b/code/datasets/wine/README.md
new file mode 100644
index 00000000..fff29174
--- /dev/null
+++ b/code/datasets/wine/README.md
@@ -0,0 +1,53 @@
+Sebastian Raschka, 2015
+
+# Python Machine Learning - Supplementary Datasets
+
+## Wine Dataset
+
+- Used in chapters 4 and 5
+
+The Wine dataset for classification.
+
+| | |
+|----------------------------|----------------|
+| Samples | 178 |
+| Features | 13 |
+| Classes | 3 |
+| Data Set Characteristics: | Multivariate |
+| Attribute Characteristics: | Integer, Real |
+| Associated Tasks: | Classification |
+| Missing Values | None |
+
+| column| attribute |
+|-----|------------------------------|
+| 1) | Class Label |
+| 2) | Alcohol |
+| 3) | Malic acid |
+| 4) | Ash |
+| 5) | Alcalinity of ash |
+| 6) | Magnesium |
+| 7) | Total phenols |
+| 8) | Flavanoids |
+| 9) | Nonflavanoid phenols |
+| 10) | Proanthocyanins |
+| 11) | intensity |
+| 12) | Hue |
+| 13) | OD280/OD315 of diluted wines |
+| 14) | Proline |
+
+
+| class | samples |
+|-------|----|
+| 0 | 59 |
+| 1 | 71 |
+| 2 | 48 |
+
+
+### References
+
+- Forina, M. et al, PARVUS -
+An Extendible Package for Data Exploration, Classification and Correlation.
+Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno,
+16147 Genoa, Italy.
+- Source: [https://archive.ics.uci.edu/ml/datasets/Wine](https://archive.ics.uci.edu/ml/datasets/Wine)
+- Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
diff --git a/code/optional-py-scripts/ch02.py b/code/optional-py-scripts/ch02.py
new file mode 100644
index 00000000..3ce52fe8
--- /dev/null
+++ b/code/optional-py-scripts/ch02.py
@@ -0,0 +1,414 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 2 - Training Machine Learning Algorithms for Classification
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+
+
+class Perceptron(object):
+ """Perceptron classifier.
+
+ Parameters
+ ------------
+ eta : float
+ Learning rate (between 0.0 and 1.0)
+ n_iter : int
+ Passes over the training dataset.
+
+ Attributes
+ -----------
+ w_ : 1d-array
+ Weights after fitting.
+ errors_ : list
+ Number of misclassifications (updates) in each epoch.
+
+ """
+ def __init__(self, eta=0.01, n_iter=10):
+ self.eta = eta
+ self.n_iter = n_iter
+
+ def fit(self, X, y):
+ """Fit training data.
+
+ Parameters
+ ----------
+ X : {array-like}, shape = [n_samples, n_features]
+ Training vectors, where n_samples is the number of samples and
+ n_features is the number of features.
+ y : array-like, shape = [n_samples]
+ Target values.
+
+ Returns
+ -------
+ self : object
+
+ """
+ self.w_ = np.zeros(1 + X.shape[1])
+ self.errors_ = []
+
+ for _ in range(self.n_iter):
+ errors = 0
+ for xi, target in zip(X, y):
+ update = self.eta * (target - self.predict(xi))
+ self.w_[1:] += update * xi
+ self.w_[0] += update
+ errors += int(update != 0.0)
+ self.errors_.append(errors)
+ return self
+
+ def net_input(self, X):
+ """Calculate net input"""
+ return np.dot(X, self.w_[1:]) + self.w_[0]
+
+ def predict(self, X):
+ """Return class label after unit step"""
+ return np.where(self.net_input(X) >= 0.0, 1, -1)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Training a perceptron model on the Iris dataset')
+print(50 * '-')
+
+df = pd.read_csv('https://archive.ics.uci.edu/ml/'
+ 'machine-learning-databases/iris/iris.data', header=None)
+print(df.tail())
+
+#############################################################################
+print(50 * '=')
+print('Plotting the Iris data')
+print(50 * '-')
+
+# select setosa and versicolor
+y = df.iloc[0:100, 4].values
+y = np.where(y == 'Iris-setosa', -1, 1)
+
+# extract sepal length and petal length
+X = df.iloc[0:100, [0, 2]].values
+
+# plot data
+plt.scatter(X[:50, 0], X[:50, 1],
+ color='red', marker='o', label='setosa')
+plt.scatter(X[50:100, 0], X[50:100, 1],
+ color='blue', marker='x', label='versicolor')
+
+plt.xlabel('sepal length [cm]')
+plt.ylabel('petal length [cm]')
+plt.legend(loc='upper left')
+
+# plt.tight_layout()
+# plt.savefig('./images/02_06.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Training the perceptron model')
+print(50 * '-')
+
+ppn = Perceptron(eta=0.1, n_iter=10)
+
+ppn.fit(X, y)
+
+plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')
+plt.xlabel('Epochs')
+plt.ylabel('Number of misclassifications')
+
+# plt.tight_layout()
+# plt.savefig('./perceptron_1.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('A function for plotting decision regions')
+print(50 * '-')
+
+
+def plot_decision_regions(X, y, classifier, resolution=0.02):
+
+ # setup marker generator and color map
+ markers = ('s', 'x', 'o', '^', 'v')
+ colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
+ cmap = ListedColormap(colors[:len(np.unique(y))])
+
+ # plot the decision surface
+ x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+ x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+ xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
+ np.arange(x2_min, x2_max, resolution))
+ Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
+ Z = Z.reshape(xx1.shape)
+ plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
+ plt.xlim(xx1.min(), xx1.max())
+ plt.ylim(xx2.min(), xx2.max())
+
+ # plot class samples
+ for idx, cl in enumerate(np.unique(y)):
+ plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
+ alpha=0.8, c=cmap(idx),
+ marker=markers[idx], label=cl)
+
+
+plot_decision_regions(X, y, classifier=ppn)
+plt.xlabel('sepal length [cm]')
+plt.ylabel('petal length [cm]')
+plt.legend(loc='upper left')
+
+# plt.tight_layout()
+# plt.savefig('./perceptron_2.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Implementing an adaptive linear neuron in Python')
+print(50 * '-')
+
+
+class AdalineGD(object):
+ """ADAptive LInear NEuron classifier.
+
+ Parameters
+ ------------
+ eta : float
+ Learning rate (between 0.0 and 1.0)
+ n_iter : int
+ Passes over the training dataset.
+
+ Attributes
+ -----------
+ w_ : 1d-array
+ Weights after fitting.
+ cost_ : list
+ Sum-of-squares cost function value in each epoch.
+
+ """
+ def __init__(self, eta=0.01, n_iter=50):
+ self.eta = eta
+ self.n_iter = n_iter
+
+ def fit(self, X, y):
+ """ Fit training data.
+
+ Parameters
+ ----------
+ X : {array-like}, shape = [n_samples, n_features]
+ Training vectors, where n_samples is the number of samples and
+ n_features is the number of features.
+ y : array-like, shape = [n_samples]
+ Target values.
+
+ Returns
+ -------
+ self : object
+
+ """
+ self.w_ = np.zeros(1 + X.shape[1])
+ self.cost_ = []
+
+ for i in range(self.n_iter):
+ output = self.net_input(X)
+ errors = (y - output)
+ self.w_[1:] += self.eta * X.T.dot(errors)
+ self.w_[0] += self.eta * errors.sum()
+ cost = (errors**2).sum() / 2.0
+ self.cost_.append(cost)
+ return self
+
+ def net_input(self, X):
+ """Calculate net input"""
+ return np.dot(X, self.w_[1:]) + self.w_[0]
+
+ def activation(self, X):
+ """Compute linear activation"""
+ return self.net_input(X)
+
+ def predict(self, X):
+ """Return class label after unit step"""
+ return np.where(self.activation(X) >= 0.0, 1, -1)
+
+
+fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
+
+ada1 = AdalineGD(n_iter=10, eta=0.01).fit(X, y)
+ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')
+ax[0].set_xlabel('Epochs')
+ax[0].set_ylabel('log(Sum-squared-error)')
+ax[0].set_title('Adaline - Learning rate 0.01')
+
+ada2 = AdalineGD(n_iter=10, eta=0.0001).fit(X, y)
+ax[1].plot(range(1, len(ada2.cost_) + 1), ada2.cost_, marker='o')
+ax[1].set_xlabel('Epochs')
+ax[1].set_ylabel('Sum-squared-error')
+ax[1].set_title('Adaline - Learning rate 0.0001')
+
+# plt.tight_layout()
+# plt.savefig('./adaline_1.png', dpi=300)
+plt.show()
+
+
+print('standardize features')
+X_std = np.copy(X)
+X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
+X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
+
+ada = AdalineGD(n_iter=15, eta=0.01)
+ada.fit(X_std, y)
+
+plot_decision_regions(X_std, y, classifier=ada)
+plt.title('Adaline - Gradient Descent')
+plt.xlabel('sepal length [standardized]')
+plt.ylabel('petal length [standardized]')
+plt.legend(loc='upper left')
+# plt.tight_layout()
+# plt.savefig('./adaline_2.png', dpi=300)
+plt.show()
+
+plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')
+plt.xlabel('Epochs')
+plt.ylabel('Sum-squared-error')
+
+# plt.tight_layout()
+# plt.savefig('./adaline_3.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Large scale machine learning and stochastic gradient descent')
+print(50 * '-')
+
+
+class AdalineSGD(object):
+ """ADAptive LInear NEuron classifier.
+
+ Parameters
+ ------------
+ eta : float
+ Learning rate (between 0.0 and 1.0)
+ n_iter : int
+ Passes over the training dataset.
+
+ Attributes
+ -----------
+ w_ : 1d-array
+ Weights after fitting.
+ cost_ : list
+ Sum-of-squares cost function value averaged over all
+ training samples in each epoch.
+ shuffle : bool (default: True)
+ Shuffles training data every epoch if True to prevent cycles.
+ random_state : int (default: None)
+ Set random state for shuffling and initializing the weights.
+
+ """
+ def __init__(self, eta=0.01, n_iter=10, shuffle=True, random_state=None):
+ self.eta = eta
+ self.n_iter = n_iter
+ self.w_initialized = False
+ self.shuffle = shuffle
+ if random_state:
+ np.random.seed(random_state)
+
+ def fit(self, X, y):
+ """ Fit training data.
+
+ Parameters
+ ----------
+ X : {array-like}, shape = [n_samples, n_features]
+ Training vectors, where n_samples is the number of samples and
+ n_features is the number of features.
+ y : array-like, shape = [n_samples]
+ Target values.
+
+ Returns
+ -------
+ self : object
+
+ """
+ self._initialize_weights(X.shape[1])
+ self.cost_ = []
+ for i in range(self.n_iter):
+ if self.shuffle:
+ X, y = self._shuffle(X, y)
+ cost = []
+ for xi, target in zip(X, y):
+ cost.append(self._update_weights(xi, target))
+ avg_cost = sum(cost) / len(y)
+ self.cost_.append(avg_cost)
+ return self
+
+ def partial_fit(self, X, y):
+ """Fit training data without reinitializing the weights"""
+ if not self.w_initialized:
+ self._initialize_weights(X.shape[1])
+ if y.ravel().shape[0] > 1:
+ for xi, target in zip(X, y):
+ self._update_weights(xi, target)
+ else:
+ self._update_weights(X, y)
+ return self
+
+ def _shuffle(self, X, y):
+ """Shuffle training data"""
+ r = np.random.permutation(len(y))
+ return X[r], y[r]
+
+ def _initialize_weights(self, m):
+ """Initialize weights to zeros"""
+ self.w_ = np.zeros(1 + m)
+ self.w_initialized = True
+
+ def _update_weights(self, xi, target):
+ """Apply Adaline learning rule to update the weights"""
+ output = self.net_input(xi)
+ error = (target - output)
+ self.w_[1:] += self.eta * xi.dot(error)
+ self.w_[0] += self.eta * error
+ cost = 0.5 * error**2
+ return cost
+
+ def net_input(self, X):
+ """Calculate net input"""
+ return np.dot(X, self.w_[1:]) + self.w_[0]
+
+ def activation(self, X):
+ """Compute linear activation"""
+ return self.net_input(X)
+
+ def predict(self, X):
+ """Return class label after unit step"""
+ return np.where(self.activation(X) >= 0.0, 1, -1)
+
+
+ada = AdalineSGD(n_iter=15, eta=0.01, random_state=1)
+ada.fit(X_std, y)
+
+plot_decision_regions(X_std, y, classifier=ada)
+plt.title('Adaline - Stochastic Gradient Descent')
+plt.xlabel('sepal length [standardized]')
+plt.ylabel('petal length [standardized]')
+plt.legend(loc='upper left')
+
+# plt.tight_layout()
+# plt.savefig('./adaline_4.png', dpi=300)
+plt.show()
+
+plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')
+plt.xlabel('Epochs')
+plt.ylabel('Average Cost')
+
+# plt.tight_layout()
+# plt.savefig('./adaline_5.png', dpi=300)
+plt.show()
+
+ada = ada.partial_fit(X_std[0, :], y[0])
diff --git a/code/optional-py-scripts/ch03.py b/code/optional-py-scripts/ch03.py
new file mode 100644
index 00000000..3edd740c
--- /dev/null
+++ b/code/optional-py-scripts/ch03.py
@@ -0,0 +1,431 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 3 - A Tour of Machine Learning Classifiers Using Scikit-Learn
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+
+import numpy as np
+from sklearn import datasets
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import accuracy_score
+from sklearn.linear_model import LogisticRegression
+from sklearn.linear_model import Perceptron
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.neighbors import KNeighborsClassifier
+# from sklearn.tree import export_graphviz
+from matplotlib.colors import ListedColormap
+import matplotlib.pyplot as plt
+import warnings
+
+# for sklearn 0.18's alternative syntax
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+ from sklearn.grid_search import train_test_split
+else:
+ from sklearn.model_selection import train_test_split
+
+#############################################################################
+print(50 * '=')
+print('Section: First steps with scikit-learn')
+print(50 * '-')
+
+iris = datasets.load_iris()
+X = iris.data[:, [2, 3]]
+y = iris.target
+print('Class labels:', np.unique(y))
+
+X_train, X_test, y_train, y_test = train_test_split(
+ X, y, test_size=0.3, random_state=0)
+
+sc = StandardScaler()
+sc.fit(X_train)
+X_train_std = sc.transform(X_train)
+X_test_std = sc.transform(X_test)
+
+#############################################################################
+print(50 * '=')
+print('Section: Training a perceptron via scikit-learn')
+print(50 * '-')
+
+ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)
+ppn.fit(X_train_std, y_train)
+print('Y array shape', y_test.shape)
+
+y_pred = ppn.predict(X_test_std)
+print('Misclassified samples: %d' % (y_test != y_pred).sum())
+print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
+
+
+def versiontuple(v):
+ return tuple(map(int, (v.split("."))))
+
+
+def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
+
+ # setup marker generator and color map
+ markers = ('s', 'x', 'o', '^', 'v')
+ colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
+ cmap = ListedColormap(colors[:len(np.unique(y))])
+
+ # plot the decision surface
+ x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+ x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+ xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
+ np.arange(x2_min, x2_max, resolution))
+ Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
+ Z = Z.reshape(xx1.shape)
+ plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
+ plt.xlim(xx1.min(), xx1.max())
+ plt.ylim(xx2.min(), xx2.max())
+
+ for idx, cl in enumerate(np.unique(y)):
+ plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
+ alpha=0.8, c=cmap(idx),
+ marker=markers[idx], label=cl)
+
+ # highlight test samples
+ if test_idx:
+ # plot all samples
+ if not versiontuple(np.__version__) >= versiontuple('1.9.0'):
+ X_test, y_test = X[list(test_idx), :], y[list(test_idx)]
+ warnings.warn('Please update to NumPy 1.9.0 or newer')
+ else:
+ X_test, y_test = X[test_idx, :], y[test_idx]
+
+ plt.scatter(X_test[:, 0],
+ X_test[:, 1],
+ c='',
+ alpha=1.0,
+ linewidths=1,
+ marker='o',
+ s=55, label='test set')
+
+
+X_combined_std = np.vstack((X_train_std, X_test_std))
+y_combined = np.hstack((y_train, y_test))
+
+plot_decision_regions(X=X_combined_std, y=y_combined,
+ classifier=ppn, test_idx=range(105, 150))
+plt.xlabel('petal length [standardized]')
+plt.ylabel('petal width [standardized]')
+plt.legend(loc='upper left')
+
+# plt.tight_layout()
+# plt.savefig('./figures/iris_perceptron_scikit.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Section: Logistic regression intuition and conditional probabilities')
+print(50 * '-')
+
+
+def sigmoid(z):
+ return 1.0 / (1.0 + np.exp(-z))
+
+
+z = np.arange(-7, 7, 0.1)
+phi_z = sigmoid(z)
+
+plt.plot(z, phi_z)
+plt.axvline(0.0, color='k')
+plt.ylim(-0.1, 1.1)
+plt.xlabel('z')
+plt.ylabel('$\phi (z)$')
+
+# y axis ticks and gridline
+plt.yticks([0.0, 0.5, 1.0])
+ax = plt.gca()
+ax.yaxis.grid(True)
+
+# plt.tight_layout()
+# plt.savefig('./figures/sigmoid.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Section: Learning the weights of the logistic cost function')
+print(50 * '-')
+
+
+def cost_1(z):
+ return - np.log(sigmoid(z))
+
+
+def cost_0(z):
+ return - np.log(1 - sigmoid(z))
+
+
+z = np.arange(-10, 10, 0.1)
+phi_z = sigmoid(z)
+
+c1 = [cost_1(x) for x in z]
+plt.plot(phi_z, c1, label='J(w) if y=1')
+
+c0 = [cost_0(x) for x in z]
+plt.plot(phi_z, c0, linestyle='--', label='J(w) if y=0')
+
+plt.ylim(0.0, 5.1)
+plt.xlim([0, 1])
+plt.xlabel('$\phi$(z)')
+plt.ylabel('J(w)')
+plt.legend(loc='best')
+# plt.tight_layout()
+# plt.savefig('./figures/log_cost.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Training a logistic regression model with scikit-learn')
+print(50 * '-')
+
+
+lr = LogisticRegression(C=1000.0, random_state=0)
+lr.fit(X_train_std, y_train)
+
+plot_decision_regions(X_combined_std, y_combined,
+ classifier=lr, test_idx=range(105, 150))
+plt.xlabel('petal length [standardized]')
+plt.ylabel('petal width [standardized]')
+plt.legend(loc='upper left')
+# plt.tight_layout()
+# plt.savefig('./figures/logistic_regression.png', dpi=300)
+plt.show()
+
+print('Predicted probabilities', lr.predict_proba(X_test_std[0, :]
+ .reshape(1, -1)))
+
+#############################################################################
+print(50 * '=')
+print('Section: Tackling overfitting via regularization')
+print(50 * '-')
+
+weights, params = [], []
+for c in np.arange(-5.0, 5.0):
+ lr = LogisticRegression(C=10**c, random_state=0)
+ lr.fit(X_train_std, y_train)
+ weights.append(lr.coef_[1])
+ params.append(10**c)
+
+weights = np.array(weights)
+plt.plot(params, weights[:, 0],
+ label='petal length')
+plt.plot(params, weights[:, 1], linestyle='--',
+ label='petal width')
+plt.ylabel('weight coefficient')
+plt.xlabel('C')
+plt.legend(loc='upper left')
+plt.xscale('log')
+# plt.savefig('./figures/regression_path.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Section: Dealing with the nonlinearly'
+ 'separable case using slack variables')
+print(50 * '-')
+
+svm = SVC(kernel='linear', C=1.0, random_state=0)
+svm.fit(X_train_std, y_train)
+
+plot_decision_regions(X_combined_std, y_combined,
+ classifier=svm, test_idx=range(105, 150))
+plt.xlabel('petal length [standardized]')
+plt.ylabel('petal width [standardized]')
+plt.legend(loc='upper left')
+# plt.tight_layout()
+# plt.savefig('./figures/support_vector_machine_linear.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Section: Solving non-linear problems using a kernel SVM')
+print(50 * '-')
+
+np.random.seed(0)
+X_xor = np.random.randn(200, 2)
+y_xor = np.logical_xor(X_xor[:, 0] > 0,
+ X_xor[:, 1] > 0)
+y_xor = np.where(y_xor, 1, -1)
+
+plt.scatter(X_xor[y_xor == 1, 0],
+ X_xor[y_xor == 1, 1],
+ c='b', marker='x',
+ label='1')
+plt.scatter(X_xor[y_xor == -1, 0],
+ X_xor[y_xor == -1, 1],
+ c='r',
+ marker='s',
+ label='-1')
+
+plt.xlim([-3, 3])
+plt.ylim([-3, 3])
+plt.legend(loc='best')
+# plt.tight_layout()
+# plt.savefig('./figures/xor.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Section: Using the kernel trick to find separating hyperplanes'
+ 'in higher dimensional space')
+print(50 * '-')
+
+svm = SVC(kernel='rbf', random_state=0, gamma=0.10, C=10.0)
+svm.fit(X_xor, y_xor)
+plot_decision_regions(X_xor, y_xor,
+ classifier=svm)
+
+plt.legend(loc='upper left')
+# plt.tight_layout()
+# plt.savefig('./figures/support_vector_machine_rbf_xor.png', dpi=300)
+plt.show()
+
+
+svm = SVC(kernel='rbf', random_state=0, gamma=0.2, C=1.0)
+svm.fit(X_train_std, y_train)
+
+plot_decision_regions(X_combined_std, y_combined,
+ classifier=svm, test_idx=range(105, 150))
+plt.xlabel('petal length [standardized]')
+plt.ylabel('petal width [standardized]')
+plt.legend(loc='upper left')
+# plt.tight_layout()
+# plt.savefig('./figures/support_vector_machine_rbf_iris_1.png', dpi=300)
+plt.show()
+
+
+svm = SVC(kernel='rbf', random_state=0, gamma=100.0, C=1.0)
+svm.fit(X_train_std, y_train)
+
+plot_decision_regions(X_combined_std, y_combined,
+ classifier=svm, test_idx=range(105, 150))
+plt.xlabel('petal length [standardized]')
+plt.ylabel('petal width [standardized]')
+plt.legend(loc='upper left')
+# plt.tight_layout()
+# plt.savefig('./figures/support_vector_machine_rbf_iris_2.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Decision tree learning')
+print(50 * '-')
+
+
+def gini(p):
+ return p * (1 - p) + (1 - p) * (1 - (1 - p))
+
+
+def entropy(p):
+ return - p * np.log2(p) - (1 - p) * np.log2((1 - p))
+
+
+def error(p):
+ return 1 - np.max([p, 1 - p])
+
+
+x = np.arange(0.0, 1.0, 0.01)
+
+ent = [entropy(p) if p != 0 else None for p in x]
+sc_ent = [e * 0.5 if e else None for e in ent]
+err = [error(i) for i in x]
+
+fig = plt.figure()
+ax = plt.subplot(111)
+for i, lab, ls, c, in zip([ent, sc_ent, gini(x), err],
+ ['Entropy', 'Entropy (scaled)',
+ 'Gini Impurity', 'Misclassification Error'],
+ ['-', '-', '--', '-.'],
+ ['black', 'lightgray', 'red', 'green', 'cyan']):
+ line = ax.plot(x, i, label=lab, linestyle=ls, lw=2, color=c)
+
+ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15),
+ ncol=3, fancybox=True, shadow=False)
+
+ax.axhline(y=0.5, linewidth=1, color='k', linestyle='--')
+ax.axhline(y=1.0, linewidth=1, color='k', linestyle='--')
+plt.ylim([0, 1.1])
+plt.xlabel('p(i=1)')
+plt.ylabel('Impurity Index')
+# plt.tight_layout()
+# plt.savefig('./figures/impurity.png', dpi=300, bbox_inches='tight')
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Building a decision tree')
+print(50 * '-')
+
+tree = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)
+tree.fit(X_train, y_train)
+
+X_combined = np.vstack((X_train, X_test))
+y_combined = np.hstack((y_train, y_test))
+plot_decision_regions(X_combined, y_combined,
+ classifier=tree, test_idx=range(105, 150))
+
+plt.xlabel('petal length [cm]')
+plt.ylabel('petal width [cm]')
+plt.legend(loc='upper left')
+# plt.tight_layout()
+# plt.savefig('./figures/decision_tree_decision.png', dpi=300)
+plt.show()
+
+# export_graphviz(tree,
+# out_file='tree.dot',
+# feature_names=['petal length', 'petal width'])
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Combining weak to strong learners via random forests')
+print(50 * '-')
+
+
+forest = RandomForestClassifier(criterion='entropy',
+ n_estimators=10,
+ random_state=1,
+ n_jobs=2)
+forest.fit(X_train, y_train)
+
+plot_decision_regions(X_combined, y_combined,
+ classifier=forest, test_idx=range(105, 150))
+
+plt.xlabel('petal length [cm]')
+plt.ylabel('petal width [cm]')
+plt.legend(loc='upper left')
+# plt.tight_layout()
+# plt.savefig('./figures/random_forest.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: K-nearest neighbors - a lazy learning algorithm')
+print(50 * '-')
+
+knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
+knn.fit(X_train_std, y_train)
+
+plot_decision_regions(X_combined_std, y_combined,
+ classifier=knn, test_idx=range(105, 150))
+
+plt.xlabel('petal length [standardized]')
+plt.ylabel('petal width [standardized]')
+plt.legend(loc='upper left')
+# plt.tight_layout()
+# plt.savefig('./figures/k_nearest_neighbors.png', dpi=300)
+plt.show()
diff --git a/code/optional-py-scripts/ch04.py b/code/optional-py-scripts/ch04.py
new file mode 100644
index 00000000..68e75873
--- /dev/null
+++ b/code/optional-py-scripts/ch04.py
@@ -0,0 +1,398 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 4 - Building Good Training Sets – Data Pre-Processing
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+
+import pandas as pd
+import numpy as np
+from io import StringIO
+from sklearn.preprocessing import Imputer
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.base import clone
+from sklearn.metrics import accuracy_score
+from itertools import combinations
+import matplotlib.pyplot as plt
+
+# for sklearn 0.18's alternative syntax
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+ from sklearn.grid_search import train_test_split
+else:
+ from sklearn.model_selection import train_test_split
+
+#############################################################################
+print(50 * '=')
+print('Section: Dealing with missing data')
+print(50 * '-')
+
+csv_data = '''A,B,C,D
+1.0,2.0,3.0,4.0
+5.0,6.0,,8.0
+10.0,11.0,12.0,'''
+
+# If you are using Python 2.7, you need
+# to convert the string to unicode:
+# csv_data = unicode(csv_data)
+
+df = pd.read_csv(StringIO(csv_data))
+print(df)
+print('\n\nExecuting df.isnull().sum():')
+print(df.isnull().sum())
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Eliminating samples or features with missing values')
+print(50 * '-')
+
+print('\n\nExecuting df.dropna()')
+print(df.dropna())
+
+print('\n\nExecuting df.dropna(axis=1)')
+print(df.dropna(axis=1))
+
+print("\n\nExecuting df.dropna(thresh=4)")
+print("(drop rows that have not at least 4 non-NaN values)")
+print(df.dropna(thresh=4))
+
+print("\n\nExecuting df.dropna(how='all')")
+print("(only drop rows where all columns are NaN)")
+print(df.dropna(how='all'))
+
+print("\n\nExecuting df.dropna(subset=['C'])")
+print("(only drop rows where NaN appear in specific columns (here: 'C'))")
+print(df.dropna(subset=['C']))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Imputing missing values')
+print(50 * '-')
+
+imr = Imputer(missing_values='NaN', strategy='mean', axis=0)
+imr = imr.fit(df)
+imputed_data = imr.transform(df.values)
+
+print('Input Array:\n', df.values)
+print('Imputed Data:\n', imputed_data)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Handling categorical data')
+print(50 * '-')
+
+df = pd.DataFrame([['green', 'M', 10.1, 'class1'],
+ ['red', 'L', 13.5, 'class2'],
+ ['blue', 'XL', 15.3, 'class1']])
+
+df.columns = ['color', 'size', 'price', 'classlabel']
+print('Input Array:\n', df)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Mapping ordinal features')
+print(50 * '-')
+
+size_mapping = {'XL': 3,
+ 'L': 2,
+ 'M': 1}
+
+df['size'] = df['size'].map(size_mapping)
+print('Mapping:\n', df)
+
+inv_size_mapping = {v: k for k, v in size_mapping.items()}
+df_inv = df['size'].map(inv_size_mapping)
+print('\nInverse mapping:\n', df_inv)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Encoding class labels')
+print(50 * '-')
+
+class_mapping = {label: idx for idx, label
+ in enumerate(np.unique(df['classlabel']))}
+print('\nClass mapping:\n', class_mapping)
+
+df['classlabel'] = df['classlabel'].map(class_mapping)
+print('Mapping:\n', df)
+
+inv_class_mapping = {v: k for k, v in class_mapping.items()}
+df_inv = df['classlabel'] = df['classlabel'].map(inv_class_mapping)
+print('\nInverse mapping:\n', df_inv)
+
+class_le = LabelEncoder()
+y = class_le.fit_transform(df['classlabel'].values)
+print('Label encoder tansform:\n', y)
+
+y_inv = class_le.inverse_transform(y)
+print('Label encoder inverse tansform:\n', y_inv)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Performing one hot encoding on nominal features')
+print(50 * '-')
+
+X = df[['color', 'size', 'price']].values
+
+color_le = LabelEncoder()
+X[:, 0] = color_le.fit_transform(X[:, 0])
+print("Input array:\n", X)
+
+ohe = OneHotEncoder(categorical_features=[0])
+X_onehot = ohe.fit_transform(X).toarray()
+print("Encoded array:\n", X_onehot)
+
+df_dummies = pd.get_dummies(df[['price', 'color', 'size']])
+print("Pandas get_dummies alternative:\n", df_dummies)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Partitioning a dataset in training and test sets')
+print(50 * '-')
+
+df_wine = pd.read_csv('https://archive.ics.uci.edu/'
+ 'ml/machine-learning-databases/wine/wine.data',
+ header=None)
+
+df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',
+ 'Alcalinity of ash', 'Magnesium', 'Total phenols',
+ 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',
+ 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines',
+ 'Proline']
+
+print('Class labels', np.unique(df_wine['Class label']))
+
+print('\nWine data excerpt:\n\n', df_wine.head())
+
+
+X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values
+
+X_train, X_test, y_train, y_test = \
+ train_test_split(X, y, test_size=0.3, random_state=0)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Bringing features onto the same scale')
+print(50 * '-')
+
+mms = MinMaxScaler()
+X_train_norm = mms.fit_transform(X_train)
+X_test_norm = mms.transform(X_test)
+
+stdsc = StandardScaler()
+X_train_std = stdsc.fit_transform(X_train)
+X_test_std = stdsc.transform(X_test)
+
+ex = pd.DataFrame([0, 1, 2, 3, 4, 5])
+print('Scaling Example:\n')
+print('\nInput array:\n', ex)
+ex[1] = (ex[0] - ex[0].mean()) / ex[0].std(ddof=0)
+
+# Please note that pandas uses ddof=1 (sample standard deviation)
+# by default, whereas NumPy's std method and the StandardScaler
+# uses ddof=0 (population standard deviation)
+
+# normalize
+ex[2] = (ex[0] - ex[0].min()) / (ex[0].max() - ex[0].min())
+ex.columns = ['input', 'standardized', 'normalized']
+print('\nOutput array after scaling:\n', ex)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Sparse solutions with L1-regularization')
+print(50 * '-')
+
+lr = LogisticRegression(penalty='l1', C=0.1)
+lr.fit(X_train_std, y_train)
+print('Training accuracy:', lr.score(X_train_std, y_train))
+print('Test accuracy:', lr.score(X_test_std, y_test))
+print('Intercept:', lr.intercept_)
+print('Model weights:', lr.coef_)
+
+fig = plt.figure()
+ax = plt.subplot(111)
+
+colors = ['blue', 'green', 'red', 'cyan',
+ 'magenta', 'yellow', 'black',
+ 'pink', 'lightgreen', 'lightblue',
+ 'gray', 'indigo', 'orange']
+
+weights, params = [], []
+for c in np.arange(-4.0, 6.0):
+ lr = LogisticRegression(penalty='l1', C=10**c, random_state=0)
+ lr.fit(X_train_std, y_train)
+ weights.append(lr.coef_[1])
+ params.append(10**c)
+
+weights = np.array(weights)
+
+for column, color in zip(range(weights.shape[1]), colors):
+ plt.plot(params, weights[:, column],
+ label=df_wine.columns[column + 1],
+ color=color)
+plt.axhline(0, color='black', linestyle='--', linewidth=3)
+plt.xlim([10**(-5), 10**5])
+plt.ylabel('weight coefficient')
+plt.xlabel('C')
+plt.xscale('log')
+plt.legend(loc='upper left')
+ax.legend(loc='upper center',
+ bbox_to_anchor=(1.38, 1.03),
+ ncol=1, fancybox=True)
+# plt.savefig('./figures/l1_path.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Sequential feature selection algorithms')
+print(50 * '-')
+
+
+class SBS():
+ def __init__(self, estimator, k_features, scoring=accuracy_score,
+ test_size=0.25, random_state=1):
+ self.scoring = scoring
+ self.estimator = clone(estimator)
+ self.k_features = k_features
+ self.test_size = test_size
+ self.random_state = random_state
+
+ def fit(self, X, y):
+
+ X_train, X_test, y_train, y_test = \
+ train_test_split(X, y, test_size=self.test_size,
+ random_state=self.random_state)
+
+ dim = X_train.shape[1]
+ self.indices_ = tuple(range(dim))
+ self.subsets_ = [self.indices_]
+ score = self._calc_score(X_train, y_train,
+ X_test, y_test, self.indices_)
+ self.scores_ = [score]
+
+ while dim > self.k_features:
+ scores = []
+ subsets = []
+
+ for p in combinations(self.indices_, r=dim - 1):
+ score = self._calc_score(X_train, y_train,
+ X_test, y_test, p)
+ scores.append(score)
+ subsets.append(p)
+
+ best = np.argmax(scores)
+ self.indices_ = subsets[best]
+ self.subsets_.append(self.indices_)
+ dim -= 1
+
+ self.scores_.append(scores[best])
+ self.k_score_ = self.scores_[-1]
+
+ return self
+
+ def transform(self, X):
+ return X[:, self.indices_]
+
+ def _calc_score(self, X_train, y_train, X_test, y_test, indices):
+ self.estimator.fit(X_train[:, indices], y_train)
+ y_pred = self.estimator.predict(X_test[:, indices])
+ score = self.scoring(y_test, y_pred)
+ return score
+
+
+knn = KNeighborsClassifier(n_neighbors=2)
+
+# selecting features
+sbs = SBS(knn, k_features=1)
+sbs.fit(X_train_std, y_train)
+
+# plotting performance of feature subsets
+k_feat = [len(k) for k in sbs.subsets_]
+
+plt.plot(k_feat, sbs.scores_, marker='o')
+plt.ylim([0.7, 1.1])
+plt.ylabel('Accuracy')
+plt.xlabel('Number of features')
+plt.grid()
+# plt.tight_layout()
+# plt.savefig('./sbs.png', dpi=300)
+plt.show()
+
+
+k5 = list(sbs.subsets_[8])
+print('Selected top 5 features:\n', df_wine.columns[1:][k5])
+
+knn.fit(X_train_std, y_train)
+print('\nPerformance using all features:\n')
+print('Training accuracy:', knn.score(X_train_std, y_train))
+print('Test accuracy:', knn.score(X_test_std, y_test))
+
+knn.fit(X_train_std[:, k5], y_train)
+print('\nPerformance using the top 5 features:\n')
+print('Training accuracy:', knn.score(X_train_std[:, k5], y_train))
+print('Test accuracy:', knn.score(X_test_std[:, k5], y_test))
+
+#############################################################################
+print(50 * '=')
+print('Section: Assessing Feature Importances with Random Forests')
+print(50 * '-')
+
+feat_labels = df_wine.columns[1:]
+
+forest = RandomForestClassifier(n_estimators=10000,
+ random_state=0,
+ n_jobs=-1)
+
+forest.fit(X_train, y_train)
+importances = forest.feature_importances_
+
+indices = np.argsort(importances)[::-1]
+
+for f in range(X_train.shape[1]):
+ print("%2d) %-*s %f" % (f + 1, 30,
+ feat_labels[indices[f]],
+ importances[indices[f]]))
+
+plt.title('Feature Importances')
+plt.bar(range(X_train.shape[1]),
+ importances[indices],
+ color='lightblue',
+ align='center')
+
+plt.xticks(range(X_train.shape[1]),
+ feat_labels[indices], rotation=90)
+plt.xlim([-1, X_train.shape[1]])
+# plt.tight_layout()
+# plt.savefig('./random_forest.png', dpi=300)
+plt.show()
+
+if Version(sklearn_version) < '0.18':
+ X_selected = forest.transform(X_train, threshold=0.15)
+else:
+ from sklearn.feature_selection import SelectFromModel
+ sfm = SelectFromModel(forest, threshold=0.15, prefit=True)
+ X_selected = sfm.transform(X_train)
+
+X_selected.shape
diff --git a/code/optional-py-scripts/ch05.py b/code/optional-py-scripts/ch05.py
new file mode 100644
index 00000000..215b0150
--- /dev/null
+++ b/code/optional-py-scripts/ch05.py
@@ -0,0 +1,639 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 5 - Compressing Data via Dimensionality Reduction
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+from sklearn.linear_model import LogisticRegression
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.datasets import make_moons
+from sklearn.datasets import make_circles
+from sklearn.decomposition import KernelPCA
+from scipy.spatial.distance import pdist, squareform
+from scipy import exp
+from scipy.linalg import eigh
+from matplotlib.ticker import FormatStrFormatter
+
+# for sklearn 0.18's alternative syntax
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+ from sklearn.grid_search import train_test_split
+ from sklearn.lda import LDA
+else:
+ from sklearn.model_selection import train_test_split
+ from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Unsupervised dimensionality reduction'
+ ' via principal component analysis')
+print(50 * '-')
+
+df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/'
+ 'machine-learning-databases/wine/wine.data',
+ header=None)
+
+df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',
+ 'Alcalinity of ash', 'Magnesium', 'Total phenols',
+ 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',
+ 'Color intensity', 'Hue',
+ 'OD280/OD315 of diluted wines', 'Proline']
+
+print('Wine data excerpt:\n\n:', df_wine.head())
+
+
+X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values
+
+X_train, X_test, y_train, y_test = \
+ train_test_split(X, y, test_size=0.3, random_state=0)
+
+sc = StandardScaler()
+X_train_std = sc.fit_transform(X_train)
+X_test_std = sc.transform(X_test)
+
+cov_mat = np.cov(X_train_std.T)
+eigen_vals, eigen_vecs = np.linalg.eig(cov_mat)
+
+print('\nEigenvalues \n%s' % eigen_vals)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Total and explained variance')
+print(50 * '-')
+
+tot = sum(eigen_vals)
+var_exp = [(i / tot) for i in sorted(eigen_vals, reverse=True)]
+cum_var_exp = np.cumsum(var_exp)
+
+plt.bar(range(1, 14), var_exp, alpha=0.5, align='center',
+ label='individual explained variance')
+plt.step(range(1, 14), cum_var_exp, where='mid',
+ label='cumulative explained variance')
+plt.ylabel('Explained variance ratio')
+plt.xlabel('Principal components')
+plt.legend(loc='best')
+# plt.tight_layout()
+# plt.savefig('./figures/pca1.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Section: Feature Transformation')
+print(50 * '-')
+
+# Make a list of (eigenvalue, eigenvector) tuples
+eigen_pairs = [(np.abs(eigen_vals[i]), eigen_vecs[:, i])
+ for i in range(len(eigen_vals))]
+
+# Sort the (eigenvalue, eigenvector) tuples from high to low
+eigen_pairs.sort(reverse=True)
+
+w = np.hstack((eigen_pairs[0][1][:, np.newaxis],
+ eigen_pairs[1][1][:, np.newaxis]))
+print('Matrix W:\n', w)
+
+X_train_pca = X_train_std.dot(w)
+colors = ['r', 'b', 'g']
+markers = ['s', 'x', 'o']
+
+for l, c, m in zip(np.unique(y_train), colors, markers):
+ plt.scatter(X_train_pca[y_train == l, 0],
+ X_train_pca[y_train == l, 1],
+ c=c, label=l, marker=m)
+
+plt.xlabel('PC 1')
+plt.ylabel('PC 2')
+plt.legend(loc='lower left')
+# plt.tight_layout()
+# plt.savefig('./figures/pca2.png', dpi=300)
+plt.show()
+
+print('Dot product:\n', X_train_std[0].dot(w))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Principal component analysis in scikit-learn')
+print(50 * '-')
+
+pca = PCA()
+X_train_pca = pca.fit_transform(X_train_std)
+print('Variance explained ratio:\n', pca.explained_variance_ratio_)
+
+plt.bar(range(1, 14), pca.explained_variance_ratio_, alpha=0.5, align='center')
+plt.step(range(1, 14), np.cumsum(pca.explained_variance_ratio_), where='mid')
+plt.ylabel('Explained variance ratio')
+plt.xlabel('Principal components')
+plt.show()
+
+pca = PCA(n_components=2)
+X_train_pca = pca.fit_transform(X_train_std)
+X_test_pca = pca.transform(X_test_std)
+
+plt.scatter(X_train_pca[:, 0], X_train_pca[:, 1])
+plt.xlabel('PC 1')
+plt.ylabel('PC 2')
+plt.show()
+
+
+def plot_decision_regions(X, y, classifier, resolution=0.02):
+
+ # setup marker generator and color map
+ markers = ('s', 'x', 'o', '^', 'v')
+ colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
+ cmap = ListedColormap(colors[:len(np.unique(y))])
+
+ # plot the decision surface
+ x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+ x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+ xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
+ np.arange(x2_min, x2_max, resolution))
+ Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
+ Z = Z.reshape(xx1.shape)
+ plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
+ plt.xlim(xx1.min(), xx1.max())
+ plt.ylim(xx2.min(), xx2.max())
+
+ # plot class samples
+ for idx, cl in enumerate(np.unique(y)):
+ plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
+ alpha=0.8, c=cmap(idx),
+ marker=markers[idx], label=cl)
+
+
+lr = LogisticRegression()
+lr = lr.fit(X_train_pca, y_train)
+
+plot_decision_regions(X_train_pca, y_train, classifier=lr)
+plt.xlabel('PC 1')
+plt.ylabel('PC 2')
+plt.legend(loc='lower left')
+# plt.tight_layout()
+# plt.savefig('./figures/pca3.png', dpi=300)
+plt.show()
+
+plot_decision_regions(X_test_pca, y_test, classifier=lr)
+plt.xlabel('PC 1')
+plt.ylabel('PC 2')
+plt.legend(loc='lower left')
+# plt.tight_layout()
+# plt.savefig('./figures/pca4.png', dpi=300)
+plt.show()
+
+pca = PCA(n_components=None)
+X_train_pca = pca.fit_transform(X_train_std)
+print('Explaind variance ratio:\n', pca.explained_variance_ratio_)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Supervised data compression via linear discriminant analysis'
+ ' - Computing the scatter matrices')
+print(50 * '-')
+
+np.set_printoptions(precision=4)
+
+mean_vecs = []
+for label in range(1, 4):
+ mean_vecs.append(np.mean(X_train_std[y_train == label], axis=0))
+ print('MV %s: %s\n' % (label, mean_vecs[label - 1]))
+
+
+d = 13 # number of features
+S_W = np.zeros((d, d))
+for label, mv in zip(range(1, 4), mean_vecs):
+ class_scatter = np.zeros((d, d)) # scatter matrix for each class
+ for row in X_train_std[y_train == label]:
+ row, mv = row.reshape(d, 1), mv.reshape(d, 1) # make column vectors
+ class_scatter += (row - mv).dot((row - mv).T)
+ S_W += class_scatter # sum class scatter matrices
+
+print('Within-class scatter matrix: %sx%s' % (S_W.shape[0], S_W.shape[1]))
+
+print('Class label distribution: %s'
+ % np.bincount(y_train)[1:])
+
+d = 13 # number of features
+S_W = np.zeros((d, d))
+for label, mv in zip(range(1, 4), mean_vecs):
+ class_scatter = np.cov(X_train_std[y_train == label].T)
+ S_W += class_scatter
+print('Scaled within-class scatter matrix: %sx%s' % (S_W.shape[0],
+ S_W.shape[1]))
+
+
+mean_overall = np.mean(X_train_std, axis=0)
+d = 13 # number of features
+S_B = np.zeros((d, d))
+for i, mean_vec in enumerate(mean_vecs):
+ n = X_train[y_train == i + 1, :].shape[0]
+ mean_vec = mean_vec.reshape(d, 1) # make column vector
+ mean_overall = mean_overall.reshape(d, 1) # make column vector
+ S_B += n * (mean_vec - mean_overall).dot((mean_vec - mean_overall).T)
+
+print('Between-class scatter matrix: %sx%s' % (S_B.shape[0], S_B.shape[1]))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Selecting linear discriminants for the new feature subspace')
+print(50 * '-')
+
+eigen_vals, eigen_vecs = np.linalg.eig(np.linalg.inv(S_W).dot(S_B))
+
+# Make a list of (eigenvalue, eigenvector) tuples
+eigen_pairs = [(np.abs(eigen_vals[i]), eigen_vecs[:, i])
+ for i in range(len(eigen_vals))]
+
+# Sort the (eigenvalue, eigenvector) tuples from high to low
+eigen_pairs = sorted(eigen_pairs, key=lambda k: k[0], reverse=True)
+
+# Visually confirm that the list is correctly sorted by decreasing eigenvalues
+
+print('Eigenvalues in decreasing order:\n')
+for eigen_val in eigen_pairs:
+ print(eigen_val[0])
+
+tot = sum(eigen_vals.real)
+discr = [(i / tot) for i in sorted(eigen_vals.real, reverse=True)]
+cum_discr = np.cumsum(discr)
+
+plt.bar(range(1, 14), discr, alpha=0.5, align='center',
+ label='individual "discriminability"')
+plt.step(range(1, 14), cum_discr, where='mid',
+ label='cumulative "discriminability"')
+plt.ylabel('"discriminability" ratio')
+plt.xlabel('Linear Discriminants')
+plt.ylim([-0.1, 1.1])
+plt.legend(loc='best')
+# plt.tight_layout()
+# plt.savefig('./figures/lda1.png', dpi=300)
+plt.show()
+
+w = np.hstack((eigen_pairs[0][1][:, np.newaxis].real,
+ eigen_pairs[1][1][:, np.newaxis].real))
+print('Matrix W:\n', w)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Projecting samples onto the new feature space')
+print(50 * '-')
+
+X_train_lda = X_train_std.dot(w)
+colors = ['r', 'b', 'g']
+markers = ['s', 'x', 'o']
+
+for l, c, m in zip(np.unique(y_train), colors, markers):
+ plt.scatter(X_train_lda[y_train == l, 0] * (-1),
+ X_train_lda[y_train == l, 1] * (-1),
+ c=c, label=l, marker=m)
+
+plt.xlabel('LD 1')
+plt.ylabel('LD 2')
+plt.legend(loc='lower right')
+# plt.tight_layout()
+# plt.savefig('./figures/lda2.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: LDA via scikit-learn')
+print(50 * '-')
+
+lda = LDA(n_components=2)
+X_train_lda = lda.fit_transform(X_train_std, y_train)
+
+lr = LogisticRegression()
+lr = lr.fit(X_train_lda, y_train)
+
+plot_decision_regions(X_train_lda, y_train, classifier=lr)
+plt.xlabel('LD 1')
+plt.ylabel('LD 2')
+plt.legend(loc='lower left')
+# plt.tight_layout()
+# plt.savefig('./images/lda3.png', dpi=300)
+plt.show()
+
+X_test_lda = lda.transform(X_test_std)
+
+plot_decision_regions(X_test_lda, y_test, classifier=lr)
+plt.xlabel('LD 1')
+plt.ylabel('LD 2')
+plt.legend(loc='lower left')
+# plt.tight_layout()
+# plt.savefig('./images/lda4.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Implementing a kernel principal component analysis in Python')
+print(50 * '-')
+
+
+def rbf_kernel_pca(X, gamma, n_components):
+ """
+ RBF kernel PCA implementation.
+
+ Parameters
+ ------------
+ X: {NumPy ndarray}, shape = [n_samples, n_features]
+
+ gamma: float
+ Tuning parameter of the RBF kernel
+
+ n_components: int
+ Number of principal components to return
+
+ Returns
+ ------------
+ X_pc: {NumPy ndarray}, shape = [n_samples, k_features]
+ Projected dataset
+
+ """
+ # Calculate pairwise squared Euclidean distances
+ # in the MxN dimensional dataset.
+ sq_dists = pdist(X, 'sqeuclidean')
+
+ # Convert pairwise distances into a square matrix.
+ mat_sq_dists = squareform(sq_dists)
+
+ # Compute the symmetric kernel matrix.
+ K = exp(-gamma * mat_sq_dists)
+
+ # Center the kernel matrix.
+ N = K.shape[0]
+ one_n = np.ones((N, N)) / N
+ K = K - one_n.dot(K) - K.dot(one_n) + one_n.dot(K).dot(one_n)
+
+ # Obtaining eigenpairs from the centered kernel matrix
+ # numpy.eigh returns them in sorted order
+ eigvals, eigvecs = eigh(K)
+
+ # Collect the top k eigenvectors (projected samples)
+ X_pc = np.column_stack((eigvecs[:, -i]
+ for i in range(1, n_components + 1)))
+
+ return X_pc
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Example 1: Separating half-moon shapes')
+print(50 * '-')
+
+X, y = make_moons(n_samples=100, random_state=123)
+
+plt.scatter(X[y == 0, 0], X[y == 0, 1], color='red', marker='^', alpha=0.5)
+plt.scatter(X[y == 1, 0], X[y == 1, 1], color='blue', marker='o', alpha=0.5)
+
+# plt.tight_layout()
+# plt.savefig('./figures/half_moon_1.png', dpi=300)
+plt.show()
+
+scikit_pca = PCA(n_components=2)
+X_spca = scikit_pca.fit_transform(X)
+
+fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(7, 3))
+
+ax[0].scatter(X_spca[y == 0, 0], X_spca[y == 0, 1],
+ color='red', marker='^', alpha=0.5)
+ax[0].scatter(X_spca[y == 1, 0], X_spca[y == 1, 1],
+ color='blue', marker='o', alpha=0.5)
+
+ax[1].scatter(X_spca[y == 0, 0], np.zeros((50, 1)) + 0.02,
+ color='red', marker='^', alpha=0.5)
+ax[1].scatter(X_spca[y == 1, 0], np.zeros((50, 1)) - 0.02,
+ color='blue', marker='o', alpha=0.5)
+
+ax[0].set_xlabel('PC1')
+ax[0].set_ylabel('PC2')
+ax[1].set_ylim([-1, 1])
+ax[1].set_yticks([])
+ax[1].set_xlabel('PC1')
+
+# plt.tight_layout()
+# plt.savefig('./figures/half_moon_2.png', dpi=300)
+plt.show()
+
+X_kpca = rbf_kernel_pca(X, gamma=15, n_components=2)
+
+fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(7, 3))
+ax[0].scatter(X_kpca[y == 0, 0], X_kpca[y == 0, 1],
+ color='red', marker='^', alpha=0.5)
+ax[0].scatter(X_kpca[y == 1, 0], X_kpca[y == 1, 1],
+ color='blue', marker='o', alpha=0.5)
+
+ax[1].scatter(X_kpca[y == 0, 0], np.zeros((50, 1)) + 0.02,
+ color='red', marker='^', alpha=0.5)
+ax[1].scatter(X_kpca[y == 1, 0], np.zeros((50, 1)) - 0.02,
+ color='blue', marker='o', alpha=0.5)
+
+ax[0].set_xlabel('PC1')
+ax[0].set_ylabel('PC2')
+ax[1].set_ylim([-1, 1])
+ax[1].set_yticks([])
+ax[1].set_xlabel('PC1')
+ax[0].xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
+ax[1].xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
+
+# plt.tight_layout()
+# plt.savefig('./figures/half_moon_3.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Example 2: Separating concentric circles')
+print(50 * '-')
+
+X, y = make_circles(n_samples=1000, random_state=123, noise=0.1, factor=0.2)
+
+plt.scatter(X[y == 0, 0], X[y == 0, 1], color='red', marker='^', alpha=0.5)
+plt.scatter(X[y == 1, 0], X[y == 1, 1], color='blue', marker='o', alpha=0.5)
+
+# plt.tight_layout()
+# plt.savefig('./figures/circles_1.png', dpi=300)
+plt.show()
+
+scikit_pca = PCA(n_components=2)
+X_spca = scikit_pca.fit_transform(X)
+
+fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(7, 3))
+
+ax[0].scatter(X_spca[y == 0, 0], X_spca[y == 0, 1],
+ color='red', marker='^', alpha=0.5)
+ax[0].scatter(X_spca[y == 1, 0], X_spca[y == 1, 1],
+ color='blue', marker='o', alpha=0.5)
+
+ax[1].scatter(X_spca[y == 0, 0], np.zeros((500, 1)) + 0.02,
+ color='red', marker='^', alpha=0.5)
+ax[1].scatter(X_spca[y == 1, 0], np.zeros((500, 1)) - 0.02,
+ color='blue', marker='o', alpha=0.5)
+
+ax[0].set_xlabel('PC1')
+ax[0].set_ylabel('PC2')
+ax[1].set_ylim([-1, 1])
+ax[1].set_yticks([])
+ax[1].set_xlabel('PC1')
+
+# plt.tight_layout()
+# plt.savefig('./figures/circles_2.png', dpi=300)
+plt.show()
+
+
+X_kpca = rbf_kernel_pca(X, gamma=15, n_components=2)
+
+fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(7, 3))
+ax[0].scatter(X_kpca[y == 0, 0], X_kpca[y == 0, 1],
+ color='red', marker='^', alpha=0.5)
+ax[0].scatter(X_kpca[y == 1, 0], X_kpca[y == 1, 1],
+ color='blue', marker='o', alpha=0.5)
+
+ax[1].scatter(X_kpca[y == 0, 0], np.zeros((500, 1)) + 0.02,
+ color='red', marker='^', alpha=0.5)
+ax[1].scatter(X_kpca[y == 1, 0], np.zeros((500, 1)) - 0.02,
+ color='blue', marker='o', alpha=0.5)
+
+ax[0].set_xlabel('PC1')
+ax[0].set_ylabel('PC2')
+ax[1].set_ylim([-1, 1])
+ax[1].set_yticks([])
+ax[1].set_xlabel('PC1')
+
+# plt.tight_layout()
+# plt.savefig('./figures/circles_3.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Projecting new data points')
+print(50 * '-')
+
+
+def rbf_kernel_pca(X, gamma, n_components):
+ """
+ RBF kernel PCA implementation.
+
+ Parameters
+ ------------
+ X: {NumPy ndarray}, shape = [n_samples, n_features]
+
+ gamma: float
+ Tuning parameter of the RBF kernel
+
+ n_components: int
+ Number of principal components to return
+
+ Returns
+ ------------
+ X_pc: {NumPy ndarray}, shape = [n_samples, k_features]
+ Projected dataset
+
+ lambdas: list
+ Eigenvalues
+
+ """
+ # Calculate pairwise squared Euclidean distances
+ # in the MxN dimensional dataset.
+ sq_dists = pdist(X, 'sqeuclidean')
+
+ # Convert pairwise distances into a square matrix.
+ mat_sq_dists = squareform(sq_dists)
+
+ # Compute the symmetric kernel matrix.
+ K = exp(-gamma * mat_sq_dists)
+
+ # Center the kernel matrix.
+ N = K.shape[0]
+ one_n = np.ones((N, N)) / N
+ K = K - one_n.dot(K) - K.dot(one_n) + one_n.dot(K).dot(one_n)
+
+ # Obtaining eigenpairs from the centered kernel matrix
+ # numpy.eigh returns them in sorted order
+ eigvals, eigvecs = eigh(K)
+
+ # Collect the top k eigenvectors (projected samples)
+ alphas = np.column_stack((eigvecs[:, -i]
+ for i in range(1, n_components + 1)))
+
+ # Collect the corresponding eigenvalues
+ lambdas = [eigvals[-i] for i in range(1, n_components + 1)]
+
+ return alphas, lambdas
+
+
+X, y = make_moons(n_samples=100, random_state=123)
+alphas, lambdas = rbf_kernel_pca(X, gamma=15, n_components=1)
+
+
+x_new = X[25]
+print('New data point x_new:', x_new)
+
+x_proj = alphas[25] # original projection
+print('Original projection x_proj:', x_proj)
+
+
+def project_x(x_new, X, gamma, alphas, lambdas):
+ pair_dist = np.array([np.sum((x_new - row)**2) for row in X])
+ k = np.exp(-gamma * pair_dist)
+ return k.dot(alphas / lambdas)
+
+
+# projection of the "new" datapoint
+x_reproj = project_x(x_new, X, gamma=15, alphas=alphas, lambdas=lambdas)
+print('Reprojection x_reproj:', x_reproj)
+
+plt.scatter(alphas[y == 0, 0], np.zeros((50)),
+ color='red', marker='^', alpha=0.5)
+plt.scatter(alphas[y == 1, 0], np.zeros((50)),
+ color='blue', marker='o', alpha=0.5)
+plt.scatter(x_proj, 0, color='black',
+ label='original projection of point X[25]', marker='^', s=100)
+plt.scatter(x_reproj, 0, color='green',
+ label='remapped point X[25]', marker='x', s=500)
+plt.legend(scatterpoints=1)
+
+# plt.tight_layout()
+# plt.savefig('./figures/reproject.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Kernel principal component analysis in scikit-learn')
+print(50 * '-')
+
+
+X, y = make_moons(n_samples=100, random_state=123)
+scikit_kpca = KernelPCA(n_components=2, kernel='rbf', gamma=15)
+X_skernpca = scikit_kpca.fit_transform(X)
+
+plt.scatter(X_skernpca[y == 0, 0], X_skernpca[y == 0, 1],
+ color='red', marker='^', alpha=0.5)
+plt.scatter(X_skernpca[y == 1, 0], X_skernpca[y == 1, 1],
+ color='blue', marker='o', alpha=0.5)
+
+plt.xlabel('PC1')
+plt.ylabel('PC2')
+# plt.tight_layout()
+# plt.savefig('./figures/scikit_kpca.png', dpi=300)
+plt.show()
diff --git a/code/optional-py-scripts/ch06.py b/code/optional-py-scripts/ch06.py
new file mode 100644
index 00000000..e7a7c79c
--- /dev/null
+++ b/code/optional-py-scripts/ch06.py
@@ -0,0 +1,443 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 6 - Learning Best Practices for Model Evaluation
+# and Hyperparameter Tuning
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import Pipeline
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.svm import SVC
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import f1_score
+from sklearn.metrics import recall_score
+from sklearn.metrics import precision_score
+from sklearn.metrics import make_scorer
+from sklearn.metrics import roc_curve
+from sklearn.metrics import auc
+from sklearn.metrics import roc_auc_score
+from sklearn.metrics import accuracy_score
+from scipy import interp
+
+# for sklearn 0.18's alternative syntax
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+ from sklearn.grid_search import train_test_split
+ from sklearn.cross_validation import StratifiedKFold
+ from sklearn.cross_validation import cross_val_score
+ from sklearn.learning_curve import learning_curve
+ from sklearn.learning_curve import validation_curve
+ from sklearn.grid_search import GridSearchCV
+else:
+ from sklearn.model_selection import train_test_split
+ from sklearn.model_selection import StratifiedKFold
+ from sklearn.model_selection import cross_val_score
+ from sklearn.model_selection import learning_curve
+ from sklearn.model_selection import validation_curve
+ from sklearn.model_selection import GridSearchCV
+
+#############################################################################
+print(50 * '=')
+print('Section: Loading the Breast Cancer Wisconsin dataset')
+print(50 * '-')
+
+df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases'
+ '/breast-cancer-wisconsin/wdbc.data', header=None)
+print('Breast Cancer dataset excerpt:\n\n')
+print(df.head())
+
+print('Breast Cancer dataset dimensions:\n\n')
+print(df.shape)
+
+X = df.loc[:, 2:].values
+y = df.loc[:, 1].values
+le = LabelEncoder()
+y = le.fit_transform(y)
+y_enc = le.transform(['M', 'B'])
+print("Label encoding example, le.transform(['M', 'B'])")
+print(le.transform(['M', 'B']))
+
+X_train, X_test, y_train, y_test = \
+ train_test_split(X, y, test_size=0.20, random_state=1)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Combining transformers and estimators in a pipeline')
+print(50 * '-')
+
+
+pipe_lr = Pipeline([('scl', StandardScaler()),
+ ('pca', PCA(n_components=2)),
+ ('clf', LogisticRegression(random_state=1))])
+
+pipe_lr.fit(X_train, y_train)
+print('Test Accuracy: %.3f' % pipe_lr.score(X_test, y_test))
+y_pred = pipe_lr.predict(X_test)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: K-fold cross-validation')
+print(50 * '-')
+
+if Version(sklearn_version) < '0.18':
+ kfold = StratifiedKFold(y=y_train,
+ n_folds=10,
+ random_state=1)
+else:
+ kfold = StratifiedKFold(n_splits=10,
+ random_state=1).split(X_train, y_train)
+
+scores = []
+for k, (train, test) in enumerate(kfold):
+ pipe_lr.fit(X_train[train], y_train[train])
+ score = pipe_lr.score(X_train[test], y_train[test])
+ scores.append(score)
+ print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k + 1,
+ np.bincount(y_train[train]), score))
+
+print('\nCV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
+
+print('Using StratifiedKFold')
+if Version(sklearn_version) < '0.18':
+ kfold = StratifiedKFold(y=y_train,
+ n_folds=10,
+ random_state=1)
+else:
+ kfold = StratifiedKFold(n_splits=10,
+ random_state=1).split(X_train, y_train)
+
+scores = []
+for k, (train, test) in enumerate(kfold):
+ pipe_lr.fit(X_train[train], y_train[train])
+ score = pipe_lr.score(X_train[test], y_train[test])
+ scores.append(score)
+ print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k + 1,
+ np.bincount(y_train[train]), score))
+
+print('\nCV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
+
+
+print('Using cross_val_score')
+scores = cross_val_score(estimator=pipe_lr,
+ X=X_train,
+ y=y_train,
+ cv=10,
+ n_jobs=1)
+
+print('CV accuracy scores: %s' % scores)
+print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Diagnosing bias and variance problems with learning curves')
+print(50 * '-')
+
+
+pipe_lr = Pipeline([('scl', StandardScaler()),
+ ('clf', LogisticRegression(penalty='l2', random_state=0))])
+
+train_sizes, train_scores, test_scores =\
+ learning_curve(estimator=pipe_lr,
+ X=X_train,
+ y=y_train,
+ train_sizes=np.linspace(0.1, 1.0, 10),
+ cv=10,
+ n_jobs=1)
+
+train_mean = np.mean(train_scores, axis=1)
+train_std = np.std(train_scores, axis=1)
+test_mean = np.mean(test_scores, axis=1)
+test_std = np.std(test_scores, axis=1)
+
+plt.plot(train_sizes, train_mean,
+ color='blue', marker='o',
+ markersize=5, label='training accuracy')
+
+plt.fill_between(train_sizes,
+ train_mean + train_std,
+ train_mean - train_std,
+ alpha=0.15, color='blue')
+
+plt.plot(train_sizes, test_mean,
+ color='green', linestyle='--',
+ marker='s', markersize=5,
+ label='validation accuracy')
+
+plt.fill_between(train_sizes,
+ test_mean + test_std,
+ test_mean - test_std,
+ alpha=0.15, color='green')
+
+plt.grid()
+plt.xlabel('Number of training samples')
+plt.ylabel('Accuracy')
+plt.legend(loc='lower right')
+plt.ylim([0.8, 1.0])
+# plt.tight_layout()
+# plt.savefig('./figures/learning_curve.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Addressing over- and underfitting with validation curves')
+print(50 * '-')
+
+param_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]
+train_scores, test_scores = validation_curve(
+ estimator=pipe_lr,
+ X=X_train,
+ y=y_train,
+ param_name='clf__C',
+ param_range=param_range,
+ cv=10)
+
+train_mean = np.mean(train_scores, axis=1)
+train_std = np.std(train_scores, axis=1)
+test_mean = np.mean(test_scores, axis=1)
+test_std = np.std(test_scores, axis=1)
+
+plt.plot(param_range, train_mean,
+ color='blue', marker='o',
+ markersize=5, label='training accuracy')
+
+plt.fill_between(param_range, train_mean + train_std,
+ train_mean - train_std, alpha=0.15,
+ color='blue')
+
+plt.plot(param_range, test_mean,
+ color='green', linestyle='--',
+ marker='s', markersize=5,
+ label='validation accuracy')
+
+plt.fill_between(param_range,
+ test_mean + test_std,
+ test_mean - test_std,
+ alpha=0.15, color='green')
+
+plt.grid()
+plt.xscale('log')
+plt.legend(loc='lower right')
+plt.xlabel('Parameter C')
+plt.ylabel('Accuracy')
+plt.ylim([0.8, 1.0])
+# plt.tight_layout()
+# plt.savefig('./figures/validation_curve.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Tuning hyperparameters via grid search')
+print(50 * '-')
+
+
+pipe_svc = Pipeline([('scl', StandardScaler()),
+ ('clf', SVC(random_state=1))])
+
+param_range = [0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0]
+
+param_grid = [{'clf__C': param_range,
+ 'clf__kernel': ['linear']},
+ {'clf__C': param_range,
+ 'clf__gamma': param_range,
+ 'clf__kernel': ['rbf']}]
+
+gs = GridSearchCV(estimator=pipe_svc,
+ param_grid=param_grid,
+ scoring='accuracy',
+ cv=10,
+ n_jobs=-1)
+gs = gs.fit(X_train, y_train)
+print('Validation accuracy', gs.best_score_)
+print('Best parameters', gs.best_params_)
+
+clf = gs.best_estimator_
+clf.fit(X_train, y_train)
+print('Test accuracy: %.3f' % clf.score(X_test, y_test))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Algorithm selection with nested cross-validation')
+print(50 * '-')
+
+gs = GridSearchCV(estimator=pipe_svc,
+ param_grid=param_grid,
+ scoring='accuracy',
+ cv=2)
+
+# Note: Optionally, you could use cv=2
+# in the GridSearchCV above to produce
+# the 5 x 2 nested CV that is shown in the figure.
+
+scores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)
+print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
+
+gs = GridSearchCV(estimator=DecisionTreeClassifier(random_state=0),
+ param_grid=[{'max_depth': [1, 2, 3, 4, 5, 6, 7, None]}],
+ scoring='accuracy',
+ cv=2)
+scores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)
+print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Reading a confusion matrix')
+print(50 * '-')
+
+pipe_svc.fit(X_train, y_train)
+y_pred = pipe_svc.predict(X_test)
+confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
+print('Confusion matrix', confmat)
+
+fig, ax = plt.subplots(figsize=(2.5, 2.5))
+ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)
+for i in range(confmat.shape[0]):
+ for j in range(confmat.shape[1]):
+ ax.text(x=j, y=i, s=confmat[i, j], va='center', ha='center')
+
+plt.xlabel('predicted label')
+plt.ylabel('true label')
+
+# plt.tight_layout()
+# plt.savefig('./figures/confusion_matrix.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Optimizing the precision and recall of a classification model')
+print(50 * '-')
+
+print('Precision: %.3f' % precision_score(y_true=y_test, y_pred=y_pred))
+print('Recall: %.3f' % recall_score(y_true=y_test, y_pred=y_pred))
+print('F1: %.3f' % f1_score(y_true=y_test, y_pred=y_pred))
+
+scorer = make_scorer(f1_score, pos_label=0)
+
+c_gamma_range = [0.01, 0.1, 1.0, 10.0]
+
+param_grid = [{'clf__C': c_gamma_range,
+ 'clf__kernel': ['linear']},
+ {'clf__C': c_gamma_range,
+ 'clf__gamma': c_gamma_range,
+ 'clf__kernel': ['rbf']}]
+
+gs = GridSearchCV(estimator=pipe_svc,
+ param_grid=param_grid,
+ scoring=scorer,
+ cv=10,
+ n_jobs=-1)
+gs = gs.fit(X_train, y_train)
+print(gs.best_score_)
+print(gs.best_params_)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Plotting a receiver operating characteristic')
+print(50 * '-')
+
+pipe_lr = Pipeline([('scl', StandardScaler()),
+ ('pca', PCA(n_components=2)),
+ ('clf', LogisticRegression(penalty='l2',
+ random_state=0,
+ C=100.0))])
+
+X_train2 = X_train[:, [4, 14]]
+
+if Version(sklearn_version) < '0.18':
+ cv = StratifiedKFold(y_train,
+ n_folds=3,
+ random_state=1)
+
+else:
+ cv = list(StratifiedKFold(n_splits=3,
+ random_state=1).split(X_train, y_train))
+
+fig = plt.figure(figsize=(7, 5))
+
+mean_tpr = 0.0
+mean_fpr = np.linspace(0, 1, 100)
+all_tpr = []
+
+for i, (train, test) in enumerate(cv):
+ probas = pipe_lr.fit(X_train2[train],
+ y_train[train]).predict_proba(X_train2[test])
+
+ fpr, tpr, thresholds = roc_curve(y_train[test],
+ probas[:, 1],
+ pos_label=1)
+ mean_tpr += interp(mean_fpr, fpr, tpr)
+ mean_tpr[0] = 0.0
+ roc_auc = auc(fpr, tpr)
+ plt.plot(fpr,
+ tpr,
+ lw=1,
+ label='ROC fold %d (area = %0.2f)'
+ % (i + 1, roc_auc))
+
+plt.plot([0, 1],
+ [0, 1],
+ linestyle='--',
+ color=(0.6, 0.6, 0.6),
+ label='random guessing')
+
+mean_tpr /= len(cv)
+mean_tpr[-1] = 1.0
+mean_auc = auc(mean_fpr, mean_tpr)
+plt.plot(mean_fpr, mean_tpr, 'k--',
+ label='mean ROC (area = %0.2f)' % mean_auc, lw=2)
+plt.plot([0, 0, 1],
+ [0, 1, 1],
+ lw=2,
+ linestyle=':',
+ color='black',
+ label='perfect performance')
+
+plt.xlim([-0.05, 1.05])
+plt.ylim([-0.05, 1.05])
+plt.xlabel('false positive rate')
+plt.ylabel('true positive rate')
+plt.title('Receiver Operator Characteristic')
+plt.legend(loc="lower right")
+
+# plt.tight_layout()
+# plt.savefig('./figures/roc.png', dpi=300)
+plt.show()
+
+pipe_lr = pipe_lr.fit(X_train2, y_train)
+y_pred2 = pipe_lr.predict(X_test[:, [4, 14]])
+
+print('ROC AUC: %.3f' % roc_auc_score(y_true=y_test, y_score=y_pred2))
+print('Accuracy: %.3f' % accuracy_score(y_true=y_test, y_pred=y_pred2))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: The scoring metrics for multiclass classification')
+print(50 * '-')
+
+pre_scorer = make_scorer(score_func=precision_score,
+ pos_label=1,
+ greater_is_better=True,
+ average='micro')
diff --git a/code/optional-py-scripts/ch07.py b/code/optional-py-scripts/ch07.py
new file mode 100644
index 00000000..c605ef9d
--- /dev/null
+++ b/code/optional-py-scripts/ch07.py
@@ -0,0 +1,595 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 7 - Combining Different Models for Ensemble Learning
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+
+import math
+import numpy as np
+import pandas as pd
+import operator
+from scipy.misc import comb
+import matplotlib.pyplot as plt
+from sklearn.base import BaseEstimator
+from sklearn.base import ClassifierMixin
+from sklearn.preprocessing import LabelEncoder
+from sklearn.externals import six
+from sklearn.base import clone
+from sklearn.pipeline import _name_estimators
+from sklearn import datasets
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import LabelEncoder
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import roc_curve
+from sklearn.metrics import auc
+from sklearn.metrics import accuracy_score
+from sklearn.ensemble import BaggingClassifier
+from sklearn.ensemble import AdaBoostClassifier
+from itertools import product
+
+# Added version check for recent scikit-learn 0.18 checks
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+ from sklearn.cross_validation import train_test_split
+ from sklearn.cross_validation import cross_val_score
+ from sklearn.cross_validation import GridSearchCV
+else:
+ from sklearn.model_selection import train_test_split
+ from sklearn.model_selection import cross_val_score
+ from sklearn.model_selection import GridSearchCV
+
+#############################################################################
+print(50 * '=')
+print('Section: Learning with ensembles')
+print(50 * '-')
+
+
+def ensemble_error(n_classifier, error):
+ k_start = math.ceil(n_classifier / 2.0)
+ probs = [comb(n_classifier, k) * error**k * (1 - error)**(n_classifier - k)
+ for k in range(k_start, n_classifier + 1)]
+ return sum(probs)
+
+print('Ensemble error', ensemble_error(n_classifier=11, error=0.25))
+
+error_range = np.arange(0.0, 1.01, 0.01)
+ens_errors = [ensemble_error(n_classifier=11, error=error)
+ for error in error_range]
+
+plt.plot(error_range,
+ ens_errors,
+ label='Ensemble error',
+ linewidth=2)
+
+plt.plot(error_range,
+ error_range,
+ linestyle='--',
+ label='Base error',
+ linewidth=2)
+
+plt.xlabel('Base error')
+plt.ylabel('Base/Ensemble error')
+plt.legend(loc='upper left')
+plt.grid()
+# plt.tight_layout()
+# plt.savefig('./figures/ensemble_err.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Implementing a simple majority vote classifier')
+print(50 * '-')
+
+np.argmax(np.bincount([0, 0, 1],
+ weights=[0.2, 0.2, 0.6]))
+
+ex = np.array([[0.9, 0.1],
+ [0.8, 0.2],
+ [0.4, 0.6]])
+
+p = np.average(ex,
+ axis=0,
+ weights=[0.2, 0.2, 0.6])
+print('Averaged prediction', p)
+print('np.argmax(p): ', np.argmax(p))
+
+
+class MajorityVoteClassifier(BaseEstimator,
+ ClassifierMixin):
+ """ A majority vote ensemble classifier
+
+ Parameters
+ ----------
+ classifiers : array-like, shape = [n_classifiers]
+ Different classifiers for the ensemble
+
+ vote : str, {'classlabel', 'probability'} (default='label')
+ If 'classlabel' the prediction is based on the argmax of
+ class labels. Else if 'probability', the argmax of
+ the sum of probabilities is used to predict the class label
+ (recommended for calibrated classifiers).
+
+ weights : array-like, shape = [n_classifiers], optional (default=None)
+ If a list of `int` or `float` values are provided, the classifiers
+ are weighted by importance; Uses uniform weights if `weights=None`.
+
+ """
+ def __init__(self, classifiers, vote='classlabel', weights=None):
+
+ self.classifiers = classifiers
+ self.named_classifiers = {key: value for key, value
+ in _name_estimators(classifiers)}
+ self.vote = vote
+ self.weights = weights
+
+ def fit(self, X, y):
+ """ Fit classifiers.
+
+ Parameters
+ ----------
+ X : {array-like, sparse matrix}, shape = [n_samples, n_features]
+ Matrix of training samples.
+
+ y : array-like, shape = [n_samples]
+ Vector of target class labels.
+
+ Returns
+ -------
+ self : object
+
+ """
+ if self.vote not in ('probability', 'classlabel'):
+ raise ValueError("vote must be 'probability' or 'classlabel'"
+ "; got (vote=%r)"
+ % self.vote)
+
+ if self.weights and len(self.weights) != len(self.classifiers):
+ raise ValueError('Number of classifiers and weights must be equal'
+ '; got %d weights, %d classifiers'
+ % (len(self.weights), len(self.classifiers)))
+
+ # Use LabelEncoder to ensure class labels start with 0, which
+ # is important for np.argmax call in self.predict
+ self.lablenc_ = LabelEncoder()
+ self.lablenc_.fit(y)
+ self.classes_ = self.lablenc_.classes_
+ self.classifiers_ = []
+ for clf in self.classifiers:
+ fitted_clf = clone(clf).fit(X, self.lablenc_.transform(y))
+ self.classifiers_.append(fitted_clf)
+ return self
+
+ def predict(self, X):
+ """ Predict class labels for X.
+
+ Parameters
+ ----------
+ X : {array-like, sparse matrix}, shape = [n_samples, n_features]
+ Matrix of training samples.
+
+ Returns
+ ----------
+ maj_vote : array-like, shape = [n_samples]
+ Predicted class labels.
+
+ """
+ if self.vote == 'probability':
+ maj_vote = np.argmax(self.predict_proba(X), axis=1)
+ else: # 'classlabel' vote
+
+ # Collect results from clf.predict calls
+ predictions = np.asarray([clf.predict(X)
+ for clf in self.classifiers_]).T
+
+ maj_vote = np.apply_along_axis(
+ lambda x:
+ np.argmax(np.bincount(x,
+ weights=self.weights)),
+ axis=1,
+ arr=predictions)
+ maj_vote = self.lablenc_.inverse_transform(maj_vote)
+ return maj_vote
+
+ def predict_proba(self, X):
+ """ Predict class probabilities for X.
+
+ Parameters
+ ----------
+ X : {array-like, sparse matrix}, shape = [n_samples, n_features]
+ Training vectors, where n_samples is the number of samples and
+ n_features is the number of features.
+
+ Returns
+ ----------
+ avg_proba : array-like, shape = [n_samples, n_classes]
+ Weighted average probability for each class per sample.
+
+ """
+ probas = np.asarray([clf.predict_proba(X)
+ for clf in self.classifiers_])
+ avg_proba = np.average(probas, axis=0, weights=self.weights)
+ return avg_proba
+
+ def get_params(self, deep=True):
+ """ Get classifier parameter names for GridSearch"""
+ if not deep:
+ return super(MajorityVoteClassifier, self).get_params(deep=False)
+ else:
+ out = self.named_classifiers.copy()
+ for name, step in six.iteritems(self.named_classifiers):
+ for key, value in six.iteritems(step.get_params(deep=True)):
+ out['%s__%s' % (name, key)] = value
+ return out
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Combining different algorithms for'
+ ' classification with majority vote')
+print(50 * '-')
+
+iris = datasets.load_iris()
+X, y = iris.data[50:, [1, 2]], iris.target[50:]
+le = LabelEncoder()
+y = le.fit_transform(y)
+
+X_train, X_test, y_train, y_test =\
+ train_test_split(X, y,
+ test_size=0.5,
+ random_state=1)
+
+clf1 = LogisticRegression(penalty='l2',
+ C=0.001,
+ random_state=0)
+
+clf2 = DecisionTreeClassifier(max_depth=1,
+ criterion='entropy',
+ random_state=0)
+
+clf3 = KNeighborsClassifier(n_neighbors=1,
+ p=2,
+ metric='minkowski')
+
+pipe1 = Pipeline([['sc', StandardScaler()],
+ ['clf', clf1]])
+pipe3 = Pipeline([['sc', StandardScaler()],
+ ['clf', clf3]])
+
+clf_labels = ['Logistic Regression', 'Decision Tree', 'KNN']
+
+print('10-fold cross validation:\n')
+for clf, label in zip([pipe1, clf2, pipe3], clf_labels):
+ scores = cross_val_score(estimator=clf,
+ X=X_train,
+ y=y_train,
+ cv=10,
+ scoring='roc_auc')
+ print("ROC AUC: %0.2f (+/- %0.2f) [%s]"
+ % (scores.mean(), scores.std(), label))
+
+
+mv_clf = MajorityVoteClassifier(classifiers=[pipe1, clf2, pipe3])
+
+clf_labels += ['Majority Voting']
+all_clf = [pipe1, clf2, pipe3, mv_clf]
+
+for clf, label in zip(all_clf, clf_labels):
+ scores = cross_val_score(estimator=clf,
+ X=X_train,
+ y=y_train,
+ cv=10,
+ scoring='roc_auc')
+ print("ROC AUC: %0.2f (+/- %0.2f) [%s]"
+ % (scores.mean(), scores.std(), label))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Evaluating and tuning the ensemble classifier')
+print(50 * '-')
+
+
+colors = ['black', 'orange', 'blue', 'green']
+linestyles = [':', '--', '-.', '-']
+for clf, label, clr, ls \
+ in zip(all_clf,
+ clf_labels, colors, linestyles):
+
+ # assuming the label of the positive class is 1
+ y_pred = clf.fit(X_train,
+ y_train).predict_proba(X_test)[:, 1]
+ fpr, tpr, thresholds = roc_curve(y_true=y_test,
+ y_score=y_pred)
+ roc_auc = auc(x=fpr, y=tpr)
+ plt.plot(fpr, tpr,
+ color=clr,
+ linestyle=ls,
+ label='%s (auc = %0.2f)' % (label, roc_auc))
+
+plt.legend(loc='lower right')
+plt.plot([0, 1], [0, 1],
+ linestyle='--',
+ color='gray',
+ linewidth=2)
+
+plt.xlim([-0.1, 1.1])
+plt.ylim([-0.1, 1.1])
+plt.grid()
+plt.xlabel('False Positive Rate')
+plt.ylabel('True Positive Rate')
+
+# plt.tight_layout()
+# plt.savefig('./figures/roc.png', dpi=300)
+plt.show()
+
+
+sc = StandardScaler()
+X_train_std = sc.fit_transform(X_train)
+
+
+all_clf = [pipe1, clf2, pipe3, mv_clf]
+
+x_min = X_train_std[:, 0].min() - 1
+x_max = X_train_std[:, 0].max() + 1
+y_min = X_train_std[:, 1].min() - 1
+y_max = X_train_std[:, 1].max() + 1
+
+xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
+ np.arange(y_min, y_max, 0.1))
+
+f, axarr = plt.subplots(nrows=2, ncols=2,
+ sharex='col',
+ sharey='row',
+ figsize=(7, 5))
+
+for idx, clf, tt in zip(product([0, 1], [0, 1]),
+ all_clf, clf_labels):
+ clf.fit(X_train_std, y_train)
+
+ Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+ Z = Z.reshape(xx.shape)
+
+ axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.3)
+
+ axarr[idx[0], idx[1]].scatter(X_train_std[y_train == 0, 0],
+ X_train_std[y_train == 0, 1],
+ c='blue',
+ marker='^',
+ s=50)
+
+ axarr[idx[0], idx[1]].scatter(X_train_std[y_train == 1, 0],
+ X_train_std[y_train == 1, 1],
+ c='red',
+ marker='o',
+ s=50)
+
+ axarr[idx[0], idx[1]].set_title(tt)
+
+plt.text(-3.5, -4.5,
+ s='Sepal width [standardized]',
+ ha='center', va='center', fontsize=12)
+plt.text(-10.5, 4.5,
+ s='Petal length [standardized]',
+ ha='center', va='center',
+ fontsize=12, rotation=90)
+
+# plt.tight_layout()
+# plt.savefig('./figures/voting_panel', bbox_inches='tight', dpi=300)
+plt.show()
+
+print(mv_clf.get_params())
+
+params = {'decisiontreeclassifier__max_depth': [1, 2],
+ 'pipeline-1__clf__C': [0.001, 0.1, 100.0]}
+
+grid = GridSearchCV(estimator=mv_clf,
+ param_grid=params,
+ cv=10,
+ scoring='roc_auc')
+grid.fit(X_train, y_train)
+
+if Version(sklearn_version) < '0.18':
+ for params, mean_score, scores in grid.grid_scores_:
+ print("%0.3f +/- %0.2f %r"
+ % (mean_score, scores.std() / 2.0, params))
+
+else:
+ cv_keys = ('mean_test_score', 'std_test_score', 'params')
+
+ for r, _ in enumerate(grid.cv_results_['mean_test_score']):
+ print("%0.3f +/- %0.2f %r"
+ % (grid.cv_results_[cv_keys[0]][r],
+ grid.cv_results_[cv_keys[1]][r] / 2.0,
+ grid.cv_results_[cv_keys[2]][r]))
+
+print('Best parameters: %s' % grid.best_params_)
+print('Accuracy: %.2f' % grid.best_score_)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Bagging -- Building an ensemble of'
+ 'classifiers from bootstrap samples')
+print(50 * '-')
+
+df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/'
+ 'machine-learning-databases/wine/wine.data',
+ header=None)
+
+df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',
+ 'Alcalinity of ash', 'Magnesium', 'Total phenols',
+ 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',
+ 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines',
+ 'Proline']
+
+# drop 1 class
+df_wine = df_wine[df_wine['Class label'] != 1]
+
+y = df_wine['Class label'].values
+X = df_wine[['Alcohol', 'Hue']].values
+
+
+le = LabelEncoder()
+y = le.fit_transform(y)
+
+X_train, X_test, y_train, y_test =\
+ train_test_split(X, y,
+ test_size=0.40,
+ random_state=1)
+
+tree = DecisionTreeClassifier(criterion='entropy',
+ max_depth=None,
+ random_state=1)
+
+bag = BaggingClassifier(base_estimator=tree,
+ n_estimators=500,
+ max_samples=1.0,
+ max_features=1.0,
+ bootstrap=True,
+ bootstrap_features=False,
+ n_jobs=1,
+ random_state=1)
+
+tree = tree.fit(X_train, y_train)
+y_train_pred = tree.predict(X_train)
+y_test_pred = tree.predict(X_test)
+
+tree_train = accuracy_score(y_train, y_train_pred)
+tree_test = accuracy_score(y_test, y_test_pred)
+print('Decision tree train/test accuracies %.3f/%.3f'
+ % (tree_train, tree_test))
+
+bag = bag.fit(X_train, y_train)
+y_train_pred = bag.predict(X_train)
+y_test_pred = bag.predict(X_test)
+
+bag_train = accuracy_score(y_train, y_train_pred)
+bag_test = accuracy_score(y_test, y_test_pred)
+print('Bagging train/test accuracies %.3f/%.3f'
+ % (bag_train, bag_test))
+
+
+x_min = X_train[:, 0].min() - 1
+x_max = X_train[:, 0].max() + 1
+y_min = X_train[:, 1].min() - 1
+y_max = X_train[:, 1].max() + 1
+
+xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
+ np.arange(y_min, y_max, 0.1))
+
+f, axarr = plt.subplots(nrows=1, ncols=2,
+ sharex='col',
+ sharey='row',
+ figsize=(8, 3))
+
+
+for idx, clf, tt in zip([0, 1],
+ [tree, bag],
+ ['Decision Tree', 'Bagging']):
+ clf.fit(X_train, y_train)
+
+ Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+ Z = Z.reshape(xx.shape)
+
+ axarr[idx].contourf(xx, yy, Z, alpha=0.3)
+ axarr[idx].scatter(X_train[y_train == 0, 0],
+ X_train[y_train == 0, 1],
+ c='blue', marker='^')
+
+ axarr[idx].scatter(X_train[y_train == 1, 0],
+ X_train[y_train == 1, 1],
+ c='red', marker='o')
+
+ axarr[idx].set_title(tt)
+
+axarr[0].set_ylabel('Alcohol', fontsize=12)
+plt.text(10.2, -1.2,
+ s='Hue',
+ ha='center', va='center', fontsize=12)
+
+# plt.tight_layout()
+# plt.savefig('./figures/bagging_region.png',
+# dpi=300,
+# bbox_inches='tight')
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Leveraging weak learners via adaptive boosting')
+print(50 * '-')
+
+tree = DecisionTreeClassifier(criterion='entropy',
+ max_depth=1,
+ random_state=0)
+
+ada = AdaBoostClassifier(base_estimator=tree,
+ n_estimators=500,
+ learning_rate=0.1,
+ random_state=0)
+
+tree = tree.fit(X_train, y_train)
+y_train_pred = tree.predict(X_train)
+y_test_pred = tree.predict(X_test)
+
+tree_train = accuracy_score(y_train, y_train_pred)
+tree_test = accuracy_score(y_test, y_test_pred)
+print('Decision tree train/test accuracies %.3f/%.3f'
+ % (tree_train, tree_test))
+
+ada = ada.fit(X_train, y_train)
+y_train_pred = ada.predict(X_train)
+y_test_pred = ada.predict(X_test)
+
+ada_train = accuracy_score(y_train, y_train_pred)
+ada_test = accuracy_score(y_test, y_test_pred)
+print('AdaBoost train/test accuracies %.3f/%.3f'
+ % (ada_train, ada_test))
+
+
+x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1
+y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1
+xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
+ np.arange(y_min, y_max, 0.1))
+
+f, axarr = plt.subplots(1, 2, sharex='col', sharey='row', figsize=(8, 3))
+
+
+for idx, clf, tt in zip([0, 1],
+ [tree, ada],
+ ['Decision Tree', 'AdaBoost']):
+ clf.fit(X_train, y_train)
+
+ Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+ Z = Z.reshape(xx.shape)
+
+ axarr[idx].contourf(xx, yy, Z, alpha=0.3)
+ axarr[idx].scatter(X_train[y_train == 0, 0],
+ X_train[y_train == 0, 1],
+ c='blue', marker='^')
+ axarr[idx].scatter(X_train[y_train == 1, 0],
+ X_train[y_train == 1, 1],
+ c='red', marker='o')
+ axarr[idx].set_title(tt)
+
+axarr[0].set_ylabel('Alcohol', fontsize=12)
+plt.text(10.2, -1.2,
+ s='Hue',
+ ha='center', va='center', fontsize=12)
+
+# plt.tight_layout()
+# plt.savefig('./figures/adaboost_region.png',
+# dpi=300,
+# bbox_inches='tight')
+plt.show()
diff --git a/code/optional-py-scripts/ch08.py b/code/optional-py-scripts/ch08.py
new file mode 100644
index 00000000..c50145c5
--- /dev/null
+++ b/code/optional-py-scripts/ch08.py
@@ -0,0 +1,290 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 8 - Applying Machine Learning To Sentiment Analysis
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+import pyprind
+import pandas as pd
+import os
+import numpy as np
+import re
+import nltk
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LogisticRegression
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.feature_extraction.text import HashingVectorizer
+from sklearn.linear_model import SGDClassifier
+from nltk.stem.porter import PorterStemmer
+from nltk.corpus import stopwords
+
+# Added version check for recent scikit-learn 0.18 checks
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+ from sklearn.cross_validation import GridSearchCV
+else:
+ from sklearn.model_selection import GridSearchCV
+
+#############################################################################
+print(50 * '=')
+print('Section: Obtaining the IMDb movie review dataset')
+print(50 * '-')
+
+print('!! This script assumes that the movie dataset is located in the'
+ ' current directory under ./aclImdb')
+
+_ = input('Please hit enter to continue.')
+
+basepath = './aclImdb'
+
+"""
+labels = {'pos': 1, 'neg': 0}
+pbar = pyprind.ProgBar(50000)
+df = pd.DataFrame()
+for s in ('test', 'train'):
+ for l in ('pos', 'neg'):
+ path = os.path.join(basepath, s, l)
+ for file in os.listdir(path):
+ with open(os.path.join(path, file), 'r',
+ encoding='utf-8') as infile:
+ txt = infile.read()
+ df = df.append([[txt, labels[l]]], ignore_index=True)
+ pbar.update()
+df.columns = ['review', 'sentiment']
+
+
+np.random.seed(0)
+df = df.reindex(np.random.permutation(df.index))
+
+df.to_csv('./movie_data.csv', index=False)
+"""
+
+df = pd.read_csv('../datasets/movie/movie_data.csv')
+print('Excerpt of the movie dataset', df.head(3))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Transforming documents into feature vectors')
+print(50 * '-')
+
+count = CountVectorizer()
+docs = np.array(['The sun is shining',
+ 'The weather is sweet',
+ 'The sun is shining and the weather is sweet'])
+bag = count.fit_transform(docs)
+
+print('Vocabulary', count.vocabulary_)
+print('bag.toarray()', bag.toarray())
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Assessing word relevancy via term frequency-inverse'
+ ' document frequency')
+print(50 * '-')
+
+np.set_printoptions(precision=2)
+tfidf = TfidfTransformer(use_idf=True, norm='l2', smooth_idf=True)
+print(tfidf.fit_transform(count.fit_transform(docs)).toarray())
+
+tf_is = 2
+n_docs = 3
+idf_is = np.log((n_docs + 1) / (3 + 1))
+tfidf_is = tf_is * (idf_is + 1)
+print('tf-idf of term "is" = %.2f' % tfidf_is)
+
+
+tfidf = TfidfTransformer(use_idf=True, norm=None, smooth_idf=True)
+raw_tfidf = tfidf.fit_transform(count.fit_transform(docs)).toarray()[-1]
+print('raw tf-idf', raw_tfidf)
+
+l2_tfidf = raw_tfidf / np.sqrt(np.sum(raw_tfidf**2))
+l2_tfidf
+print('l2 tf-idf', l2_tfidf)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Cleaning text data')
+print(50 * '-')
+
+print('Excerpt:\n\n', df.loc[0, 'review'][-50:])
+
+
+def preprocessor(text):
+ text = re.sub('<[^>]*>', '', text)
+ emoticons = re.findall('(?::|;|=)(?:-)?(?:\)|\(|D|P)', text)
+ text = re.sub('[\W]+', ' ', text.lower()) +\
+ ' '.join(emoticons).replace('-', '')
+ return text
+
+
+print('Preprocessor on Excerpt:\n\n', preprocessor(df.loc[0, 'review'][-50:]))
+
+res = preprocessor("This :) is :( a test :-)!")
+print('Preprocessor on "This :) is :( a test :-)!":\n\n', res)
+
+df['review'] = df['review'].apply(preprocessor)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Processing documents into tokens')
+print(50 * '-')
+
+porter = PorterStemmer()
+
+
+def tokenizer(text):
+ return text.split()
+
+
+def tokenizer_porter(text):
+ return [porter.stem(word) for word in text.split()]
+
+
+t1 = tokenizer('runners like running and thus they run')
+print("Tokenize: 'runners like running and thus they run'")
+print(t1)
+
+t2 = tokenizer_porter('runners like running and thus they run')
+print("\nPorter-Tokenize: 'runners like running and thus they run'")
+print(t2)
+
+nltk.download('stopwords')
+
+
+print('remove stop words')
+stop = stopwords.words('english')
+
+r = [w for w in tokenizer_porter('a runner likes running and runs a lot')[-10:]
+ if w not in stop]
+
+print(r)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Training a logistic regression model'
+ ' for document classification')
+print(50 * '-')
+
+
+X_train = df.loc[:25000, 'review'].values
+y_train = df.loc[:25000, 'sentiment'].values
+X_test = df.loc[25000:, 'review'].values
+y_test = df.loc[25000:, 'sentiment'].values
+
+
+tfidf = TfidfVectorizer(strip_accents=None,
+ lowercase=False,
+ preprocessor=None)
+
+param_grid = [{'vect__ngram_range': [(1, 1)],
+ 'vect__stop_words': [stop, None],
+ 'vect__tokenizer': [tokenizer, tokenizer_porter],
+ 'clf__penalty': ['l1', 'l2'],
+ 'clf__C': [1.0, 10.0, 100.0]},
+ {'vect__ngram_range': [(1, 1)],
+ 'vect__stop_words': [stop, None],
+ 'vect__tokenizer': [tokenizer, tokenizer_porter],
+ 'vect__use_idf':[False],
+ 'vect__norm':[None],
+ 'clf__penalty': ['l1', 'l2'],
+ 'clf__C': [1.0, 10.0, 100.0]},
+ ]
+
+lr_tfidf = Pipeline([('vect', tfidf),
+ ('clf', LogisticRegression(random_state=0))])
+
+gs_lr_tfidf = GridSearchCV(lr_tfidf, param_grid,
+ scoring='accuracy',
+ cv=5,
+ verbose=1,
+ n_jobs=-1)
+
+gs_lr_tfidf.fit(X_train, y_train)
+
+print('Best parameter set: %s ' % gs_lr_tfidf.best_params_)
+print('CV Accuracy: %.3f' % gs_lr_tfidf.best_score_)
+
+
+clf = gs_lr_tfidf.best_estimator_
+print('Test Accuracy: %.3f' % clf.score(X_test, y_test))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Working with bigger data - online'
+ ' algorithms and out-of-core learning')
+print(50 * '-')
+
+stop = stopwords.words('english')
+
+
+def tokenizer(text):
+ text = re.sub('<[^>]*>', '', text)
+ emoticons = re.findall('(?::|;|=)(?:-)?(?:\)|\(|D|P)', text.lower())
+ text = re.sub('[\W]+', ' ', text.lower()) +\
+ ' '.join(emoticons).replace('-', '')
+ tokenized = [w for w in text.split() if w not in stop]
+ return tokenized
+
+
+def stream_docs(path):
+ with open(path, 'r', encoding='utf-8') as csv:
+ next(csv) # skip header
+ for line in csv:
+ text, label = line[:-3], int(line[-2])
+ yield text, label
+
+
+next(stream_docs(path='./movie_data.csv'))
+
+
+def get_minibatch(doc_stream, size):
+ docs, y = [], []
+ try:
+ for _ in range(size):
+ text, label = next(doc_stream)
+ docs.append(text)
+ y.append(label)
+ except StopIteration:
+ return None, None
+ return docs, y
+
+
+vect = HashingVectorizer(decode_error='ignore',
+ n_features=2**21,
+ preprocessor=None,
+ tokenizer=tokenizer)
+
+clf = SGDClassifier(loss='log', random_state=1, n_iter=1)
+doc_stream = stream_docs(path='./movie_data.csv')
+
+pbar = pyprind.ProgBar(45)
+
+classes = np.array([0, 1])
+for _ in range(45):
+ X_train, y_train = get_minibatch(doc_stream, size=1000)
+ if not X_train:
+ break
+ X_train = vect.transform(X_train)
+ clf.partial_fit(X_train, y_train, classes=classes)
+ pbar.update()
+
+
+X_test, y_test = get_minibatch(doc_stream, size=5000)
+X_test = vect.transform(X_test)
+print('Accuracy: %.3f' % clf.score(X_test, y_test))
+
+clf = clf.partial_fit(X_test, y_test)
diff --git a/code/optional-py-scripts/ch09.py b/code/optional-py-scripts/ch09.py
new file mode 100644
index 00000000..fa75836a
--- /dev/null
+++ b/code/optional-py-scripts/ch09.py
@@ -0,0 +1,23 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 9 - Embedding a Machine Learning Model into a Web Application
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+
+s = """
+
+Due to the complexity of this chapter, and the many files involved,
+please refer to the IPython Notebook at
+https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb
+
+The web application files can be obtained from
+https://github.com/rasbt/python-machine-learning-book/tree/master/code/ch09
+
+"""
+print(s)
diff --git a/code/optional-py-scripts/ch10.py b/code/optional-py-scripts/ch10.py
new file mode 100644
index 00000000..17e8eb98
--- /dev/null
+++ b/code/optional-py-scripts/ch10.py
@@ -0,0 +1,510 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 10 - Predicting Continuous Target Variables with Regression Analysis
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import RANSACRegressor
+from sklearn.cross_validation import train_test_split
+from sklearn.metrics import r2_score
+from sklearn.metrics import mean_squared_error
+from sklearn.linear_model import Lasso
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor
+
+# Added version check for recent scikit-learn 0.18 checks
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+ from sklearn.cross_validation import train_test_split
+else:
+ from sklearn.model_selection import train_test_split
+
+#############################################################################
+print(50 * '=')
+print('Section: Exploring the Housing dataset')
+print(50 * '-')
+
+df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/'
+ 'housing/housing.data',
+ header=None,
+ sep='\s+')
+
+df.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS',
+ 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
+ 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
+print('Dataset excerpt:\n\n', df.head())
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Visualizing the important characteristics of a dataset')
+print(50 * '-')
+
+sns.set(style='whitegrid', context='notebook')
+cols = ['LSTAT', 'INDUS', 'NOX', 'RM', 'MEDV']
+
+sns.pairplot(df[cols], size=2.5)
+# plt.tight_layout()
+# plt.savefig('./figures/scatter.png', dpi=300)
+plt.show()
+
+
+cm = np.corrcoef(df[cols].values.T)
+sns.set(font_scale=1.5)
+hm = sns.heatmap(cm,
+ cbar=True,
+ annot=True,
+ square=True,
+ fmt='.2f',
+ annot_kws={'size': 15},
+ yticklabels=cols,
+ xticklabels=cols)
+
+# plt.tight_layout()
+# plt.savefig('./figures/corr_mat.png', dpi=300)
+plt.show()
+
+sns.reset_orig()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Solving regression for regression'
+ ' parameters with gradient descent')
+print(50 * '-')
+
+
+class LinearRegressionGD(object):
+
+ def __init__(self, eta=0.001, n_iter=20):
+ self.eta = eta
+ self.n_iter = n_iter
+
+ def fit(self, X, y):
+ self.w_ = np.zeros(1 + X.shape[1])
+ self.cost_ = []
+
+ for i in range(self.n_iter):
+ output = self.net_input(X)
+ errors = (y - output)
+ self.w_[1:] += self.eta * X.T.dot(errors)
+ self.w_[0] += self.eta * errors.sum()
+ cost = (errors**2).sum() / 2.0
+ self.cost_.append(cost)
+ return self
+
+ def net_input(self, X):
+ return np.dot(X, self.w_[1:]) + self.w_[0]
+
+ def predict(self, X):
+ return self.net_input(X)
+
+
+X = df[['RM']].values
+y = df['MEDV'].values
+
+sc_x = StandardScaler()
+sc_y = StandardScaler()
+X_std = sc_x.fit_transform(X)
+y_std = sc_y.fit_transform(y[:, np.newaxis]).flatten()
+
+lr = LinearRegressionGD()
+lr.fit(X_std, y_std)
+
+
+plt.plot(range(1, lr.n_iter+1), lr.cost_)
+plt.ylabel('SSE')
+plt.xlabel('Epoch')
+# plt.tight_layout()
+# plt.savefig('./figures/cost.png', dpi=300)
+plt.show()
+
+
+def lin_regplot(X, y, model):
+ plt.scatter(X, y, c='lightblue')
+ plt.plot(X, model.predict(X), color='red', linewidth=2)
+ return
+
+
+lin_regplot(X_std, y_std, lr)
+plt.xlabel('Average number of rooms [RM] (standardized)')
+plt.ylabel('Price in $1000\'s [MEDV] (standardized)')
+# plt.tight_layout()
+# plt.savefig('./figures/gradient_fit.png', dpi=300)
+plt.show()
+
+
+print('Slope: %.3f' % lr.w_[1])
+print('Intercept: %.3f' % lr.w_[0])
+
+
+num_rooms_std = sc_x.transform(np.array([[5.0]]))
+price_std = lr.predict(num_rooms_std)
+print("Price in $1000's: %.3f" % sc_y.inverse_transform(price_std))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Estimating the coefficient of a'
+ ' regression model via scikit-learn')
+print(50 * '-')
+
+slr = LinearRegression()
+slr.fit(X, y)
+y_pred = slr.predict(X)
+print('Slope: %.3f' % slr.coef_[0])
+print('Intercept: %.3f' % slr.intercept_)
+
+lin_regplot(X, y, slr)
+plt.xlabel('Average number of rooms [RM]')
+plt.ylabel('Price in $1000\'s [MEDV]')
+# plt.tight_layout()
+# plt.savefig('./figures/scikit_lr_fit.png', dpi=300)
+plt.show()
+
+
+# adding a column vector of "ones"
+Xb = np.hstack((np.ones((X.shape[0], 1)), X))
+w = np.zeros(X.shape[1])
+z = np.linalg.inv(np.dot(Xb.T, Xb))
+w = np.dot(z, np.dot(Xb.T, y))
+
+print('Slope: %.3f' % w[1])
+print('Intercept: %.3f' % w[0])
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Fitting a robust regression model using RANSAC')
+print(50 * '-')
+
+
+if Version(sklearn_version) < '0.18':
+ ransac = RANSACRegressor(LinearRegression(),
+ max_trials=100,
+ min_samples=50,
+ residual_metric=lambda x: np.sum(
+ np.abs(x), axis=1),
+ residual_threshold=5.0,
+ random_state=0)
+else:
+ ransac = RANSACRegressor(LinearRegression(),
+ max_trials=100,
+ min_samples=50,
+ loss='absolute_loss',
+ residual_threshold=5.0,
+ random_state=0)
+ransac.fit(X, y)
+inlier_mask = ransac.inlier_mask_
+outlier_mask = np.logical_not(inlier_mask)
+
+line_X = np.arange(3, 10, 1)
+line_y_ransac = ransac.predict(line_X[:, np.newaxis])
+plt.scatter(X[inlier_mask], y[inlier_mask],
+ c='blue', marker='o', label='Inliers')
+plt.scatter(X[outlier_mask], y[outlier_mask],
+ c='lightgreen', marker='s', label='Outliers')
+plt.plot(line_X, line_y_ransac, color='red')
+plt.xlabel('Average number of rooms [RM]')
+plt.ylabel('Price in $1000\'s [MEDV]')
+plt.legend(loc='upper left')
+
+# plt.tight_layout()
+# plt.savefig('./figures/ransac_fit.png', dpi=300)
+plt.show()
+
+print('Slope: %.3f' % ransac.estimator_.coef_[0])
+print('Intercept: %.3f' % ransac.estimator_.intercept_)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Evaluating the performance of linear regression models')
+print(50 * '-')
+
+X = df.iloc[:, :-1].values
+y = df['MEDV'].values
+
+X_train, X_test, y_train, y_test = train_test_split(
+ X, y, test_size=0.3, random_state=0)
+
+slr = LinearRegression()
+
+slr.fit(X_train, y_train)
+y_train_pred = slr.predict(X_train)
+y_test_pred = slr.predict(X_test)
+
+plt.scatter(y_train_pred, y_train_pred - y_train,
+ c='blue', marker='o', label='Training data')
+plt.scatter(y_test_pred, y_test_pred - y_test,
+ c='lightgreen', marker='s', label='Test data')
+plt.xlabel('Predicted values')
+plt.ylabel('Residuals')
+plt.legend(loc='upper left')
+plt.hlines(y=0, xmin=-10, xmax=50, lw=2, color='red')
+plt.xlim([-10, 50])
+# plt.tight_layout()
+
+# plt.savefig('./figures/slr_residuals.png', dpi=300)
+plt.show()
+
+
+print('MSE train: %.3f, test: %.3f' % (
+ mean_squared_error(y_train, y_train_pred),
+ mean_squared_error(y_test, y_test_pred)))
+print('R^2 train: %.3f, test: %.3f' % (
+ r2_score(y_train, y_train_pred),
+ r2_score(y_test, y_test_pred)))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Using regularized methods for regression')
+print(50 * '-')
+
+print('LASSO Coefficients')
+
+lasso = Lasso(alpha=0.1)
+lasso.fit(X_train, y_train)
+y_train_pred = lasso.predict(X_train)
+y_test_pred = lasso.predict(X_test)
+print(lasso.coef_)
+
+print('MSE train: %.3f, test: %.3f' % (
+ mean_squared_error(y_train, y_train_pred),
+ mean_squared_error(y_test, y_test_pred)))
+print('R^2 train: %.3f, test: %.3f' % (
+ r2_score(y_train, y_train_pred),
+ r2_score(y_test, y_test_pred)))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Turning a linear regression model into a curve'
+ ' - polynomial regression')
+print(50 * '-')
+
+X = np.array([258.0, 270.0, 294.0,
+ 320.0, 342.0, 368.0,
+ 396.0, 446.0, 480.0, 586.0])[:, np.newaxis]
+
+y = np.array([236.4, 234.4, 252.8,
+ 298.6, 314.2, 342.2,
+ 360.8, 368.0, 391.2,
+ 390.8])
+
+lr = LinearRegression()
+pr = LinearRegression()
+quadratic = PolynomialFeatures(degree=2)
+X_quad = quadratic.fit_transform(X)
+
+
+# fit linear features
+lr.fit(X, y)
+X_fit = np.arange(250, 600, 10)[:, np.newaxis]
+y_lin_fit = lr.predict(X_fit)
+
+# fit quadratic features
+pr.fit(X_quad, y)
+y_quad_fit = pr.predict(quadratic.fit_transform(X_fit))
+
+# plot results
+plt.scatter(X, y, label='training points')
+plt.plot(X_fit, y_lin_fit, label='linear fit', linestyle='--')
+plt.plot(X_fit, y_quad_fit, label='quadratic fit')
+plt.legend(loc='upper left')
+
+# plt.tight_layout()
+# plt.savefig('./figures/poly_example.png', dpi=300)
+plt.show()
+
+y_lin_pred = lr.predict(X)
+y_quad_pred = pr.predict(X_quad)
+
+
+print('Training MSE linear: %.3f, quadratic: %.3f' % (
+ mean_squared_error(y, y_lin_pred),
+ mean_squared_error(y, y_quad_pred)))
+print('Training R^2 linear: %.3f, quadratic: %.3f' % (
+ r2_score(y, y_lin_pred),
+ r2_score(y, y_quad_pred)))
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Modeling nonlinear relationships in the Housing Dataset')
+print(50 * '-')
+
+X = df[['LSTAT']].values
+y = df['MEDV'].values
+
+regr = LinearRegression()
+
+# create quadratic features
+quadratic = PolynomialFeatures(degree=2)
+cubic = PolynomialFeatures(degree=3)
+X_quad = quadratic.fit_transform(X)
+X_cubic = cubic.fit_transform(X)
+
+# fit features
+X_fit = np.arange(X.min(), X.max(), 1)[:, np.newaxis]
+
+regr = regr.fit(X, y)
+y_lin_fit = regr.predict(X_fit)
+linear_r2 = r2_score(y, regr.predict(X))
+
+regr = regr.fit(X_quad, y)
+y_quad_fit = regr.predict(quadratic.fit_transform(X_fit))
+quadratic_r2 = r2_score(y, regr.predict(X_quad))
+
+regr = regr.fit(X_cubic, y)
+y_cubic_fit = regr.predict(cubic.fit_transform(X_fit))
+cubic_r2 = r2_score(y, regr.predict(X_cubic))
+
+
+# plot results
+plt.scatter(X, y, label='training points', color='lightgray')
+
+plt.plot(X_fit, y_lin_fit,
+ label='linear (d=1), $R^2=%.2f$' % linear_r2,
+ color='blue',
+ lw=2,
+ linestyle=':')
+
+plt.plot(X_fit, y_quad_fit,
+ label='quadratic (d=2), $R^2=%.2f$' % quadratic_r2,
+ color='red',
+ lw=2,
+ linestyle='-')
+
+plt.plot(X_fit, y_cubic_fit,
+ label='cubic (d=3), $R^2=%.2f$' % cubic_r2,
+ color='green',
+ lw=2,
+ linestyle='--')
+
+plt.xlabel('% lower status of the population [LSTAT]')
+plt.ylabel('Price in $1000\'s [MEDV]')
+plt.legend(loc='upper right')
+
+# plt.tight_layout()
+# plt.savefig('./figures/polyhouse_example.png', dpi=300)
+plt.show()
+
+
+print('Transforming the dataset')
+X = df[['LSTAT']].values
+y = df['MEDV'].values
+
+# transform features
+X_log = np.log(X)
+y_sqrt = np.sqrt(y)
+
+# fit features
+X_fit = np.arange(X_log.min()-1, X_log.max()+1, 1)[:, np.newaxis]
+
+regr = regr.fit(X_log, y_sqrt)
+y_lin_fit = regr.predict(X_fit)
+linear_r2 = r2_score(y_sqrt, regr.predict(X_log))
+
+# plot results
+plt.scatter(X_log, y_sqrt, label='training points', color='lightgray')
+
+plt.plot(X_fit, y_lin_fit,
+ label='linear (d=1), $R^2=%.2f$' % linear_r2,
+ color='blue',
+ lw=2)
+
+plt.xlabel('log(% lower status of the population [LSTAT])')
+plt.ylabel('$\sqrt{Price \; in \; \$1000\'s [MEDV]}$')
+plt.legend(loc='lower left')
+
+# plt.tight_layout()
+# plt.savefig('./figures/transform_example.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Section: Decision tree regression')
+print(50 * '-')
+
+
+X = df[['LSTAT']].values
+y = df['MEDV'].values
+
+tree = DecisionTreeRegressor(max_depth=3)
+tree.fit(X, y)
+
+sort_idx = X.flatten().argsort()
+
+lin_regplot(X[sort_idx], y[sort_idx], tree)
+plt.xlabel('% lower status of the population [LSTAT]')
+plt.ylabel('Price in $1000\'s [MEDV]')
+# plt.savefig('./figures/tree_regression.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Random forest regression')
+print(50 * '-')
+
+X = df.iloc[:, :-1].values
+y = df['MEDV'].values
+
+X_train, X_test, y_train, y_test = train_test_split(
+ X, y, test_size=0.4, random_state=1)
+
+forest = RandomForestRegressor(n_estimators=1000,
+ criterion='mse',
+ random_state=1,
+ n_jobs=-1)
+forest.fit(X_train, y_train)
+y_train_pred = forest.predict(X_train)
+y_test_pred = forest.predict(X_test)
+
+print('MSE train: %.3f, test: %.3f' % (
+ mean_squared_error(y_train, y_train_pred),
+ mean_squared_error(y_test, y_test_pred)))
+print('R^2 train: %.3f, test: %.3f' % (
+ r2_score(y_train, y_train_pred),
+ r2_score(y_test, y_test_pred)))
+
+
+plt.scatter(y_train_pred,
+ y_train_pred - y_train,
+ c='black',
+ marker='o',
+ s=35,
+ alpha=0.5,
+ label='Training data')
+plt.scatter(y_test_pred,
+ y_test_pred - y_test,
+ c='lightgreen',
+ marker='s',
+ s=35,
+ alpha=0.7,
+ label='Test data')
+
+plt.xlabel('Predicted values')
+plt.ylabel('Residuals')
+plt.legend(loc='upper left')
+plt.hlines(y=0, xmin=-10, xmax=50, lw=2, color='red')
+plt.xlim([-10, 50])
+# plt.tight_layout()
+# plt.savefig('./figures/slr_residuals.png', dpi=300)
+plt.show()
diff --git a/code/optional-py-scripts/ch11.py b/code/optional-py-scripts/ch11.py
new file mode 100644
index 00000000..4c0e4657
--- /dev/null
+++ b/code/optional-py-scripts/ch11.py
@@ -0,0 +1,375 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 11 - Working with Unlabeled Data – Clustering Analysis
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+import matplotlib.pyplot as plt
+from matplotlib import cm
+import numpy as np
+import pandas as pd
+from sklearn.datasets import make_blobs
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_samples
+from scipy.spatial.distance import squareform
+from scipy.spatial.distance import pdist
+from scipy.cluster.hierarchy import linkage
+from scipy.cluster.hierarchy import dendrogram
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.datasets import make_moons
+from sklearn.cluster import DBSCAN
+
+#############################################################################
+print(50 * '=')
+print('Section: Grouping objects by similarity using k-means')
+print(50 * '-')
+
+X, y = make_blobs(n_samples=150,
+ n_features=2,
+ centers=3,
+ cluster_std=0.5,
+ shuffle=True,
+ random_state=0)
+
+
+plt.scatter(X[:, 0], X[:, 1], c='white', marker='o', s=50)
+plt.grid()
+# plt.tight_layout()
+# plt.savefig('./figures/spheres.png', dpi=300)
+plt.show()
+
+km = KMeans(n_clusters=3,
+ init='random',
+ n_init=10,
+ max_iter=300,
+ tol=1e-04,
+ random_state=0)
+y_km = km.fit_predict(X)
+
+plt.scatter(X[y_km == 0, 0],
+ X[y_km == 0, 1],
+ s=50,
+ c='lightgreen',
+ marker='s',
+ label='cluster 1')
+plt.scatter(X[y_km == 1, 0],
+ X[y_km == 1, 1],
+ s=50,
+ c='orange',
+ marker='o',
+ label='cluster 2')
+plt.scatter(X[y_km == 2, 0],
+ X[y_km == 2, 1],
+ s=50,
+ c='lightblue',
+ marker='v',
+ label='cluster 3')
+plt.scatter(km.cluster_centers_[:, 0],
+ km.cluster_centers_[:, 1],
+ s=250,
+ marker='*',
+ c='red',
+ label='centroids')
+plt.legend()
+plt.grid()
+# plt.tight_layout()
+# plt.savefig('./figures/centroids.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Section: Using the elbow method to find the optimal number of clusters')
+print(50 * '-')
+
+print('Distortion: %.2f' % km.inertia_)
+
+distortions = []
+for i in range(1, 11):
+ km = KMeans(n_clusters=i,
+ init='k-means++',
+ n_init=10,
+ max_iter=300,
+ random_state=0)
+ km.fit(X)
+ distortions.append(km.inertia_)
+plt.plot(range(1, 11), distortions, marker='o')
+plt.xlabel('Number of clusters')
+plt.ylabel('Distortion')
+# plt.tight_layout()
+# plt.savefig('./figures/elbow.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Section: Quantifying the quality of clustering via silhouette plots')
+print(50 * '-')
+
+km = KMeans(n_clusters=3,
+ init='k-means++',
+ n_init=10,
+ max_iter=300,
+ tol=1e-04,
+ random_state=0)
+y_km = km.fit_predict(X)
+
+cluster_labels = np.unique(y_km)
+n_clusters = cluster_labels.shape[0]
+silhouette_vals = silhouette_samples(X, y_km, metric='euclidean')
+y_ax_lower, y_ax_upper = 0, 0
+yticks = []
+for i, c in enumerate(cluster_labels):
+ c_silhouette_vals = silhouette_vals[y_km == c]
+ c_silhouette_vals.sort()
+ y_ax_upper += len(c_silhouette_vals)
+ color = cm.jet(i / n_clusters)
+ plt.barh(range(y_ax_lower, y_ax_upper), c_silhouette_vals, height=1.0,
+ edgecolor='none', color=color)
+
+ yticks.append((y_ax_lower + y_ax_upper) / 2.)
+ y_ax_lower += len(c_silhouette_vals)
+
+silhouette_avg = np.mean(silhouette_vals)
+plt.axvline(silhouette_avg, color="red", linestyle="--")
+
+plt.yticks(yticks, cluster_labels + 1)
+plt.ylabel('Cluster')
+plt.xlabel('Silhouette coefficient')
+
+# plt.tight_layout()
+# plt.savefig('./figures/silhouette.png', dpi=300)
+plt.show()
+
+
+print('A bad clunstering:')
+
+km = KMeans(n_clusters=2,
+ init='k-means++',
+ n_init=10,
+ max_iter=300,
+ tol=1e-04,
+ random_state=0)
+y_km = km.fit_predict(X)
+
+plt.scatter(X[y_km == 0, 0],
+ X[y_km == 0, 1],
+ s=50,
+ c='lightgreen',
+ marker='s',
+ label='cluster 1')
+plt.scatter(X[y_km == 1, 0],
+ X[y_km == 1, 1],
+ s=50,
+ c='orange',
+ marker='o',
+ label='cluster 2')
+
+plt.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1],
+ s=250, marker='*', c='red', label='centroids')
+plt.legend()
+plt.grid()
+# plt.tight_layout()
+# plt.savefig('./figures/centroids_bad.png', dpi=300)
+plt.show()
+
+
+cluster_labels = np.unique(y_km)
+n_clusters = cluster_labels.shape[0]
+silhouette_vals = silhouette_samples(X, y_km, metric='euclidean')
+y_ax_lower, y_ax_upper = 0, 0
+yticks = []
+for i, c in enumerate(cluster_labels):
+ c_silhouette_vals = silhouette_vals[y_km == c]
+ c_silhouette_vals.sort()
+ y_ax_upper += len(c_silhouette_vals)
+ color = cm.jet(i / n_clusters)
+ plt.barh(range(y_ax_lower, y_ax_upper), c_silhouette_vals, height=1.0,
+ edgecolor='none', color=color)
+
+ yticks.append((y_ax_lower + y_ax_upper) / 2.)
+ y_ax_lower += len(c_silhouette_vals)
+
+silhouette_avg = np.mean(silhouette_vals)
+plt.axvline(silhouette_avg, color="red", linestyle="--")
+
+plt.yticks(yticks, cluster_labels + 1)
+plt.ylabel('Cluster')
+plt.xlabel('Silhouette coefficient')
+
+# plt.tight_layout()
+# plt.savefig('./figures/silhouette_bad.png', dpi=300)
+plt.show()
+
+#############################################################################
+print(50 * '=')
+print('Section: Organizing clusters as a hierarchical tree')
+print(50 * '-')
+
+
+np.random.seed(123)
+
+variables = ['X', 'Y', 'Z']
+labels = ['ID_0', 'ID_1', 'ID_2', 'ID_3', 'ID_4']
+
+X = np.random.random_sample([5, 3])*10
+df = pd.DataFrame(X, columns=variables, index=labels)
+print('DataFrame:\n\n', df)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Performing hierarchical clustering on a distance matrix')
+print(50 * '-')
+
+row_dist = pd.DataFrame(squareform(pdist(df, metric='euclidean')),
+ columns=labels,
+ index=labels)
+print('Row distances:\n\n', row_dist)
+
+print('1. incorrect approach: Squareform distance matrix')
+
+row_clusters = linkage(row_dist, method='complete', metric='euclidean')
+df1 = pd.DataFrame(row_clusters,
+ columns=['row label 1', 'row label 2',
+ 'distance', 'no. of items in clust.'],
+ index=['cluster %d' % (i + 1)
+ for i in range(row_clusters.shape[0])])
+
+
+print('2. correct approach: Condensed distance matrix')
+
+row_clusters = linkage(pdist(df, metric='euclidean'), method='complete')
+df2 = pd.DataFrame(row_clusters,
+ columns=['row label 1', 'row label 2',
+ 'distance', 'no. of items in clust.'],
+ index=['cluster %d' % (i + 1)
+ for i in range(row_clusters.shape[0])])
+
+
+print('3. correct approach: Input sample matrix')
+
+row_clusters = linkage(df.values, method='complete', metric='euclidean')
+df3 = pd.DataFrame(row_clusters,
+ columns=['row label 1', 'row label 2',
+ 'distance', 'no. of items in clust.'],
+ index=['cluster %d' % (i + 1)
+ for i in range(row_clusters.shape[0])])
+
+
+# make dendrogram black (part 1/2)
+# from scipy.cluster.hierarchy import set_link_color_palette
+# set_link_color_palette(['black'])
+
+row_dendr = dendrogram(row_clusters,
+ labels=labels,
+ # make dendrogram black (part 2/2)
+ # color_threshold=np.inf
+ )
+# plt.tight_layout()
+plt.ylabel('Euclidean distance')
+# plt.savefig('./figures/dendrogram.png', dpi=300,
+# bbox_inches='tight')
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Attaching dendrograms to a heat map')
+print(50 * '-')
+
+# plot row dendrogram
+fig = plt.figure(figsize=(8, 8), facecolor='white')
+axd = fig.add_axes([0.09, 0.1, 0.2, 0.6])
+
+# note: for matplotlib < v1.5.1, please use orientation='right'
+row_dendr = dendrogram(row_clusters, orientation='left')
+
+# reorder data with respect to clustering
+df_rowclust = df.ix[row_dendr['leaves'][::-1]]
+
+axd.set_xticks([])
+axd.set_yticks([])
+
+# remove axes spines from dendrogram
+for i in axd.spines.values():
+ i.set_visible(False)
+
+# plot heatmap
+axm = fig.add_axes([0.23, 0.1, 0.6, 0.6]) # x-pos, y-pos, width, height
+cax = axm.matshow(df_rowclust, interpolation='nearest', cmap='hot_r')
+fig.colorbar(cax)
+axm.set_xticklabels([''] + list(df_rowclust.columns))
+axm.set_yticklabels([''] + list(df_rowclust.index))
+
+# plt.savefig('./figures/heatmap.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Applying agglomerative clustering via scikit-learn')
+print(50 * '-')
+
+
+ac = AgglomerativeClustering(n_clusters=2,
+ affinity='euclidean',
+ linkage='complete')
+labels = ac.fit_predict(X)
+print('Cluster labels: %s' % labels)
+
+
+#############################################################################
+print(50 * '=')
+print('Section: Attaching dendrograms to a heat map')
+print(50 * '-')
+
+X, y = make_moons(n_samples=200, noise=0.05, random_state=0)
+plt.scatter(X[:, 0], X[:, 1])
+# plt.tight_layout()
+# plt.savefig('./figures/moons.png', dpi=300)
+plt.show()
+
+
+f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
+
+km = KMeans(n_clusters=2, random_state=0)
+y_km = km.fit_predict(X)
+ax1.scatter(X[y_km == 0, 0], X[y_km == 0, 1],
+ c='lightblue', marker='o', s=40, label='cluster 1')
+ax1.scatter(X[y_km == 1, 0], X[y_km == 1, 1],
+ c='red', marker='s', s=40, label='cluster 2')
+ax1.set_title('K-means clustering')
+
+ac = AgglomerativeClustering(n_clusters=2,
+ affinity='euclidean',
+ linkage='complete')
+y_ac = ac.fit_predict(X)
+ax2.scatter(X[y_ac == 0, 0], X[y_ac == 0, 1], c='lightblue',
+ marker='o', s=40, label='cluster 1')
+ax2.scatter(X[y_ac == 1, 0], X[y_ac == 1, 1], c='red',
+ marker='s', s=40, label='cluster 2')
+ax2.set_title('Agglomerative clustering')
+
+plt.legend()
+# plt.tight_layout()
+# plt.savefig('./figures/kmeans_and_ac.png', dpi=300)
+plt.show()
+
+print('DBSCAN')
+
+db = DBSCAN(eps=0.2, min_samples=5, metric='euclidean')
+y_db = db.fit_predict(X)
+plt.scatter(X[y_db == 0, 0], X[y_db == 0, 1],
+ c='lightblue', marker='o', s=40,
+ label='cluster 1')
+plt.scatter(X[y_db == 1, 0], X[y_db == 1, 1],
+ c='red', marker='s', s=40,
+ label='cluster 2')
+plt.legend()
+# plt.tight_layout()
+# plt.savefig('./figures/moons_dbscan.png', dpi=300)
+plt.show()
diff --git a/code/optional-py-scripts/ch12.py b/code/optional-py-scripts/ch12.py
new file mode 100644
index 00000000..e79beba9
--- /dev/null
+++ b/code/optional-py-scripts/ch12.py
@@ -0,0 +1,943 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 12 - Training Artificial Neural Networks for Image Recognition
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+import os
+import struct
+import numpy as np
+from scipy.special import expit
+import sys
+import matplotlib.pyplot as plt
+
+#############################################################################
+print(50 * '=')
+print('Obtaining the MNIST dataset')
+print(50 * '-')
+
+
+s = """
+The MNIST dataset is publicly available at http://yann.lecun.com/exdb/mnist/
+and consists of the following four parts:
+
+- Training set images: train-images-idx3-ubyte.gz
+ (9.9 MB, 47 MB unzipped, 60,000 samples)
+- Training set labels: train-labels-idx1-ubyte.gz
+ (29 KB, 60 KB unzipped, 60,000 labels)
+- Test set images: t10k-images-idx3-ubyte.gz
+ (1.6 MB, 7.8 MB, 10,000 samples)
+- Test set labels: t10k-labels-idx1-ubyte.gz
+ (5 KB, 10 KB unzipped, 10,000 labels)
+
+In this section, we will only be working with a subset of MNIST, thus,
+we only need to download the training set images and training set labels.
+After downloading the files, I recommend unzipping the files using
+the Unix/Linux gzip tool from
+the terminal for efficiency, e.g., using the command
+
+ gzip *ubyte.gz -d
+
+in your local MNIST download directory, or, using your
+favorite unzipping tool if you are working with a machine
+running on Microsoft Windows. The images are stored in byte form,
+and using the following function, we will read them into NumPy arrays
+that we will use to train our MLP.
+
+
+"""
+
+print(s)
+
+_ = input("Please hit enter to continue.")
+
+
+def load_mnist(path, kind='train'):
+ """Load MNIST data from `path`"""
+ labels_path = os.path.join(path,
+ '%s-labels-idx1-ubyte' % kind)
+ images_path = os.path.join(path,
+ '%s-images-idx3-ubyte' % kind)
+
+ with open(labels_path, 'rb') as lbpath:
+ magic, n = struct.unpack('>II',
+ lbpath.read(8))
+ labels = np.fromfile(lbpath,
+ dtype=np.uint8)
+
+ with open(images_path, 'rb') as imgpath:
+ magic, num, rows, cols = struct.unpack(">IIII",
+ imgpath.read(16))
+ images = np.fromfile(imgpath,
+ dtype=np.uint8).reshape(len(labels), 784)
+
+ return images, labels
+
+
+X_train, y_train = load_mnist('mnist', kind='train')
+print('Training rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))
+
+X_test, y_test = load_mnist('mnist', kind='t10k')
+print('Test rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))
+
+fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True,)
+ax = ax.flatten()
+for i in range(10):
+ img = X_train[y_train == i][0].reshape(28, 28)
+ ax[i].imshow(img, cmap='Greys', interpolation='nearest')
+
+ax[0].set_xticks([])
+ax[0].set_yticks([])
+# plt.tight_layout()
+# plt.savefig('./figures/mnist_all.png', dpi=300)
+plt.show()
+
+
+fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)
+ax = ax.flatten()
+for i in range(25):
+ img = X_train[y_train == 7][i].reshape(28, 28)
+ ax[i].imshow(img, cmap='Greys', interpolation='nearest')
+
+ax[0].set_xticks([])
+ax[0].set_yticks([])
+# plt.tight_layout()
+# plt.savefig('./figures/mnist_7.png', dpi=300)
+plt.show()
+
+"""
+Uncomment the following lines to optionally save the data in CSV format.
+However, note that those CSV files will take up a
+substantial amount of storage space:
+
+- train_img.csv 1.1 GB (gigabytes)
+- train_labels.csv 1.4 MB (megabytes)
+- test_img.csv 187.0 MB
+- test_labels 144 KB (kilobytes)
+"""
+
+# np.savetxt('train_img.csv', X_train, fmt='%i', delimiter=',')
+# np.savetxt('train_labels.csv', y_train, fmt='%i', delimiter=',')
+# X_train = np.genfromtxt('train_img.csv', dtype=int, delimiter=',')
+# y_train = np.genfromtxt('train_labels.csv', dtype=int, delimiter=',')
+
+# np.savetxt('test_img.csv', X_test, fmt='%i', delimiter=',')
+# np.savetxt('test_labels.csv', y_test, fmt='%i', delimiter=',')
+# X_test = np.genfromtxt('test_img.csv', dtype=int, delimiter=',')
+# y_test = np.genfromtxt('test_labels.csv', dtype=int, delimiter=',')
+
+#############################################################################
+print(50 * '=')
+print('Implementing a multi-layer perceptron')
+print(50 * '-')
+
+
+class NeuralNetMLP(object):
+ """ Feedforward neural network / Multi-layer perceptron classifier.
+
+ Parameters
+ ------------
+ n_output : int
+ Number of output units, should be equal to the
+ number of unique class labels.
+ n_features : int
+ Number of features (dimensions) in the target dataset.
+ Should be equal to the number of columns in the X array.
+ n_hidden : int (default: 30)
+ Number of hidden units.
+ l1 : float (default: 0.0)
+ Lambda value for L1-regularization.
+ No regularization if l1=0.0 (default)
+ l2 : float (default: 0.0)
+ Lambda value for L2-regularization.
+ No regularization if l2=0.0 (default)
+ epochs : int (default: 500)
+ Number of passes over the training set.
+ eta : float (default: 0.001)
+ Learning rate.
+ alpha : float (default: 0.0)
+ Momentum constant. Factor multiplied with the
+ gradient of the previous epoch t-1 to improve
+ learning speed
+ w(t) := w(t) - (grad(t) + alpha*grad(t-1))
+ decrease_const : float (default: 0.0)
+ Decrease constant. Shrinks the learning rate
+ after each epoch via eta / (1 + epoch*decrease_const)
+ shuffle : bool (default: True)
+ Shuffles training data every epoch if True to prevent circles.
+ minibatches : int (default: 1)
+ Divides training data into k minibatches for efficiency.
+ Normal gradient descent learning if k=1 (default).
+ random_state : int (default: None)
+ Set random state for shuffling and initializing the weights.
+
+ Attributes
+ -----------
+ cost_ : list
+ Sum of squared errors after each epoch.
+
+ """
+ def __init__(self, n_output, n_features, n_hidden=30,
+ l1=0.0, l2=0.0, epochs=500, eta=0.001,
+ alpha=0.0, decrease_const=0.0, shuffle=True,
+ minibatches=1, random_state=None):
+
+ np.random.seed(random_state)
+ self.n_output = n_output
+ self.n_features = n_features
+ self.n_hidden = n_hidden
+ self.w1, self.w2 = self._initialize_weights()
+ self.l1 = l1
+ self.l2 = l2
+ self.epochs = epochs
+ self.eta = eta
+ self.alpha = alpha
+ self.decrease_const = decrease_const
+ self.shuffle = shuffle
+ self.minibatches = minibatches
+
+ def _encode_labels(self, y, k):
+ """Encode labels into one-hot representation
+
+ Parameters
+ ------------
+ y : array, shape = [n_samples]
+ Target values.
+
+ Returns
+ -----------
+ onehot : array, shape = (n_labels, n_samples)
+
+ """
+ onehot = np.zeros((k, y.shape[0]))
+ for idx, val in enumerate(y):
+ onehot[val, idx] = 1.0
+ return onehot
+
+ def _initialize_weights(self):
+ """Initialize weights with small random numbers."""
+ w1 = np.random.uniform(-1.0, 1.0,
+ size=self.n_hidden*(self.n_features + 1))
+ w1 = w1.reshape(self.n_hidden, self.n_features + 1)
+ w2 = np.random.uniform(-1.0, 1.0,
+ size=self.n_output*(self.n_hidden + 1))
+ w2 = w2.reshape(self.n_output, self.n_hidden + 1)
+ return w1, w2
+
+ def _sigmoid(self, z):
+ """Compute logistic function (sigmoid)
+
+ Uses scipy.special.expit to avoid overflow
+ error for very small input values z.
+
+ """
+ # return 1.0 / (1.0 + np.exp(-z))
+ return expit(z)
+
+ def _sigmoid_gradient(self, z):
+ """Compute gradient of the logistic function"""
+ sg = self._sigmoid(z)
+ return sg * (1 - sg)
+
+ def _add_bias_unit(self, X, how='column'):
+ """Add bias unit (column or row of 1s) to array at index 0"""
+ if how == 'column':
+ X_new = np.ones((X.shape[0], X.shape[1]+1))
+ X_new[:, 1:] = X
+ elif how == 'row':
+ X_new = np.ones((X.shape[0]+1, X.shape[1]))
+ X_new[1:, :] = X
+ else:
+ raise AttributeError('`how` must be `column` or `row`')
+ return X_new
+
+ def _feedforward(self, X, w1, w2):
+ """Compute feedforward step
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ----------
+ a1 : array, shape = [n_samples, n_features+1]
+ Input values with bias unit.
+ z2 : array, shape = [n_hidden, n_samples]
+ Net input of hidden layer.
+ a2 : array, shape = [n_hidden+1, n_samples]
+ Activation of hidden layer.
+ z3 : array, shape = [n_output_units, n_samples]
+ Net input of output layer.
+ a3 : array, shape = [n_output_units, n_samples]
+ Activation of output layer.
+
+ """
+ a1 = self._add_bias_unit(X, how='column')
+ z2 = w1.dot(a1.T)
+ a2 = self._sigmoid(z2)
+ a2 = self._add_bias_unit(a2, how='row')
+ z3 = w2.dot(a2)
+ a3 = self._sigmoid(z3)
+ return a1, z2, a2, z3, a3
+
+ def _L2_reg(self, lambda_, w1, w2):
+ """Compute L2-regularization cost"""
+ return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) +
+ np.sum(w2[:, 1:] ** 2))
+
+ def _L1_reg(self, lambda_, w1, w2):
+ """Compute L1-regularization cost"""
+ return (lambda_/2.0) * (np.abs(w1[:, 1:]).sum() +
+ np.abs(w2[:, 1:]).sum())
+
+ def _get_cost(self, y_enc, output, w1, w2):
+ """Compute cost function.
+
+ Parameters
+ ----------
+ y_enc : array, shape = (n_labels, n_samples)
+ one-hot encoded class labels.
+ output : array, shape = [n_output_units, n_samples]
+ Activation of the output layer (feedforward)
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ---------
+ cost : float
+ Regularized cost.
+
+ """
+ term1 = -y_enc * (np.log(output))
+ term2 = (1 - y_enc) * np.log(1 - output)
+ cost = np.sum(term1 - term2)
+ L1_term = self._L1_reg(self.l1, w1, w2)
+ L2_term = self._L2_reg(self.l2, w1, w2)
+ cost = cost + L1_term + L2_term
+ return cost
+
+ def _get_gradient(self, a1, a2, a3, z2, y_enc, w1, w2):
+ """ Compute gradient step using backpropagation.
+
+ Parameters
+ ------------
+ a1 : array, shape = [n_samples, n_features+1]
+ Input values with bias unit.
+ a2 : array, shape = [n_hidden+1, n_samples]
+ Activation of hidden layer.
+ a3 : array, shape = [n_output_units, n_samples]
+ Activation of output layer.
+ z2 : array, shape = [n_hidden, n_samples]
+ Net input of hidden layer.
+ y_enc : array, shape = (n_labels, n_samples)
+ one-hot encoded class labels.
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ---------
+ grad1 : array, shape = [n_hidden_units, n_features]
+ Gradient of the weight matrix w1.
+ grad2 : array, shape = [n_output_units, n_hidden_units]
+ Gradient of the weight matrix w2.
+
+ """
+ # backpropagation
+ sigma3 = a3 - y_enc
+ z2 = self._add_bias_unit(z2, how='row')
+ sigma2 = w2.T.dot(sigma3) * self._sigmoid_gradient(z2)
+ sigma2 = sigma2[1:, :]
+ grad1 = sigma2.dot(a1)
+ grad2 = sigma3.dot(a2.T)
+
+ # regularize
+ grad1[:, 1:] += (w1[:, 1:] * (self.l1 + self.l2))
+ grad2[:, 1:] += (w2[:, 1:] * (self.l1 + self.l2))
+
+ return grad1, grad2
+
+ def predict(self, X):
+ """Predict class labels
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+
+ Returns:
+ ----------
+ y_pred : array, shape = [n_samples]
+ Predicted class labels.
+
+ """
+ if len(X.shape) != 2:
+ raise AttributeError('X must be a [n_samples, n_features] array.\n'
+ 'Use X[:,None] for 1-feature classification,'
+ '\nor X[[i]] for 1-sample classification')
+
+ a1, z2, a2, z3, a3 = self._feedforward(X, self.w1, self.w2)
+ y_pred = np.argmax(z3, axis=0)
+ return y_pred
+
+ def fit(self, X, y, print_progress=False):
+ """ Learn weights from training data.
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+ y : array, shape = [n_samples]
+ Target class labels.
+ print_progress : bool (default: False)
+ Prints progress as the number of epochs
+ to stderr.
+
+ Returns:
+ ----------
+ self
+
+ """
+ self.cost_ = []
+ X_data, y_data = X.copy(), y.copy()
+ y_enc = self._encode_labels(y, self.n_output)
+
+ delta_w1_prev = np.zeros(self.w1.shape)
+ delta_w2_prev = np.zeros(self.w2.shape)
+
+ for i in range(self.epochs):
+
+ # adaptive learning rate
+ self.eta /= (1 + self.decrease_const*i)
+
+ if print_progress:
+ sys.stderr.write('\rEpoch: %d/%d' % (i+1, self.epochs))
+ sys.stderr.flush()
+
+ if self.shuffle:
+ idx = np.random.permutation(y_data.shape[0])
+ X_data, y_enc = X_data[idx], y_enc[:, idx]
+
+ mini = np.array_split(range(y_data.shape[0]), self.minibatches)
+ for idx in mini:
+
+ # feedforward
+ a1, z2, a2, z3, a3 = self._feedforward(X_data[idx],
+ self.w1,
+ self.w2)
+ cost = self._get_cost(y_enc=y_enc[:, idx],
+ output=a3,
+ w1=self.w1,
+ w2=self.w2)
+ self.cost_.append(cost)
+
+ # compute gradient via backpropagation
+ grad1, grad2 = self._get_gradient(a1=a1, a2=a2,
+ a3=a3, z2=z2,
+ y_enc=y_enc[:, idx],
+ w1=self.w1,
+ w2=self.w2)
+
+ delta_w1, delta_w2 = self.eta * grad1, self.eta * grad2
+ self.w1 -= (delta_w1 + (self.alpha * delta_w1_prev))
+ self.w2 -= (delta_w2 + (self.alpha * delta_w2_prev))
+ delta_w1_prev, delta_w2_prev = delta_w1, delta_w2
+
+ return self
+
+
+nn = NeuralNetMLP(n_output=10,
+ n_features=X_train.shape[1],
+ n_hidden=50,
+ l2=0.1,
+ l1=0.0,
+ epochs=1000,
+ eta=0.001,
+ alpha=0.001,
+ decrease_const=0.00001,
+ minibatches=50,
+ shuffle=True,
+ random_state=1)
+
+nn.fit(X_train, y_train, print_progress=True)
+
+plt.plot(range(len(nn.cost_)), nn.cost_)
+plt.ylim([0, 2000])
+plt.ylabel('Cost')
+plt.xlabel('Epochs * 50')
+# plt.tight_layout()
+# plt.savefig('./figures/cost.png', dpi=300)
+plt.show()
+
+batches = np.array_split(range(len(nn.cost_)), 1000)
+cost_ary = np.array(nn.cost_)
+cost_avgs = [np.mean(cost_ary[i]) for i in batches]
+
+
+plt.plot(range(len(cost_avgs)), cost_avgs, color='red')
+plt.ylim([0, 2000])
+plt.ylabel('Cost')
+plt.xlabel('Epochs')
+# plt.tight_layout()
+# plt.savefig('./figures/cost2.png', dpi=300)
+plt.show()
+
+
+y_train_pred = nn.predict(X_train)
+
+if sys.version_info < (3, 0):
+ acc = ((np.sum(y_train == y_train_pred, axis=0)).astype('float') /
+ X_train.shape[0])
+else:
+ acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]
+
+print('Training accuracy: %.2f%%' % (acc * 100))
+
+
+y_test_pred = nn.predict(X_test)
+
+if sys.version_info < (3, 0):
+ acc = ((np.sum(y_test == y_test_pred, axis=0)).astype('float') /
+ X_test.shape[0])
+else:
+ acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]
+
+print('Test accuracy: %.2f%%' % (acc * 100))
+
+
+miscl_img = X_test[y_test != y_test_pred][:25]
+correct_lab = y_test[y_test != y_test_pred][:25]
+miscl_lab = y_test_pred[y_test != y_test_pred][:25]
+
+fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)
+ax = ax.flatten()
+for i in range(25):
+ img = miscl_img[i].reshape(28, 28)
+ ax[i].imshow(img, cmap='Greys', interpolation='nearest')
+ ax[i].set_title('%d) t: %d p: %d' % (i+1, correct_lab[i], miscl_lab[i]))
+
+ax[0].set_xticks([])
+ax[0].set_yticks([])
+# plt.tight_layout()
+# plt.savefig('./figures/mnist_miscl.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Debugging neural networks with gradient checking')
+print(50 * '-')
+
+
+class MLPGradientCheck(object):
+ """ Feedforward neural network / Multi-layer perceptron classifier.
+
+ Parameters
+ ------------
+ n_output : int
+ Number of output units, should be equal to the
+ number of unique class labels.
+ n_features : int
+ Number of features (dimensions) in the target dataset.
+ Should be equal to the number of columns in the X array.
+ n_hidden : int (default: 30)
+ Number of hidden units.
+ l1 : float (default: 0.0)
+ Lambda value for L1-regularization.
+ No regularization if l1=0.0 (default)
+ l2 : float (default: 0.0)
+ Lambda value for L2-regularization.
+ No regularization if l2=0.0 (default)
+ epochs : int (default: 500)
+ Number of passes over the training set.
+ eta : float (default: 0.001)
+ Learning rate.
+ alpha : float (default: 0.0)
+ Momentum constant. Factor multiplied with the
+ gradient of the previous epoch t-1 to improve
+ learning speed
+ w(t) := w(t) - (grad(t) + alpha*grad(t-1))
+ decrease_const : float (default: 0.0)
+ Decrease constant. Shrinks the learning rate
+ after each epoch via eta / (1 + epoch*decrease_const)
+ shuffle : bool (default: False)
+ Shuffles training data every epoch if True to prevent circles.
+ minibatches : int (default: 1)
+ Divides training data into k minibatches for efficiency.
+ Normal gradient descent learning if k=1 (default).
+ random_state : int (default: None)
+ Set random state for shuffling and initializing the weights.
+
+ Attributes
+ -----------
+ cost_ : list
+ Sum of squared errors after each epoch.
+
+ """
+ def __init__(self, n_output, n_features, n_hidden=30,
+ l1=0.0, l2=0.0, epochs=500, eta=0.001,
+ alpha=0.0, decrease_const=0.0, shuffle=True,
+ minibatches=1, random_state=None):
+
+ np.random.seed(random_state)
+ self.n_output = n_output
+ self.n_features = n_features
+ self.n_hidden = n_hidden
+ self.w1, self.w2 = self._initialize_weights()
+ self.l1 = l1
+ self.l2 = l2
+ self.epochs = epochs
+ self.eta = eta
+ self.alpha = alpha
+ self.decrease_const = decrease_const
+ self.shuffle = shuffle
+ self.minibatches = minibatches
+
+ def _encode_labels(self, y, k):
+ """Encode labels into one-hot representation
+
+ Parameters
+ ------------
+ y : array, shape = [n_samples]
+ Target values.
+
+ Returns
+ -----------
+ onehot : array, shape = (n_labels, n_samples)
+
+ """
+ onehot = np.zeros((k, y.shape[0]))
+ for idx, val in enumerate(y):
+ onehot[val, idx] = 1.0
+ return onehot
+
+ def _initialize_weights(self):
+ """Initialize weights with small random numbers."""
+ w1 = np.random.uniform(-1.0, 1.0,
+ size=self.n_hidden*(self.n_features + 1))
+ w1 = w1.reshape(self.n_hidden, self.n_features + 1)
+ w2 = np.random.uniform(-1.0, 1.0,
+ size=self.n_output*(self.n_hidden + 1))
+ w2 = w2.reshape(self.n_output, self.n_hidden + 1)
+ return w1, w2
+
+ def _sigmoid(self, z):
+ """Compute logistic function (sigmoid)
+
+ Uses scipy.special.expit to avoid overflow
+ error for very small input values z.
+
+ """
+ # return 1.0 / (1.0 + np.exp(-z))
+ return expit(z)
+
+ def _sigmoid_gradient(self, z):
+ """Compute gradient of the logistic function"""
+ sg = self._sigmoid(z)
+ return sg * (1 - sg)
+
+ def _add_bias_unit(self, X, how='column'):
+ """Add bias unit (column or row of 1s) to array at index 0"""
+ if how == 'column':
+ X_new = np.ones((X.shape[0], X.shape[1]+1))
+ X_new[:, 1:] = X
+ elif how == 'row':
+ X_new = np.ones((X.shape[0]+1, X.shape[1]))
+ X_new[1:, :] = X
+ else:
+ raise AttributeError('`how` must be `column` or `row`')
+ return X_new
+
+ def _feedforward(self, X, w1, w2):
+ """Compute feedforward step
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ----------
+ a1 : array, shape = [n_samples, n_features+1]
+ Input values with bias unit.
+ z2 : array, shape = [n_hidden, n_samples]
+ Net input of hidden layer.
+ a2 : array, shape = [n_hidden+1, n_samples]
+ Activation of hidden layer.
+ z3 : array, shape = [n_output_units, n_samples]
+ Net input of output layer.
+ a3 : array, shape = [n_output_units, n_samples]
+ Activation of output layer.
+
+ """
+ a1 = self._add_bias_unit(X, how='column')
+ z2 = w1.dot(a1.T)
+ a2 = self._sigmoid(z2)
+ a2 = self._add_bias_unit(a2, how='row')
+ z3 = w2.dot(a2)
+ a3 = self._sigmoid(z3)
+ return a1, z2, a2, z3, a3
+
+ def _L2_reg(self, lambda_, w1, w2):
+ """Compute L2-regularization cost"""
+ return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) +
+ np.sum(w2[:, 1:] ** 2))
+
+ def _L1_reg(self, lambda_, w1, w2):
+ """Compute L1-regularization cost"""
+ return (lambda_/2.0) * (np.abs(w1[:, 1:]).sum() +
+ np.abs(w2[:, 1:]).sum())
+
+ def _get_cost(self, y_enc, output, w1, w2):
+ """Compute cost function.
+
+ Parameters
+ ----------
+ y_enc : array, shape = (n_labels, n_samples)
+ one-hot encoded class labels.
+ output : array, shape = [n_output_units, n_samples]
+ Activation of the output layer (feedforward)
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ---------
+ cost : float
+ Regularized cost.
+
+ """
+ term1 = -y_enc * (np.log(output))
+ term2 = (1 - y_enc) * np.log(1 - output)
+ cost = np.sum(term1 - term2)
+ L1_term = self._L1_reg(self.l1, w1, w2)
+ L2_term = self._L2_reg(self.l2, w1, w2)
+ cost = cost + L1_term + L2_term
+ return cost
+
+ def _get_gradient(self, a1, a2, a3, z2, y_enc, w1, w2):
+ """ Compute gradient step using backpropagation.
+
+ Parameters
+ ------------
+ a1 : array, shape = [n_samples, n_features+1]
+ Input values with bias unit.
+ a2 : array, shape = [n_hidden+1, n_samples]
+ Activation of hidden layer.
+ a3 : array, shape = [n_output_units, n_samples]
+ Activation of output layer.
+ z2 : array, shape = [n_hidden, n_samples]
+ Net input of hidden layer.
+ y_enc : array, shape = (n_labels, n_samples)
+ one-hot encoded class labels.
+ w1 : array, shape = [n_hidden_units, n_features]
+ Weight matrix for input layer -> hidden layer.
+ w2 : array, shape = [n_output_units, n_hidden_units]
+ Weight matrix for hidden layer -> output layer.
+
+ Returns
+ ---------
+ grad1 : array, shape = [n_hidden_units, n_features]
+ Gradient of the weight matrix w1.
+ grad2 : array, shape = [n_output_units, n_hidden_units]
+ Gradient of the weight matrix w2.
+
+ """
+ # backpropagation
+ sigma3 = a3 - y_enc
+ z2 = self._add_bias_unit(z2, how='row')
+ sigma2 = w2.T.dot(sigma3) * self._sigmoid_gradient(z2)
+ sigma2 = sigma2[1:, :]
+ grad1 = sigma2.dot(a1)
+ grad2 = sigma3.dot(a2.T)
+
+ # regularize
+ grad1[:, 1:] += (w1[:, 1:] * (self.l1 + self.l2))
+ grad2[:, 1:] += (w2[:, 1:] * (self.l1 + self.l2))
+
+ return grad1, grad2
+
+ def _gradient_checking(self, X, y_enc, w1, w2, epsilon, grad1, grad2):
+ """ Apply gradient checking (for debugging only)
+
+ Returns
+ ---------
+ relative_error : float
+ Relative error between the numerically
+ approximated gradients and the backpropagated gradients.
+
+ """
+ num_grad1 = np.zeros(np.shape(w1))
+ epsilon_ary1 = np.zeros(np.shape(w1))
+ for i in range(w1.shape[0]):
+ for j in range(w1.shape[1]):
+ epsilon_ary1[i, j] = epsilon
+ a1, z2, a2, z3, a3 = self._feedforward(X,
+ w1 - epsilon_ary1, w2)
+ cost1 = self._get_cost(y_enc, a3, w1-epsilon_ary1, w2)
+ a1, z2, a2, z3, a3 = self._feedforward(X,
+ w1 + epsilon_ary1, w2)
+ cost2 = self._get_cost(y_enc, a3, w1 + epsilon_ary1, w2)
+ num_grad1[i, j] = (cost2 - cost1) / (2 * epsilon)
+ epsilon_ary1[i, j] = 0
+
+ num_grad2 = np.zeros(np.shape(w2))
+ epsilon_ary2 = np.zeros(np.shape(w2))
+ for i in range(w2.shape[0]):
+ for j in range(w2.shape[1]):
+ epsilon_ary2[i, j] = epsilon
+ a1, z2, a2, z3, a3 = self._feedforward(X, w1,
+ w2 - epsilon_ary2)
+ cost1 = self._get_cost(y_enc, a3, w1, w2 - epsilon_ary2)
+ a1, z2, a2, z3, a3 = self._feedforward(X, w1,
+ w2 + epsilon_ary2)
+ cost2 = self._get_cost(y_enc, a3, w1, w2 + epsilon_ary2)
+ num_grad2[i, j] = (cost2 - cost1) / (2 * epsilon)
+ epsilon_ary2[i, j] = 0
+
+ num_grad = np.hstack((num_grad1.flatten(), num_grad2.flatten()))
+ grad = np.hstack((grad1.flatten(), grad2.flatten()))
+ norm1 = np.linalg.norm(num_grad - grad)
+ norm2 = np.linalg.norm(num_grad)
+ norm3 = np.linalg.norm(grad)
+ relative_error = norm1 / (norm2 + norm3)
+ return relative_error
+
+ def predict(self, X):
+ """Predict class labels
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+
+ Returns:
+ ----------
+ y_pred : array, shape = [n_samples]
+ Predicted class labels.
+
+ """
+ if len(X.shape) != 2:
+ raise AttributeError('X must be a [n_samples, n_features] array.\n'
+ 'Use X[:,None] for 1-feature classification,'
+ '\nor X[[i]] for 1-sample classification')
+
+ a1, z2, a2, z3, a3 = self._feedforward(X, self.w1, self.w2)
+ y_pred = np.argmax(z3, axis=0)
+ return y_pred
+
+ def fit(self, X, y, print_progress=False):
+ """ Learn weights from training data.
+
+ Parameters
+ -----------
+ X : array, shape = [n_samples, n_features]
+ Input layer with original features.
+ y : array, shape = [n_samples]
+ Target class labels.
+ print_progress : bool (default: False)
+ Prints progress as the number of epochs
+ to stderr.
+
+ Returns:
+ ----------
+ self
+
+ """
+ self.cost_ = []
+ X_data, y_data = X.copy(), y.copy()
+ y_enc = self._encode_labels(y, self.n_output)
+
+ delta_w1_prev = np.zeros(self.w1.shape)
+ delta_w2_prev = np.zeros(self.w2.shape)
+
+ for i in range(self.epochs):
+
+ # adaptive learning rate
+ self.eta /= (1 + self.decrease_const*i)
+
+ if print_progress:
+ sys.stderr.write('\rEpoch: %d/%d' % (i+1, self.epochs))
+ sys.stderr.flush()
+
+ if self.shuffle:
+ idx = np.random.permutation(y_data.shape[0])
+ X_data, y_enc = X_data[idx], y_enc[idx]
+
+ mini = np.array_split(range(y_data.shape[0]), self.minibatches)
+ for idx in mini:
+
+ # feedforward
+ a1, z2, a2, z3, a3 = self._feedforward(X[idx],
+ self.w1,
+ self.w2)
+ cost = self._get_cost(y_enc=y_enc[:, idx],
+ output=a3,
+ w1=self.w1,
+ w2=self.w2)
+ self.cost_.append(cost)
+
+ # compute gradient via backpropagation
+ grad1, grad2 = self._get_gradient(a1=a1, a2=a2,
+ a3=a3, z2=z2,
+ y_enc=y_enc[:, idx],
+ w1=self.w1,
+ w2=self.w2)
+
+ # start gradient checking
+ grad_diff = self._gradient_checking(X=X_data[idx],
+ y_enc=y_enc[:, idx],
+ w1=self.w1,
+ w2=self.w2,
+ epsilon=1e-5,
+ grad1=grad1,
+ grad2=grad2)
+
+ if grad_diff <= 1e-7:
+ print('Ok: %s' % grad_diff)
+ elif grad_diff <= 1e-4:
+ print('Warning: %s' % grad_diff)
+ else:
+ print('PROBLEM: %s' % grad_diff)
+
+ # update weights; [alpha * delta_w_prev] for momentum learning
+ delta_w1, delta_w2 = self.eta * grad1, self.eta * grad2
+ self.w1 -= (delta_w1 + (self.alpha * delta_w1_prev))
+ self.w2 -= (delta_w2 + (self.alpha * delta_w2_prev))
+ delta_w1_prev, delta_w2_prev = delta_w1, delta_w2
+
+ return self
+
+
+nn_check = MLPGradientCheck(n_output=10,
+ n_features=X_train.shape[1],
+ n_hidden=10,
+ l2=0.0,
+ l1=0.0,
+ epochs=10,
+ eta=0.001,
+ alpha=0.0,
+ decrease_const=0.0,
+ minibatches=1,
+ shuffle=False,
+ random_state=1)
+
+nn_check.fit(X_train[:5], y_train[:5], print_progress=False)
diff --git a/code/optional-py-scripts/ch13.py b/code/optional-py-scripts/ch13.py
new file mode 100644
index 00000000..57941805
--- /dev/null
+++ b/code/optional-py-scripts/ch13.py
@@ -0,0 +1,424 @@
+# Sebastian Raschka, 2015 (http://sebastianraschka.com)
+# Python Machine Learning - Code Examples
+#
+# Chapter 13 - Parallelizing Neural Network Training with Theano
+#
+# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
+# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
+#
+# License: MIT
+# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
+
+import os
+import theano
+from theano import tensor as T
+import numpy as np
+import struct
+import matplotlib.pyplot as plt
+from keras.utils import np_utils
+from keras.models import Sequential
+from keras.layers.core import Dense
+from keras.optimizers import SGD
+
+
+#############################################################################
+print(50 * '=')
+print('First steps with Theano')
+print(50 * '-')
+
+# initialize
+x1 = T.scalar()
+w1 = T.scalar()
+w0 = T.scalar()
+z1 = w1 * x1 + w0
+
+# compile
+net_input = theano.function(inputs=[w1, x1, w0], outputs=z1)
+
+# execute
+net_input(2.0, 1.0, 0.5)
+
+
+#############################################################################
+print(50 * '=')
+print('Configuring Theano')
+print(50 * '-')
+
+print('theano.config.floatX', theano.config.floatX)
+theano.config.floatX = 'float32'
+
+print('print(theano.config.device)', print(theano.config.device))
+
+
+#############################################################################
+print(50 * '=')
+print('Working with array structures')
+print(50 * '-')
+
+
+# initialize
+# if you are running Theano on 64 bit mode,
+# you need to use dmatrix instead of fmatrix
+x = T.fmatrix(name='x')
+x_sum = T.sum(x, axis=0)
+
+# compile
+calc_sum = theano.function(inputs=[x], outputs=x_sum)
+
+# execute (Python list)
+ary = [[1, 2, 3], [1, 2, 3]]
+print('Column sum:', calc_sum(ary))
+
+# execute (NumPy array)
+ary = np.array([[1, 2, 3], [1, 2, 3]], dtype=theano.config.floatX)
+print('Column sum:', calc_sum(ary))
+
+
+# initialize
+x = T.fmatrix(name='x')
+w = theano.shared(np.asarray([[0.0, 0.0, 0.0]],
+ dtype=theano.config.floatX))
+z = x.dot(w.T)
+update = [[w, w + 1.0]]
+
+# compile
+net_input = theano.function(inputs=[x],
+ updates=update,
+ outputs=z)
+
+# execute
+data = np.array([[1, 2, 3]], dtype=theano.config.floatX)
+for i in range(5):
+ print('z%d:' % i, net_input(data))
+
+
+"""
+We can use the `givens` variable to insert values into the graph
+before compiling it. Using this approach we can reduce the number
+of transfers from RAM (via CPUs) to GPUs to speed up learning with
+shared variables. If we use `inputs`, a datasets is transferred from
+the CPU to the GPU multiple times, for example, if we iterate over a
+dataset multiple times (epochs) during gradient descent. Via `givens`,
+we can keep the dataset on the GPU if it fits (e.g., a mini-batch).
+"""
+
+# initialize
+data = np.array([[1, 2, 3]],
+ dtype=theano.config.floatX)
+x = T.fmatrix(name='x')
+w = theano.shared(np.asarray([[0.0, 0.0, 0.0]],
+ dtype=theano.config.floatX))
+z = x.dot(w.T)
+update = [[w, w + 1.0]]
+
+# compile
+net_input = theano.function(inputs=[],
+ updates=update,
+ givens={x: data},
+ outputs=z)
+
+# execute
+for i in range(5):
+ print('z:', net_input())
+
+
+#############################################################################
+print(50 * '=')
+print('Wrapping things up: A linear regression example')
+print(50 * '-')
+
+X_train = np.asarray([[0.0], [1.0], [2.0], [3.0], [4.0],
+ [5.0], [6.0], [7.0], [8.0], [9.0]],
+ dtype=theano.config.floatX)
+
+y_train = np.asarray([1.0, 1.3, 3.1, 2.0, 5.0,
+ 6.3, 6.6, 7.4, 8.0, 9.0],
+ dtype=theano.config.floatX)
+
+
+def train_linreg(X_train, y_train, eta, epochs):
+
+ costs = []
+ # Initialize arrays
+ eta0 = T.fscalar('eta0')
+ y = T.fvector(name='y')
+ X = T.fmatrix(name='X')
+ w = theano.shared(np.zeros(
+ shape=(X_train.shape[1] + 1),
+ dtype=theano.config.floatX),
+ name='w')
+
+ # calculate cost
+ net_input = T.dot(X, w[1:]) + w[0]
+ errors = y - net_input
+ cost = T.sum(T.pow(errors, 2))
+
+ # perform gradient update
+ gradient = T.grad(cost, wrt=w)
+ update = [(w, w - eta0 * gradient)]
+
+ # compile model
+ train = theano.function(inputs=[eta0],
+ outputs=cost,
+ updates=update,
+ givens={X: X_train,
+ y: y_train})
+
+ for _ in range(epochs):
+ costs.append(train(eta))
+
+ return costs, w
+
+
+costs, w = train_linreg(X_train, y_train, eta=0.001, epochs=10)
+
+plt.plot(range(1, len(costs) + 1), costs)
+
+plt.tight_layout()
+plt.xlabel('Epoch')
+plt.ylabel('Cost')
+# plt.tight_layout()
+# plt.savefig('./figures/cost_convergence.png', dpi=300)
+plt.show()
+
+
+def predict_linreg(X, w):
+ Xt = T.matrix(name='X')
+ net_input = T.dot(Xt, w[1:]) + w[0]
+ predict = theano.function(inputs=[Xt], givens={w: w}, outputs=net_input)
+ return predict(X)
+
+
+plt.scatter(X_train, y_train, marker='s', s=50)
+plt.plot(range(X_train.shape[0]),
+ predict_linreg(X_train, w),
+ color='gray',
+ marker='o',
+ markersize=4,
+ linewidth=3)
+
+plt.xlabel('x')
+plt.ylabel('y')
+
+# plt.tight_layout()
+# plt.savefig('./figures/linreg.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Wrapping things up: A linear regression example')
+print(50 * '-')
+
+
+# note that first element (X[0] = 1) to denote bias unit
+
+X = np.array([[1, 1.4, 1.5]])
+w = np.array([0.0, 0.2, 0.4])
+
+
+def net_input(X, w):
+ z = X.dot(w)
+ return z
+
+
+def logistic(z):
+ return 1.0 / (1.0 + np.exp(-z))
+
+
+def logistic_activation(X, w):
+ z = net_input(X, w)
+ return logistic(z)
+
+
+print('P(y=1|x) = %.3f' % logistic_activation(X, w)[0])
+
+
+# W : array, shape = [n_output_units, n_hidden_units+1]
+# Weight matrix for hidden layer -> output layer.
+# note that first column (A[:][0] = 1) are the bias units
+W = np.array([[1.1, 1.2, 1.3, 0.5],
+ [0.1, 0.2, 0.4, 0.1],
+ [0.2, 0.5, 2.1, 1.9]])
+
+# A : array, shape = [n_hidden+1, n_samples]
+# Activation of hidden layer.
+# note that first element (A[0][0] = 1) is for the bias units
+
+A = np.array([[1.0],
+ [0.1],
+ [0.3],
+ [0.7]])
+
+# Z : array, shape = [n_output_units, n_samples]
+# Net input of output layer.
+
+Z = W.dot(A)
+y_probas = logistic(Z)
+print('Probabilities:\n', y_probas)
+
+y_class = np.argmax(Z, axis=0)
+print('predicted class label: %d' % y_class[0])
+
+
+#############################################################################
+print(50 * '=')
+print('Estimating probabilities in multi-class'
+ ' classification via the softmax function')
+print(50 * '-')
+
+
+def softmax(z):
+ return np.exp(z) / np.sum(np.exp(z))
+
+
+def softmax_activation(X, w):
+ z = net_input(X, w)
+ return softmax(z)
+
+
+y_probas = softmax(Z)
+print('Probabilities:\n', y_probas)
+
+print('Sum of probabilities', y_probas.sum())
+
+y_class = np.argmax(Z, axis=0)
+print('Predicted class', y_class)
+
+
+#############################################################################
+print(50 * '=')
+print('Broadening the output spectrum using a hyperbolic tangent')
+print(50 * '-')
+
+
+def tanh(z):
+ e_p = np.exp(z)
+ e_m = np.exp(-z)
+ return (e_p - e_m) / (e_p + e_m)
+
+
+z = np.arange(-5, 5, 0.005)
+log_act = logistic(z)
+tanh_act = tanh(z)
+
+# alternatives:
+# from scipy.special import expit
+# log_act = expit(z)
+# tanh_act = np.tanh(z)
+
+plt.ylim([-1.5, 1.5])
+plt.xlabel('net input $z$')
+plt.ylabel('activation $\phi(z)$')
+plt.axhline(1, color='black', linestyle='--')
+plt.axhline(0.5, color='black', linestyle='--')
+plt.axhline(0, color='black', linestyle='--')
+plt.axhline(-1, color='black', linestyle='--')
+
+plt.plot(z, tanh_act,
+ linewidth=2,
+ color='black',
+ label='tanh')
+plt.plot(z, log_act,
+ linewidth=2,
+ color='lightgreen',
+ label='logistic')
+
+plt.legend(loc='lower right')
+# plt.tight_layout()
+# plt.savefig('./figures/activation.png', dpi=300)
+plt.show()
+
+
+#############################################################################
+print(50 * '=')
+print('Broadening the output spectrum using a hyperbolic tangent')
+print(50 * '-')
+
+_ = input("Please make sure that you've downloaded and unzipped the"
+ " MNIST dataset as described in the previous chapter. The following"
+ " code assumes that you have created a mnist directory within"
+ " this script's directory. Please hit 'enter' to continue.")
+
+
+def load_mnist(path, kind='train'):
+ """Load MNIST data from `path`"""
+ labels_path = os.path.join(path,
+ '%s-labels-idx1-ubyte' % kind)
+ images_path = os.path.join(path,
+ '%s-images-idx3-ubyte'
+ % kind)
+
+ with open(labels_path, 'rb') as lbpath:
+ magic, n = struct.unpack('>II',
+ lbpath.read(8))
+ labels = np.fromfile(lbpath,
+ dtype=np.uint8)
+
+ with open(images_path, 'rb') as imgpath:
+ magic, num, rows, cols = struct.unpack(">IIII",
+ imgpath.read(16))
+ images = np.fromfile(imgpath,
+ dtype=np.uint8).reshape(len(labels), 784)
+
+ return images, labels
+
+
+X_train, y_train = load_mnist('mnist', kind='train')
+print('Training rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))
+
+X_test, y_test = load_mnist('mnist', kind='t10k')
+print('Test rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))
+
+
+#############################################################################
+print(50 * '=')
+print('Multi-layer Perceptron in Keras')
+print(50 * '-')
+
+theano.config.floatX = 'float32'
+X_train = X_train.astype(theano.config.floatX)
+X_test = X_test.astype(theano.config.floatX)
+
+print('First 3 labels: ', y_train[:3])
+
+y_train_ohe = np_utils.to_categorical(y_train)
+print('\nFirst 3 labels (one-hot):\n', y_train_ohe[:3])
+
+np.random.seed(1)
+
+model = Sequential()
+model.add(Dense(input_dim=X_train.shape[1],
+ output_dim=50,
+ init='uniform',
+ activation='tanh'))
+
+model.add(Dense(input_dim=50,
+ output_dim=50,
+ init='uniform',
+ activation='tanh'))
+
+model.add(Dense(input_dim=50,
+ output_dim=y_train_ohe.shape[1],
+ init='uniform',
+ activation='softmax'))
+
+sgd = SGD(lr=0.001, decay=1e-7, momentum=.9)
+model.compile(loss='categorical_crossentropy', optimizer=sgd)
+
+model.fit(X_train, y_train_ohe,
+ nb_epoch=50,
+ batch_size=300,
+ verbose=1,
+ validation_split=0.1,
+ show_accuracy=True)
+
+y_train_pred = model.predict_classes(X_train, verbose=0)
+print('First 3 predictions: ', y_train_pred[:3])
+
+train_acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]
+print('Training accuracy: %.2f%%' % (train_acc * 100))
+
+y_test_pred = model.predict_classes(X_test, verbose=0)
+test_acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]
+print('Test accuracy: %.2f%%' % (test_acc * 100))
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diff --git a/docs/equations/pymle-equations.tex b/docs/equations/pymle-equations.tex
new file mode 100644
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@@ -0,0 +1,2336 @@
+\documentclass[letterpaper]{report}
+
+\usepackage{hyperref}
+\usepackage{fancyhdr}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{enumerate}
+\usepackage{caption}
+
+\setlength\parindent{0pt}
+
+% start meta data
+
+\title{Python Machine Learning\\ Equation Reference}
+\author{Sebastian Raschka \\ \texttt{mail@sebastianraschka.com}}
+\date{ \vspace{2cm} 05\slash 04\slash 2015 (last updated: 11\slash 29\slash 2016) \\\begin{flushleft} \vspace{2cm} \noindent\rule{10cm}{0.4pt} \\ Code Repository and Resources:: \href{https://github.com/rasbt/python-machine-learning-book}{https://github.com/rasbt/python-machine-learning-book} \vspace{2cm} \endgraf @book\{raschka2015python,\\
+ title=\{Python Machine Learning\},\\
+ author=\{Raschka, Sebastian\},\\
+ year=\{2015\},\\
+ publisher=\{Packt Publishing\} \} \end{flushleft}}
+
+% end meta data
+
+% start header and footer
+\pagestyle{fancy}
+\lhead{Sebastian Raschka}
+\rhead{Python Machine Learning -- Equation Reference -- Ch. \thechapter}
+\cfoot{\thepage} % centered footer
+\renewcommand{\headrulewidth}{0.4pt}
+\renewcommand{\footrulewidth}{0.4pt}
+\renewcommand{\chaptermark}[1]{%
+{}}
+
+
+
+% end header and footer
+
+
+
+
+\begin{document} % start main document
+
+
+\maketitle
+
+
+\tableofcontents
+
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 1
+%%%%%%%%%%%%%%%
+
+
+\chapter{Giving Computers the Ability to Learn from Data}
+
+\section{Building intelligent machines to transform data into knowledge}
+\section{The three different types of machine learning}
+\section{Making predictions about the future with supervised learning}
+\subsection{Classification for predicting class labels}
+\subsection{Regression for predicting continuous outcomes}
+\section{Solving interactive problems with reinforcement learning}
+\section{Discovering hidden structures with unsupervised learning}
+\subsection{Finding subgroups with clustering}
+\subsection{Dimensionality reduction for data compression}
+\section{An introduction to the basic terminology and notations}
+
+\newpage
+
+The Iris dataset, consisting of 150 samples and 4 features, can then be written as a $150 \times 4$ matrix $\mathbf{X} \in \mathbb{R}^{150 \times 4}:$
+
+\[
+\begin{bmatrix}
+ x_{1}^{(1)} & x_{2}^{(1)} & x_{3}^{(1)} & \dots & x_{4}^{(1)} \\
+ x_{1}^{(2)} & x_{2}^{(2)} & x_{3}^{(2)} & \dots & x_{4}^{(2)} \\
+ \vdots & \vdots & \vdots & \ddots & \vdots \\
+ x_{1}^{(150)} & x_{2}^{(150)} & x_{3}^{(150)} & \dots & x_{4}^{(150)}
+\end{bmatrix}
+\]
+
+For the rest of this book, unless noted otherwise, we will use the superscript $(i)$ to refer to the $i$th training sample, and the subscript $j$ to refer to the $j$th dimension of the training dataset.
+
+We use lower-case, bold-face letters to refer to vectors ($\mathbf{x} \in \mathbb{R}^{n \times 1}$) and upper-case, bold-face letters to refer to matrices, respectively ($\mathbf{X} \in \mathbb{R}^{n \times m}$), where $n$ refers to the number of rows, and $m$ refers to the number of columns, respectively. To refer to single elements in a vector or matrix, we write the letters in italics $x^{(n)}$ or $x^{(n)}_{m}$, respectively. For example, $x^{150}_1$ refers to the refers to the first dimension of the flower sample 150, the sepal length. Thus, each row in this feature matrix represents one flower instance and can be written as four-dimensional row vector $\mathbf{x}^{(i)} \in \mathbb{R}^{1 \times 4}$
+
+\[ \mathbf{x}^{(i)} = \bigg[x^{(i)}_1 \; x^{(i)}_2 \; x^{(i)}_3 \; x^{(i)}_4 \bigg]. \]
+
+Each feature dimension is a 150-dimensional column vector $\mathbf{x}_{j} \in \mathbb{R}^{150 \times 1}$, for example
+
+\[
+\mathbf{x_j} = \begin{bmatrix}
+ x_{j}^{(1)} \\
+ x_{j}^{(2)} \\
+ \vdots \\
+ x_{j}^{(150)}
+\end{bmatrix}
+.\]
+
+Similarly, we store the target variables (here: class labels) as a 150-dimensional column vector
+
+\[
+\mathbf{y} = \begin{bmatrix}
+ y^{(1)} \\
+ y^{(2)} \\
+ \vdots \\
+ y^{(150)}
+\end{bmatrix}
+, (y \in \{ \text{Setosa, Versicolor, Virginica \}}).\]
+
+\newpage
+
+\section{A roadmap for building machine learning systems}
+\subsection{Preprocessing -- getting data into shape}
+\subsection{Training and selecting a predictive model}
+\subsection{Evaluating models and predicting unseen data instances}
+\section{Using Python for machine learning}
+\subsection{Installing Python packages}
+\section{Summary}
+
+
+
+
+
+
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 2
+%%%%%%%%%%%%%%%
+
+
+\chapter{Training Machine Learning Algorithms for Classification}
+
+\section{Artificial neurons -- a brief glimpse into the early history of machine learning}
+
+We can then define an activation function $\phi(z)$ that takes a linear combination of certain
+input values $\mathbf{x}$ and a corresponding weight vector $\mathbf{w}$ where $z$ is the so-called net
+input ($z = w_1 x_1 + \dots + w_m x_m$):
+
+\[
+\mathbf{w} = \begin{bmatrix}
+ w_{1} \\
+ w_{2} \\
+ \vdots \\
+ w_{m}
+\end{bmatrix}, \quad
+\mathbf{x} = \begin{bmatrix}
+ x_{1} \\
+ x_{2} \\
+ \vdots \\
+ x_{m}
+\end{bmatrix}.
+\]
+
+Now, if the activation of a particular sample $x^{(i)}$, that is, the output of $\phi(z)$, is greater than a defined threshold $\theta$, we predict class 1 and class -1, otherwise. In the perceptron algorithm, the activation function $\phi(\cdot)$ is a simple \textit{unit step function}, which is sometimes also called the \textit{Heaviside step function}:
+
+\[ \phi(z) = \begin{cases}
+ 1 & \text{ if } z \ge \theta \\
+ -1 & \text{ otherwise }.
+ \end{cases}
+\]
+
+For simplicity, we can bring the threshold $\theta$ to the left side of the equation and define a weight-zero as $w_0 = -\theta$ and $x_0=1$, so that we write $\mathbf{z}$ in a more compact form
+
+\[
+z = w_0 x_0 + w_1 x_1 + \dots + w_m x_m = \mathbf{w^T x}
+\]
+
+and
+
+\[ \phi(z) = \begin{cases}
+ 1 & \text{ if } z \ge 0 \\
+ -1 & \text{ otherwise }.
+ \end{cases}
+\]
+
+
+In the following sections, we will often make use of basic notations from linear algebra. For example, we will abbreviate the sum of the products of the values in $\mathbf{x}$ and $\mathbf{w}$ using a \textit{vector dot product}, whereas superscript $T$ stands for \textit{transpose}, which is an operation that transforms a column vector into a row vector and vice versa:
+
+\[
+z = w_0 x_0 + w_1 x_1 + \dots + w_m x_m = \mathbf{w^T x} = \sum_{j=0}^{m} \mathbf{w_j} \mathbf{x_j} = \mathbf{w}^T \mathbf{x}.
+\]
+
+For example:
+
+\[
+\big[1 \quad 2 \quad 3 \big] \times \begin{bmatrix}
+ 4 \\
+ 5 \\
+ 6
+\end{bmatrix} = 1 \times 4 + 2 \times 5 + 3 \times 6 = 32.
+\]
+
+Furthermore, the transpose operation can also be applied to a matrix to
+reflect it over its diagonal, for example:
+
+\[
+ \begin{bmatrix}
+ 1 & 2 \\
+ 3 & 4 \\
+ 5 & 6
+\end{bmatrix}^T = \begin{bmatrix}
+ 1 & 3 & 5 \\
+ 2 & 4 & 6
+\end{bmatrix}
+\]
+
+Rosenblatt's initial perceptron rule is fairly simple and can be summarized by the following steps:
+
+\begin{enumerate}
+\item Initialize the weights to 0 or small random numbers.
+\item For each training sample $\mathbf{x}^{(i)}$, perform the following steps:
+\begin{enumerate}
+\item Compute the output value $\hat{y}$.
+\item Update the weights.
+\end{enumerate}
+\end{enumerate}
+
+Here, the output value is the class label predicted by the unit step function that we defined earlier, and the simultaneous update of each weight $w_j$ in the weight vector $\mathbf{w}$ can be more formally written as:
+
+\[
+w_j := w_j + \Delta w_j
+\]
+
+The value of $\Delta w_j$, which is used to update the weight $w_j$, is calculated by the perceptron rule:
+
+\[
+\Delta w_j = \eta \bigg( y^{(i)} - \hat{y}^{(i)} \bigg)x_{j}^{(i)}
+\]
+
+Where $\eta$ is the learning rate (a constant between 0.0 and 1.0), $y^{(i)}$ is the true class label of the $i$th training sample, and $\hat{y}^{(i)}$ is the predicted class label. It is important to note that all weights in the weight vector are being updated simultaneously, which means that we don't recompute $\hat{y}^{(i)}$ before all of the weights $\Delta w_j$ were updated. Concretely, for a 2D dataset, we would write the update as follows:
+
+\[
+\Delta w_0 = \eta \bigg( y^{(i)} - \hat{y}^{(i)} \bigg)
+\]
+
+\[
+\Delta w_1 = \eta \bigg( y^{(i)} - \hat{y}^{(i)} \bigg) x_{1}^{(i)}
+\]
+
+\[
+\Delta w_2 = \eta \bigg( y^{(i)} - \hat{y}^{(i)} \bigg) x_{2}^{(i)}
+\]
+
+Before we implement the perceptron rule in Python, let us make a simple thought experiment to illustrate how beautifully simple this learning rule really is. In the two scenarios where the perceptron predicts the class label correctly, the weights remain unchanged:
+
+\[
+\Delta w_j = \eta \bigg( -1 -- 1 \bigg)x_{j}^{(i)} = 0
+\]
+
+\[
+\Delta w_j = \eta \bigg( 1-1 \bigg)x_{j}^{(i)} = 0
+\]
+
+However, in the case of a wrong prediction, the weights are being pushed towards the direction of the positive or negative target class, respectively:
+
+\[
+\Delta w_j = \eta \bigg( 1 -- 1 \bigg)x_{j}^{(i)} = \eta(2)x_{j}^{(i)}
+\]
+
+\[
+\Delta w_j = \eta \bigg( -1-1 \bigg)x_{j}^{(i)} = \eta(-2)x_{j}^{(i)}
+\]
+
+
+To get a better intuition for the multiplicative factor $x_{j}^{(i)}$, let us go through another
+simple example, where:
+
+\[
+y^{(i)} = +1, \quad \hat{y}^{(i)} = -1, \quad \eta = 1
+ \]
+
+Let's assume that $x_{j}^{(i)}=0.5$ and we misclassify this sample as $-1$. In this case, we would increase the corresponding weight by $1$ so that the net input $x_{j}^{i} \times w_{j}^{(i)}$ will be more positive the next time we encounter this sample and thus will be more likely to be above the threshold of the unit step function to classify the sample as $+1$:
+
+\[
+\Delta w_{j} = (1--1)0.5 = (2)0.5 = 1
+\]
+
+The weight update is proportional to the value of $x_{j}^{(i)}$. For example, if we have another sample $x_{j}^{(i)}=2$ that is incorrectly classified as $-1$, we'd push the decision boundary by an even larger extent to classify this sample correctly the next time:
+
+\[
+\Delta w_{j} = (1--1)2 = (2)2 = 4.
+\]
+
+
+\section{Implementing a perceptron learning algorithm in Python}
+
+\subsection{Training a perceptron model on the Iris dataset}
+
+\section{Adaptive linear neurons and the convergence of learning}
+
+The key difference between the Adaline rule (also known as the Widrow-Hoff rule) and Rosenblatt's perceptron is that the weights are updated based on a linear activation function rather than a unit step function like in the perceptron. In Adaline, this linear activation function $\phi{z}$ is simply the identity function of the net input so that
+
+\[
+\phi \big( \mathbf{w}^T \mathbf{x} \big) = \mathbf{w}^T \mathbf{x}
+\]
+
+\subsection{Minimizing cost functions with gradient descent}
+
+One of the key ingredients of supervised machine learning algorithms is to define an objective function that is to be optimized during the learning process. This objective function is often a cost function that we want to minimize. In the case of Adaline, we can define the cost function $J(\cdot)$ to learn the weights as the Sum of Squared Errors (SSE) between the calculated outcomes and the true class labels
+
+\[
+J(\mathbf{w}) = \frac{1}{2} \sum_i \bigg(y^{(i)} - \phi \big(z^{(i)} \big) \bigg)^2.
+\]
+
+Using gradient descent, we can now update the weights by taking a step away from the gradient $\nabla J(\mathbf{w})$ of our cost function $J(\mathbf{\cdot})$:
+
+\[
+\mathbf{w} := \mathbf{w} + \Delta \mathbf{w}.
+\]
+
+To compute the gradient of the cost function, we need to compute the partial derivative of the cost function with respect to each weight $w_j$,
+
+\[
+\frac{\partial J}{\partial w_j} = - \sum_i \bigg( y^{(i)} - \phi \big(z^{(i)} \big) \bigg) x_{j}^{(i)},
+\]
+
+so that we can write the update of weight $w_j$ as
+
+\[
+\Delta w_j = - \eta \frac{\partial J}{\partial w_j} = \eta \sum_i \bigg( y^{(i)} - \phi \big(z^{(i)} \big) \bigg) x_{j}^{(i)}
+\]
+
+Since we update all weights simultaneously, our Adaline learning rule becomes
+
+\[
+\mathbf{w} := \mathbf{w} + \Delta \mathbf{w}.
+\]
+
+
+For those who are familiar with calculus, the partial derivative of the SSE cost function with respect to the $j$th weight in can be obtained as follows:
+
+\begin{equation*}
+\begin{split}
+& \frac{\partial J}{\partial w_j} = \frac{\partial}{\partial w_j} \frac{1}{2} \sum_i \bigg( y^{(i)} - \phi \big( z^{(i)} \big) \bigg)^2 \\
+& = \frac{1}{2} \frac{\partial}{\partial w_j} \sum_i \bigg( y^{(i)} - \phi \big( z^{(i)} \big) \bigg)^2 \\
+& = \frac{1}{2} \sum_i 2 \big( y^{(i)} - \phi(z^{(i)})\big) \frac{\partial}{\partial w_j} \Big( y^{(i)} - \phi({z^{(i)}}) \Big) \\
+& = \sum_i \big( y^{(i)} - \phi (z^{(i)}) \big) \frac{\partial}{\partial w_j} \Big( y^{(i)} - \sum_i \big(w^{(i)}_{j} x^{(i)}_{j} \big) \Big) \\
+& = \sum_i \bigg( y^{(i)} - \phi \big( z^{(i)} \big) \bigg) \bigg( - x_{j}^{(i)} \bigg) \\
+& = - \sum_i \bigg( y^{(i)} - \phi \big( z^{(i)} \big) \bigg) x_{j}^{(i)} \\
+\end{split}
+\end{equation*}
+
+Performing a matrix-vector multiplication is similar to calculating a vector dot product where each row in the matrix is treated as a single row vector. This vectorized approach represents a more compact notation and results in a more efficient computation using NumPy. For example:
+
+\[
+ \begin{bmatrix}
+ 1 & 2 & 3\\
+ 4 & 5 & 6
+\end{bmatrix} \times \begin{bmatrix}
+ 7 \\
+ 8 \\
+ 9
+\end{bmatrix} = \begin{bmatrix}
+ 1 \times 7 + 2 \times 8 + 3 \times 9 \\
+ 4 \times 7 + 5 \times 8 + 6 \times 9
+\end{bmatrix} = \begin{bmatrix}
+ 50 \\
+ 122
+\end{bmatrix}
+\]
+
+\subsection{Implementing an Adaptive Linear Neuron in Python}
+
+Here, we will use a feature scaling method called standardization, which gives our data the property of a standard normal distribution. The mean of each feature
+is centered at value 0 and the feature column has a standard deviation of 1. For example, to standardize the $j$th feature, we simply need to subtract the sample mean $\mu_j$ from every training sample and divide it by its standard deviation $\sigma_j$:
+
+\[
+\mathbf{x'}_j = \frac{\mathbf{x} - \mathbf{\mathbf{\mu_j}}}{\sigma_j}.
+\]
+
+
+Here $\mathbf{x}_j$ is a vector consisting of the $j$th feature values of all training samples $n$.
+
+\subsection{Large scale machine learning and stochastic gradient descent}
+
+A popular alternative to the batch gradient descent algorithm is stochastic gradient descent, sometimes also called iterative or on-line gradient descent. Instead of updating the weights based on the sum of the accumulated errors over all samples $\mathbf{x}^{(i)}$:
+
+\[
+\Delta \mathbf{w} = \eta \sum_i \bigg( y^{(i)} - \phi \big( z^{(i)}\big) \bigg) \mathbf{x}^{(i)}.
+\]
+
+We update the weights incrementally for each training sample:
+
+\[
+\Delta \mathbf{w} = \eta \bigg( y^{(i)} - \phi \big( z^{(i)}\big) \bigg) \mathbf{x}^{(i)}.
+\]
+
+\section{Summary}
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 3
+%%%%%%%%%%%%%%%
+
+\chapter{A Tour of Machine Learning Classifiers Using Scikit-learn}
+
+\section{Choosing a classification algorithm}
+\section{First steps with scikit-learn}
+\subsection{Training a perceptron via scikit-learn}
+\section{Modeling class probabilities via logistic regression}
+\subsection{Logistic regression intuition and conditional probabilities}
+
+The odds ratio can be written as
+
+\[
+\frac{p}{(1-p)},
+\]
+
+where $p$ stands for the probability of the positive (1? p)
+ event. The term positive event does not necessarily mean good, but refers to the event that we want to predict, for example, the probability that a patient has a certain disease; we can think of the positive event as class label $y =1$. We can then further define the logit function, which is simply the logarithm of the odds ratio (log-odds):
+
+\[
+logit(p) = \log \frac{p}{1-p}
+\]
+
+The logit function takes input values in the range 0 to 1 and transforms them to values over the entire real number range, which we can use to express a linear relationship between feature values and the log-odds:
+
+\[
+logit ( p (y=1 | \mathbf{x})) = w_0 x_0 + w_1 x_1 + \cdots + x_m w_m = \sum^{m}_{i=0} w_i x_i = \mathbf{w}^T \mathbf{x}.
+\]
+
+Here, $p(y=1 | \mathbf{x})$ s the conditional probability that a particular sample belongs to class 1 given its features $\mathbf{x}$. Now what we are actually interested in is predicting the probability that a certain sample belongs to a particular class, which is the inverse form of the logit function. It is also called the logistic function, sometimes simply abbreviated as sigmoid function due to its characteristic S-shape
+
+\[
+\phi(z) = \frac{1}{1+e^{-z}}.
+\]
+
+The output of the sigmoid function is then interpreted as the probability of particular sample belonging to class 1
+
+\[
+\phi(z) = P(y=1 | \mathbf{x}; \mathbf{w})
+\]
+
+given its features $\mathbf{x}$ parameterized by the weights $\mathbf{w}$. For example, if we compute $\phi(z) = 0.8$ for a particular flower sample, it means that the chance that this sample is an Iris-Versicolor flower is 80 percent. Similarly, the probability that this ower is an Iris-Setosa ower can be calculated as $P(y=0 | \mathbf{x};\mathbf{w})=1 - P (y=1 | \mathbf{x}; \mathbf{w}) = 0.2$ or 20 percent. The predicted probability can then simply be converted into a binary outcome via a quantizer (unit step function):
+
+\[ \hat{y}= \begin{cases}
+ 1 & \text{ if } \phi(z) \ge 0.5 \\
+ 0 & \text{ otherwise }.
+ \end{cases}
+\]
+
+If we look at the preceding sigmoid plot, this is equivalent to the following:
+
+\[ \hat{y}= \begin{cases}
+ 1 & \text{ if } \phi(z) \ge 0.0 \\
+ 0 & \text{ otherwise }.
+ \end{cases}
+\]
+
+\subsection{Learning the weights of the logistic cost function}
+
+In the previous chapter, we defined the sum-squared-error cost function:
+
+\[
+J(\mathbf{w}) = \frac{1}{2} \sum_i \bigg( \phi \big( z^{(i)} \big) - y^{(i)} \bigg)^2.
+\]
+
+We minimized this in order to learn the weights w for our Adaline classification model. To explain how we can derive the cost function for logistic regression, let's first define the likelihood L that we want to maximize when we build a logistic regression model, assuming that the individual samples in our dataset are independent of one another. The formula is as follows:
+
+\[
+L(\mathbf{w}) = P(\mathbf{y} | \mathbf{x}; \mathbf{w}) = \prod_{i=1}^{n} P \big( y^{(i)} | x^{(i)}; \mathbf{w} \big) = \prod_{i=1}^{n} \bigg( \phi \big(z^{(i)} \big) \bigg) ^ {y^{(i)}} \bigg( 1 - \phi \big( z^{(i)} \big) \bigg)^{1-y^{(i)}}
+\]
+
+In practice, it is easier to maximize the (natural) log of this equation, which is called
+the log-likelihood function:
+
+\[
+l(\mathbf{w}) = \log L(\mathbf{w}) = \sum_{i=1}^{n} \Bigg[ y^{(i)} \log \bigg(\phi \big( z^{(i)} \big) \bigg) + \bigg(1 - y^{(i)} \bigg) \log \bigg( 1 - \phi \big( z^{(i)} \big) \bigg) \Bigg]
+\]
+
+Firstly, applying the log function reduces the potential for numerical under ow, which can occur if the likelihoods are very small. Secondly, we can convert the product of factors into a summation of factors, which makes it easier to obtain the derivative of this function via the addition trick, as you may remember
+from calculus.
+
+Now we could use an optimization algorithm such as gradient ascent to maximize this log-likelihood function. Alternatively, let's rewrite the log-likelihood as a cost function $J(\cdot)$ that can be minimized using gradient descent as in \textit{Chapter 2, Training Machine Learning Algorithms for Classification}:
+
+\[
+J(\mathbf{w}) = \sum_{i=1}^{n} \Bigg[- y^{(i)} \log \bigg(\phi \big( z^{(i)} \big) \bigg) - \bigg(1 - y^{(i)} \bigg) \log \bigg( 1 - \phi \big( z^{(i)} \big) \bigg) \Bigg]
+\]
+
+To get a better grasp on this cost function, let's take a look at the cost that we
+calculate for one single-sample instance:
+
+\[
+J\big( \phi(z), y; \mathbf{w} \big) = -y \log \big( \phi(z) \big) - (1-y) \log \big(1 - \phi(z) \big).
+\]
+
+Looking at the preceding equation, we can see that the rst term becomes zero if
+$y = 0$ , and the second term becomes zero if $y = 1$, respectively:
+
+
+\[
+J \big( \phi(z), y; \mathbf{w} \big)= \begin{cases}
+ - \log \big( \phi(z) \big) \text{ if } y=1\\
+ - \log \big( 1 - \phi(z) \big) \text{ if } y=0
+ \end{cases}
+\]
+
+\subsection{Training a logistic regression model with scikit-learn}
+
+If we were to implement logistic regression ourselves, we could simply substitute the cost function $J(\cdot)$ in our Adaline implementation from \textit{Chapter 2, Training Machine Learning Algorithms for Classification}, by the new cost function:
+
+\[
+J(\mathbf{w}) = \sum_{i=1}^{n} \Bigg[- y^{(i)} \log \bigg(\phi \big( z^{(i)} \big) \bigg) - \bigg(1 - y^{(i)} \bigg) \log \bigg( 1 - \phi \big( z^{(i)} \big) \bigg) \Bigg]
+\]
+
+We can show that the weight update in logistic regression via gradient descent is indeed equal to the equation that we used in Adaline in \textit{Chapter 2, Training Machine Learning Algorithms for Classification}. Let's start by calculating the partial derivative of the log-likelihood function with respect to the $j$th weight:
+
+\[
+\frac{\partial}{\partial w_j} l(\mathbf{w}) = \Bigg( y \frac{1}{\phi(z)} - (1-y) \frac{1}{1-\phi(z)} \Bigg) \frac{\partial}{\partial w_j} \phi(z)
+\]
+
+Before we continue, let's calculate the partial derivative of the sigmoid function first:
+
+\[
+\frac{\partial}{\partial z} \phi(z) = \frac{\partial}{\partial z} \frac{1}{1 + e^{-1}} \frac{1}{\big( 1 + e^{-z}\big)^2} e^{-z} = \frac{1}{1 + e^{-z}} = \frac{1}{1 + e^{-z}} \bigg( 1 - \frac{1}{1 + e^{-z}} \bigg) \\\\
+\]
+\[
+= \phi(z)(1-\phi(z)).
+\]
+
+Now we can resubstitute $\frac{\partial}{\partial z} \phi(z) = \phi(z)(1-\phi(z))$ in our first equation to obtain the following:
+
+\begin{equation*}
+\begin{split}
+& \Bigg( y \frac{1}{\phi(z)} - (1-y) \frac{1}{1-\phi(z)} \Bigg) \frac{\partial}{\partial w_j} \phi(z) \\
+& = \Bigg( y \frac{1}{\phi(z)} - (1-y) \frac{1}{1-\phi(z)} \Bigg) \phi(z) \big(1 - \phi(z)\big) \frac{\partial}{\partial w_j} z \\
+& = \bigg( y \big( 1 - \phi(z) \big) - (1-y) \phi(z) \bigg) x_j \\
+& = \big( y - \phi(z) \big) x_j
+\end{split}
+\end{equation*}
+
+Remember that the goal is to find the weights that maximize the log-likelihood so that we would perform the update for each weight as follows:
+
+\[
+w_j := w_j + \eta \sum_{i=1}^{n} \bigg( y^{(i)} - \phi(z^{(i)}) \bigg) x_{j}^{(i)}
+\]
+
+Since we update all weights simultaneously, we can write the general update rule as follows:
+
+\[
+\mathbf{w} := \mathbf{w} + \Delta \mathbf{w}
+\]
+
+We define $\Delta \mathbf{w}$ as follows:
+
+\[
+\Delta \mathbf{w} = \eta \nabla l (\mathbf{w})
+\]
+
+Since maximizing the log-likelihood is equal to minimizing the cost function $J(\cdot)$ that we defined earlier, we can write the gradient descent update rule as follows:
+
+\[
+\Delta w_j = - \eta \frac{\partial J}{\partial w_j} = \eta \sum_{i=1}^{n} \bigg( y^{(i)} - \phi(z^{(i)}) \bigg)x_{j}^{(i)}
+\]
+
+\[
+\mathbf{w} := \mathbf{w} + \Delta \mathbf{w}, \; \Delta \mathbf{w} = - \eta \nabla J(\mathbf{w})
+\]
+
+This is equal to the gradient descent rule in Adaline in \textit{Chapter 2, Training Machine Learning Algorithms for Classification}.
+
+
+\subsection{Tackling overfitting via regularization}
+
+The most common form of regularization is the so-called L2 regularization (sometimes also called L2 shrinkage or weight decay), which can be written as follows:
+
+\[
+\frac{\lambda}{2} \lVert \mathbf{w} \rVert^2 = \frac{\lambda}{2} \sum_{j=1}^m w_{j}^{2}
+\]
+
+Here, $\lambda$ is the so-called regularization parameter.
+
+In order to apply regularization, we just need to add the regularization term to the cost function that we defined for logistic regression to shrink the weights:
+
+\[
+J(\mathbf{w}) = - \sum_{i=1}^{n} \bigg[ y^{(i)} \log \big( \phi(z^{(i)}) \big) - \big( 1 - y ^{(i)} \big) \log \big( 1 - \phi(z^{(i)}) \big) \bigg] + \frac{\lambda}{2} \lVert \mathbf{w}\rVert^2
+\]
+
+Then, we have the following regularized weight updates for weight $w_j$:
+
+\[
+\Delta w_j = - \eta \frac{\partial J}{\partial w_j} = \eta \sum_{i=1}^{n} \bigg( y^{(i)} - \phi(z^{(i)}) \bigg)x_{j}^{(i)} - \eta \lambda w_j,
+\] for $j \in \{1, 2, ..., m \}$ (i.e., $j \neq 0 $) since we don't regularize the bias unit $w_0$. \\
+
+
+
+
+Via the regularization parameter $\lambda$, we can then control how well we fit the training data while keeping the weights small. By increasing the value of $\lambda$, we increase the regularization strength.
+
+The parameter \textit{C} that is implemented for the \textit{LogisticRegression} class in scikit-learn comes from a convention in support vector machines, which will be the topic of the next section. \textit{C} is directly related to the regularization parameter $\lambda$ , which is its inverse:
+
+\[
+C = \frac{1}{\lambda}
+\]
+
+So, we can rewrite the regularized cost function of logistic regression as follows:
+
+\[
+J(\mathbf{w}) = C \Bigg[ \sum_{i=1}^{n} \Big( -y^{(i)} \log \big( \phi(z^{(i)} \big) - \big( 1 - y^{(i)} \big) \Big) \log \bigg( 1 - \phi(z^{(i)}) \bigg) \Bigg] + \frac{1}{2} \lVert \mathbf{w} \rVert^2
+\]
+
+
+
+
+\section{Maximum margin classification with support vector machines}
+
+\subsection{Maximum margin intuition}
+
+To get an intuition for the margin maximization, let's take a closer look at those \textit{positive} and \textit{negative} hyperplanes that are parallel to the decision boundary, which can be expressed as follows:
+
+\[
+w_0 + \mathbf{w}^T \mathbf{x}_{pos} = 1 \quad (1)
+\]
+
+\[
+w_0 + \mathbf{w}^T \mathbf{x}_{neg} = -1 \quad (2)
+\]
+
+If we subtract those two linear equations (1) and (2) from each other, we get:
+
+\[
+\Rightarrow \mathbf{w}^T \big( \mathbf{x}_{pos} - \mathbf{x}_{neg} \big) = 2
+\]
+
+We can normalize this by the length of the vector $\mathbf{w}$, which is defined as follows:
+
+\[
+\lVert \mathbf{w} \rVert = \sqrt{\sum_{j=1}^{m} w_{j}^{2}}
+\]
+
+So we arrive at the following equation:
+
+\[
+\frac{\mathbf{w}^T ( \mathbf{x}_{pos} - \mathbf{x}_{neg} )}{\lVert \mathbf{w} \rVert} = \frac{2}{\lVert \mathbf{w} \rVert}
+\]
+
+The left side of the preceding equation can then be interpreted as the distance between the positive and negative hyperplane, which is the so-called margin that we want to maximize.
+
+Now the objective function of the SVM becomes the maximization of this margin by maximizing $\frac{2}{\lVert \mathbf{w} \rVert}$ under the constraint that the samples are classi ed correctly, which can be written as follows:
+
+
+\[
+w_0 + \mathbf{w}^T \mathbf{x}^{(i)} \ge 1 \text{ if } y^{(i)} = 1
+\]
+
+\[
+w_0 + \mathbf{w}^T \mathbf{x}^{(i)} < -1 \text{ if } y^{(i)} = -1
+\]
+
+These two equations basically say that all negative samples should fall on one side of the negative hyperplane, whereas all the positive samples should fall behind the positive hyperplane. This can also be written more compactly as follows:
+
+\[
+y^{(i)} \big( w_0 + \mathbf{w}^T \mathbf{x}^{(i)} \big) \ge 1 \quad \forall_i
+\]
+
+In practice, though, it is easier to minimize the reciprocal term $\frac{1}{2} \lVert \mathbf{w} \rVert^2$, which can be solved by quadratic programming.
+
+\subsection{Dealing with the nonlinearly separable case using slack variables}
+
+The motivation for introducing the slack variable $\xi$ was that the linear constraints need to be relaxed for nonlinearly separable data to allow convergence of the optimization in the presence of misclassifications under the appropriate cost penalization. The positive-values slack variable is simply added to the linear constraints:
+
+\[
+\mathbf{w}^T \mathbf{x}^{(i)} \ge 1 - \xi^{(i)} \text{ if } y^{(i)} = 1
+\]
+
+\[
+\mathbf{w}^T \mathbf{x}^{(i)} < -1 + \xi^{(i)} \text{ if } y^{(i)} = -1
+\]
+
+So the new objective to be minimized (subject to the preceding constraints) becomes:
+
+\[
+\frac{1}{2} \lVert \mathbf{w} \rVert^2 + C \Big(\sum_i \xi^{(i)} \Big)
+\]
+
+
+\subsection{Alternative implementations in scikit-learn}
+\section{Solving nonlinear problems using a kernel SVM}
+
+As shown in the next figure, we can transform a two-dimensional dataset onto a new three-dimensional feature space where the classes become separable via the following projection:
+
+\[
+\phi(x_1, x_2) = (z_1, z_2, z_3) = (x_1, x_2, x_{1}^{2} + x_{2}^{2})
+\]
+
+\subsection{Using the kernel trick to find separating hyperplanes in higher dimensional space}
+
+To solve a nonlinear problem using an SVM, we transform the training data onto a higher dimensional feature space via a mapping function $\phi(\cdot)$ and train a linear SVM model to classify the data in this new feature space. Then we can use the same mapping function $\phi(\cdot)$ to transform new, unseen data to classify it using the linear SVM model.
+
+However, one problem with this mapping approach is that the construction of the new features is computationally very expensive, especially if we are dealing with high-dimensional data. This is where the so-called kernel trick comes into play. Although we didn't go into much detail about how to solve the quadratic programming task to train an SVM, in practice all we need is to replace the dot product
+
+\[
+\mathbf{x}^{(i) \; T} \mathbf{x}^{(j)} \text{ by } \phi \big( \mathbf{x}^{(i)} \big)^T \phi \big( \mathbf{x}^{(j)} \big)
+\]
+
+
+In order to save the expensive step of calculating this dot product between two points explicitly, we de define a so-called kernel function:
+
+\[
+k \big( \mathbf{x}^{(i)}, \mathbf{x}^{(j)} \big) = \phi \big( \mathbf{x}^{(i)} \big)^T \phi \big( \mathbf{x}^{(j)} \big)
+\]
+
+One of the most widely used kernels is the \textit{Radial Basis Function kernel} (RBF kernel) or Gaussian kernel:
+
+\[
+k \big( \mathbf{x}^{(i)}, \mathbf{x}^{(j)} \big) = \exp \Bigg( - \frac{ \lVert \mathbf{x}^{(i)} - \mathbf{x}^{(j)} \rVert^2 }{2 \sigma^2} \Bigg)
+\]
+
+This is often simplified to:
+
+\[
+k \big( \mathbf{x}^{(i)}, \mathbf{x}^{(j)} \big) = \exp \bigg( -\gamma\ \lVert \mathbf{x}^{(i)} - \mathbf{x}^{(j)} \rVert^2 \bigg)
+\]
+
+Here, $\gamma = \frac{1}{2 \sigma^2}$ is a free parameter that is to be optimized.
+
+\section{Decision tree learning}
+
+In order to split the nodes at the most informative features, we need to define an objective function that we want to optimize via the tree learning algorithm. Here, our objective function is to maximize the information gain at each split, which we define as follows:
+
+\[
+IG(D_p, f) = I(D_p) - \sum_{j=1}^{m} \frac{N_j}{N_p} I(D_j)
+\]
+
+Here, $f$ is the feature to perform the split, $D_p$ and $D_j$ are the dataset of the parent $p$ and $j$th child node; $I$ is our impurity measure, $N_p$ is the total number of samples at the parent node, and $N_j$ is the number of samples at the $j$th child node. As we can see, the information gain is simply the difference between the impurity of the parent node and the sum of the child node impurities?the lower the impurity of the child nodes, the larger the information gain. However, for simplicity and to reduce the combinatorial search space, most libraries (including scikit-learn) implement binary decision trees. This means that each parent node is split into two child nodes, $D_{left}$ and $D_{right}$:
+
+\[
+IG(D_p, f) = 1 (D_p) - \frac{N_{left}}{N_p} I(D_{left}) - \frac{N_{right}}{N_p} I (D_{right})
+\]
+
+Now, the three impurity measures or splitting criteria that are commonly used in
+binary decision trees are \textit{Gini impurity} ($I_G$), \textit{Entropy} ($I_H$) and the \textit{classification error} ($I_E$). Let's start with the definition of Entropy for all non-empty classes $p(i | t) \neq 0$:
+
+
+\[
+I_H(t) = - \sum_{i=1}^{c} p(i | t) \log_2 p(i|t)
+\]
+
+Here, $p(i | t)$ is the proportion of the samples that belongs to class $i$ for a particular node $t$. The entropy is therefore 0 if all samples at a node belong to the same class, and the entropy is maximal if we have a uniform class distribution. For example, in a binary class setting, the entropy is 0 if $p(i=1 | t) = 1$ or $p(i=0| t)=0$. If the classes are distributed uniformly with $p(i=1|t)=0.5$ and $p(i=0|t)=0.5$, the entropy is 1. Therefore, we can say that the entropy criterion attempts to maximize the mutual information in the tree.
+
+Intuitively, the Gini impurity can be understood as a criterion to minimize the probability of misclassification:
+
+\[
+I_G(t) = \sum_{i=1}^{c} p(i | t) (1 - p (i | t)) = 1 - \sum_{i=1}^{c} p(i|t)^2
+\]
+
+Similar to entropy, the Gini impurity is maximal if the classes are perfectly mixed, for example, in a binary class setting ($c = 2 $):
+
+\[
+I_G(t) = 1 - \sum_{i=1}^{c} 0.5^2 = 0.5.
+\]
+
+...
+
+Another impurity measure is the classification error:
+
+\[
+I_E(t) = 1 - max \{ p(i|t) \}
+\]
+
+\subsection{Maximizing information gain -- getting the most bang for the buck}
+\subsection{Building a decision tree}
+\subsection{Combining weak to strong learners via random forests}
+\section{K-nearest neighbors -- a lazy learning algorithm}
+
+The \textit{minkowski} distance that we used in the previous code example is just a generalization of the Euclidean and Manhattan distances that can be written as follows:
+
+\[
+d \big(\mathbf{x}^{(i)}, \mathbf{x}^{(j)}\big) = \sqrt[p]{\sum_k \big| x_{k}^{(i)} - x_{k}^{(j)} \big|^p }
+\]
+
+
+\section{Summary}
+
+
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 4
+%%%%%%%%%%%%%%%
+
+
+\chapter{Building Good Training Sets -- Data Pre-Processing}
+
+\section{Dealing with missing data}
+\subsection{Eliminating samples or features with missing values}
+\subsection{Imputing missing values}
+\subsection{Understanding the scikit-learn estimator API}
+\section{Handling categorical data}
+\subsection{Mapping ordinal features}
+\subsection{Encoding class labels}
+\subsection{Performing one-hot encoding on nominal features}
+\section{Partitioning a dataset in training and test sets}
+\section{Bringing features onto the same scale}
+
+Now, there are two common approaches to bringing different features onto the same
+scale: \textit{normalization} and \textit{standardization}. Those terms are often used quite loosely
+in different fields, and the meaning has to be derived from the context. Most often,
+\textit{normalization} refers to the rescaling of the features to a range of [0, 1], which is a
+special case of min-max scaling. To normalize our data, we can simply apply the
+min-max scaling to each feature column, where the new value $x_{norm}^{(i)}$ of a sample $x^{(i)}$:
+
+\[
+x_{norm}^{(i)} = \frac{x^{(i)} - \mathbf{x}_{min}}{\mathbf{x}_{max} - \mathbf{x}_{min}}
+\]
+
+Here, $x^{(i)}$ is a particular sample, $x_{min}$ is the smallest value in a feature column, and $x_{max}$ the largest value, respectively.
+
+[...] Furthermore, standardization maintains useful information about outliers and makes the algorithm less sensitive to them in contrast to min-max scaling, which scales
+the data to a limited range of values.
+
+The procedure of standardization can be expressed by the following equation:
+
+\[
+x_{std}^{(i)} = \frac{x^{(i)} - \mu_{x}}{\sigma_{x}}
+\]
+
+Here, $\mu_{x}$ is the sample mean of a particular feature column and $\sigma_{x}$ the corresponding standard deviation, respectively.
+
+\section{Selecting meaningful features}
+\subsection{Sparse solutions with L1 regularization}
+
+We recall from \textit{Chapter 3, A Tour of Machine Learning Classfiers Using Scikit-learn}, that L2 regularization is one approach to reduce the complexity of a model by penalizing large individual weights, where we defined the L2 norm of our weight vector w as follows:
+
+\[
+L2: \lVert \mathbf{w} \rVert^{2}_{2} = \sum_{j=1}^{m} w^{2}_{j}
+\]
+
+Another approach to reduce the model complexity is the related \textit{L1 regularization}:
+
+\[
+L1: \lVert \mathbf{w} \rVert_{1} = \sum_{j=1}^{m} |w_j|
+\]
+
+
+\subsection{Sequential feature selection algorithms}
+
+Based on the preceding definition of SBS, we can outline the algorithm in 4 simple steps:
+
+\begin{enumerate}
+\item Initialize the algorithm with $k=d$, where $d$ is the dimensionality of the full feature space $\mathbf{X}_d$
+\item Determine the feature $x^{-}$ that maximizes the criterion $x^{-} = \text{arg max} J(\mathbf{X_k} - x)$, where $x \in \mathbf{X}_k$.
+\item Remove the feature $x^-$ from the feature set: $\mathbf{X}_{k-l} := \mathbf{X}_k - x^{-}; \quad k:= k-1$.
+\item Terminate if $k$ equals the number of desired features, if not, got to step 2.
+
+\end{enumerate}
+
+
+
+
+\section{Assessing feature importance with random forests}
+\section{Summary}
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 5
+%%%%%%%%%%%%%%%
+
+\chapter{Compressing Data via Dimensionality Reduction}
+
+\section{Unsupervised dimensionality reduction via principal component analysis}
+
+When we use PCA for dimensionality reduction, we construct a $d \times k$-dimensional transformation matrix $\mathbf{W}$ that allows us to map a sample vector $\mathbf{x}$ onto a new $k$-dimensional feature subspace that has fewer dimensions than the original $d$-dimensional feature space:
+
+\[
+\mathbf{x} = [ x_1, x_2, \dots, x_j], \mathbf{x} \in \mathbb{R}^4
+\]
+
+\[
+\downarrow \mathbf{x W}, \quad \mathbf{W} \in \mathbb{R}^{d \times k}
+\]
+
+\[
+\mathbf{z} = [z_1, z_2, \dots, z_k], \quad \mathbf{z} \in \mathbb{R}^4
+\]
+
+As a result of transforming the original $d$-dimensional data onto this new
+$k$-dimensional subspace (typically $k << d$ ), the rst principal component will have
+the largest possible variance, and all consequent principal components will have the largest possible variance given that they are uncorrelated (orthogonal) to the other principal components. Note that the PCA directions are highly sensitive to data scaling, and we need to standardize the features prior to PCA if the features were measured on different scales and we want to assign equal importance to all features.
+
+Before looking at the PCA algorithm for dimensionality reduction in more detail, let's summarize the approach in a few simple steps:
+
+\begin{enumerate}
+\item Standardize the $d$-dimensional dataset.
+\item Construct the covariance matrix.
+\item Decompose the covariance matrix into its eigenvectors and eigenvalues.
+\item Select $k$ eigenvectors that correspond to the $k$ largest eigenvalues, where $k$ is the dimensionality of the new feature subspace $(k \le d)$.
+\item Construct a projection matrix $\mathbf{W}$ from the "top" $k$ eigenvectors.
+\item Transform the $d$-dimensional input dataset $\mathbf{X}$ using the projection matrix $\mathbf{W}$ to obtain the new $k$-dimensional feature subspace.
+\end{enumerate}
+
+\subsection{Total and explained variance}
+
+After completing the mandatory preprocessing steps by executing the preceding code, let's advance to the second step: constructing the covariance matrix. The symmetric $d \times d$ -dimensional covariance matrix, where $d$ is the number of dimensions in the dataset, stores the pairwise covariances between the different features. For example, the covariance between two features $\mathbf{x}_j$ and $\mathbf{x}_k$ on the population level can be calculated via the following equation:
+
+\[
+\sigma_{jk} = \frac{1}{n} \sum_{i=1}^{n} \big( x_{j}^{(i)} - \mu_j \big) \big( x_{k}^{(i)} - \mu_k \big)
+\]
+
+Here, $\mu_j$ and $\mu_k$ are the sample means of feature $j$ and $k$, respectively. [...] For example, a covariance matrix of three features can then be written as
+
+\[
+\Sigma = \begin{bmatrix}
+\sigma_{1}^2 & \sigma_{12} & \sigma_{13} \\
+\sigma_{21} & \sigma_{2}^{2} & \sigma_{23} \\
+\sigma_{31} & \sigma_{32} & \sigma_{3}^{2}
+\end{bmatrix}
+\]
+
+[...] an eigenvector $\mathbf{v}$ satisfies the following condition:
+
+\[
+\Sigma \mathbf{v} = \lambda \mathbf{v}
+\]
+
+Here, $\lambda$ is a scalar: the eigenvector.
+
+...
+
+The variance explained ratio of an eigenvalue $\lambda_j$ is simply the fraction of an eigenvalue $\lambda_j$ and the total sum of the eigenvalues:
+
+\[
+\frac{\lambda_j}{\sum_{j=1}^{d} \lambda_j}
+\]
+
+\subsection{Feature transformation}
+
+Using the projection matrix, we can now transform a sample $\mathbf{x}$ onto the PCA subspace obtaining $\mathbf{x}'$, a now two-dimensional sample vector consisting of two new features:
+
+\[
+\mathbf{x}' = \mathbf{xW}
+\]
+
+\subsection{Principal component analysis in scikit-learn}
+
+
+\section{Supervised data compression via linear discriminant analysis}
+
+Before we take a look into the inner workings of LDA in the following subsections, let's summarize the key steps of the LDA approach:
+
+\begin{enumerate}
+\item Standardize the $d$-dimensional dataset ($d$ is the number of features).
+\item For each class, compute the $d$ dimensional mean vector.
+\item Construct the between-class scatter matrix $\mathbf{S}_B$ and the within-class scatter matrix $\mathbf{S}_W$.
+\item Compute the eigenvectors and corresponding eigenvalues of the matrix $\mathbf{S}_{W}^{-1} \mathbf{S}_B$.
+\item Choose the $k$ eigenvectors that correspond to the $k$ largest eigenvalues to construct a $d \times k$-dimensional transformation matrix $\mathbf{W}$; the eigenvectors are the columns of this matrix.
+\item Project the samples onto the new feature subspace using the transformation matrix $\mathbf{W}$.
+\end{enumerate}
+
+\subsection{Computing the scatter matrices}
+
+Each mean vector $\mathbf{m}_i$ stores the mean feature value $\mu_m$ with respect to the samples of class $i$:
+
+\[
+\mathbf{m}_i = \frac{1}{n_i} \sum_{x \in D_i}^{c} \mathbf{x}_m
+\]
+
+This results in three mean vectors:
+
+\[
+\mathbf{m}_i = \begin{bmatrix}
+\mu_{i, \text{alcohol}} \\
+\mu_{i, \text{malic-acid}} \\
+\mu_{i, \text{proline}}
+ \end{bmatrix}^T, i \in \{ 1, 2, 3 \}
+\]
+
+Using the mean vectors, we can now compute the within-class scatter matrix $\mathbf{S}_W$
+
+\[
+\mathbf{S}_W = \sum^{c}_{i=1} \mathbf{S}_i
+\]
+
+This is calculated by summing up the individual scatter matrices $S_i$ of each
+individual class $i$:
+
+\[
+\mathbf{S}_i = \sum_{x \in D_i}^{c} (\mathbf{x} - \mathbf{m}_i) (\mathbf{x} - \mathbf{m}_i)^T
+\]
+
+The assumption that we are making when we are computing the scatter matrices is that the class labels in the training set are uniformly distributed. [...] Thus, we want to scale the individual scatter matrices $\mathbf{S}_i$ before we sum them up as scatter matrix $\mathbf{S}_W$ When we divide the scatter matrices by the number of class samples $\mathbf{N}_i$, we can see that computing the scatter matrix is in fact the same as computing the covariance matrix $\mathbf{Sigma}_i$ The covariance matrix is a normalized version of the scatter matrix:
+
+\[
+\Sigma_i = \frac{1}{N_i} \mathbf{S}_W = \frac{1}{N_i} \sum^{c}_{x \in D_i} (\mathbf{x} - \mathbf{m}_i) (\mathbf{x} - \mathbf{m}_i)^T
+\]
+
+After we have computed the scaled within-class scatter matrix (or covariance matrix), we can move on to the next step and compute the between-class scatter matrix $\mathbf{S}_B$
+
+\[
+\mathbf{S}_B = \sum^{c}_{i=1} N_i (\mathbf{m}_i - \mathbf{m})(\mathbf{m}_i - \mathbf{m})^T
+\]
+
+Here, $\mathbf{m}$ is the overall mean that is computed, including samples from all classes.
+
+\subsection{Selecting linear discriminants for the new feature subspace}
+\subsection{Projecting samples onto the new feature space}
+
+\[
+\mathbf{X'} = \mathbf{XW}
+\]
+
+\subsection{LDA via scikit-learn}
+\section{Using kernel principal component analysis for nonlinear mappings}
+\subsection{Kernel functions and the kernel trick}
+
+To transform the samples $\mathbf{x} \in \mathbb{R}^d$ onto this higher $k$-dimensional subspace, we defined a nonlinear mapping function $\phi$:
+
+\[
+\phi : \mathbb{R}^d \rightarrow \mathbb{R}^k \quad (k >> d)
+\]
+
+We can think of $\phi$ as a function that creates nonlinear combinations of the original features to map the original $d$-dimensional dataset onto a larger, $k$-dimensional feature space. For example, if we had feature vector $\mathbf{x} \in \mathbb{R}^d$ ($\mathbf{x}$ is a column vector consisting of $d$ features) with two dimensions ($d=2$), a potential mapping onto a 3D space could be as follows:
+
+\[
+\mathbf{x} = [x_1, x_2]^T
+\]
+
+\[
+\downarrow \phi
+\]
+
+\[
+\mathbf{z} = \bigg[ x_{1}^{2}, \sqrt{2x_1x_2}, x_{2}^{2} \bigg]^T
+\]
+
+ [...] We computed the covariance between two features $k$ and $j$ as follows:
+
+ \[
+ \sigma_{jk} = \frac{1}{n} \sum_{n}^{i=1} \big( x_{j}^{(i)} - \mu_j \big) \big( x_{k}^{(i)} - \mu_k \big)
+ \]
+
+ Since the standardizing of features centers them at mean zero, for instance, $\mu_j = 0$ and $\mu_k = 0$, we can simplify this equation as follows:
+
+ \[
+ \sigma_{jk} = \frac{1}{n} \sum_{i=1}^{n} x_{j}^{(i)} x_{k}^{(i)}
+ \]
+
+Note that the preceding equation refers to the covariance between two features; now, let's write the general equation to calculate the covariance matrix $\Sigma$:
+
+\[
+\Sigma = \frac{1}{n} \sum_{i=1}^{n} \mathbf{x}^{(i)} \mathbf{x}^{(i)\;T}
+\]
+
+ Bernhard Scholkopf generalized this approach (B. Scholkopf, A. Smola, and
+K.-R. Muller. \textit{Kernel Principal Component Analysis}. pages 583-588, 1997) so that we can replace the dot products between samples in the original feature space by the nonlinear feature combinations via $\phi$:
+
+ \[
+ \Sigma = \frac{1}{n} \sum_{i=1}^{n} \phi \big( \mathbf{x}^{(i)} \big) \phi \big( \mathbf{x}^{(i)} \big)^T
+ \]
+
+ To obtain the eigenvectors?the principal components?from this covariance matrix,
+we have to solve the following equation:
+
+
+\begin{equation*}
+\begin{split}
+ & \Sigma \mathbf{v} = \lambda \mathbf{v} \\
+ & \Rightarrow \frac{1}{n} \sum_{i=1}^{n} \phi \big( \mathbf{x}^{(i)} \big) \phi \big( \mathbf{x}^{(i)} \big)^T \mathbf{v} = \lambda \mathbf{v} \\
+ & \Rightarrow \frac{1}{n \lambda} \sum^{n}_{i=1} \phi \big( \mathbf{x}^{(i)} \big) \big( \mathbf{x}^{(i)} \big)^T \mathbf{v} = \frac{1}{n} \sum^{n}_{(i=1)} \mathbf{a}^{(i)} \phi (\mathbf{x}^{(i)})
+\end{split}
+\end{equation*}
+
+Here, $\lambda$ and v are the eigenvalues and eigenvectors of the covariance matrix $\Sigma$, and $\mathbf{a}$ can be obtained by extracting the eigenvectors of the kernel (similarity) matrix $\mathbf{K}$ as we will see in the following paragraphs.
+
+The derivation of the kernel matrix is as follows:
+
+
+
+\subsection{Implementing a kernel principal component analysis in Python}
+
+First, let's write the covariance matrix as in matrix notation, where $\phi(X)$ is an $n \times k$-dimensional matrix:
+
+\[
+\Sigma = \frac{1}{n} \sum_{i=1}^{n} \phi \big( \mathbf{x}^{(i)} \big) \big( \mathbf{x}^{(i)} \big)^T = \frac{1}{n} \phi ( \mathbf{X})^T \phi (\mathbf{X})
+\]
+
+Now, we can write the eigenvector equation as follows:
+
+\[
+mathbf{v} = \frac{1}{n} \sum_{i=1}^{n} \mathbf{a}^{(i)} \phi(\mathbf{x}^{(i)}) = \lambda \phi (\mathbf{X})^T \mathbf{a}
+\]
+
+Since $\Sigma \mathbf{v} = \lambda \mathbf{v}$, we get:
+
+\[
+\frac{1}{n} \phi (\mathbf{X})^T \phi (\mathbf{X}) \phi (\mathbf{X})^T \mathbf{a} = \lambda \phi (\mathbf{X})^T \mathbf{a}
+\]
+
+Multiplying it by $\phi(\mathbf{X})$ on both sides yields the following result:
+
+\begin{equation*}
+\begin{split}
+& \frac{1}{n} \phi(\mathbf{X}) \phi(\mathbf{X})^T \phi(\mathbf{X}) \phi(\mathbf{X})^T \mathbf{a} = \lambda \phi(\mathbf{X}) \phi(\mathbf{X})^T \mathbf{a} \\
+& \Rightarrow \frac{1}{n} \phi(\mathbf{X}) \phi(\mathbf{X})^T \mathbf{a} = \lambda \mathbf{a} \\
+& \Rightarrow \frac{1}{n} \mathbf{Ka} = \lambda \mathbf{a}
+\end{split}
+\end{equation*}
+
+Here, K is the similarity (kernel) matrix:
+
+\[
+\mathbf{K} = \phi (\mathbf{X}) \phi (\mathbf{X})^T
+\]
+
+As we recall from the SVM section in \textit{Chapter 3, A Tour of Machine Learning Classifiers Using Scikit-learn}, we use the kernel trick to avoid calculating the pairwise dot products of the samples $\mathbf{x}$ under $\phi$ explicitly by using a kernel function $\kappa(\cdot)$ so that we don't need to calculate the eigenvectors explicitly:
+
+[...] The most commonly used kernels are the following ones:
+
+\begin{itemize}
+\item The polynomial kernel:
+\[
+\kappa \big( \mathbf{x}^{(i)}, \mathbf{x}^{(j)} \big) = \big( \mathbf{x}^{(i)\;T} \mathbf{x}^{(j)} + \theta \big)^p
+ \]
+ Here, $\theta$ is the threshold and $p$ is the power that has to be specified by the user.
+\item the hyperbolic tangent (sigmoid) kernel:
+\[
+\kappa \big( \mathbf{x}^{(i)}, \mathbf{x}^{(j)} \big) = \tanh \big( \eta \mathbf{x}^{(i)\; T} \mathbf{x}^{(j)} + \theta \big)
+\]
+\item The \textit{Radial Basis Function (RBF)} or Gaussian kernel that we will use in the following examples in the next subsection:
+\[
+\kappa \big( \mathbf{x}^{(i)}, \mathbf{x}^{(j)} \big) = \exp \Bigg( - \frac{\lVert \mathbf{x}^{(i)} - \mathbf{x}^{(j)} \rVert^2}{2\sigma^2} \Bigg),
+\]
+which is also often written as
+\[
+\kappa \big( \mathbf{x}^{(i)}, \mathbf{x}^{(j)} \big) = \exp \big( -\gamma \lVert \mathbf{x}^{(i)} - \mathbf{x}^{(j)} \rVert^2 \big),
+\]
+
+where $\gamma = \frac{1}{2 \sigma^2}$.
+
+\end{itemize}
+
+To summarize what we have discussed so far, we can define the following three steps to implement an RBF kernel PCA:
+
+\begin{enumerate}
+\item We compute the kernel (similarity) matrix $\mathbf{K}$, where we need to calculate the following:
+\[
+\kappa \big( \mathbf{x}^{(i)}, \mathbf{x}^{(j)} \big) = \exp \big( -\gamma \lVert \mathbf{x}^{(i)} - \mathbf{x}^{(j)} \rVert^2 \big)
+\]
+We do this for each pair of samples:
+
+
+\[
+\mathbf{K} = \begin{bmatrix}
+ \kappa \big(\mathbf{x}^{(1)},\mathbf{x}^{(1)}\big)& \kappa \big(\mathbf{x}^{(1)},\mathbf{x}^{(2)}\big) & \dots & \kappa \big(\mathbf{x}^{(1)},\mathbf{x}^{(n)}\big) \\
+ \kappa \big(\mathbf{x}^{(2)},\mathbf{x}^{(1)}\big) & \kappa \big(\mathbf{x}^{(2)},\mathbf{x}^{(2)}\big) & \dots & \kappa \big(\mathbf{x}^{(2)},\mathbf{x}^{(n)}\big)\\
+ \vdots & \vdots & \ddots & \vdots \\
+ \kappa \big(\mathbf{x}^{(n)},\mathbf{x}^{(1)}\big) & \kappa \big(\mathbf{x}^{(n)},\mathbf{x}^{(2)}\big) & \dots & \kappa \big(\mathbf{x}^{(n)},\mathbf{x}^{(n)}\big)
+\end{bmatrix}.
+\]
+
+For example, if our dataset contains 100 training samples, the symmetric kernel matrix of the pair-wise similarities would be $100 \times 100$ dimensional.
+
+\item We center the kernel matrix $\mathbf{K}$ using the following equation:
+
+\[
+\mathbf{K}' = \mathbf{K} - 1_n \mathbf{K} - \mathbf{K} - \mathbf{K}1_n + 1_n \mathbf{K}1_n
+\]
+
+Here, $1_n$ is an $n \times n$-dimensional matrix (the same dimensions as the kernel matrix) where all values are equal to $\frac{1}{n}$.
+
+\item We collect the top $k$ eigenvectors of the centered kernel matrix based on their corresponding eigenvalues, which are ranked by decreasing magnitude. In contrast to standard PCA, the eigenvectors are not the principal component axes but the samples projected onto those axes
+
+\end{enumerate}
+
+
+\subsubsection{Example 1 -- separating half-moon shapes}
+\subsubsection{Example 2 -- separating concentric circles}
+\subsection{Projecting new data points}
+
+[...] Thus, if we want to project a new sample $\mathbf{x}'$ onto this principal component axis, we'd need to compute the following:
+
+\[
+\phi(\mathbf{x}')^T \mathbf{v}
+\]
+
+Fortunately, we can use the kernel trick so that we don't have to calculate the projection $\phi(\mathbf{x}')^T \mathbf{v}$ explicitly. However, it is worth noting that kernel PCA, in contrast to standard PCA, is a memory-based method, which means that we have to reuse the original training set each time to project new samples. We have to calculate the pairwise RBF kernel (similarity) between each $i$th sample in the training dataset and the new sample $\mathbf{x}'$:
+
+\[
+\phi(\mathbf{x'})^T \mathbf{v} = \sum_i \mathbf{a}^{(i)} \phi(\mathbf{x'})^T \phi (\mathbf{x^{(i)}})
+\]
+
+\[
+= \sum_i \mathbf{a}^{(i)} k (\mathbf{x'}, \mathbf{x}^{(i)})^T
+\]
+
+Here, eigenvectors $\mathbf{a}$ and eigenvalues $\lambda$ of the Kernel matrix $\mathbf{K}$ satisfy the following condition in the equation
+
+\[
+\mathbf{Ka} = \lambda \mathbf{a}
+\]
+
+
+
+\subsection{Kernel principal component analysis in scikit-learn}
+\section{Summary}
+
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 6
+%%%%%%%%%%%%%%%
+
+\chapter{Learning Best Practices for Model Evaluation and Hyperparameter Tuning}
+
+\section{Streamlining workflows with pipelines}
+\subsection{Loading the Breast Cancer Wisconsin dataset}
+\subsection{Combining transformers and estimators in a pipeline}
+\section{Using k-fold cross-validation to assess model performance}
+\subsection{The holdout method}
+\subsection{K-fold cross-validation}
+\section{Debugging algorithms with learning and validation curves}
+\subsection{Diagnosing bias and variance problems with learning curves}
+\subsection{Addressing overfitting and underfitting with validation curves}
+
+\newpage
+
+\section{Fine-tuning machine learning models via grid search}
+\subsection{Tuning hyperparameters via grid search}
+\subsection{Algorithm selection with nested cross-validation}
+\section{Looking at different performance evaluation metrics}
+\subsection{Reading a confusion matrix}
+\subsection{Optimizing the precision and recall of a classification model}
+
+Both the prediction error (ERR) and accuracy (ACC) provide general information about how many samples are misclassi ed. The error can be understood as the sum of all false predictions divided by the number of total predictions, and the accuracy is calculated as the sum of correct predictions divided by the total number of predictions, respectively:
+
+\[
+ERR = \frac{FP + FN}{FP + FN + TP + TN}
+\]
+
+(TP = true positives, FP = false positives, TN = true negatives, FN = false negatives)
+
+The prediction accuracy can then be calculated directly from the error:
+
+\[
+ACC = \frac{TP + TN}{FP + FN + TP + TN} = 1 - ERR
+\]
+
+The true \textit{positive rate} (TPR) and \textit{false positive rate} (FPR) are performance metrics that are especially useful for imbalanced class problems:
+
+\[
+FPR = \frac{FP}{N} = \frac{FP}{FP + TN}
+\]
+
+\[
+TPR = \frac{TP}{P} = \frac{TP}{FN+TP}
+\]
+
+\textit{Precision (PRE)} and \textit{recall} (REC) are performance metrics that are related to those true positive and true negative rates, and in fact, recall is synonymous to the true positive rate:
+
+\[
+PRE = \frac{TP}{TP + FP}
+\]
+
+\[
+REC = TPR = \frac{TP}{P} = \frac{TP}{FN + TP}
+\]
+
+In practice, often a combination of precision and recall is used, the so-called \textit{F1-score}:
+
+\[
+\text{F1} = 2 \times \frac{PRE \times REC}{PRE + REC}
+\]
+
+\subsection{Plotting a receiver operating characteristic}
+\subsection{The scoring metrics for multiclass classification}
+
+he micro-average is calculated from the individual true positives, true negatives, false positives, and false negatives of the system. For example, the micro-average of the precision score in a k-class system can be calculated as follows:
+
+\[
+PRE_{micro} = \frac{TP_1 + \dots + TP_k}{TP_1 + \dots + TP_k + FP_1 + \dots + FP_k}
+\]
+
+The macro-average is simply calculated as the average scores of the different systems:
+
+\[
+PRE_{macro} = \frac{PRE_1 + \dots + PRE_k}{k}
+\]
+
+\section{Summary}
+
+
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 7
+%%%%%%%%%%%%%%%
+
+\chapter{Combining Different Models for Ensemble Learning}
+
+\section{Learning with ensembles}
+
+To predict a class label via a simple majority or plurality voting, we combine the predicted class labels of each individual classifier $C_j$ and select the class label $\hat{y}$ that received the most votes:
+
+\[
+\hat{y} = mode \{ C_1 (\mathbf{x}), C_2 (\mathbf{x}), \dots, C_m (\mathbf{x}) \}
+\]
+
+For example, in a binary classification task where $class1 = -1$ and $class2 = +1$, we can write the majority vote prediction as follows:
+
+\[
+C(\mathbf{x}) = sign \Bigg[ \sum_{j}^{m} C_j (\mathbf{x} \Bigg] = \begin{cases}
+ 1 & \text{ if } \sum_j C_j (\mathbf{x}) \ge 0 \\
+ -1 & \text{ otherwise }.
+ \end{cases}
+\]
+
+To illustrate why ensemble methods can work better than individual classifiers alone, let's apply the simple concepts of combinatorics. For the following example, we make the assumption that all $n$ base classifiers for a binary classification task have an equal error rate $\epsilon$. Furthermore, we assume that the classifiers are independent and the error rates are not correlated. Under those assumptions, we can simply express the error probability of an ensemble of base classifiers as a probability
+mass function of a binomial distribution:
+
+\[
+P(y \ge k) = \sum_{k}^{n} \binom{n}{k} \epsilon^k (1 - \epsilon)^{n-k} = \epsilon_{\text{ensemble}}
+\]
+
+Here, $\binom{n}{k}$ is the binomial coefficient \textit{n choose k}. In other words, we compute the probability that the prediction of the ensemble is wrong. Now let's take a look at a more concrete example of 11 base classifiers ($n=11$) with an error rate of 0.25 ($\epsilon = 0.25$):
+
+\[
+P(y \ge k) = \sum_{k=6}^{11} \binom{11}{k} 0.25^k (1 - 0.25)^{11-k} = 0.034
+\]
+
+\section{Implementing a simple majority vote classifier}
+
+Our goal is to build a stronger meta-classifier that balances out the individual classifiers' weaknesses on a particular dataset. In more precise mathematical terms, we can write the weighted majority vote as follows:
+
+\[
+\hat{y} = \text{arg} \max_i \sum_{j=1}^{m} w_j \chi_A \big(C_j (\mathbf{x})=i\big)
+\]
+
+Let's assume that we have an ensemble of three base classifiers $C_j ( j \in {0,1})$ and want to predict the class label of a given sample instance x. Two out of three base classi ers predict the class label 0, and one $C_3$ predicts that the sample belongs to class 1. If we weight the predictions of each base classifier equally, the majority vote will predict that the sample belongs to class 0:
+
+\[
+C_1(\mathbf{x}) \rightarrow 0, C_2 (\mathbf{x}) \rightarrow 0, C_3(\mathbf{x}) \rightarrow 1
+\]
+
+\[
+\hat{y} = mode{0, 0, 1} = 0
+\]
+
+Now let's assign a weight of 0.6 to $C_3$ and weight $C_1$ and $C_2$ by a coefficient of 0.2, respectively.
+
+\[
+\hat{y} = \text{arg}\max_i \sum_{j=1}^{m} w_j \chi_A \big( C_j(\mathbf{x}) = i \big)
+\]
+
+\[
+= \text{arg}\max_i \big[0.2 \times i_0 + 0.2 \times i_0 + 0.6 \times i_1 \big] = 1
+\]
+
+More intuitively, since $3 \times 0.2 = 0.6$, we can say that the prediction made by $C_3$ has three times more weight than the predictions by $C_1$ or $C_2$ , respectively. We can write this as follows:
+
+\[
+\hat{y} = mode\{0,0,1,1,1\} = 1
+\]
+
+[...] The modified version of the majority vote for predicting class labels from probabilities can be written as follows:
+
+\[
+\hat{y} = \text{arg} \max_i \sum^{m}_{j=1} w_j p_{ij}
+\]
+
+Here, $p_{ij}$ is the predicted probability of the $j$th classifier for class label $i$.
+
+To continue with our previous example, let's assume that we have a binary classification problem with class labels $i \in \{0, 1\}$ and an ensemble of three classifiers $C_j (j \in \{1, 2, 3\}$. Let's assume that the classifier $C_j$ returns the following class membership probabilities for a particular sample $\mathbf{x}$:
+
+\[
+C_1(\mathbf{x}) \rightarrow [0.9, 0.1], C_2 (\mathbf{x}) \rightarrow [0.8, 0.2], C_3(\mathbf{x}) \rightarrow [0.4, 0.6]
+\]
+
+We can then calculate the individual class probabilities as follows:
+
+\[
+p(i_0 | \mathbf{x}) = 0.2 \times 0.9 + 0.2 \times 0.8 + 0.6 \times 0.4 = 0.58
+\]
+
+\[
+p(i_1 | \mathbf{x}) = 0.2 \times 0.1 + 0.2 \times 0.2 + 0.6 \times 0.06 = 0.42
+\]
+
+\[
+\hat{y} = \text{arg} \max_i \big[ p(i_0 | \mathbf{x}), p(i_1 | \mathbf{x}) \big] = 0
+\]
+
+\subsection{Combining different algorithms for classification with majority vote}
+\section{Evaluating and tuning the ensemble classifier}
+\section{Bagging -- building an ensemble of classifiers from bootstrap samples}
+\section{Leveraging weak learners via adaptive boosting}
+
+[...] The original boosting procedure is summarized in four key steps as follows:
+
+\begin{enumerate}
+\item Draw a random subset of training samples $d_1$ without replacement from the training set $D$ to train a weak learner $C_1$.
+\item Draw second random training subset $d_2$ without replacement from the training set and add 50 percent of the samples that were previously misclassified to train a weak learner $C_2$.
+\item Find the training samples $d_3$ in the training set $D$ on which $C_1$ and $C_2$ disagree to train a third weak learner $C_3$
+\item Combine the weak learners $C_1, C_2$, and $C_3$ via majority voting.
+\end{enumerate}
+
+[...] Now that have a better understanding behind the basic concept of AdaBoost, let's take a more detailed look at the algorithm using pseudo code. For clarity, we will denote element-wise multiplication by the cross symbol $(\times)$ and the dot product between two vectors by a dot symbol $(\cdot)$, respectively. The steps are as follows:
+
+\begin{enumerate}
+\item Set weight vector $\mathbf{w}$ to uniform weights where $\sum_i w_i = 1$.
+\item For $j$ in $m$ boosting rounds, do the following:
+\begin{enumerate}
+\item Train a weighted weak learner: $C_j = train(\mathbf{X, y, w})$.
+\item Predict class labels: $\hat{y} = predict(C_j, \mathbf{X})$.
+\item Compute the weighted error rate: $\epsilon = \mathbf{w} \cdot (\mathbf{\hat{y}} \neq \mathbf{y})$.
+\item Compute the coefficient $\alpha_j$: $\alpha_j=0.5 \log \frac{1 - \epsilon}{\epsilon}$.
+\item Update the weights: $\mathbf{w} := \mathbf{w} \times \exp \big( -\alpha_j \times \mathbf{\hat{y}} \times \mathbf{y} \big)$.
+\item Normalize weights to sum to 1: $\mathbf{w}:= \mathbf{w} / \sum_i w_i$.
+\end{enumerate}
+\item Compute the final prediction: $\mathbf{\hat{y}} = \big( \sum^{m}_{j=1} \big( \mathbf{\alpha}_j \times predict(C_j, \mathbf{X}) \big) > 0 \big)$.
+\end{enumerate}
+
+Note that the expression ($\mathbf{\hat{y}} == \mathbf{y}$) in step 5 refers to a vector of 1s and 0s, where a 1 is assigned if the prediction is incorrect and 0 is assigned otherwise.
+
+\begin{table}[!htbp]
+\centering
+\caption*{}
+\label{}
+\begin{tabular}{r | c c c c c | l}
+\hline
+Sample indices & x & y & Weights & $\hat{y}$(x $\le$ 3.0)? & Correct? & Updated weights \\ \hline
+1 & 1.0 & 1 & 0.1 & 1 & Yes & 0.072 \\
+2 & 2.0 & 1 & 0.1 & 1 & Yes & 0.072 \\
+3 & 3.0 & 1 & 0.1 & 1 & Yes & 0.072 \\
+4 & 4.0 & -1 & 0.1 & -1 & Yes & 0.072 \\
+5 & 5.0 & -1 & 0.1 & -1 & Yes & 0.072 \\
+6 & 6.0 & -1 & 0.1 & -1 & Yes & 0.072 \\
+7 & 7.0 & 1 & 0.1 & -1 & No & 0.167 \\
+8 & 8.0 & 1 & 0.1 & -1 & No & 0.167 \\
+9 & 9.0 & 1 & 0.1 & -1 & No & 0.167 \\
+10 & 10.0 & -1 & 0.1 & -1 & Yes & 0.072 \\ \hline
+\end{tabular}
+\end{table}
+
+Since the computation of the weight updates may look a little bit complicated at rst, we will now follow the calculation step by step. We start by computing the weighted error rate $\epsilon$ as described in step 5:
+
+\[
+\epsilon = 0.1\times 0+0.1\times 0+0.1 \times 0+0.1 \times 0+0.1 \times 0+0.1 \times 0+0.1\times 1+0.1 \times 1 + 0.1 \times 1+0.1 \times 0
+\]
+\[
+= \frac{3}{10} = 0.3
+\]
+
+Next we compute the coefficient $\alpha_j$ (shown in step 6), which is later used in step 7 to update the weights as well as for the weights in majority vote prediction (step 10):
+
+\[
+\alpha_j = 0.5 \log \Bigg( \frac{1 - \epsilon}{\epsilon} \Bigg) \approx 0.424
+\]
+
+After we have computed the coefficient $\alpha_j$ we can now update the weight vector using the following equation:
+
+\[
+\mathbf{w} := \mathbf{w} \times \exp ( -\alpha_j \times \mathbf{\hat{y}} \times \mathbf{y})
+\]
+
+Here, $\mathbf{\hat{y}} \times \mathbf{y}$ is an element-wise multiplication between the vectors of the predicted and true class labels, respectively. Thus, if a prediction $\hat{y}_i$ is correct, $\hat{y}_i \times y_i$ will have a positive sign so that we decrease the $i$th weight since $\alpha_j$ is a positive number as well:
+
+\[
+0.1 \times \exp (-0.424 \times 1 \times 1) \approx 0.065
+\]
+
+Similarly, we will increase the $i$th weight if $\hat{y}_i$ predicted the label incorrectly
+ like this:
+
+\[
+0.1 \times \exp (-0.424 \times 1 \times (-1)) \approx 0.153
+\]
+
+Or like this:
+
+\[
+0.1 \times \exp (-0.424 \times (-1) \times 1) \approx 0.153
+\]
+
+After we update each weight in the weight vector, we normalize the weights so that they sum up to 1 (step 8):
+
+\[
+\mathbf{w} := \frac{\mathbf{w}}{\sum_i w_i}
+\]
+
+Here, $\sum_i w_i = 7 \times 0.065 + 3 \times 0.153 = 0.914$.
+
+Thus, each weight that corresponds to a correctly classified sample will be reduced from the initial value of $0.1$ to $0.065 / 0.914 \approx 0.071$ for the next round of boosting. Similarly, the weights of each incorrectly classified sample will increase from $0.1$ to $0.153 / 0.914 \approx 0.167$.
+
+\section{Summary}
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 8
+%%%%%%%%%%%%%%%
+
+\chapter{Applying Machine Learning to Sentiment Analysis}
+
+\section{Obtaining the IMDb movie review dataset}
+\section{Introducing the bag-of-words model}
+\subsection{Transforming words into feature vectors}
+\subsection{Assessing word relevancy via term frequency-inverse document frequency}
+
+The \textit{tf-idf} can be defined as the product of the \textit{term frequency} and the \textit{inverse document frequency}:
+
+\[
+\text{tf-idf}(t, d) = \text{tf} (t, d) \times \text{idf}(t, d)
+\]
+
+Here the $\text{tf}(t, d)$ is the term frequency that we introduced in the previous section,
+and the inverse document frequency $\text{idf}(t, d)$ can be calculated as:
+
+\[
+\text{idf}(t, d) = \log \frac{n_d}{1 + \text{df}(d, t)},
+\]
+
+where $n_d$ is the total number of documents, and $\text{df}(d, t)$ is the number of documents $d$ that contain the term $t$. Note that adding the constant 1 to the denominator is optional and serves the purpose of assigning a non-zero value to terms that occur in all training samples; the log is used to ensure that low document frequencies are not given too much weight.
+
+However, if we'd manually calculated the tf-idfs of the individual terms in our feature vectors, we'd have noticed that the \textit{TfidfTransformer} calculates the tf-idfs slightly differently compared to the standard textbook equations that we defined earlier. The equations for the idf and tf-idf that were implemented in scikit-learn are:
+
+\[
+\text{idf}(t, d) = \log \frac{1 + n_d}{1 + \text{df}(d, t)}
+\]
+
+The tf-idf equation that was implemented in scikit-learn is as follows:
+
+\[
+\text{tf-idf(t, d)} = \text{tf}(t, d) \times (\text{idf} (t, d) + 1).
+\]
+
+While it is also more typical to normalize the raw term frequencies before calculating the tf-idfs, the \textit{TfidfTransformer} normalizes the tf-idfs directly. By default (\text{norm='l2'}), scikit-learn's \textit{TfidfTransformer} applies the L2-normalization, which returns a vector of length 1 by dividing an un-normalized feature vector $\mathbf{v}$ by its L2-norm:
+
+\[
+\mathbf{v}_{norm} = \frac{\mathbf{v}}{\lVert \mathbf{v} \rVert}_2 = \frac{\mathbf{v}}{\sqrt{v_{1}^{2} + v_{2}^{2} + \cdots + v_{n}^{2}}} = \frac{\mathbf{v}}{ \big( \sum_{i=1}^{n} v_{i}^{2} \big)^{1/2} }
+\]
+
+\subsection{Cleaning text data}
+\subsection{Processing documents into tokens}
+\section{Training a logistic regression model for document classification}
+\section{Working with bigger data - online algorithms and out-of-core learning}
+\section{Summary}
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 9
+%%%%%%%%%%%%%%%
+
+\chapter{Embedding a Machine Learning Model into a Web Application}
+
+\section{Chapter 8 recap - Training a model for movie review classification}
+\section{Serializing fitted scikit-learn estimators}
+\section{Setting up a SQLite database for data storage Developing a web application with Flask}
+\section{Our first Flask web application}
+\subsection{Form validation and rendering}
+\subsection{Turning the movie classifier into a web application}
+\section{Deploying the web application to a public server}
+\subsection{Updating the movie review classifier}
+\section{Summary}
+
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 10
+%%%%%%%%%%%%%%%
+
+\chapter{Predicting Continuous Target Variables with Regression Analysis}
+
+\section{Introducing a simple linear regression model}
+
+The goal of simple (univariate) linear regression is to model the relationship between a single feature (explanatory variable x) and a continuous valued \textit{response} (target variable \textit{y}). The equation of a linear model with one explanatory variable is defined as follows:
+
+\[
+y = w_0 + w_1 + x
+\]
+
+Here, the weight $w_0$ represents the $y$ axis intercepts and $w_1$ is the coefficient of the explanatory variable.
+
+[...] The special case of one explanatory variable is also called \textit{simple linear regression}, but of course we can also generalize the linear regression model to multiple explanatory variables. Hence, this process is called \textit{multiple linear regression}:
+
+\[
+y = w_0 x_0 + w_1 x_1 + \cdots + w_m x_m = \sum_{i=0}^{m} w_i x_i = \mathbf{w}^T \mathbf{x}
+\]
+
+Here, $w_0$ is the $y$-axis intercept with $x_0$ =1.
+
+\section{Exploring the Housing Dataset}
+\subsection{Visualizing the important characteristics of a dataset}
+
+The correlation matrix is a square matrix that contains the Pearson product-moment correlation coeffcients (often abbreviated as Pearson's $r$), which measure the linear dependence between pairs of features. The correlation coefficients are bounded to the range $-1$ and $1$. Two features have a perfect positive correlation if $r =1$, no correlation if $r = 0$, and a perfect negative correlation if $r = ?1$, respectively. As mentioned previously, Pearson's correlation coefficient can simply be calculated as the covariance between two features $x$ and $y$ (numerator) divided by the product of their standard deviations (denominator):
+
+\[
+r = \frac{\sum_{i=1}^{n} \Big[ \big( x^{(i)} - \mu_x \big) \big( y^{(i)} - \mu_y \big) \Big] }{ \sqrt{\sum_{i=1}^{n} \big( x^{(i)} - \mu_x \big)^2} \sqrt{\sum_{i=1}^{n} \big( y^{(i)} - \mu_y \big)^2}} = \frac{\sigma_{xy}}{\sigma_x \sigma_y}
+\]
+
+Here, $\mu$ denotes the sample mean of the corresponding feature, $\sigma_{xy}$ is the covariance between the features $x$ and $y$, and $\sigma_x$ and $\sigma_y$ are the features'
+standard deviations, respectively.
+
+We can show that the covariance between standardized features is in fact equal to their linear correlation coefficient. Let's first standardize the features $x$ and $y$, to obtain their $z$-scores which we will denote as $x'$ and $y'$ , respectively:
+
+\[
+x' = \frac{x-\mu_x}{\sigma_x}, \; y' = \frac{y - \mu_y}{\sigma_y}
+\]
+
+Remember that we calculate the (population) covariance between two features as follows:
+
+\[
+\sigma_xy = \frac{1}{n} \sum_{i}^{n} \big( x^{(i)} - \mu_x \big) \big( y^{(i)} - \mu_y \big)
+\]
+
+Since standardization centers a feature variable at mean 0, we can now calculate the covariance between the scaled features as follows:
+
+\[
+\sigma'_{xy} = \frac{1}{n} \sum_{i}^{n} (x' - 0)(y' - 0)
+\]
+
+Through resubstitution, we get the following result:
+
+\[
+\frac{1}{n} \sum_{i}^{n} \Big( \frac{x - \mu_x}{\sigma_x} \Big) \Big( \frac{y - \mu_y}{\sigma_y} \Big)
+\]
+
+\[
+= \frac{1}{n \cdot \sigma_x \sigma_y} \sum^{n}_{i} \sum_{i}^{n} \big(x^{(i)} - \mu_x \big) \big(y^{(i)} - \mu_y \big)
+\]
+
+We can simplify it as follows:
+
+\[
+\sigma'_{xy} = \frac{\sigma_{xy}}{\sigma_x \sigma_y}
+\]
+
+\section{Implementing an ordinary least squares linear regression model}
+\subsection{Solving regression for regression parameters with gradient descent}
+
+Consider our implementation of the \textit{ADAptive LInear NEuron (Adaline)} from \textit{Chapter 2, Training Machine Learning Algorithms for Classifcation}; we remember that the artificial neuron uses a linear activation function and we defined a cost function $J (\cdot)$, which we minimized to learn the weights via optimization algorithms, such as \textit{Gradient Descent (GD)} and \textit{Stochastic Gradient Descent (SGD)}. This cost function in Adaline is the \textit{Sum of Squared Errors (SSE)}. This is identical to the OLS cost function that we defined:
+
+\[
+J(w) = \frac{1}{2} \sum_{i=1}^{n} \big( y^{(i)} - \hat{y}^{(i)} \big)^2
+\]
+
+Here, $\hat{y}$ is the predicted value $\hat{y} = \mathbf{w}^T\mathbf{x}$ (note that the term $1/2$ is just used for convenience to derive the update rule of GD). Essentially, OLS linear regression can be understood as Adaline without the unit step function so that we obtain continuous target values instead of the class labels $-1$ and $1$.
+
+[...] As an alternative to using machine learning libraries, there is also
+a closed-form solution for solving OLS involving a system of linear equations that can be found in most introductory statistics textbooks:
+
+\[
+\mathbf{w} = (\mathbf{X}^T \mathbf{X})^{(-1)} \mathbf{X}^T \mathbf{y}
+\]
+
+\subsection{Estimating the coefficient of a regression model via scikit-learn}
+\section{Fitting a robust regression model using RANSAC}
+\section{Evaluating the performance of linear regression models}
+
+Another useful quantitative measure of a model's performance is the so-called \textit{Mean Squared Error (MSE)}, which is simply the average value of the SSE cost function that we minimize to fit the linear regression model. The MSE is useful to for comparing different regression models or for tuning their parameters via a grid search and cross-validation:
+
+\[
+MSE = \frac{1}{n} \sum_{i=1}^{n} \big( y^{(i)} - \hat{y}^{(i)} \big)^2
+\]
+
+[...] Sometimes it may be more useful to report the coef cient of determination ($R^2$), which can be understood as a standardized version of the MSE, for better interpretability of the model performance. In other words, $R^2$ is the fraction of response variance that is captured by the model. The $R^2$ value is defined as follows:
+
+\[
+R^2 = 1 - \frac{SSE}{SST}
+\]
+
+Here, SSE is the sum of squared errors and SST is the total sum of squares $SST = \sum^{n}_{i=1} \big( y^{(i)} - \mu_y \big)^2$, or in other words, it is simply the variance of the response. Let's quickly show that $R^2$ is indeed just the rescaled version of the MSE:
+
+\[
+R^2 = 1 - \frac{SSE}{SST}
+\]
+
+\[
+= 1 - \frac{\frac{1}{n} \sum^{n}_{i=1} \big( y^{(i)} - \hat{y}^{(i)} \big)^2 }{\frac{1}{n} \sum_{i=1}^{n} \big( y^{(i)} - \mu_y \big)^2 }
+\]
+
+\[
+= 1 - \frac{MSE}{Var(y)}
+\]
+
+For the training dataset, $R^2$ is bounded between 0 and 1, but it can become negative for the test set. If $R^2$ =1, the model fits the data perfectly with a
+corresponding $MSE = 0$.
+
+\section{Using regularized methods for regression}
+
+The most popular approaches to regularized linear regression are the so-called \textit{Ridge Regression}, \textit{Least Absolute Shrinkage and Selection Operator (LASSO)}, and the \textit{Elastic Net} method.
+
+Ridge regression is an L2 penalized model where we simply add the squared sum of the weights to our least-squares cost function:
+
+\[
+J(\mathbf{w})_{ridge} = \sum^{n}_{i=1} \big( y^{(i)} - \hat{y}^{(i)} \big)^2 + \lambda \lVert \mathbf{w} \rVert^{2}_{2}
+\]
+
+Here:
+
+\[
+\text{L2}: \quad \lambda \lVert \mathbf{w} \rVert^{2}_{2} = \lambda \sum^{m}_{j=1} w_{j}^{2}
+\]
+
+By increasing the value of the hyperparameter $\lambda$ , we increase the regularization strength and shrink the weights of our model. Please note that we don't regularize the intercept term $w_0$.
+
+An alternative approach that can lead to sparse models is the LASSO. Depending on the regularization strength, certain weights can become zero, which makes the LASSO also useful as a supervised feature selection technique:
+
+\[
+J(\mathbf{w})_{LASSO} = \sum^{n}_{i=1} \big( y^{(i)} - \hat{y}^{(i)} \big)^2 + \lambda \lVert w \rVert_1
+\]
+
+Here:
+
+\[
+L1: \quad \lambda \lVert \mathbf{w} \rVert_1 = \lambda \sum^{m}_{j=1} | w_j |
+\]
+
+However, a limitation of the LASSO is that it selects at most $n$ variables if $m > n$. A compromise between Ridge regression and the LASSO is the Elastic Net, which has a L1 penalty to generate sparsity and a L2 penalty to overcome some of the limitations of the LASSO, such as the number of selected variables.
+
+\[
+J(\mathbf{w})_{ElasticNet} = \sum_{i=1}^{n} \big( y^{(i)} - \hat{y}^{(i)} \big)^2 + \lambda_1 \sum^{m}_{j=1} w_{j}^2+ \lambda_2 \sum^{m}_{j=1} |w_j|
+\]
+
+\section{Turning a linear regression model into a curve - polynomial regression}
+
+In the previous sections, we assumed a linear relationship between explanatory and response variables. One way to account for the violation of linearity assumption is to use a polynomial regression model by adding polynomial terms:
+
+\[
+y = w_0 + w_1 x + w_2 x^2 + \dots + w_d x^d,
+\]
+
+where $d$ denotes the degree of the polynomial.
+
+\subsection{Modeling nonlinear relationships in the Housing Dataset}
+\subsection{Dealing with nonlinear relationships using random forests}
+\subsubsection{Decision tree regression}
+
+When we used decision trees for classi cation, we defined entropy as a measure of impurity to determine which feature split maximizes the \textit{Information Gain (IG)}, which can be defined as follows for a binary split:
+
+\[
+IG(D_p, x_i) = I(D_p) - \frac{N_{left}}{N_{p}} I (D_{left}) - \frac{N_{right}}{N_p} I (D_{right})
+\]
+
+To use a decision tree for regression, we will replace entropy as the impurity measure of a node $t$ by the MSE:
+
+\[
+I(t) - MSE(t) = \frac{1}{N_t} \sum_{i \in D_t} \big( y^{(i)} - \hat{y}_t \big)^2
+\]
+
+Here, $N_t$ is the number of training samples at node $t$, $D_t$ is the training subset at node $t$, $y^{(i)}$ is the true target value, and $\hat{y}^{(i)}$ is the predicted target value (sample mean):
+
+\[
+\hat{y}_t = \frac{1}{N} \sum_{i \in D_t} y^{(i)}
+\]
+
+In the context of decision tree regression, the MSE is often also referred to as within-node variance, which is why the splitting criterion is also better known
+as \textit{variance reduction}.
+
+\subsubsection{Random forest regression}
+\section{Summary}
+
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 11
+%%%%%%%%%%%%%%%
+
+\chapter{Working with Unlabeled Data -- Clustering Analysis}
+
+
+\section{Grouping objects by similarity using k-means}
+
+Thus, our goal is to group the samples based on their feature similarities, which we can be achieved using the k-means algorithm that can be summarized by the following four steps:
+
+\begin{enumerate}
+\item Randomly pick $k$ centroids from the sample points as initial cluster centers.
+\item Assign each sample to the nearest centroid $\mu^{(j)}, \quad j \in {1, ..., k}.$
+\item Move the centroids to the center of the samples that were assigned to it.
+\item Repeat steps 2 and 3 until the cluster assignments do not change or a user-defined tolerance or a maximum number of iterations is reached.
+\end{enumerate}
+
+Now the next question is \textit{how do we measure similarity between objects?} We can de ne similarity as the opposite of distance, and a commonly used distance for clustering samples with continuous features is the \textit{squared Euclidean distance} between two points $\mathbf{x}$ and $\mathbf{y}$ in $m$-dimensional space:
+
+\[
+d(\mathbf{x}, \mathbf{y})^2 = \sum_{j=1}^{m} \big(x_j - y_j \big)^2 = \lVert \mathbf{x} - \mathbf{y} \rVert^{2}_{2}.
+\]
+
+Note that, in the preceding equation, the index $j$ refers to the $j$th dimension
+(feature column) of the sample points x and y. In the rest of this section, we will use the superscripts $i$ and $j$ to refer to the sample index and cluster index, respectively.
+
+
+Based on this Euclidean distance metric, we can describe the k-means algorithm
+as a simple optimization problem, an iterative approach for minimizing the \textit{within-cluster sum of squared errors (SSE)}, which is sometimes also called \textit{cluster inertia}:
+
+\[
+SSE = \sum_{i=1}^{n} \sum^{k}_{j=1} w^{(i, j)} \big \lVert \mathbf{x}^{(i)} - \mu^{(j)} \big \rVert^{2}_{2}
+\]
+
+Here, $\mu^{(j)}$ is the representative point (centroid) for cluster $j$, and $w^{(i, j)} = 1$ if the sample $\mathbf{x}^{(i)}$ is in cluster $j$; $w^{(i, j)}=0$ otherwise.
+
+\subsection{K-means++}
+
+[...] The initialization in k-means++ can be summarized as follows:
+\begin{enumerate}
+\item Initialize an empty set $M$ to store the $k$ centroids being selected.
+\item Randomly choose the first centroid $\mu^{(j)}$ from the input samples and assign it to $M$
+\item For each sample $\mathbf{x}^{(i)}$ that is not in $M$, find the minimum distance $d \big( x^{(i)}, M \big)^2$ to any of the centroids in $M$.
+\item To randomly select the next centroid $\mu^{(p)}$, use a weighted probability distribution equal to $\frac{d(\mu^{(p)}, M)^2 }{\sum_i d(x^{(i)}M)^2}$
+\item Repeat steps 2 and 3 until $k$ centroids are chosen.
+\item Proceed with the classic \textit{k}-means algorithm.
+\end{enumerate}
+
+\subsection{Hard versus soft clustering}
+
+The $fuzzy c-means (FCM)$ procedure is very similar to k-means. However, we replace the hard cluster assignment by probabilities for each point belonging to each cluster. In $k$-means, we could express the cluster membership of a sample $x$ by a sparse vector of binary values:
+
+\[
+\begin{bmatrix}
+\mathbf{\mu}^{(1)} \rightarrow 0 \\
+\mathbf{\mu}^{(2)} \rightarrow 1 \\
+\mathbf{\mu}^{(3)} \rightarrow 0
+\end{bmatrix}
+\]
+
+Here, the index position with value 1 indicates the cluster centroid $\mathbf{\mu}^{(j)}$ the sample is assigned to (assuming $k=3, \; j \in \{ 1, 2, 3 \}$). In contrast, a membership vector in FCM could be represented as follows:
+
+\[
+\begin{bmatrix}
+\mathbf{\mu}^{(1)} \rightarrow 0.1 \\
+\mathbf{\mu}^{(2)} \rightarrow 0.85 \\
+\mathbf{\mu}^{(3)} \rightarrow 0.05
+\end{bmatrix}
+\]
+
+Here, each value falls in the range $[0, 1]$ and represents a probability of membership to the respective cluster centroid. The sum of the memberships for a given sample is equal to 1. Similarly to the k-means algorithm, we can summarize the FCM algorithm in four key steps:
+
+\begin{enumerate}
+\item Specify the number of $k$ centroids and randomly assign the cluster memberships for each point.
+\item Compute the cluster centroids $\mathbf{\mu^{(j)}}, j \in \{1, \dots, k \}$.
+\item Update the cluster memberships for each point.
+\item Repeat steps 2 and 3 until the membership coefficients do not change or a user-defined tolerance or a maximum number of iterations is reached.
+\end{enumerate}
+
+The objective function of FCM -- we abbreviate it by $J_m$ -- looks very similar to the within cluster sum-squared-error that we minimize in $k$-means:
+
+\[
+J_m = \sum_{i=1}^{n} \sum_{j=1}^{k} w^{m(i, j)} \big \lVert \mathbf{x}^{(i)} - \mathbf{\mu}^{(j)} \big \rVert^{2}_{2} \; m \in [1, \infty)
+\]
+
+However, note that the membership indicator $w^{(i, j)}$ is not a binary value as in $k$-means $\big( w^{(i, j)} \in \{0, 1\} \big)$ but a real value that denotes the cluster membership probability $\big( w^{(i, j)} \in [0, 1] \big).$ You also may have noticed that we added an additional exponent to $w^{(i, j)}$; the exponent $m$, any number greater or equal to 1 (typically $m=2$), is the so-called \textit{fuzziness coefficient} (or simply \textit{fuzzifier}) that controls the degree of \textit{fuzziness}. The larger the value of $m$, the smaller the cluster membership $w^{(i, j)}$ becomes, which leads to fuzzier clusters. The cluster membership probability itself is calculated as follows:
+
+\[
+w^{(i, j)} = \Bigg[ \sum^{k}_{p=1} \Bigg( \frac{\lVert \mathbf{x}^{(i)} - \mathbf{\mu}^{(j)} \rVert_2}{\lVert \mathbf{x}^{(i)} - \mathbf{\mu}^{(p)} \rVert_2} \Bigg)^{\frac{2}{m-1}} \Bigg]^{-1}
+\]
+
+For example, if we chose three cluster centers as in the previous $k$-means example, we could calculate the membership of the $\mathbf{x}^{(i)}$ sample belonging to its own cluster:
+
+\[
+w^{(i, j)} = \Bigg[ \sum^{k}_{p=1} \Bigg( \frac{\lVert \mathbf{x}^{(i)} - \mathbf{\mu}^{(j)} \rVert_2}{\lVert \mathbf{x}^{(i)} - \mathbf{\mu}^{(1)} \rVert_2} \Bigg)^{\frac{2}{m-1}} + \sum^{k}_{p=1} \Bigg( \frac{\lVert \mathbf{x}^{(i)} - \mathbf{\mu}^{(j)} \rVert_2}{\lVert \mathbf{x}^{(i)} - \mathbf{\mu}^{(2)} \rVert_2} \Bigg)^{\frac{2}{m-1}} + \sum^{k}_{p=1} \Bigg( \frac{\lVert \mathbf{x}^{(i)} - \mathbf{\mu}^{(j)} \rVert_2}{\lVert \mathbf{x}^{(i)} - \mathbf{\mu}^{(3)} \rVert_2} \Bigg)^{\frac{2}{m-1}} \Bigg]^{-1}
+\]
+
+The center $\mu^{(j)}$ of a cluster itself is calculated as the mean of all samples in the cluster weighted by the membership degree of belonging to its own cluster:
+
+\[
+\mathbf{\mu}^{(j)} = \frac{\sum_{i=1}^{n} w^{m(i, j)} \mathbf{x}^{(i)}}{\sum_{i=1}^{n} w^{m(i, j)}}
+\]
+
+\subsection{Using the elbow method to find the optimal number of clusters}
+\subsection{Quantifying the quality of clustering via silhouette plots}
+
+To calculate the \textit{silhouette coefficient} of a single sample in our dataset, we can apply the following three steps:
+
+\begin{enumerate}
+\item Calculate the cluster cohesion $a^{(i)}$ as the average distance between a sample $\mathbf{x}^{(i)}$ and all other points in the same cluster.
+\item Calculate the cluster separation $b^{(i)}$ from the next closest cluster as the average distance between the sample $\mathbf{x}^{(i)}$ and all samples in the nearest cluster.
+\item Calculate the silhouette $s^{(i)}$ as the difference between cluster cohesion and separation divided by the greater of the two, as shown here:
+\[
+s^{(i)} = \frac{b^{(i)} - a^{(i)}}{\max \{ b^{(i)}, a^{(i)} \}}.
+\]
+\end{enumerate}
+
+The silhouette coefficient is bounded in the range $-1$ to $1$. Based on the preceding formula, we can see that the silhouette coefficient is 0 if the cluster separation
+and cohesion are equal $(b^{(i)} = a^{(i)})$. Furthermore, we get close to an ideal silhouette coefficient of $1$ if $b^{(i)} >> a^{(i)}$, since $b^{(i)}$ quantifies how dissimilar a sample is to other clusters, and $a^{(i)}$ tells us how similar it is to the other samples in its own cluster, respectively.
+
+\section{Organizing clusters as a hierarchical tree}
+\subsection{Performing hierarchical clustering on a distance matrix}
+\subsection{Attaching dendrograms to a heat map}
+\subsection{Applying agglomerative clustering via scikit-learn}
+\section{Locating regions of high density via DBSCAN}
+
+[...] In \textit{Density-based Spatial Clustering of Applications with Noise} (DBSCAN), a special label is assigned to each sample (point) using the following criteria:
+
+\begin{itemize}
+\item A point is considered as \textit{core point} if at least a specified number (\textit{MinPts}) of neighboring points fall within the specified radius $\epsilon$.
+\item A \textit{border point} is a point that has fewer neighbors than MinPts within $\epsilon$, but lies within the $\epsilon$ radius of a core point.
+\item All other points that are neither core nor border points are considered as \textit{noise points}.
+\end{itemize}
+
+After labeling the points as core, border, or noise points, the DBSCAN algorithm can be summarized in two simple steps:
+
+\begin{enumerate}
+\item Form a separate cluster for each core point or a connected group of core points (core points are connected if they are no farther away than $\epsilon$).
+\item Assign each border point to the cluster of its corresponding core poin.
+\end{enumerate}
+
+\section{Summary}
+
+
+
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 12
+%%%%%%%%%%%%%%%
+
+\chapter{Training Artificial Neural Networks for Image Recognition}
+
+\section{Modeling complex functions with artificial neural networks}
+\subsection{Single-layer neural network recap}
+
+In \textit{Chapter 2, Training Machine Learning Algorithms for Classification}, we implemented the Adaline algorithm to perform binary classification, and we used a gradient descent optimization algorithm to learn the weight coefficients of the model. In every epoch (pass over the training set), we updated the weight vector $\mathbf{w}$ using the following update rule:
+
+\[
+\mathbf{w} := \mathbf{w} + \Delta \mathbf{w}, \quad \text{where } \Delta \mathbf{w} = - \eta \nabla J (\mathbf{w})
+\]
+
+In other words, we computed the gradient based on the whole training set and updated the weights of the model by taking a step into the opposite direction of the gradient $\nabla J(\mathbf{w})$. In order to find the optimal weights of the model, we optimized an objective function that we defined as the \textit{Sum of Squared Errors (SSE)} cost function $J(\mathbf{w})$. Furthermore, we multiplied the gradient by a factor, the learning rate $\eta$ , which we chose carefully to balance the speed of learning against the risk of overshooting the global minimum of the cost function.
+
+In gradient descent optimization, we updated all weights simultaneously after each epoch, and we defined the partial derivative for each weight $w_j$ in the weight vector
+$\mathbf{w}$ as follows:
+
+\[
+\frac{\partial}{\partial w_j} J(\mathbf{w}) = - \sum_i \big( y^{(i)} - a^{(i)} \big) x_{j}^{(i)}
+\]
+
+Here $y^{(i)}$ is the target class label of a particular sample $x^{(i)}$ , and $a^{(i)}$ is the \textit{activation} of the neuron, which is a linear function in the special case of Adaline. Furthermore, we defined the \textit{activation function} $\phi(\cdot)$ as follows:
+
+\[
+\phi(z) = z = a
+\]
+
+Here, the net input $z$ is a linear combination of the weights that are connecting the
+input to the output layer:
+
+\[
+z = \sum_j w_j x_j = \mathbf{w}^T \mathbf{x}
+\]
+
+While we used the activation $\phi(z)$ to compute the gradient update, we implemented a \textit{threshold function} (Heaviside function) $g(\cdot)$ to squash the continuous-valued output into binary class labels for prediction:
+
+\[ \phi(z) = \begin{cases}
+ 1 & \text{ if } g(z) \ge 0 \\
+ -1 & \text{ otherwise }.
+ \end{cases}
+\]
+
+\subsection{Introducing the multi-layer neural network architecture}
+
+[...] As shown in the preceding figure, we denote the $i$th activation unit in the $l$th layer as $a_{i}^{l}$ , and the activation units $a_{0}^{1}$ and $a_{0}^{2}$ are the \textit{bias units}, respectively, which we set equal to 1. The activation of the units in the input layer is just its input plus the bias unit:
+
+\[
+\mathbf{a}^{(i)} =
+\begin{bmatrix}
+ a_{0}^{(1)} \\
+ a_{1}^{(1)} \\
+ \vdots \\
+ a_{m}^{(1)}
+\end{bmatrix}
+=
+\begin{bmatrix}
+ 1 \\
+ x_{1}^{(i)} \\
+ \vdots \\
+ x_{m}^{(i)}
+\end{bmatrix}
+\]
+
+Each unit in layer $l$ is connected to all units in layer $l +1$ via a weight coefficient. For example, the connection between the $k$th unit in layer $l$ to the $j$th unit in layer
+$l+1$ would be written as $w^{(l)}_{j, k}$ . Please note that the superscript $i$ in $x^{(i)}_{m}$ stands for the $i$th sample, not the $i$th layer. In the following paragraphs, we will often omit the superscript $i$ for clarity.
+
+[...] To better understand how this works, remember the one-hot representation of categorical variables that we introduced in \textit{Chapter 4, Building Good Training Sets -- Data Preprocessing}. For example, we would encode the three class labels in the familiar Iris dataset (\textit{0=Setosa, 1=Versicolor, 2=Virginica})
+as follows:
+
+\[
+0 =
+\begin{bmatrix}
+1 \\
+0 \\
+0
+\end{bmatrix},
+1 =
+\begin{bmatrix}
+0 \\
+1 \\
+0
+\end{bmatrix},
+2 =
+\begin{bmatrix}
+0 \\
+0 \\
+1
+\end{bmatrix}.
+\]
+
+This one-hot vector representation allows us to tackle classification tasks with an arbitrary number of unique class labels present in the training set.
+
+
+[...] If you are new to neural network representations, the terminology around the indices (subscripts and superscripts) may look a little bit confusing at first. You may wonder
+ why we wrote $w^{(l)}_{j, k}$ and not $w^{(l)}_{k, j}$ to refer to the weight coefficient that connects the $k$th unit in layer $l$ to the $j$th unit in layer $l +1$. What may seem a little bit quirky at first will make much more sense in later sections when we vectorize the neural network representation. For example, we will summarize the weights that connect the input and hidden layer by a matrix $\mathbf{W}^{(1)} \in \mathbb{R}^{h \times [m+1]}$ , where $h$ is the number of hidden units and $m + 1$ is the number of input units plus bias unit.
+
+\subsection{Activating a neural network via forward propagation}
+
+[...] Now, let's walk through the individual steps of forward propagation to generate
+an output from the patterns in the training data. Since each unit in the hidden layer
+is connected to all units in the input layers, we first calculate the activation $a_{1}^{(2)}$ as follows:
+
+\[
+z_{1}^{(2)} = a_{0}^{(1)} w_{1,0}^{(1)} + a_{1}^{(1)} w_{1, 1}^{(1)} + \dots + a_{m}^{(1)} w_{l, m}^{(1)}
+\]
+
+\[
+a_{1}^{(2)} = \phi \big( z_{1}^{(2)} \big)
+\]
+
+Here, $z_{1}^{(2)}$ is the net input and $\phi(\cdot)$ is the activation function, which has to be differentiable to learn the weights that connect the neurons using a gradient-based approach. To be able to solve complex problems such as image classification, we need nonlinear activation functions in our MLP model, for example, the sigmoid (logistic) function that we discussed in previous chapters:
+
+\[
+\phi(z) = \frac{1}{1 + e^{-z}}.
+\]
+
+For purposes of computational efficiency and code readability, we will now write the activation in a more compact form using the concepts of basic linear algebra, which will allow us to vectorize our code implementation:
+
+\[
+\mathbf{z}^{(2)} = \mathbf{W}^{(1)} \mathbf{a}^{(1)}
+\]
+
+\[
+\mathbf{a}^{(2)} = \phi \big( \mathbf{z}^{(2)} \big)
+\]
+
+\textit{Note: Everywhere you read $h$ in the following paragraphs of this section, you can think of $h$ as $h+1$ to include the bias unit (and in order to get the dimensions right).}
+
+Here, $\mathbf{a}^{(1)}$ is our $[m+1] \times 1$ dimensional feature vector a sample $\mathbf{x}^{(i)}$ plus bias unit. $\mathbf{W}^{(i)}$ is an $h \times [m + 1]$-dimensional weight matrix where $h$ is the number of hidden units in our neural network. After matrix-vector multiplication, we obtain the $h \times 1$-dimensional net input vector $\mathbf{z}^{(2)}$ to calculate the activation $\mathbf{a}^{(2)}$ (where $\mathbf{a}^{(2)} \in \mathbb{R}^{h \times 1}$). Furthermore, we can generalize this computation to all $n$ samples in the training set:
+
+\[
+\mathbf{Z}^{(2)} = \mathbf{W}^{(1)} \big[ \mathbf{A}^{(1)} \big]^T
+\]
+
+Here, $\mathbf{A}^{(1)}$ is now an $n \times [m+1]$ matrix, and the matrix-matrix multiplication will result in an $h \times n$-dimensional net input matrix $\mathbf{Z}^{(2)}$. Finally, we apply the activation function $\phi(\cdot)$ to each value in the net input matrix to get the $h \times n$ activation matrix $\mathbf{A}^{(2)}$ for the next layer (here, output layer):
+
+\[
+\mathbf{A}^{(2)} = \phi \big( \mathbf{Z}^{(2)} \big)
+\]
+
+Similarly, we can rewrite the activation of the output layer in the vectorized form:
+
+\[
+\mathbf{Z}^{(3)} \mathbf{W}^{(2)} \mathbf{A}^{(2)}
+\]
+
+Here, we multiply the $t \times h$ matrix $\mathbf{W}^{(2)}$ ($t$ is the number of output units) by the $h \times n$ dimensional matrix $\mathbf{A}^{(2)}$ to obtain the $t \times n$ dimensional matrix $\mathbf{Z}^{(3)}$ (the columns in this matrix represent the outputs for each sample). Lastly, we apply the sigmoid activation function to obtain the continuous valued output of our network:
+
+\[
+\mathbf{A}^{(3)} = \phi \big( \mathbf{Z}^{(3)} \big), \; \mathbf{A}^{(3)} \in \mathbb{R}^{t \times n}.
+\]
+
+
+
+\section{Classifying handwritten digits}
+\subsection{Obtaining the MNIST dataset}
+\subsection{Implementing a multi-layer perceptron}
+
+As you may have noticed, by going over our preceding MLP implementation, we also implemented some additional features, which are summarized here:
+
+\begin{itemize}
+\item \textit{l2}: the $\lambda$ parameter for L2 regularization to decrease the degree of overfitting; equivalently, \textit{l1} is the $\lambda$ parameter for L1 regularization.
+\item \textit{epochs}: The number of passes over the training set.
+\item \textit{eta}: The learning rate $\eta$
+\item \textit{alpha}: A parameter for momentum learning to add a factor of the previous gradient to the weight update for faster learning
+\[
+\Delta \mathbf{w}_t = \eta \nabla J(\mathbf{w}_t) + \alpha \Delta \mathbf{w}_t-1,
+\]
+where $t$ is the current time step or epoch.
+\item \textit{decrease\_const}: The decrease constant $d$ for an adaptive learning rate $\eta$ that decreases over time for better convergence $\eta / 1 + t \times d .$
+\item \textit{shuffle}: Shuffling the training set prior to every epoch to prevent the algorithm from getting stuck in cycles.
+\item \textit{Minibatches}: Splitting of the training data into k mini-batches in each epoch. The gradient is computed for each mini-batch separately instead of the entire training data for faster learning.
+\end{itemize}
+
+\section{Training an artificial neural network}
+
+\subsection{Computing the logistic cost function}
+
+The logistic cost function that we implemented as the \textit{\_get\_cost} method is actually pretty simple to follow since it is the same cost function that we described in the logistic regression section in \textit{Chapter 3, A Tour of Machine Learning Classifiers Using Scikit-learn.}
+
+\[
+J(\mathbf{w}) = -\sum_{i=1}^{n} y^{(i)} \log \big( a^{(i)} \big) + \big( 1 - y^{(i)} \big) \log \big( 1 - a^{(i)}\big)
+\]
+
+Here, $a^{(i)}$ is the sigmoid activation of the $i$th unit in one of the layers which we compute in the forward propagation step:
+
+\[
+a^{(i)} = \phi \big( z^{(i)} \big).
+\]
+
+Now, let's add a regularization term, which allows us to reduce the degree of over tting. As you will recall from earlier chapters, the L2 and L1 regularization terms are defined as follows (remember that we don't regularize the bias units):
+
+\[
+L2 = \lambda \lVert \mathbf{w} \rVert^{2}_{2} = \lambda \sum_{j=1}^{m} w_{j}^{2} \text{ and } L1 = \lambda \lVert \mathbf{w} \rVert_{1} = \lambda \sum_{j=1}^{m} | w_j |.
+\]
+
+[...] By adding the L2 regularization term to our logistic cost function, we obtain the following equation:
+
+\[
+J(\mathbf{w}) = - \Bigg[ \sum_{i=1}^{n} y^{(i)} \log \big( a^{(i)} \big) + \big(1 - y^{(i)} \big) \log \big(1- a^{(i)} \big) \Bigg] + \frac{\lambda}{2} \lVert \mathbf{w} \rVert^{2}_{2}
+\]
+
+Since we implemented an MLP for multi-class classification, this returns an output vector of $t$ elements, which we need to compare with the $t \times 1$ dimensional target vector in the one-hot encoding representation. For example, the activation of the third layer and the target class (here: class 2) for a particular sample may look like this:
+
+\[
+a^{(3)} =
+\begin{bmatrix}
+0.1 \\
+0.9 \\
+\vdots \\
+0.3
+\end{bmatrix}
+ ,\; \mathbf{y} =
+ \begin{bmatrix}
+0 \\
+1 \\
+\vdots \\
+0
+\end{bmatrix}
+\]
+
+Thus, we need to generalize the logistic cost function to all activation units $j$ in our network. So our cost function (without the regularization term) becomes:
+
+\[
+J(\mathbf{w}) = - \sum_{i=1}^{n} \sum_{j=1}^{t} = y_{j}^{(i)} \log \big( 1 - a^{(1)}_{j} \big)
+\]
+
+Here, the superscript $i$ is the index of a particular sample in our training set.
+The following generalized regularization term may look a little bit complicated at first, but here we are just calculating the sum of all weights of a layer $l$ (without the bias term) that we added to the first column:
+
+\[
+J(\mathbf{w}) = - \Bigg[ \sum_{i=1}^{n} \sum_{j=1}^{m} y_{j}^{(i)} \log \bigg( \phi \Big( z_{j}^{(i)} \Big) \bigg) + \Big(1 - y_{j}^{(i)} \Big) \log \bigg(1 - \phi \Big( z_{j}^{(i)} \Big) \bigg) \Bigg] + \frac{\lambda}{2} \sum_{l=1}^{L-1} \sum_{i=1}^{u_l} \sum_{j=1}^{u_{l+1}} \Big(w_{j, i}^{(l)}\Big)^2
+\]
+
+The following expression represents the L2-penalty term:
+
+\[
+\frac{\lambda}{2} \sum_{l=1}^{L-1} \sum_{i=1}^{u_l} \sum_{j=1}^{u_{l+1}} \Big(w_{j, i}^{(l)}\Big)^2
+\]
+
+Remember that our goal is to minimize the cost function $J(\mathbf{w})$. Thus, we need to calculate the partial derivative of matrix $\mathbf{W}$ with respect to each weight for every layer in the network:
+
+\[
+\frac{\partial}{\partial w_{j, i}^{l}} J(\mathbf{W}).
+\]
+
+
+\subsection{Training neural networks via backpropagation}
+
+[...] As we recall from the beginning of this chapter, we first need to apply forward propagation in order to obtain the activation of the output layer, which we formulated as follows:
+
+\[
+\mathbf{Z}^{(2)} = \mathbf{W}^{(1)} \Big[ \mathbf{A}^{(1)} \Big]^T \quad \text{(net input of the hidden layer)}
+\]
+
+\[
+\mathbf{A}^{(2)} = \phi \big( \mathbf{Z}^{(2)} = \big) \quad \text{(activation of the hidden layer)}
+\]
+
+\[
+\mathbf{Z}^{(3)} = \mathbf{W}^{(2)} \mathbf{A}^{(2)} \quad \text{(net input of the output layer)}
+\]
+
+\[
+\mathbf{A}^{(3)} = \phi \big( \mathbf{Z}^{(3)} \big) \quad \text{(activation of the output layer)}
+\]
+
+[...] In backpropagation, we propagate the error from right to left. We start by calculating the error vector of the output layer:
+
+\[
+\delta^{(3)} = \mathbf{a}^{(3)} - \mathbf{y}
+\]
+
+Here, $\mathbf{y}$ is the vector of the true class labels. Next, we calculate the error term of the hidden layer:
+
+\[
+\delta^{(2)} = \big( \mathbf{W}^{(2)} \big)^T \delta^{(3)} \cdot \frac{\partial \phi \big( z^{(2)} \big) }{\partial z^{(2)}}
+.\]
+
+Here, $\frac{\partial \phi \big( z^{(2)} \big)}{\partial z^{(2)}}$ is simply the derivative of the sigmoid activation function, which we implemented as \textit{\_sigmoid\_gradient}:
+
+\[
+\frac{\partial \phi \big( z^{(2)} \big)}{\partial z^{(2)}} = \Big( a^{(2)} \cdot \big( 1 - a^{(2)} \big) \Big).
+\]
+
+Note that the asterisk symbol ($\cdot$) means element-wise multiplication in this context.
+
+Although, it is not important to follow the next equations, you may be curious as to how I obtained the derivative of the activation function. I summarized the derivation step by step here:
+
+\begin{equation*}
+\begin{split}
+& \phi' (z) = \frac{\partial}{\partial z} \Big( \frac{1}{1 + e^{-z}} \Big) \\
+& = \frac{e^{-z}}{(1 + e^{-z})^2} \\
+& = \frac{1 + e^{-z}}{\big( 1 + e^{-z} \big)^2} - \Big( \frac{1}{1 + e^{-z}} \Big)^2 \\
+& = \frac{1}{\big( 1 + e^{-z} \big)^2} - \Big( \frac{1}{1 + e^{-z}} \Big)^2 \\
+& = \phi(z) - \big(\phi(z)\big)^2 \\
+& = \phi(z) - \big( 1 - \phi(z) \big) \\
+& = a(1 -a)
+\end{split}
+\end{equation*}
+
+To better understand how we compute the $\delta^P{(3)}$ term, let's walk through it in more detail. In the preceding equation, we multiplied the transpose $(\mathbf{W}^{(2)})^T$ of the $t \times h$ dimensional matrix $\mathbf{W}^{(2)}$; $t$ is the number of output class labels and $h$ is the number of hidden units. Now, $(\mathbf{W}^{(2)})^T$ becomes an $h \times t$ dimensional matrix with $\delta^{(3)}$, which is a $t \times 1$ dimensional vector. We then performed a pair-wise multiplication between $(\mathbf{W}^{(2)})^T \delta^{(3)}$ and $\Big( a^{(2)} \cdot \big( 1 - a^{(2)} \big) \Big)$, which is also a $t \times 1$ dimensional vector. Eventually, after obtaining the ? terms, we can now write the derivation of the cost function as follows:
+
+\[
+\frac{\partial}{\partial w_{i, j}^{l}} J(\mathbf{W}) = a_{j}^{l} \delta_{i}^{(l+1)}
+\]
+
+Next, we need to accumulate the partial derivative of every $j$th node in layer $l$ and the $i$th error of the node in layer $l +1$:
+
+\[
+\Delta^{(l)}_{i, j} := \Delta_{i, j}^{(l)} + a_{j}^{(l)} \delta_{i}^{(l+1)}
+\]
+
+Remember that we need to compute $\Delta_{i, j}^{(l)}$ for every sample in the training set. Thus, it is easier to implement it as a vectorized version like in our preceding MLP code implementation:
+
+\[
+\Delta^{(l)} := \Delta^{(l)} \delta^{(l + 1)} \big( \mathbf{A}^{(l)} \big)^T
+\]
+
+After we have accumulated the partial derivatives, we can add the regularization term as follows:
+
+\[
+\Delta^{(l)} := \Delta^{(l)} + \lambda^{(l)} \quad \text{(except for the bias term)}
+\]
+
+Lastly, after we have computed the gradients, we can now update the weights by
+taking an opposite step towards the gradient:
+
+\[
+\mathbf{W}^{(l)} := \mathbf{W}^{(l)} - \eta\Delta^{(l)}
+\]
+
+
+\section{Developing your intuition for backpropagation}
+\section{Debugging neural networks with gradient checking}
+
+In the previous sections, we defined a cost function $J(\mathbf{W})$ where $\mathbf{W}$ is the matrix
+of the weight coefficients of an artificial network. Note that $J(\mathbf{W})$ is -- roughly speaking -- a "stacked" matrix consisting of the matrices $\mathbf{W}^{(1)} $ and $W^{(2)}$ in a multi-layer perceptron with one hidden unit. We defined $\mathbf{W}^{(1)}$ as the $h \times [m+1]$-dimensional matrix that connects the input layer to the hidden layer, where $h$ is the number of hidden units and $m$ is the number of features (input units). The matrix $\mathbf{W}^{(2)}$ that connects the hidden layer to the output layer has the dimensions $t \times h$, where $t$ is the number of output units. We then calculated the derivative of the cost function for a weight $w_{i, j}^{l}$ as follows:
+
+\[
+\frac{\partial}{\partial w_{i, j}^{(i)}}
+\]
+
+Remember that we are updating the weights by taking an opposite step towards the direction of the gradient. In gradient checking, we compare this analytical solution to a numerically approximated gradient:
+
+\[
+\frac{\partial}{\partial w_{i, j}^{(l)}} J(\mathbf{W}) \approx \frac{J\big( w_{i, j}^{(l)} + \epsilon \big) - J \big( w_{i, j}^{(l)}\big)}{\epsilon}
+\]
+
+Here, $\epsilon$ is typically a very small number, for example 1e-5 (note that 1e-5 is just
+a more convenient notation for 0.00001). Intuitively, we can think of this finite difference approximation as the slope of the secant line connecting the points of the cost function for the two weights w and $w + \epsilon$ (both are scalar values), as shown in the following figure. We are omitting the superscripts and subscripts for simplicity.
+
+[...] An even better approach that yields a more accurate approximation of the gradient is to compute the symmetric (or centered) difference quotient given by the two-point formula:
+
+\[
+\frac{ J\big( w_{i, j}^{(l)} + \epsilon \big) - J\big( w_{i, j}^{(l)} - \epsilon \big) }{2 \epsilon}
+\]
+
+Typically, the approximated difference between the numerical gradient $J'_{n}$ and analytical gradient $J'_{a}$ is then calculated as the L2 vector norm. For practical a reasons, we unroll the computed gradient matrices into at vectors so that we can calculate the error (the difference between the gradient vectors) more conveniently:
+
+\[
+\text{error} = \lVert J'_n - J'_a \rVert_2
+\]
+
+One problem is that the error is not scale invariant (small errors are more significant if the weight vector norms are small, too). Thus, it is recommended to calculate a normalized difference:
+
+\[
+\text{relative error} = \frac{\lVert J'_n - J'_a \rVert_2}{\lVert J'_n \rVert_2 + \lVert J'_a \rVert_2 }
+\]
+
+
+\section{Convergence in neural networks}
+\section{Other neural network architectures}
+\subsection{Convolutional Neural Networks}
+\subsection{Recurrent Neural Networks}
+\section{A few last words about neural network implementation}
+\section{Summary}
+
+
+
+%%%%%%%%%%%%%%%
+% CHAPTER 13
+%%%%%%%%%%%%%%%
+
+\chapter{Parallelizing Neural Network Training with Theano}
+
+\section{Building, compiling, and running expressions with Theano}
+\subsection{What is Theano?}
+\subsection{First steps with Theano}
+\subsection{Configuring Theano}
+\subsection{Working with array structures}
+\subsection{Wrapping things up -- a linear regression example}
+\section{Choosing activation functions for feedforward neural networks}
+\subsection{Logistic function recap}
+
+We recall from the section on logistic regression in \textit{Chapter 3, A Tour of Machine Learning Classifiers Using Scikit-learn} that we can use the logistic function to model the probability that sample $x$ belongs to the positive class (class 1) in a binary classification task:
+
+\[
+\phi_{logistic} (z) = \frac{1}{1 + e^{-z}}
+\]
+
+Here, the scalar variable $z$ is defined as the net input:
+
+\[
+z = w_0 x_0 + \dots + w_m x_m = \sum_{j=0}^{m} x_j w_j = \mathbf{w}^T \mathbf{x}
+\]
+
+Note that $w_0$ is the bias unit (y-axis intercept, $x_0 =1$).
+
+\subsection{Estimating probabilities in multi-class classification via the softmax function}
+
+The softmax function is a generalization of the logistic function that allows us
+to compute meaningful class-probabilities in multi-class settings (multinomial logistic regression). In softmax, the probability of a particular sample with net input z belongs to the i th class can be computed with a normalization term in the denominator that is the sum of all M linear functions:
+
+$P(y=i | z) = \phi_{softmax}(z) = \frac{e_{i}^{z}}{\sum_{m=1}^{M} e_{m}^{z}}$.
+
+\subsection{Broadening the output spectrum by using a hyperbolic tangent}
+
+Another sigmoid function that is often used in the hidden layers of artificial neural networks is the \textit{hyperbolic tangent (tanh)}, which can be interpreted as a rescaled version of the logistic function.
+
+\[
+\phi_{tanh} (z) = 2 \times \phi_{logistic} (2 \times z) - 1 = \frac{e^{z} - e^{-z} }{e^{z} + e^{-z}}
+\]
+
+\[
+\phi_{logistic}(z) = \frac{1}{1 + e^{-z}}
+\]
+
+\section{Training neural networks efficiently using Keras}
+\section{Summary}
+
+\end{document} % end main document
\ No newline at end of file
diff --git a/docs/equations/pymle-equations.toc b/docs/equations/pymle-equations.toc
new file mode 100644
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--- /dev/null
+++ b/docs/equations/pymle-equations.toc
@@ -0,0 +1,189 @@
+\contentsline {chapter}{\numberline {1}Giving Computers the Ability to Learn from Data}{7}{chapter.1}
+\contentsline {section}{\numberline {1.1}Building intelligent machines to transform data into knowledge}{8}{section.1.1}
+\contentsline {section}{\numberline {1.2}The three different types of machine learning}{8}{section.1.2}
+\contentsline {section}{\numberline {1.3}Making predictions about the future with supervised learning}{8}{section.1.3}
+\contentsline {subsection}{\numberline {1.3.1}Classification for predicting class labels}{8}{subsection.1.3.1}
+\contentsline {subsection}{\numberline {1.3.2}Regression for predicting continuous outcomes}{8}{subsection.1.3.2}
+\contentsline {section}{\numberline {1.4}Solving interactive problems with reinforcement learning}{8}{section.1.4}
+\contentsline {section}{\numberline {1.5}Discovering hidden structures with unsupervised learning}{8}{section.1.5}
+\contentsline {subsection}{\numberline {1.5.1}Finding subgroups with clustering}{8}{subsection.1.5.1}
+\contentsline {subsection}{\numberline {1.5.2}Dimensionality reduction for data compression}{8}{subsection.1.5.2}
+\contentsline {section}{\numberline {1.6}An introduction to the basic terminology and notations}{8}{section.1.6}
+\contentsline {section}{\numberline {1.7}A roadmap for building machine learning systems}{10}{section.1.7}
+\contentsline {subsection}{\numberline {1.7.1}Preprocessing -- getting data into shape}{10}{subsection.1.7.1}
+\contentsline {subsection}{\numberline {1.7.2}Training and selecting a predictive model}{10}{subsection.1.7.2}
+\contentsline {subsection}{\numberline {1.7.3}Evaluating models and predicting unseen data instances}{10}{subsection.1.7.3}
+\contentsline {section}{\numberline {1.8}Using Python for machine learning}{10}{section.1.8}
+\contentsline {subsection}{\numberline {1.8.1}Installing Python packages}{10}{subsection.1.8.1}
+\contentsline {section}{\numberline {1.9}Summary}{10}{section.1.9}
+\contentsline {chapter}{\numberline {2}Training Machine Learning Algorithms for Classification}{11}{chapter.2}
+\contentsline {section}{\numberline {2.1}Artificial neurons -- a brief glimpse into the early history of machine learning}{11}{section.2.1}
+\contentsline {section}{\numberline {2.2}Implementing a perceptron learning algorithm in Python}{14}{section.2.2}
+\contentsline {subsection}{\numberline {2.2.1}Training a perceptron model on the Iris dataset}{14}{subsection.2.2.1}
+\contentsline {section}{\numberline {2.3}Adaptive linear neurons and the convergence of learning}{14}{section.2.3}
+\contentsline {subsection}{\numberline {2.3.1}Minimizing cost functions with gradient descent}{14}{subsection.2.3.1}
+\contentsline {subsection}{\numberline {2.3.2}Implementing an Adaptive Linear Neuron in Python}{15}{subsection.2.3.2}
+\contentsline {subsection}{\numberline {2.3.3}Large scale machine learning and stochastic gradient descent}{16}{subsection.2.3.3}
+\contentsline {section}{\numberline {2.4}Summary}{16}{section.2.4}
+\contentsline {chapter}{\numberline {3}A Tour of Machine Learning Classifiers Using Scikit-learn}{17}{chapter.3}
+\contentsline {section}{\numberline {3.1}Choosing a classification algorithm}{17}{section.3.1}
+\contentsline {section}{\numberline {3.2}First steps with scikit-learn}{17}{section.3.2}
+\contentsline {subsection}{\numberline {3.2.1}Training a perceptron via scikit-learn}{17}{subsection.3.2.1}
+\contentsline {section}{\numberline {3.3}Modeling class probabilities via logistic regression}{17}{section.3.3}
+\contentsline {subsection}{\numberline {3.3.1}Logistic regression intuition and conditional probabilities}{17}{subsection.3.3.1}
+\contentsline {subsection}{\numberline {3.3.2}Learning the weights of the logistic cost function}{18}{subsection.3.3.2}
+\contentsline {subsection}{\numberline {3.3.3}Training a logistic regression model with scikit-learn}{19}{subsection.3.3.3}
+\contentsline {subsection}{\numberline {3.3.4}Tackling overfitting via regularization}{21}{subsection.3.3.4}
+\contentsline {section}{\numberline {3.4}Maximum margin classification with support vector machines}{22}{section.3.4}
+\contentsline {subsection}{\numberline {3.4.1}Maximum margin intuition}{22}{subsection.3.4.1}
+\contentsline {subsection}{\numberline {3.4.2}Dealing with the nonlinearly separable case using slack variables}{23}{subsection.3.4.2}
+\contentsline {subsection}{\numberline {3.4.3}Alternative implementations in scikit-learn}{23}{subsection.3.4.3}
+\contentsline {section}{\numberline {3.5}Solving nonlinear problems using a kernel SVM}{23}{section.3.5}
+\contentsline {subsection}{\numberline {3.5.1}Using the kernel trick to find separating hyperplanes in higher dimensional space}{23}{subsection.3.5.1}
+\contentsline {section}{\numberline {3.6}Decision tree learning}{24}{section.3.6}
+\contentsline {subsection}{\numberline {3.6.1}Maximizing information gain -- getting the most bang for the buck}{25}{subsection.3.6.1}
+\contentsline {subsection}{\numberline {3.6.2}Building a decision tree}{25}{subsection.3.6.2}
+\contentsline {subsection}{\numberline {3.6.3}Combining weak to strong learners via random forests}{25}{subsection.3.6.3}
+\contentsline {section}{\numberline {3.7}K-nearest neighbors -- a lazy learning algorithm}{25}{section.3.7}
+\contentsline {section}{\numberline {3.8}Summary}{25}{section.3.8}
+\contentsline {chapter}{\numberline {4}Building Good Training Sets -- Data Pre-Processing}{26}{chapter.4}
+\contentsline {section}{\numberline {4.1}Dealing with missing data}{26}{section.4.1}
+\contentsline {subsection}{\numberline {4.1.1}Eliminating samples or features with missing values}{26}{subsection.4.1.1}
+\contentsline {subsection}{\numberline {4.1.2}Imputing missing values}{26}{subsection.4.1.2}
+\contentsline {subsection}{\numberline {4.1.3}Understanding the scikit-learn estimator API}{26}{subsection.4.1.3}
+\contentsline {section}{\numberline {4.2}Handling categorical data}{26}{section.4.2}
+\contentsline {subsection}{\numberline {4.2.1}Mapping ordinal features}{26}{subsection.4.2.1}
+\contentsline {subsection}{\numberline {4.2.2}Encoding class labels}{26}{subsection.4.2.2}
+\contentsline {subsection}{\numberline {4.2.3}Performing one-hot encoding on nominal features}{26}{subsection.4.2.3}
+\contentsline {section}{\numberline {4.3}Partitioning a dataset in training and test sets}{26}{section.4.3}
+\contentsline {section}{\numberline {4.4}Bringing features onto the same scale}{26}{section.4.4}
+\contentsline {section}{\numberline {4.5}Selecting meaningful features}{27}{section.4.5}
+\contentsline {subsection}{\numberline {4.5.1}Sparse solutions with L1 regularization}{27}{subsection.4.5.1}
+\contentsline {subsection}{\numberline {4.5.2}Sequential feature selection algorithms}{27}{subsection.4.5.2}
+\contentsline {section}{\numberline {4.6}Assessing feature importance with random forests}{28}{section.4.6}
+\contentsline {section}{\numberline {4.7}Summary}{28}{section.4.7}
+\contentsline {chapter}{\numberline {5}Compressing Data via Dimensionality Reduction}{29}{chapter.5}
+\contentsline {section}{\numberline {5.1}Unsupervised dimensionality reduction via principal component analysis}{29}{section.5.1}
+\contentsline {subsection}{\numberline {5.1.1}Total and explained variance}{30}{subsection.5.1.1}
+\contentsline {subsection}{\numberline {5.1.2}Feature transformation}{30}{subsection.5.1.2}
+\contentsline {subsection}{\numberline {5.1.3}Principal component analysis in scikit-learn}{31}{subsection.5.1.3}
+\contentsline {section}{\numberline {5.2}Supervised data compression via linear discriminant analysis}{31}{section.5.2}
+\contentsline {subsection}{\numberline {5.2.1}Computing the scatter matrices}{31}{subsection.5.2.1}
+\contentsline {subsection}{\numberline {5.2.2}Selecting linear discriminants for the new feature subspace}{32}{subsection.5.2.2}
+\contentsline {subsection}{\numberline {5.2.3}Projecting samples onto the new feature space}{32}{subsection.5.2.3}
+\contentsline {subsection}{\numberline {5.2.4}LDA via scikit-learn}{32}{subsection.5.2.4}
+\contentsline {section}{\numberline {5.3}Using kernel principal component analysis for nonlinear mappings}{32}{section.5.3}
+\contentsline {subsection}{\numberline {5.3.1}Kernel functions and the kernel trick}{32}{subsection.5.3.1}
+\contentsline {subsection}{\numberline {5.3.2}Implementing a kernel principal component analysis in Python}{34}{subsection.5.3.2}
+\contentsline {subsubsection}{Example 1 -- separating half-moon shapes}{36}{section*.2}
+\contentsline {subsubsection}{Example 2 -- separating concentric circles}{36}{section*.3}
+\contentsline {subsection}{\numberline {5.3.3}Projecting new data points}{36}{subsection.5.3.3}
+\contentsline {subsection}{\numberline {5.3.4}Kernel principal component analysis in scikit-learn}{36}{subsection.5.3.4}
+\contentsline {section}{\numberline {5.4}Summary}{36}{section.5.4}
+\contentsline {chapter}{\numberline {6}Learning Best Practices for Model Evaluation and Hyperparameter Tuning}{37}{chapter.6}
+\contentsline {section}{\numberline {6.1}Streamlining workflows with pipelines}{37}{section.6.1}
+\contentsline {subsection}{\numberline {6.1.1}Loading the Breast Cancer Wisconsin dataset}{37}{subsection.6.1.1}
+\contentsline {subsection}{\numberline {6.1.2}Combining transformers and estimators in a pipeline}{37}{subsection.6.1.2}
+\contentsline {section}{\numberline {6.2}Using k-fold cross-validation to assess model performance}{37}{section.6.2}
+\contentsline {subsection}{\numberline {6.2.1}The holdout method}{37}{subsection.6.2.1}
+\contentsline {subsection}{\numberline {6.2.2}K-fold cross-validation}{37}{subsection.6.2.2}
+\contentsline {section}{\numberline {6.3}Debugging algorithms with learning and validation curves}{37}{section.6.3}
+\contentsline {subsection}{\numberline {6.3.1}Diagnosing bias and variance problems with learning curves}{37}{subsection.6.3.1}
+\contentsline {subsection}{\numberline {6.3.2}Addressing overfitting and underfitting with validation curves}{37}{subsection.6.3.2}
+\contentsline {section}{\numberline {6.4}Fine-tuning machine learning models via grid search}{38}{section.6.4}
+\contentsline {subsection}{\numberline {6.4.1}Tuning hyperparameters via grid search}{38}{subsection.6.4.1}
+\contentsline {subsection}{\numberline {6.4.2}Algorithm selection with nested cross-validation}{38}{subsection.6.4.2}
+\contentsline {section}{\numberline {6.5}Looking at different performance evaluation metrics}{38}{section.6.5}
+\contentsline {subsection}{\numberline {6.5.1}Reading a confusion matrix}{38}{subsection.6.5.1}
+\contentsline {subsection}{\numberline {6.5.2}Optimizing the precision and recall of a classification model}{38}{subsection.6.5.2}
+\contentsline {subsection}{\numberline {6.5.3}Plotting a receiver operating characteristic}{39}{subsection.6.5.3}
+\contentsline {subsection}{\numberline {6.5.4}The scoring metrics for multiclass classification}{39}{subsection.6.5.4}
+\contentsline {section}{\numberline {6.6}Summary}{39}{section.6.6}
+\contentsline {chapter}{\numberline {7}Combining Different Models for Ensemble Learning}{40}{chapter.7}
+\contentsline {section}{\numberline {7.1}Learning with ensembles}{40}{section.7.1}
+\contentsline {section}{\numberline {7.2}Implementing a simple majority vote classifier}{41}{section.7.2}
+\contentsline {subsection}{\numberline {7.2.1}Combining different algorithms for classification with majority vote}{42}{subsection.7.2.1}
+\contentsline {section}{\numberline {7.3}Evaluating and tuning the ensemble classifier}{42}{section.7.3}
+\contentsline {section}{\numberline {7.4}Bagging -- building an ensemble of classifiers from bootstrap samples}{42}{section.7.4}
+\contentsline {section}{\numberline {7.5}Leveraging weak learners via adaptive boosting}{42}{section.7.5}
+\contentsline {section}{\numberline {7.6}Summary}{44}{section.7.6}
+\contentsline {chapter}{\numberline {8}Applying Machine Learning to Sentiment Analysis}{45}{chapter.8}
+\contentsline {section}{\numberline {8.1}Obtaining the IMDb movie review dataset}{45}{section.8.1}
+\contentsline {section}{\numberline {8.2}Introducing the bag-of-words model}{45}{section.8.2}
+\contentsline {subsection}{\numberline {8.2.1}Transforming words into feature vectors}{45}{subsection.8.2.1}
+\contentsline {subsection}{\numberline {8.2.2}Assessing word relevancy via term frequency-inverse document frequency}{45}{subsection.8.2.2}
+\contentsline {subsection}{\numberline {8.2.3}Cleaning text data}{46}{subsection.8.2.3}
+\contentsline {subsection}{\numberline {8.2.4}Processing documents into tokens}{46}{subsection.8.2.4}
+\contentsline {section}{\numberline {8.3}Training a logistic regression model for document classification}{46}{section.8.3}
+\contentsline {section}{\numberline {8.4}Working with bigger data - online algorithms and out-of-core learning}{46}{section.8.4}
+\contentsline {section}{\numberline {8.5}Summary}{46}{section.8.5}
+\contentsline {chapter}{\numberline {9}Embedding a Machine Learning Model into a Web Application}{47}{chapter.9}
+\contentsline {section}{\numberline {9.1}Chapter 8 recap - Training a model for movie review classification}{47}{section.9.1}
+\contentsline {section}{\numberline {9.2}Serializing fitted scikit-learn estimators}{47}{section.9.2}
+\contentsline {section}{\numberline {9.3}Setting up a SQLite database for data storage Developing a web application with Flask}{47}{section.9.3}
+\contentsline {section}{\numberline {9.4}Our first Flask web application}{47}{section.9.4}
+\contentsline {subsection}{\numberline {9.4.1}Form validation and rendering}{47}{subsection.9.4.1}
+\contentsline {subsection}{\numberline {9.4.2}Turning the movie classifier into a web application}{47}{subsection.9.4.2}
+\contentsline {section}{\numberline {9.5}Deploying the web application to a public server}{47}{section.9.5}
+\contentsline {subsection}{\numberline {9.5.1}Updating the movie review classifier}{47}{subsection.9.5.1}
+\contentsline {section}{\numberline {9.6}Summary}{47}{section.9.6}
+\contentsline {chapter}{\numberline {10}Predicting Continuous Target Variables with Regression Analysis}{48}{chapter.10}
+\contentsline {section}{\numberline {10.1}Introducing a simple linear regression model}{48}{section.10.1}
+\contentsline {section}{\numberline {10.2}Exploring the Housing Dataset}{48}{section.10.2}
+\contentsline {subsection}{\numberline {10.2.1}Visualizing the important characteristics of a dataset}{48}{subsection.10.2.1}
+\contentsline {section}{\numberline {10.3}Implementing an ordinary least squares linear regression model}{50}{section.10.3}
+\contentsline {subsection}{\numberline {10.3.1}Solving regression for regression parameters with gradient descent}{50}{subsection.10.3.1}
+\contentsline {subsection}{\numberline {10.3.2}Estimating the coefficient of a regression model via scikit-learn}{50}{subsection.10.3.2}
+\contentsline {section}{\numberline {10.4}Fitting a robust regression model using RANSAC}{50}{section.10.4}
+\contentsline {section}{\numberline {10.5}Evaluating the performance of linear regression models}{50}{section.10.5}
+\contentsline {section}{\numberline {10.6}Using regularized methods for regression}{51}{section.10.6}
+\contentsline {section}{\numberline {10.7}Turning a linear regression model into a curve - polynomial regression}{52}{section.10.7}
+\contentsline {subsection}{\numberline {10.7.1}Modeling nonlinear relationships in the Housing Dataset}{52}{subsection.10.7.1}
+\contentsline {subsection}{\numberline {10.7.2}Dealing with nonlinear relationships using random forests}{52}{subsection.10.7.2}
+\contentsline {subsubsection}{Decision tree regression}{52}{section*.5}
+\contentsline {subsubsection}{Random forest regression}{53}{section*.6}
+\contentsline {section}{\numberline {10.8}Summary}{53}{section.10.8}
+\contentsline {chapter}{\numberline {11}Working with Unlabeled Data -- Clustering Analysis}{54}{chapter.11}
+\contentsline {section}{\numberline {11.1}Grouping objects by similarity using k-means}{54}{section.11.1}
+\contentsline {subsection}{\numberline {11.1.1}K-means++}{55}{subsection.11.1.1}
+\contentsline {subsection}{\numberline {11.1.2}Hard versus soft clustering}{55}{subsection.11.1.2}
+\contentsline {subsection}{\numberline {11.1.3}Using the elbow method to find the optimal number of clusters}{57}{subsection.11.1.3}
+\contentsline {subsection}{\numberline {11.1.4}Quantifying the quality of clustering via silhouette plots}{57}{subsection.11.1.4}
+\contentsline {section}{\numberline {11.2}Organizing clusters as a hierarchical tree}{57}{section.11.2}
+\contentsline {subsection}{\numberline {11.2.1}Performing hierarchical clustering on a distance matrix}{57}{subsection.11.2.1}
+\contentsline {subsection}{\numberline {11.2.2}Attaching dendrograms to a heat map}{57}{subsection.11.2.2}
+\contentsline {subsection}{\numberline {11.2.3}Applying agglomerative clustering via scikit-learn}{57}{subsection.11.2.3}
+\contentsline {section}{\numberline {11.3}Locating regions of high density via DBSCAN}{57}{section.11.3}
+\contentsline {section}{\numberline {11.4}Summary}{58}{section.11.4}
+\contentsline {chapter}{\numberline {12}Training Artificial Neural Networks for Image Recognition}{59}{chapter.12}
+\contentsline {section}{\numberline {12.1}Modeling complex functions with artificial neural networks}{59}{section.12.1}
+\contentsline {subsection}{\numberline {12.1.1}Single-layer neural network recap}{59}{subsection.12.1.1}
+\contentsline {subsection}{\numberline {12.1.2}Introducing the multi-layer neural network architecture}{60}{subsection.12.1.2}
+\contentsline {subsection}{\numberline {12.1.3}Activating a neural network via forward propagation}{61}{subsection.12.1.3}
+\contentsline {section}{\numberline {12.2}Classifying handwritten digits}{62}{section.12.2}
+\contentsline {subsection}{\numberline {12.2.1}Obtaining the MNIST dataset}{62}{subsection.12.2.1}
+\contentsline {subsection}{\numberline {12.2.2}Implementing a multi-layer perceptron}{62}{subsection.12.2.2}
+\contentsline {section}{\numberline {12.3}Training an artificial neural network}{63}{section.12.3}
+\contentsline {subsection}{\numberline {12.3.1}Computing the logistic cost function}{63}{subsection.12.3.1}
+\contentsline {subsection}{\numberline {12.3.2}Training neural networks via backpropagation}{64}{subsection.12.3.2}
+\contentsline {section}{\numberline {12.4}Developing your intuition for backpropagation}{66}{section.12.4}
+\contentsline {section}{\numberline {12.5}Debugging neural networks with gradient checking}{66}{section.12.5}
+\contentsline {section}{\numberline {12.6}Convergence in neural networks}{68}{section.12.6}
+\contentsline {section}{\numberline {12.7}Other neural network architectures}{68}{section.12.7}
+\contentsline {subsection}{\numberline {12.7.1}Convolutional Neural Networks}{68}{subsection.12.7.1}
+\contentsline {subsection}{\numberline {12.7.2}Recurrent Neural Networks}{68}{subsection.12.7.2}
+\contentsline {section}{\numberline {12.8}A few last words about neural network implementation}{68}{section.12.8}
+\contentsline {section}{\numberline {12.9}Summary}{68}{section.12.9}
+\contentsline {chapter}{\numberline {13}Parallelizing Neural Network Training with Theano}{69}{chapter.13}
+\contentsline {section}{\numberline {13.1}Building, compiling, and running expressions with Theano}{69}{section.13.1}
+\contentsline {subsection}{\numberline {13.1.1}What is Theano?}{69}{subsection.13.1.1}
+\contentsline {subsection}{\numberline {13.1.2}First steps with Theano}{69}{subsection.13.1.2}
+\contentsline {subsection}{\numberline {13.1.3}Configuring Theano}{69}{subsection.13.1.3}
+\contentsline {subsection}{\numberline {13.1.4}Working with array structures}{69}{subsection.13.1.4}
+\contentsline {subsection}{\numberline {13.1.5}Wrapping things up -- a linear regression example}{69}{subsection.13.1.5}
+\contentsline {section}{\numberline {13.2}Choosing activation functions for feedforward neural networks}{69}{section.13.2}
+\contentsline {subsection}{\numberline {13.2.1}Logistic function recap}{69}{subsection.13.2.1}
+\contentsline {subsection}{\numberline {13.2.2}Estimating probabilities in multi-class classification via the softmax function}{70}{subsection.13.2.2}
+\contentsline {subsection}{\numberline {13.2.3}Broadening the output spectrum by using a hyperbolic tangent}{70}{subsection.13.2.3}
+\contentsline {section}{\numberline {13.3}Training neural networks efficiently using Keras}{70}{section.13.3}
+\contentsline {section}{\numberline {13.4}Summary}{70}{section.13.4}
diff --git a/docs/errata.md b/docs/errata.md
index f641df26..ff4ce1bb 100644
--- a/docs/errata.md
+++ b/docs/errata.md
@@ -1,32 +1,76 @@
The *Known Errors* Leaderboard
========================
+
+
+
I tried my best to cut all the little typos, errors, and formatting bugs that slipped through the copy editing stage. Even so, I think it is just human to have a little typo here and there in a first edition. I know that this can be annoying as a reader, and I was thinking to associate it with something positive. Let's have a little leaderboard (inspired by Donald Knuth's "[Known Errors in My Books](http://www-cs-faculty.stanford.edu/~uno/books.html)").
**Every error that is not listed in the *Errata* yet will be rewarded with $1.**
The only catch here is that you won't receive any single cent. Instead, I am going to donate the amount to [UNICEF USA](http://www.unicefusa.org), the US branch of the United Nations agency for raising funds to provide emergency food and healthcare for children in developing countries.
+
+
+
I would be happy if you just write me a short [mail](mailto:mail@sebastianraschka.com) including the error and page number, and please let me know if you want to be listed on the public leaderboard.
+
## Donations
-- Current amount for the next donation: $34.00
-- Amount donated to charity: $0.00
+- Current amount for the next donation: $7.00
+- Amount donated to charity:
+ - [$39.00 2016-04-07](./2016-04-07-unicef.pdf)
+ - [$76.00 2016-03-03](./2016-03-03-unicef.pdf)
+ - [$50.00 2016-11-10](./2016-11-10-commoncause.png)
+
## Leaderboard
-1. Ryan S. ($16.00)
-2. S.R. ($4.00)
-3. Joseph Gordon ($3.00)
-4. T.S. Jayram ($2.00)
-5. Andrei R. ($2.00)
-9. Ilmo S. ($2.00)
-5. Elias R. ($1.00)
-6. Haitham H. Saleh ($1.00)
-7. Muqueet M. ($1.00)
-10. Renato R. ($1.00)
-11. Michael L. ($1.00)
+1. Jeremy N. ($40.00)
+1. Ryan S. ($24.00)
+2. Claude C. ($11.00)
+2. Christopher Galpin ($8.00)
+18. David C. ($6.00)
+2. Edgar C. ($5.00)
+27. Jozef Genzor ($5.00)
+3. S.R. ($4.00)
+3. Will P. ($4.00)
+3. Ignacio A. ($4.00)
+4. Joseph Gordon ($3.00)
+6. Ilmo S. ($3.00)
+7. Hyun L. ($3.00)
+5. Ailin M. ($2.00)
+8. T.S. Jayram ($2.00)
+9. Andrei R. ($2.00)
+10. Panos N. ($2.00)
+19. F. Liu ($2.00)
+20. Stefan P. ($2.00)
+32. Adam S. ($2.00)
+33. Harry Hummel ($2.00)
+11. Elias R. ($1.00)
+12. Haitham H. Saleh ($1.00)
+13. Muqueet M. ($1.00)
+14. Renato R. ($1.00)
+15. Michael L. ($1.00)
+16. Olaf R. ($1.00)
+17. Neeraj K. ($1.00)
+18. Dan I. ($1.00)
+19. Evan Colvin ($1.00)
+21. Dominik S. ($1.00)
+22. Andrei R. ($1.00)
+23. Richard L. ($1.00)
+24. Justin H. ($1.00)
+25. Neeraj K. ($1.00)
+26. Attila B. ($1.00)
+27. Simon C. ($1.00)
+28. Andrew R. ($1.00)
+29. Haesun P. ($1.00)
+30. Raga M. ($1.00)
+31. Ryszard T. Kaleta ($1.00)
+32. Baiyu Z. ($1.00)
+
+
...
@@ -35,6 +79,46 @@ I would be happy if you just write me a short [mail](mailto:mail@sebastianraschk
**I am really sorry about you seeing many typos up in the equations so far. Unfortunately, the layout team needed to retype the equations for compatibility reasons. There were a lot of typos introduced during this process, and I tried my very best to eliminate all of these by comparing the pre-finals against my draft. Cross-comparing 450 pages was a tedious task, and it appears that several typos slipped through, so if you see something that does not make quite sense to you, please let me know.**
+
+**Note**
+
+Due to the large file sizes of the PDFs, I moved the errata documents to an external source to keep this GitHub repository reasonably slim. For similar reasons, I decided to split the errata into 2 parts, a "technical" errata and a "language" errata.
+
+### Current Errata:
+
+
+**How can I tell if I have the initial or updated version of the book?**
+
+This time, the easiest way may be to go to page 6 in chapter 1. Finally, the labeling in the *reinforcement learning* figure should be correct!
+
+
+
+
+#### [Technical Errata PDF v.3](http://sebastianraschka.com/pdf/books/pymle/errata_3rd_technical.pdf)
+
+#### [Language Errata PDF v.3](http://sebastianraschka.com/pdf/books/pymle/errata_3rd_language.pdf)
+
+
+
+
+
+
+
+
+
+
+
+### Old Errata: Oct 25, 2015 - Apr 22, 2016
+
+
+
+
+#### [Technical Errata PDF v.2](http://sebastianraschka.com/pdf/books/pymle/errata_2nd_technical.pdf)
+
+#### [Language Errata PDF v.2](http://sebastianraschka.com/pdf/books/pymle/errata_2nd_language.pdf)
+
+(Left-click to view it in the browser; right-click for select a direct download option from the context menu.)
+
**E-book Update (2015-10-20):**
Good news! I just heard back from the publisher; all the typos and errors which are listed below will be fixed by next week. If you bought the book via Packt, you'd just need to re-download the book. I hope that the updates will also be reflected in the Amazon Kindle version. If not, please contact me, and I'll be happy to make an arrangement with the publisher so that you'll get the updated e-book.
@@ -52,16 +136,20 @@ The easiest way may be to go to page viii, the second page of the **Preface**. I
-
+---
-### Previous Errata: Sep 23 - Oct 25, 2015
+### Old Errata: Sep 23 - Oct 25, 2015
I converted the errata into a PDF document where I highlighted the errors and inserted the corrections as they appear in the book as many readers suggested. Please find the PDF version of the errata below.
Thanks so much for your feedback so far, I truly appreciate it and will do my best to get this fixed as soon as possible!
-####[View PDF on GitHub](./errata_pdf/errata_2015-10-23.pdf)
+**Note**
+
+Due to the large file sizes of the PDFs, I moved the errata documents to an external source to keep this GitHub repository reasonably slim.
+
+#### [Technical Errata PDF v.1](http://sebastianraschka.com/pdf/books/pymle/errata_1st_technical.pdf)
-#### [External View Link](http://sebastianraschka.com/pdf/books/pymle/errata_2015-10-23.pdf)
+#### [Language Errata PDF v.1](http://sebastianraschka.com/pdf/books/pymle/errata_1st_language.pdf)
-####[Download PDF](https://github.com/rasbt/python-machine-learning-book/raw/master/docs/errata_pdf/errata_2015-10-23.pdf)
+(Left-click to view it in the browser; right-click for select a direct download option from the context menu.)
diff --git a/docs/errata_pdf/errata_2015-10-23.pdf b/docs/errata_pdf/errata_2015-10-23.pdf
deleted file mode 100644
index d9638db2..00000000
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diff --git a/docs/feedback.md b/docs/feedback.md
index ceabd89b..f6b2fb42 100644
--- a/docs/feedback.md
+++ b/docs/feedback.md
@@ -22,6 +22,14 @@ I must honestly say that I was truly relieved and very happy about all the feedb
+> [...] The text contains lots of practical advice as well as inline links to other sources of information. While this book is a good end-to-end read, it would also serve as a useful reference text. Recommended.
+Score: 10 of 10
+-- British Computer Society, review by Patrick Hill
+
+Source: [http://www.bcs.org/content/conWebDoc/55586](http://www.bcs.org/content/conWebDoc/55586)
+
+
+
> Technical, but not too much. Let's face it, machine learning algorithms are technical in nature. However, this book allows you to gloss over the actual technical details if you don't really need to understand them right away and view the implementation of the logic in the code snippets. Though, I must say, the presentation of the technical subjects are explained clearly and with supporting graphs and images to help visualize the concepts. It was a wonderful experience to understand the code, even though the theory was also given. This allows most people to jump right in and start writing in python. For the mathematicians out there, you can take the equations and verify them if need be. [...]
The fundamental concepts I've learned have opened the door to an enormous amount of possibilities I could not have even thought of doing had I not read this book. I used to think that true machine learning was only for super geniuses. But now I feel like I have another set of tools I can use to perform nearly superhero tasks. Python Machine Learning will be a reference book I use for many years to come.
-- Perry Nally on [Amazon](http://www.amazon.com/gp/product/1783555130/ref=s9_simh_gw_p14_d0_i2?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=desktop-1&pf_rd_r=0QKDZ9QNCW8269FMDSQG&pf_rd_t=36701&pf_rd_p=2079475242&pf_rd_i=desktop)
@@ -67,6 +75,10 @@ your book is the best for me to use as a textbook.
+[](https://twitter.com/syc22/status/661963391100133377)
+
+
+
> I am a big fan of Sebastian Raschka's machine learning tutorials on his personal website, and have found him to be an excellent teacher for complex concepts in ML, so I've been looking forward to reading this book. I found the book similarly well-written and the explanations clear. It is heavy on examples (in Python 3! well done), so it's a good hands-on resource.
-- Carlos Faham, Security Data Scientist at LinkedIn
@@ -77,13 +89,48 @@ your book is the best for me to use as a textbook.
> An amazing book, really can't praise it enough. Presentation of technical subjects are explained very clearly. The author is very thorough in his writing making sure to fill in the details so you don't get left behind. It was a wonderful experience. Clearly written examples showing the theory and practice of machine learning. An invaluable tutorial.
As a bonus, chapter on "Parallelizing Neural Network Training with Theano" was a great addition! A very exciting topic, author really went all out. Hands down the best Python Machine learning book available.
-
-NazHuz via [Amazon](http://www.amazon.com/gp/customer-reviews/RURYHN1G3SRMZ/ref=cm_cr_pr_rvw_ttl?ie=UTF8&ASIN=1783555130)
+-- NazHuz via [Amazon](http://www.amazon.com/gp/customer-reviews/RURYHN1G3SRMZ/ref=cm_cr_pr_rvw_ttl?ie=UTF8&ASIN=1783555130)
-I am reading your recent book on ML and I think this is on the best book I ever read on machine learning. Especially with my background as software developer.
+> I am reading your recent book on ML and I think this is on the best book I ever read on machine learning. Especially with my background as software developer.
-- Michaël Lambé
[](https://twitter.com/arm_gilles/status/658927560932401154)
+
+
+
+
+> This is the first Python Machine Learning book that actually made sense. Sebastian managed to combine theory (math behind the models, how to implement an algorithm from scratch) with practice (how to actually implement it using scikit-learn) in a way no book has done thus far. I was really impressed (and surprised) to see how 'easy' it is to implement the more simple algorithms from scratch [...]
+-- C. Herther via [Amazon](http://www.amazon.com/gp/customer-reviews/R2I0D8HNIQODVA/ref=cm_cr_pr_rvw_ttl?ie=UTF8&ASIN=B00YSILNL0)
+
+
+
+[](https://twitter.com/mattmayo13/status/686614780589797376)
+
+> This is a great book.
+It gives the mathematical definitions of popular machine learning algorithms and shows you how to implement them. Then it explains how to use them with scikit-learn which has much more efficient implementations.
+What is great is that this book has chapters on data cleaning, what to do with missing data, etc. Compliments greatly the Andrew Ng's ML course which lacked lectures about all of these things.
+-- Peteris via [Goodreads](https://www.goodreads.com/review/show/1475215520?book_show_action=true&from_review_page=1)
+
+
+
+
+[](https://twitter.com/thenome/status/668805677951922176)
+
+
+
+> A great book, it will teach you exactly what it promises - how to use the most common ML algorithms using Python and libraries like sklearn/numpy/pandas.
+I found it to have a great balance between the theoretical math and implementation in Python; the split is somewhere around 20/80 in favour of implementation and actually using the algorithms on real data-sets. If you are trying to get a good understanding of the theory, then this book is a good starting point but you will most definitely need to supplement it with something else. I would recommend it even if you have no previous experience with Machine Learning. [...]
+-- Rafal Szymanski via [Goodreads](https://www.goodreads.com/review/show/1514154895?book_show_action=true&from_review_page=1)
+
+
+
+> Love it! Clearly written with nice examples. I have a certificate in data science, and was looking for something to fill in the gaps, consolidate and to learn new topics. This book does exactly that. I would recommend to people who are totally new to machine learning, and also those with some background.
+-- Reader via [Amazon](http://www.amazon.com/gp/customer-reviews/R2P8OGDU7XIIL5/ref=cm_cr_pr_rvw_ttl?ie=UTF8&ASIN=B00YSILNL0)
+
+
+
+> Absolutely the best machine machine learning book for Python out there. Very clear examples and very well written. The author gives a clear explanation of machine learning algorithms and techniques, as well as going into some of the math and provides code in the form of Juypter notebooks on github. Highly recommended!
+-- David M. Comfort via [Amazon](http://www.amazon.com/gp/customer-reviews/R1KU3VMAU3PY05/ref=cm_cr_pr_rvw_ttl?ie=UTF8&ASIN=1783555130)
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diff --git a/docs/references.md b/docs/references.md
index 3ddbd28e..92985d15 100644
--- a/docs/references.md
+++ b/docs/references.md
@@ -26,6 +26,8 @@ A BibTeX version for your favorite reference manager is available [here](./pymle
##### Additional Resources & Further Reading
+- [Python Tutorial](https://www.scaler.com/topics/python)
+
diff --git a/docs/running_jupyter_nb.pdf b/docs/running_jupyter_nb.pdf
new file mode 100644
index 00000000..ff128a63
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diff --git a/faq/README.md b/faq/README.md
index 7315235a..68fd5c5f 100644
--- a/faq/README.md
+++ b/faq/README.md
@@ -17,11 +17,158 @@ Sebastian
## FAQ
+
+
+### General Questions about Machine Learning and 'Data Science'
+
+- [What are machine learning and data science?](./datascience-ml.md)
+- [Why do you and other people sometimes implement machine learning algorithms from scratch?](./implementing-from-scratch.md)
+- [What learning path/discipline in data science I should focus on?](./data-science-career.md)
+- [At what point should one start contributing to open source?](./open-source.md)
+- [How important do you think having a mentor is to the learning process?](./mentor.md)
+- [Where are the best online communities centered around data science/machine learning or python?](./ml-python-communities.md)
+- [How would you explain machine learning to a software engineer?](./ml-to-a-programmer.md)
+- [How would your curriculum for a machine learning beginner look like?](./ml-curriculum.md)
+- [What is the Definition of Data Science?](./definition_data-science.md)
+- [How do Data Scientists perform model selection? Is it different from Kaggle?](./model-selection-in-datascience.md)
+
+### Questions about the Machine Learning Field
+
+- [How are Artificial Intelligence and Machine Learning related?](./ai-and-ml.md)
+- [What are some real-world examples of applications of machine learning in the field?](./ml-examples.md)
+- [What are the different fields of study in data mining?](./datamining-overview.md)
+- [What are differences in research nature between the two fields: machine learning & data mining?](./datamining-vs-ml.md)
+- [How do I know if the problem is solvable through machine learning?](./ml-solvable.md)
+- [What are the origins of machine learning?](./ml-origins.md)
+- [How was classification, as a learning machine, developed?](./classifier-history.md)
+- [Which machine learning algorithms can be considered as among the best?](./best-ml-algo.md)
+- [What are the broad categories of classifiers?](./classifier-categories.md)
+- [What is the difference between a classifier and a model?](./difference_classifier_model.md)
+- [What is the difference between a parametric learning algorithm and a nonparametric learning algorithm?](./parametric_vs_nonparametric.md)
+- [What is the difference between a cost function and a loss function in machine learning?](./cost-vs-loss.md)
+
+### Questions about Machine Learning Concepts and Statistics
+
+##### Cost Functions and Optimization
+
+- [Fitting a model via closed-form equations vs. Gradient Descent vs Stochastic Gradient Descent vs Mini-Batch Learning -- what is the difference?](./closed-form-vs-gd.md)
+- [How do you derive the Gradient Descent rule for Linear Regression and Adaline?](./linear-gradient-derivative.md)
+
+##### Regression Analysis
+
+- [What is the difference between Pearson R and Simple Linear Regression?](./pearson-r-vs-linear-regr.md)
+
+##### Tree models
+
+- [How does the random forest model work? How is it different from bagging and boosting in ensemble models?](./bagging-boosting-rf.md)
+- [What are the disadvantages of using classic decision tree algorithm for a large dataset?](./decision-tree-disadvantages.md)
+- [Why are implementations of decision tree algorithms usually binary, and what are the advantages of the different impurity metrics?](./decision-tree-binary.md)
+- [Why are we growing decision trees via entropy instead of the classification error?](./decisiontree-error-vs-entropy.md)
+- [When can a random forest perform terribly?](./random-forest-perform-terribly.md)
+
+##### Model evaluation
+
+- [What is overfitting?](./overfitting.md)
+- [How can I avoid overfitting?](./avoid-overfitting.md)
+- [Is it always better to have the largest possible number of folds when performing cross validation?](./number-of-kfolds.md)
+- [When training an SVM classifier, is it better to have a large or small number of support vectors?](./num-support-vectors.md)
+- [How do I evaluate a model?](./evaluate-a-model.md)
+- [What is the best validation metric for multi-class classification?](./multiclass-metric.md)
+- [What factors should I consider when choosing a predictive model technique?](./choosing-technique.md)
+- [What are the best toy datasets to help visualize and understand classifier behavior?](./clf-behavior-data.md)
+- [How do I select SVM kernels?](./select_svm_kernels.md)
+- [Interlude: Comparing and Computing Performance Metrics in Cross-Validation -- Imbalanced Class Problems and 3 Different Ways to Compute the F1 Score](./computing-the-f1-score.md)
+
+
+##### Logistic Regression
+
+- [What is Softmax regression and how is it related to Logistic regression?](./softmax_regression.md)
+- [Why is logistic regression considered a linear model?](./logistic_regression_linear.md)
+- [What is the probabilistic interpretation of regularized logistic regression?](./probablistic-logistic-regression.md)
+- [Does regularization in logistic regression always results in better fit and better generalization?](./regularized-logistic-regression-performance.md)
+- [What is the major difference between naive Bayes and logistic regression?](./naive-bayes-vs-logistic-regression.md)
+- [What exactly is the "softmax and the multinomial logistic loss" in the context of machine learning?](./softmax.md)
+- [What is the relation between Loigistic Regression and Neural Networks and when to use which?](./logisticregr-neuralnet.md)
+- [Logistic Regression: Why sigmoid function?](./logistic-why-sigmoid.md)
+- [Is there an analytical solution to Logistic Regression similar to the Normal Equation for Linear Regression?](./logistic-analytical.md)
+
+##### Neural Networks and Deep Learning
+
+- [What is the difference between deep learning and usual machine learning?](./difference-deep-and-normal-learning.md)
+- [Can you give a visual explanation for the back propagation algorithm for neural networks?](./visual-backpropagation.md)
+- [Why did it take so long for deep networks to be invented?](./inventing-deeplearning.md)
+- [What are some good books/papers for learning deep learning?](./deep-learning-resources.md)
+- [Why are there so many deep learning libraries?](./many-deeplearning-libs.md)
+- [Why do some people hate neural networks/deep learning?](./deeplearning-criticism.md)
+- [How can I know if Deep Learning works better for a specific problem than SVM or random forest?](./deeplearn-vs-svm-randomforest.md)
+- [What is wrong when my neural network's error increases?](./neuralnet-error.md)
+- [How do I debug an artificial neural network algorithm?](./nnet-debugging-checklist.md)
+- [What is the difference between a Perceptron, Adaline, and neural network model?](./diff-perceptron-adaline-neuralnet.md)
+- [What is the basic idea behind the dropout technique?](./dropout.md)
+
+##### Other Algorithms for Supervised Learning
+
+- [Why is Nearest Neighbor a Lazy Algorithm?](./lazy-knn.md)
+
+##### Unsupervised Learning
+
+- [What are some of the issues with clustering?](./issues-with-clustering.md)
+
+##### Semi-Supervised Learning
+
+- [What are the advantages of semi-supervised learning over supervised and unsupervised learning?](./semi-vs-supervised.md)
+
+##### Ensemble Methods
+
+- [Is Combining Classifiers with Stacking Better than Selecting the Best One?](./logistic-boosting.md)
+
+##### Preprocessing, Feature Selection and Extraction
+
+- [Why do we need to re-use training parameters to transform test data?](./scale-training-test.md)
+- [What are the different dimensionality reduction methods in machine learning?](./dimensionality-reduction.md)
+- [What is the difference between LDA and PCA for dimensionality reduction?](./lda-vs-pca.md)
+- [When should I apply data normalization/standardization?](./when-to-standardize.md)
+- [Does mean centering or feature scaling affect a Principal Component Analysis?](./pca-scaling.md)
+- [How do you attack a machine learning problem with a large number of features?](./large-num-features.md)
+- [What are some common approaches for dealing with missing data?](./missing-data.md)
+- [What is the difference between filter, wrapper, and embedded methods for feature selection?](./feature_sele_categories.md)
+- [Should data preparation/pre-processing step be considered one part of feature engineering? Why or why not?](./dataprep-vs-dataengin.md)
+- [Is a bag of words feature representation for text classification considered as a sparse matrix?](./bag-of-words-sparsity.md)
+- [How can I apply an SVM to categorical data?](./svm_for_categorical_data.md)
+
+##### Naive Bayes
+
+- [Why is the Naive Bayes Classifier naive?](./naive-naive-bayes.md)
+- [What is the decision boundary for Naive Bayes?](./naive-bayes-boundary.md)
+- [Is it possible to mix different variable types in Naive Bayes, for example, binary and continues features?](./naive-bayes-vartypes.md)
+
+##### Other
+
+- [What is Euclidean distance in terms of machine learning?](./euclidean-distance.md)
+- [When should one use median, as opposed to the mean or average?](./median-vs-mean.md)
+
+##### Programming Languages and Libraries for Data Science and Machine Learning
+
+- [Is R used extensively today in data science?](./r-in-datascience.md)
+- [What is the main difference between TensorFlow and scikit-learn?](./tensorflow-vs-scikitlearn.md)
+
+
+
+
+
+
+
+
+
+
+### Questions about the Book
+
- [Can I use paragraphs and images from the book in presentations or my blog?](./copyright.md)
- [How is this different from other machine learning books?](./different.md)
- [Which version of Python was used in the code examples?](./py2py3.md)
- [Which technologies and libraries are being used?](./technologies.md)
- [Which book version/format would you recommend?](./version.md)
-- [Why did you choose Python for machine learning?](./why_python.md)
-- [Why do you use so many leading and trailing underscores in the code examples?](./underscore_convention.md)
-- [Are There Any Prerequisites and Recommended Pre-Readings?](./prerequisites.md)
+- [Why did you choose Python for machine learning?](./why-python.md)
+- [Why do you use so many leading and trailing underscores in the code examples?](./underscore-convention.md)
+- [What is the purpose of the `return self` idioms in your code examples?](./return_self_idiom.md)
+- [Are there any prerequisites and recommended pre-readings?](./prerequisites.md)
diff --git a/faq/ai-and-ml.md b/faq/ai-and-ml.md
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+++ b/faq/ai-and-ml.md
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+# How are Artificial Intelligence and Machine Learning related?
+
+Artifical Intellicence (AI) started as a subfield of computer science with the focus on solving tasks that humans can but computers can't do (for instance, image recognition). AI can be approached in many ways, for example, writing a computer program that implements a set of rules devised by domain experts. Now, hand-crafting rules can be very laborious and time consuming.
+
+The field of machine learning -- originally, we can consider it as a subfield of AI -- was concerned with the development of algorithms so that computers can automatically learn (predictive) models from data.
+
+For instance, say we want to develop a program that can recognize handwritten digits from images. One would be to look at all of these images and come-up with a set of (nested) if-this-than-that rules to say which image is displayed in a particular image (for instance, by looking at the relative locations of pixels). Another approach would be to use a machine learning algorithm, which can fit a predictive model based on a thousands of labeled image samples that we may have collected in a database. Now, there's also deep learning, which in turn is a subfield of machine learning, referring to a particular subset of models that are particularly good at certain tasks such as image recognition and natural language processing.
+
+Or in short, machine learning (and deep learning) definitely helps to develop "AI," however, AI doesn't necessarily have to be developed using machine learning -- although, machine learning makes "AI" much more convenient ;).
+
+tldr; to summarize my point of view visually:
+
+
diff --git a/faq/ai-and-ml/ai-and-ml-1.png b/faq/ai-and-ml/ai-and-ml-1.png
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diff --git a/faq/avoid-overfitting.md b/faq/avoid-overfitting.md
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+# How can I avoid overfitting?
+
+In short, the general strategies are to
+
+1. collect more data
+2. use ensembling methods that "average" models
+3. choose simpler models / penalize complexity
+
+For the first point, it may help to plot learning curves, plotting the training vs. the validation or cross-validation performance. If you see a trend that more data helps with closing the cap between the two, and if you could afford collecting more data, then this would probably the best choice.
+
+In my experience, ensembling is probably the most convenient way to build robust predictive models on somewhat small-sized datasets. As in real life, consulting a bunch of "experts" is usually not a bad idea before making a decision ;).
+
+Regarding the third point, I usually start a predictive modeling task with the simplest model as a benchmark: usually logistic regression. Overfitting can be a real problem if our model has too much capacity — too many model parameters to fit, and too many hyperparameters to tune. If the dataset is small, a simple model is always a good option to prevent overfitting, and it is also a good benchmark for comparison to more "complex" alternatives.
diff --git a/faq/bag-of-words-sparsity.md b/faq/bag-of-words-sparsity.md
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+++ b/faq/bag-of-words-sparsity.md
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+# Is a bag of words feature representation for text classification considered as a sparse matrix?
+
+It depends on your vocabulary and dataset, but typically: Yes, definitely!
+
+
+By definition, a sparse matrix is called "sparse" if most of its elements are zero. In the bag of words model, each document is represented as a word-count vector. These counts can be binary counts (does a word occur or not) or absolute counts (term frequencies, or normalized counts), and the size of this vector is equal to the number of elements in your vocabulary. Thus, if most of your feature vectors are sparse, our bag-of-words feature matrix is most likely sparse as well!
+
+
+Now, the question is: "When are these feature vectors sparse?" It reallly depends on the size of our vocabulary, and the length and variety of the documents in our training corpus. For instance, the shorter and more similar the documents in our training set are, the more likely it is that we end up with a dense matrix, ---- it's still very unlikely in practice, though!
+
+Here's a trivial example ... Let's suppose we have 3 documents:
+
+
+- Doc1: Hello, World, the sun is shining
+- Doc2: Hello world, the weather is nice
+- Doc3: Hello world, the wind is cold
+
+
+Then, our vocabulary would look like this (using 1-grams without stop word removal):
+
+
+
+Vocabulary: [hello, world, the, wind, weather, sun, is, shining, nice, cold]
+
+
+The corresponding, binary feature vectors are:
+
+
+- Doc1: [1, 1, 1, 0, 0, 0, 1, 1, 0, 0]
+- Doc2: [1, 1, 1, 0, 0, 1, 0, 1, 1, 0]
+- Doc3: [1, 1, 1, 1, 0, 0, 1, 0, 0, 1]
+
+
+Which we use to construct the dense matrix:
+
+[[1, 1, 1, 0, 0, 0, 1, 1, 0, 0]
+[1, 1, 1, 0, 0, 1, 0, 1, 1, 0]
+[1, 1, 1, 1, 0, 0, 1, 0, 0, 1] ]
+
+
+As we can see, we have 17 x 1 and 13 x 0; so, by definition, this wouldn't be a sparse matrix. However, we can also guess how unlikely this scenario is in a real-world application ;)
diff --git a/faq/bagging-boosting-rf.md b/faq/bagging-boosting-rf.md
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+# How does the random forest model work? How is it different from bagging and boosting in ensemble models?
+
+Let's assume we use a decision tree algorithm as base classifier for all three: boosting, bagging, and (obviously :)) the random forest.
+
+
+Why and when do we want to use any of these? Given a fixed-size number of training samples, our model will increasingly suffer from the "curse of dimensionality" if we increase the number of features. The challenge of individual, unpruned decision trees is that the hypothesis often ends up being too complex for the underlying training data -- decision trees are prone to overfitting.
+
+
+**tl;dr: Bagging and random forests are "bagging" algorithms that aim to reduce the complexity of models that overfit the training data. In contrast, boosting is an approach to increase the complexity of models that suffer from high bias, that is, models that underfit the training data.**
+
+
+## Bagging
+
+
+Now, let's take a look at the probably "simplest" case, bagging. Here, we train a number (ensemble) of decision trees from bootstrap samples of our training set. Bootstrap sampling means drawing random samples from our training set with replacement. E.g., if our training set consists of 7 training samples, our bootstrap samples (here: n=7) can look as follows, where C1, C2, ... Cm shall symbolize the decision tree classifiers:
+
+
+
+
+
+After we trained our (m) decision trees, we can use them to classify new data via majority rule. For instance, we'd let each decision tree make a decision and predict the class label that received more votes. Typically, this would result in a less complex decision boundary, and the bagging classifier would have a lower variance (less overfitting) than an individual decision tree. Below is a plot comparing a single decision tree (left) to a bagging classifier (right) for 2 variables from the Wine dataset (Alcohol and Hue).
+
+
+
+
+
+## Boosting
+
+
+In contrast to bagging, we use very simple classifiers as base classifiers, so-called "weak learners." Picture these weak learners as "decision tree stumps" -- decision trees with only 1 splitting rule. Below, we will refer to the probably most popular example of boosting, AdaBoost. Here, we start with one decision tree stump (1) and "focus" on the samples it got wrong. In the next round, we train another decision tree stump that attempts to get these samples right (2); we achieve this by putting a larger weight on these training samples. Again, this 2nd classifier will likely get some other samples wrong, so we'd re-adjust the weights ...
+
+
+
+
+
+In a nutshell, we can summarize "Adaboost" as "adaptive" or "incremental"
+learning from mistakes. Eventually, we will come up with a model that has a lower bias than an individual decision tree (thus, it is less likely to underfit the training data).
+
+
+
+
+
+## Random forests
+
+
+The random forest algorithm is actually a bagging algorithm: also here, we draw random bootstrap samples from our training set. However, in addition to the bootstrap samples, we also draw random subsets of features for training the individual trees; in bagging, we provide each tree with the full set of features. Due to the random feature selection, the trees are more independent of each other compared to regular bagging, which often results in better predictive performance (due to better variance-bias trade-offs), and I'd say that it's also faster than bagging, because each tree learns only from a subset of features.
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diff --git a/faq/best-ml-algo.md b/faq/best-ml-algo.md
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+++ b/faq/best-ml-algo.md
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+# Which machine learning algorithms can be considered as among the best?
+
+I recommend taking a look at
+
+Wolpert, D.H., Macready, W.G. (1997), "[No Free Lunch Theorems for Optimization](http://ti.arc.nasa.gov/m/profile/dhw/papers/78.pdf)", IEEE Transactions on Evolutionary Computation 1, 67.
+
+Unfortunately, there's no real answer to this question: different datasets, questions, and assumptions require different algorithms -- or in other words: we haven't found the Master Algorithm, yet.
+
+But let me write down thoughts about different classifiers at least:
+
+- both logistic regression and SVMs work great for linear problems, logistic regression may be preferable for very noisy data
+- naive Bayes may work better than logistic regression for small training set sizes; the former is also pretty fast, e.g., if you have a large multi-class problem, you'd only have to train one classifier whereas you'd have to use One-vs-Rest or One-vs-One with in SVMs or logistic regression (alternatively, you could implement multinomial/softmax regression though); another point is that you don't have to worry so much about hyperparameter optimization -- if you are estimating the class priors from the training set, there are actually no hyperparameters
+- kernel SVM/logistic regression is preferable for nonlinear data vs. the linear models
+- k-nearest neighbor can also work quite well in practice for datasets with large number of samples and relatively low dimensionality
+- Random Forests & Extremely Randomized trees are very robust and work well across a whole range of problems -- linear and/or nonlinear problems
+
+Personally, I tend to prefer a multi-layer neural network in most cases, given that my dataset is sufficiently large. In my experience, the generalization performance was almost always superior to one of the other approaches I listed above. But again, it really depends on the given dataset.
diff --git a/faq/choosing-technique.md b/faq/choosing-technique.md
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+++ b/faq/choosing-technique.md
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+
+
+# What factors should I consider when choosing a predictive model technique?
+
+This is a very broad question, and the answer would basically fill an entire book. In a nutshell, I would come up with the
+
+### 1. How does your target variable look like?
+
+- continuous target variable? -> regression
+- categorical (nominal) target variable? -> classification
+- ordinal target variable? -> ranked classification
+- no target variable and want to find structure in data? -> cluster analysis, projection
+
+### 2. Is computational performance an issue?
+
+- use "cheaper" models/algorithms
+- dimensionality reduction
+- feature selection
+- lazy learner (e.g,. k-nearest neighbors)
+
+### 3. Does my dataset fit into memory? If no:
+
+- out of core learning
+- distributed systems
+
+### 4. Is my data linearly separable?
+
+- hard to know the answer upfront
+- always a good idea to compare different models
+
+### 5. Finding a good bias variance threshold. Does my model overfit?
+
+- increase regularization strength if supported by the model
+- dimensionality reduction or feature selection otherwise
+- collect more training data if possible (check via learning curves first)
+
+### 6. Are you planning to update your model with new data on the fly?
+
+- one option are lazy learners (e.g., K-nearest neighbors); needs to keep training data around; no learning necessary but more expensive predictions
+- it's generally relatively cheap to update generative models
+- another option is stochastic gradient descent for online learning
+
+...
+
+The list goes on and on :). I think Andreas Mueller's scikit-learn algorithm "cheat-sheet" is an excellent resource. (Click on the image to view the original, interactive version on scikit-learn)
+
+[](http://scikit-learn.org/dev/tutorial/machine_learning_map/index.html)
+
+[Source: http://scikit-learn.org/dev/tutorial/machine_learning_map/index.html]
diff --git a/faq/choosing-technique/scikit-cheatsheet.png b/faq/choosing-technique/scikit-cheatsheet.png
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diff --git a/faq/classifier-categories.md b/faq/classifier-categories.md
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+++ b/faq/classifier-categories.md
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+# What are the broad categories of classifiers?
+
+
+### A (broad) categorization could be "discriminative" vs. "generative" classifiers:
+
+
+Discriminative algorithms:
+- a direct mapping of x -> y
+- intuition: "Distinguishing between people who are speaking different languages without actually learning the language"
+- e.g., Logistic regression, SVMs, Neural networks, ...
+
+Generative algorithms:
+- model how the data was generated (joint probability distributions p(x, y))
+- e.g., naive Bayes, Bayesian belief networks, Restricted Boltzmann machines
+
+
+### Or, we could categorize classifiers as "lazy" vs. "eager" learners:
+
+Lazy learners:
+- don't "learn" a decision rule (or function)
+- no learning step involved but require to keep training data around
+- e.g., K-nearest neighbor classifiers
+
+### A third possibility could be "parametric" vs. "non-parametric"
+
+(in context of machine learning; the field of statistics interprets use terms a little bit differently.)
+
+non-parametric:
+- representations grow with the training data size
+- e.g., Decision trees, K-nearest neighbors
+
+parametric:
+- representations are "fixed"
+- e.g., most linear classifiers like logistic regression etc.
+
+### Pedro Domingo's 5 Tribes of Machine Learning
+
+In his new book ([The Master Algorithm](http://www.amazon.com/Master-Algorithm-Ultimate-Learning-Machine/dp/0465065708/ref=sr_1_1?ie=UTF8&qid=1447045562&sr=8-1&keywords=pedro+domingos)), Pedro Domingo's mentioned the 5 tribes of machine learning, which is another nice categorization. Summarizing from the book (pp. 51-53)
+
+**Symbolists**
+- manipulating symbols (like mathematicians replace expressions by expressions), or in other words, using pre-existing knowledge to fill in the missing pieces
+- "master algorithm:" inverse deduction
+
+
+**Connectionists**
+- reverse-engineering a biological brain, i.e., strengthening the connections between neurons
+- "master algorithm:" backpropagation
+
+
+**Evolutionaries**
+- whereas connectionism is about fine-tuning the brain, evolution is about creating the brain
+- "master algorithm:" genetic programming
+
+**Bayesians**
+- based on probabilistic inference, i.e., incorporating a priori knowledge: certain outcomes are more likely
+- "master algorithm:" Bayes' theorem and its derivatives
+
+**Analogizers**
+- generalizing from similarity, i.e., recognizing similarities or in other words: remember experiences (training data) and how to combine them to make new predictions
+- "master algorithm:" support vector machine
+
+
\ No newline at end of file
diff --git a/faq/classifier-history.md b/faq/classifier-history.md
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+++ b/faq/classifier-history.md
@@ -0,0 +1,46 @@
+# How was classification, as a learning machine, developed?
+
+
+There are two fundamental milestones I'd say.
+The first one is Fisher's Linear Discriminant [1], later generalized by Rao [2] to what we know as Linear Discriminant Analysis (LDA). Essentially, LDA is a linear transformation (or projection) technique, which is mainly used for dimensionality reduction (i.e., the objective is to find the k-dimensional feature subspace that -- linearly -- separates the samples from different classes best.
+Given the objective to maximize class separability, projecting the 2D dataset below onto "x-axis component," would be a better choice than the "y-axis component."
+
+
+
+
+
+Keep in mind though that LDA is a projection technique; the feature axes of your new feature subspace are (almost certainly) different from your original axes.
+In other words, LDA aims to find a new feature subspace that retains most of the class-discriminatory information. Where do I want to go with this? Intuitively, we can come up with a criterion function to minimize the ratio of the distance between the (class) sample means, and the within class scatter. And maximizing our criterion function plus plugging in a threshold function yields us a linear classifier.
+Anyway, subjectively, I would group LDA with its closed-form solution into the more classical statistics field (or probabilistic learning if you will due to its relation to ANOVA and Bayes' Theorem).
+
+
+Another timely take on classification would be the perceptron algorithm, which is in turn based on the concept of the McCulloch-Pitt (MCP) Neuron -- an early (maybe first?) model of how a neuron in a mammal's brain could work [3]. In contrast to the LDA classifier, Rosenblatt's perceptron [4] is an incremental learner. For each training sample, it compares the predicted class labes to the actual class label and modify the model weights accordingly.
+Rosenblatt's initial perceptron rule is fairly simple and can be summarized by the following steps:
+
+1. Initialize the weights to 0 or small random numbers.
+2. For each training sample x(i):
+- Calculate the output value.
+- Update the weights.
+The value for updating the weights at each increment is calculated by the learning rule
+
+
+
+
+
+There were few problems with this approach, i.e., the algorithm will never converge if the data is not perfectly separable by a line or hyperplane.
+An improvement over the perceptron was the adaptive linear neuron (Adaline) [5]. It is closely related to linear regression (if you use an optimization algorithm like gradient descent rather than the closed-form solution); we update the weights by comparing the real-value output to the actual class label. (Remember, the perceptron algorithms compares the binary-values class labels, i.e., the predicted and actual class labels.) After we trained the model, we use a threshold function to turn the real-valued output into a class label:
+
+
+
+A very similar concept is Logistic Regression, however, instead of a linear function, we minimize a logistic function [6]. Again, it wouldn't say the early beginning of logistic regression would be necessarily the "machine learning" approach until incremental learning (gradient descent, stochastic gradient descent, and other optimization algorithms were being used to learn the model weights). In any case, scientists came up with many other cost functions until then, I'd say they are likely inspired by the perceptron, Adaline, and logistic regression.
+
+
+For example, the hinge loss for SVM. Another direction of the analogizer's SVM approach would be the "connectionism," i.e., combining neuron units to multi-layer neural networks. Although scientists combined Adaline units to multiple Adalines (Madaline), the problem with Adaline was that a combination of linear units is ... well, it's still linear. In any case, there is so much to write about, but I hope that satisfies your curiosity towards the early beginning to some extend.
+
+
+[1] Fisher, R. A. (1936). "The Use of Multiple Measurements in Taxonomic Problems". Annals of Eugenics 7 (2): 179–188. doi:10.1111/j.1469-1809.1936.tb02137.x
+[2] Rao, R. C. (1948). "The utilization of multiple measurements in problems of biological classification". Journal of the Royal Statistical Society, Series B 10 (2): 159–203.
+[3] McCulloch, W. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115–133.
+[4] F. Rosenblatt. The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory, 1957.
+[5] B. Widrow et al. Adaptive ”Adaline” neuron using chemical ”memistors”. Number Technical Report 1553-2. Stanford Electron. Labs., Stanford, CA, October 1960.
+[6] Berkson, Joseph. "Application of the logistic function to bio-assay." Journal of the American Statistical Association 39.227 (1944): 357-365.
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+# What are the best toy datasets to help visualize and understand classifier behavior?
+
+The visualization part is a bit tricky since we as humans are limited to 1-3 D graphics. However, I'd still say Iris is one of the most useful toy datasets for looking at classifier behavior (see image below).
+
+
+
+(I've implemented this simple function here if you are interested: [mlxtend plot_decision_regions](http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions/).)
+Other than that, I think that synthetic datasets like "XOR," "half-moons," or concentric circles would be good candidates for evaluating classifier on non-linear problems:
+
+
+
+
+
+
+
+
+
+
+
+
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+# Fitting a model via closed-form equations vs. Gradient Descent vs Stochastic Gradient Descent vs Mini-Batch Learning. What is the difference?
+
+
+In order to explain the differences between alternative approaches to estimating the parameters of a model, let's take a look at a concrete example: Ordinary Least Squares (OLS) Linear Regression.
+The illustration below shall serve as a quick reminder to recall the different components of a simple linear regression model:
+
+
+
+
+
+In Ordinary Least Squares (OLS) Linear Regression, our goal is to find the line (or hyperplane) that minimizes the vertical offsets. Or, in other words, we define the best-fitting line as the line that minimizes the sum of squared errors (SSE) or mean squared error (MSE) between our target variable (y) and our predicted output over all samples *i* in our dataset of size *n*.
+
+
+
+
+
+Now, we can implement a linear regression model for performing ordinary least squares regression using one of the following approaches:
+
+
+- Solving the model parameters analytically (closed-form equations)
+- Using an optimization algorithm (Gradient Descent, Stochastic Gradient Descent, Newton's Method, Simplex Method, etc.)
+
+
+### 1) Normal Equations (closed-form solution)
+
+
+The closed-form solution may (should) be preferred for "smaller" datasets -- if computing (a "costly") matrix inverse is not a concern. For very large datasets, or datasets where the inverse of **X**T**X** may not exist (the matrix is non-invertible or singular, e.g., in case of perfect multicollinearity), the GD or SGD approaches are to be preferred.
+The linear function (linear regression model) is defined as:
+
+
+
+
+
+where *y* is the response variable, ***x*** is an *m*-dimensional sample vector, and ***w*** is the weight vector (vector of coefficients). Note that *w0* represents the y-axis intercept of the model and therefore *x0=1*.
+Using the closed-form solution (normal equation), we compute the weights of the model as follows:
+
+
+
+
+### 2) Gradient Descent (GD)
+
+Using the Gradient Decent (GD) optimization algorithm, the weights are updated incrementally after each epoch (= pass over the training dataset).
+
+
+
+The cost function *J(⋅)*, the sum of squared errors (SSE), can be written as:
+
+
+
+
+
+The magnitude and direction of the weight update is computed by taking a step in the opposite direction of the cost gradient
+
+
+
+
+where *η* is the learning rate. The weights are then updated after each epoch via the following update rule:
+
+
+
+
+
+where **Δw** is a vector that contains the weight updates of each weight coefficient *w*, which are computed as follows:
+
+
+
+Essentially, we can picture GD optimization as a hiker (the weight coefficient) who wants to climb down a mountain (cost function) into a valley (cost minimum), and each step is determined by the steepness of the slope (gradient) and the leg length of the hiker (learning rate). Considering a cost function with only a single weight coefficient, we can illustrate this concept as follows:
+
+
+
+
+
+### 3) Stochastic Gradient Descent (SGD)
+
+
+In GD optimization, we compute the cost gradient based on the complete training set; hence, we sometimes also call it *batch GD*. In case of very large datasets, using GD can be quite costly since we are only taking a single step for one pass over the training set -- thus, the larger the training set, the slower our algorithm updates the weights and the longer it may take until it converges to the global cost minimum (note that the SSE cost function is convex).
+
+In Stochastic Gradient Descent (SGD; sometimes also referred to as *iterative* or *on-line* GD), we **don't** accumulate the weight updates as we've seen above for GD:
+
+
+
+
+
+Instead, we update the weights after each training sample:
+
+
+
+
+
+Here, the term "stochastic" comes from the fact that the gradient based on a single training sample is a "stochastic approximation" of the "true" cost gradient. Due to its stochastic nature, the path towards the global cost minimum is not "direct" as in GD, but may go "zig-zag" if we are visualizing the cost surface in a 2D space. However, it has been shown that SGD almost surely converges to the global cost minimum if the cost function is convex (or pseudo-convex)[1].
+Furthermore, there are different tricks to improve the GD-based learning, for example:
+
+
+- An adaptive learning rate η Choosing a decrease constant *d* that shrinks the learning rate over time:
+
+
+
+- Momentum learning by adding a factor of the previous gradient to the weight update for faster updates:
+
+
+
+#### A note about shuffling
+
+
+There are several different flavors of SGD, which can be all seen throughout the literature. Let's take a look at the three most common variants:
+
+
+
+
+
+
+
+##### A)
+
+- randomly shuffle samples in the training set
+ - for one or more epochs, or until approx. cost minimum is reached
+ - for training sample *i*
+ - compute gradients and perform weight updates
+
+##### B)
+
+- for one or more epochs, or until approx. cost minimum is reached
+ - randomly shuffle samples in the training set
+ - for training sample *i*
+ - compute gradients and perform weight updates
+
+##### C)
+
+- for iterations *t*, or until approx. cost minimum is reached:
+ - draw random sample from the training set
+ - compute gradients and perform weight updates
+
+
+In scenario A [3], we shuffle the training set only one time in the beginning; whereas in scenario B, we shuffle the training set after each epoch to prevent repeating update cycles. In both scenario A and scenario B, each training sample is only used once per epoch to update the model weights.
+
+
+In scenario C, we draw the training samples randomly with replacement from the training set [2]. If the number of iterations *t* is equal to the number of training samples, we learn the model based on a *bootstrap sample* of the training set.
+
+### 4) Mini-Batch Gradient Descent (MB-GD)
+
+Mini-Batch Gradient Descent (MB-GD) a compromise between batch GD and SGD. In MB-GD, we update the model based on smaller groups of training samples; instead of computing the gradient from 1 sample (SGD) or all *n* training samples (GD), we compute the gradient from *1 < k < n* training samples (a common mini-batch size is *k=50*).
+
+MB-GD converges in fewer iterations than GD because we update the weights more frequently; however, MB-GD let's us utilize vectorized operation, which typically results in a computational performance gain over SGD.
+
+
+### References
+
+- [1] Bottou, Léon (1998). "Online Algorithms and Stochastic Approximations". Online Learning and Neural Networks. Cambridge University Press. ISBN 978-0-521-65263-6
+- [2] Bottou, Léon. "Large-scale machine learning with SGD." Proceedings of COMPSTAT'2010. Physica-Verlag HD, 2010. 177-186.
+- [3] Bottou, Léon. "SGD tricks." Neural Networks: Tricks of the Trade. Springer Berlin Heidelberg, 2012. 421-436.
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@@ -0,0 +1,120 @@
+This is an excerpt of an upcoming blog article of mine. Unfortunately, the blog article turned out to be quite lengthy, too lengthy. In the process of pruning, there are hard choices to be made, and this tangent, eh, section needs to go ...
+Before I hit the delete button ... maybe this section is useful to others!?
+
+Struggling to continue story where I left off: The "way" we select a model and select amongst different machine learning algorithms all depends on how we evaluate the different models, which in turn depends upon the performance metric we choose. To summarize, the topics we mostly care about are
+
+- estimation of the generalization performance
+- algorithm selection
+- hyperparameter tuning techniques
+- (cross)-validation and sampling techniques
+- performance metrics
+- class imbalances
+
+But now to the actual section I wanted to share ...
+
+## Interlude: Comparing and Computing Performance Metrics in Cross-Validation -- Imbalanced Class Problems and 3 Different Ways to Compute the F1 Score
+
+Not too long ago, George Forman and Martin Scholz wrote a thought-provoking paper dealing with the comparison and computation of performance metrics across literature, especially when dealing with class imbalances: [Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement (2010)](http://www.hpl.hp.com/techreports/2009/HPL-2009-359.pdf). This is such a nicely written, very accessible paper (and such an important topic)! I highly recommend given this a read. Given that it's not old hat to you, it might change your perspective, the way you read papers, the way you evaluate and benchmark your machine learning models -- and if you decide to publish your results, your readers will benefit as well, that's for sure.
+
+Now, imagine that we want to compare the performance of our new, shiny algorithm to the efforts made in the past. First, we want to make sure that we are comparing "fruits to fruits." Assuming we evaluate on the same dataset, we want to make sure that we use the same cross-validation technique and evaluation metric. I know, this sounds trivial, but we first want to establish this ground rule that we can't compare ROC areas under the curves (AUC) measures to F1 scores ...
+On a side note, the use of ROC AUC metrics is still a hot topic of discussion, e.g.,
+
+- JM. Lobo, A. Jiménez-Valverde, and R. Real 2008: [AUC: a misleading measure of the performance of predictive distribution models](http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2007.00358.x/abstract;jsessionid=40E65D14D4CEEC38F203699F5DCC18C7.f01t03?userIsAuthenticated=false&deniedAccessCustomisedMessage=)
+- Jin Huang & C. X. Ling 2005: [Using AUC and accuracy in evaluating learning algorithms](http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1388242&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1388242)
+- AP. Bradley 1997 [The use of the area under the ROC curve in the evaluation of machine learning algorithms](http://www.sciencedirect.com/science/article/pii/S0031320396001422)
+
+In any case, let's focus on the F1 score for now summarizing some ideas from Forman & Scholz' paper after defining some of the relevant terminology.
+
+As we probably heard or read before, the F1-score is simply the harmonic mean of precision (PRE) and recall (REC)
+
+F1 = 2 * (PRE * REC) / (PRE + REC)
+
+***What we are trying to achieve with the F1-score metric is to find an equal balance between precision and recall, which is extremely useful in most scenarios when we are working with imbalanced datasets (i.e., a dataset with a non-uniform distribution of class labels).***
+
+---
+If we write the two metrics PRE and REC in terms of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN), we get:
+
+- PRE = TP / (TP + FP)
+- REC = TP / (TP + FN)
+
+Thus, the precision score gives us an idea (expressed as a score from 1.0 to 0.0, from good to bad) of the proportion of how many actual spam emails (TP) we correctly classified as spam among all the emails we classified as spam (TP + FP).
+In contrast, the recall (also ranging from 1.0 to 0.0) tells us about how many of the actual spam emails (TP) we "retrieved" or "recalled" (TP + FN).
+
+---
+
+Okay, let's assume we settled on the F1-score as our performance metric of choice to benchmark our new algorithm; coincidentally, the algorithm in a certain paper, which should serve as our reference performance, was also evaluated using the F1 score. Using the same cross-validation technique on the same dataset, this should make this comparison, fair, right? No, no, no, not so fast! On top of choosing the appropriate performance metric -- comparing "fruits to fruits" -- we also have to care about how it's computed in order to compare "apples to apples." This is extremely important if we are comparing performance metrics on imbalanced datasets, which I will explain in a second (based on the results from Forman & Martin Scholz' paper). ***Also, keep in mind that even if our dataset doesn't seem to be imbalanced at first glance, let's think of the Iris dataset with 50 Setosa, 50 Virginica, and 50 Versicolor flowers: What happens if we use a One-vs-Rest (OVR; or One-vs-All, OVA) classification scheme?***
+
+In any case, let's focus on a binary classification problem (a *positive* and a *negative* class) for now using k-fold cross-validation as our cross-validation technique of choice for model selection.
+
+As mentioned before, we calculate the F1 score as
+
+F1 = 2 * (PRE * REC) / (PRE + REC)
+
+Now, what happens if we have a highly imbalanced dataset and perform our k-fold cross validation procedure in the training set? Well, chances are that a particular fold may not contain *a positive* sample so that TP=FN=0. If this doesn't sound too bad, have another look at the recall equation above -- yes, that's a zero-division error! Or, what happens if our classifier predicts the negative class almost all the time (i.e., it has a low false-positive rate)? Again, we get a zero-division error in the precision equation since TP = FP = 0.
+
+What can we do about it? There are two things. Firstly, let's stratify our folds -- stratification means that the random sampling procedure attempts to maintain the class-label proportion across the different folds. Thus, we are unlikely to face problems like having "no samples from the *positive* class" given that *k* is not larger than the number of *positive* samples in the training dataset.
+
+***In practice, different software packages handle the zero-division errors differently: Some don't hesitate throwing run-time exceptions; some may silently substitute the precision and/or recall by a 0 -- make sure what it's doing!*** On top of that, we can compute the F1 score in several distinct ways (and in multi-class problems, we can put the micro- and macro-averaging techniques on top of that, but this is beyond of the scope of this section). As listed by Forman and Scholz, these three different scenarios are
+
+#### (1)
+
+We compute the F1 score for each fold (iteration); then, we compute the average F1 score
+from these individual F1 scores.
+
+F1avg = 1/k Σki=1 F1(i)
+
+
+#### (2)
+
+We compute the average precision and recall scores across the *k* folds; then, we use these average scores to compute the final F1 score.
+
+PRE = 1/k Σki=1 PRE(i)
+
+REC = 1/k Σki=1 REC(i)
+
+F1PRE, REC = 2 * (PRE * REC) / (PRE + REC)
+
+
+
+#### (3)
+
+We compute the number of TP, FP, and FN separately for each fold or iteration, and compute the final F1 score based on these "micro" metrics.
+
+TP = Σki=1 TP(i)
+
+FP = Σki=1 FP(i)
+
+FN = Σki=1 FN(i)
+
+F1TP, FP, FN = (2 * TP) / (2 * TP + FP + FN)
+
+(Note that this equation doesn't suffer from the zero-division issue.)
+
+---
+
+Please note that we don't have to worry about the different ways to compute the classification error or accuracy, which we are working with in this blog article aside from this section. The reason is that it doesn't matter whether we we compute the accuracy as
+
+
+ACCavg = 1/k Σki=1 ACC(i)
+
+or
+
+TP = Σki=1 TP(i)
+
+TN = Σki=1 TN(i)
+
+ACC avg = (TP + TN) / N
+
+The two approaches are identical, or with a more concrete example: (30 + 40) / 100 = (30/50 + 40/50) / 2 = 0.7.
+
+---
+
+Eventually, Forman and Scholz plaid this game of using different ways to compute the F1 score based on a benchmark dataset with a high-class imbalance (a bit exaggerated for demonstration purposes but not untypical when working with text data). It turns out that the resulting scores (from the identical model) differed substantially:
+
+- F1avg: 69%
+- F1PRE, REC: 73%
+- F1TP, FP, FN: 58%
+
+***Finally, based on further simulations, Forman and Scholz concluded that the computation of F1TP, FP, FN (compared to the alternative ways of computing the F1 score), were yielded the "most unbiased" estimate of the generalization performance using *k*-fold cross-validation.***
+
+In any case, the bottom line is that we should not only choose the appropriate performance metric and cross-validation technique for the task, but we also take a ***closer look at how the different performance metrics are computed in case we cite papers or rely on off-the-shelve machine learning libraries.***
diff --git a/faq/copyright.md b/faq/copyright.md
index 5053f3c0..b8f239d8 100644
--- a/faq/copyright.md
+++ b/faq/copyright.md
@@ -2,8 +2,8 @@
Probably, you know me as a big advocate of *open source* and I like to share useful resources wherever and whenever I can! Most of my projects really benefit from resources people shared with me, the community, and the Internet as a whole. I am more than happy to do so likewise, that's one of the reasons why Python is so great, but this is a topic for another story ... :)
-I am really passionate about machine learning, and other than using it for my own interests, I am truly excited about sharing knowledge, getting you excited about this field, and helping you on your path of becoming an affluent machine learning practitioner. It would make me really happy to hear if I was successful with my mission, and that you find the contents of my book are beneficial. If you are developing learning resources for other people, that's great, and if figures, code examples, math formulae, or passages are helpful for that, by all means, go for it!
-Unfortunately though, writing a book for a/most publisher/s comes with some restrictions, but I have spoken with the content manager of this book, who in turn talked to the *Copyright Team*.
+I am really passionate about machine learning, and other than using it for my own interests, I am truly excited about sharing knowledge, getting you excited about this field, and helping you on your path of becoming an affluent machine learning practitioner. It would make me really happy to hear if I was successful with my mission, and that you find the contents of my book are beneficial. If you are developing learning resources for other people, that's great, and if figures, code examples, math formulas, or passages are helpful for that, by all means, go for it!
+Unfortunately though, writing a book for most publishers comes with some restrictions, but I have spoken with the content manager of this book, who in turn talked to the *Copyright Team*.
> I hope you don’t mind if ask you a few quick questions about the copyright for the images since I couldn’t find anything specific about the rules on the Packt website.
For example, if someone wants to use one of the images from the book in
diff --git a/faq/cost-vs-loss.md b/faq/cost-vs-loss.md
new file mode 100644
index 00000000..e425ef56
--- /dev/null
+++ b/faq/cost-vs-loss.md
@@ -0,0 +1,12 @@
+# What is the difference between a cost function and a loss function in machine learning?
+
+The terms *cost* and *loss* functions are synonymous (some people also call it error function). The more general scenario is to define an objective function first, which we want to optimize. This objective function could be to
+
+- maximize the posterior probabilities (e.g., naive Bayes)
+- maximize a fitness function (genetic programming)
+- maximize the total reward/value function (reinforcement learning)
+- maximize information gain/minimize child node impurities (CART decision tree classification)
+- minimize a mean squared error cost (or loss) function (CART, decision tree regression, linear regression, adaptive linear neurons, ...
+- maximize log-likelihood or minimize cross-entropy loss (or cost) function
+- minimize hinge loss (support vector machine)
+...
diff --git a/faq/data-science-career.md b/faq/data-science-career.md
new file mode 100644
index 00000000..609ae201
--- /dev/null
+++ b/faq/data-science-career.md
@@ -0,0 +1,7 @@
+# What learning path/discipline in data science I should focus on?
+
+The tl;dr: "Data science" is a broad field including many different specializations; which particular sub-role do you find most appealing? Statistics, machine learning, software engineering, data visualization...?
+
+Hm, how can I say this … it’s a pity that our day only has 24 hours, and there is only so much that we can learn as an individual person. So, I think it is somewhat important to reflect on your goals and your interest when you are picking your study topics. Although it has a “bad ring” to it, the phrase “being a jack of all trades” is certainly very tempting and useful if you work as an individual. As a “data scientist” there are endless topics you can get lost in, from programming to statistics, databases, differential calculus, … Of course, it’s useful to know a bit of everything, but I think it is impossible to become a master in everything in a given amount of time. For example, data scientists rarely work on their own but are often part of bigger teams with different areas of responsibilities. Some people are really good at stats; some people are responsible for building the framework to collect, clean, and extract data. Some people develop new algorithms, and some are “data scientist-programmers” who focus on implementing them most efficiently. For example, I’d say that I am a pretty good Python programmer, but my Java skills are really rudimentary. I think a better knowledge of Java would help me here and there, but my focus is more on the Machine Learning part; Python is already sufficient for me to implement all my ideas. So rather than investing in learning a brand new programming language, I try to focus more on my strength, e.g., picking up fresh ML concepts and staying on top with the current developments in the field. Of course, I’d like to learn a new programming language since it could be useful, but I also know that I only have so much time to learn all these things … ;)
+
+What I am trying to say is that it is probably not a bad idea to think about where you want to be in xx years, and what does it take to get there. Which are the things and skills that are necessary to get you there? I would focus on these first!
diff --git a/faq/datamining-overview.md b/faq/datamining-overview.md
new file mode 100644
index 00000000..b85e4954
--- /dev/null
+++ b/faq/datamining-overview.md
@@ -0,0 +1,21 @@
+# What are the different fields of study in data mining?
+
+
+I would roughly define the different application areas as
+
+1) Clustering (unsupervised learning)
+e.g., to find groups of customers based on some similarity
+
+2) Predictive modeling (supervised learning)
+2.1) Classification
+ e.g., medical diagnosis (sick/healthy), image classification etc.
+2.2) Regression
+ e.g., stock trade change prediction
+2.3) Ranking
+ e.g., search engine results
+
+3) Association rule mining
+e.g., which products do customers frequently buy together
+
+4) Anomaly detection
+e.g., credit fraud detection
diff --git a/faq/datamining-vs-ml.md b/faq/datamining-vs-ml.md
new file mode 100644
index 00000000..06abe0e9
--- /dev/null
+++ b/faq/datamining-vs-ml.md
@@ -0,0 +1,4 @@
+# What are differences in research nature between the two fields: Machine Learning & Data Mining?
+
+In a nutshell, Data Mining is about the discovery of patterns in datasets or "gaining knowledge and insights" from data. Machine Learning is closely related though. We can think of Machine Learning algorithms as one of the work horses of Data Mining; most Data Mining approaches are based on Machine Learning algorithms. Maybe it helps to think of Data Mining as a pipeline of steps and approaches, and the use of a Machine Learning algorithm is one part of this pipeline.
+Or in other words, Data Mining is not "just" Machine Learning. E.g., data visualization or summarization is also part of Data Mining. What I was trying to say is that Machine Learning is one part, one set of techniques, that is/are being used in Data Mining.
diff --git a/faq/dataprep-vs-dataengin.md b/faq/dataprep-vs-dataengin.md
new file mode 100644
index 00000000..9a6ff4f3
--- /dev/null
+++ b/faq/dataprep-vs-dataengin.md
@@ -0,0 +1,6 @@
+# Should data preparation/pre-processing step be considered one part of feature engineering? Why or why not?
+
+I think there's a fuzzy boundary between these two areas of tasks. I see data preparation more as a technical/computational task. E.g., if you think about getting the data into the "right" format, choosing the appropriate data structure / database, and so forth.
+Then, there's data cleaning, which can also be grouped into the "preparation / pre-processing" category. Here, you may want to think about detecting duplications, how to deal with outliers, and how to deal with missing data.
+
+To me, feature engineering is a bit different. I see it more as a "data/feature creation" step rather than a data "sanitizing" step. Feature engineering may include all different sorts of feature transformations in both directions: Higher-dimensional feature spaces (e.g., polynomials), lower dimensional feature spaces (dimensionality reduction like PCA, LDA, etc., hashing, clustering), or you keep the dimensions but change the distribution of your data (e.g., log transformation, standardization, min-max scaling etc.)
diff --git a/faq/datascience-ml.md b/faq/datascience-ml.md
new file mode 100644
index 00000000..4fb5f38b
--- /dev/null
+++ b/faq/datascience-ml.md
@@ -0,0 +1,30 @@
+# What are data science and machine learning?
+
+
+### Let's start with machine learning
+
+In short, machine learning algorithms are algorithms that learn (often predictive) models from data. I.e., instead of formulating "rules" manually, a machine learning algorithm will learn the model for you.
+
+
+
+So, let me give you an example to illustrate what that means! Say you are interested in implementing a spam filter. The probably most conservative approach would be to let a person sort these emails manually. Now, the "traditional" programming approach would be to look at some example emails (and/or use your "domain knowledge") to come up with a chain of rules like
+
+*"if this email contains word X, label it as spam, else if email contains ..."*
+
+Now, machine learning algorithms help you formulating these rules. Or in other words, (supervised) machine learning algorithms will look at a dataset of labeled emails (spam and non-spam) and derive rules from there to separate the two classes.
+
+
+### So, what is data science then?
+
+First of all, "data science" is a pretty ambiguous, ill-defined term and interdisciplinary field; and people mean (expect) different things in different contexts. In my opinion, in practice, data science is pretty much the same as what we've known as *Data Mining* or *KDD* (Knowledge Discovery in Databases). The typical skills of a data scientists are
+
+- Computer science: programming, hardware understanding, etc.
+- Math: Linear algebra, calculus, statistics
+- Communication: visualization and presentation
+- Domain knowledge
+
+Where machine learning -- at its core -- is about the use and development of these learning algorithms, data science is more about the extraction of knowledge from data to answer particular question or solve particular problems.
+
+Machine learning is often a big part of a "data science" project, e.g., it is often heavily used for exploratory analysis and discovery (clustering algorithms) and building predictive models (supervised learning algorithms). However, in data science, you often also worry about the collection, wrangling, and cleaning of your data (i.e., data engineering), and eventually, you want to draw conclusions from your data that help you solve a particular problem.
+
+There are numerous examples of data science applications. Assume you are working for a credit company. Your boss gives you the task to find out whether a customer is creditworthy or not. You collect transaction data, maybe shipping records and customer ratings and so forth. Next, you'll probably use a machine learning algorithm to learn a predictive model. For example, let's assume you chose to grow a decision tree, and you concluded that this particular customer is not creditworthy. Finally, you prepare a nice presentation visualizing the decision tree to answer your boss' next question: Why is this customer not creditworthy? ...
\ No newline at end of file
diff --git a/faq/datascience-ml/ml-overview.jpg b/faq/datascience-ml/ml-overview.jpg
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diff --git a/faq/decision-tree-binary.md b/faq/decision-tree-binary.md
new file mode 100644
index 00000000..88b083a4
--- /dev/null
+++ b/faq/decision-tree-binary.md
@@ -0,0 +1,60 @@
+# Why are implementations of decision tree algorithms usually binary and what are the advantages of the different impurity metrics?
+
+For practical reasons (combinatorial explosion) most libraries implement decision trees with binary splits. The nice thing is that they are NP-complete (Hyafil, Laurent, and Ronald L. Rivest. "Constructing optimal binary decision trees is NP-complete." Information Processing Letters 5.1 (1976): 15-17.)
+
+Our objective function (e.g., in CART) is to maximize the information gain (IG) at each split:
+
+
+
+
+
+where *f* is the feature to perform the split, and *D_p* and *D_j* are the datasets of the parent and *j*th child node, respectively. *I* is the impurity measure. *N* is the total number of samples, and *N_j* is the number of samples at the *j*th child node.
+Now, let's take a look at the most commonly used splitting criteria for classification (as described in CART). For simplicity, I will write the equations for the binary split, but of course it can be generalized for multiway splits. So, for a binary split we can compute *IG* as
+
+
+
+Now, the two impurity measures or splitting criteria that are commonly used in binary decision trees are Gini Impurity(*I_G*) and Entropy (*I_H*) and the Classification Error (*I_E*). Let us start with the definition of Entropy, which is defined as
+
+
+
+for all “non-empty” classes
+
+
+
+and *p(i|t)* is the proportion of the samples that belong to class *c* for a particular node *t*. The entropy is therefore 0 if all samples at a node belong to the same class, and the Entropy is maximal if we have an uniform class distribution
+Intuitively, the Gini Impurity can be understood as a criterion to minimize the probability of misclassification
+
+
+
+Similar to the Entropy, the Gini Impurity is maximal if the classes are perfectly mixed.
+However, in practice both Gini Impurity and Entropy typically yield very similar results and it is often not worth spending much time on evaluating trees using different impurity criteria rather than experimenting with different pruning cut-offs.
+Another impurity measure is the Classification Error
+
+
+
+which is a useful criterion for pruning but not recommend for growing a decision tree since it is less sensitive to changes in the class probabilities of the nodes.
+
+
+
+So let me illustrate what I mean by "the classification error is less sensitive to changes in the class probabilities" by looking at the two possible splitting scenarios shown in the figure below.
+
+
+
+We start with a data set *D_p* at the parent node that consists 40 samples from class 1 and 40 samples from class 2 that we split into two datasets D_left and D_right, respectively. The information gain using the Classification Error as splitting criterion would be the same (*IG_E* = 0.25) in both scenario *A* and *B*:
+
+
+
+
+
+However, the Gini Impurity would favor the split in scenario B (0.1666) over scenario A (0.125), which is indeed more “pure”:
+
+
+
+Similarly, the entropy criterion would favor scenario B(IGH = 0.31) over scenario A(IGH = 0.19):
+
+
+
+
+
+
+Maybe some more words about Gini vs. Entropy. As mentioned before, the resulting trees are typically very similar in practice. Maybe an advantage of Gini would be that you don't need to compute the log, which can make it a bit faster in your implementation.
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diff --git a/faq/decision-tree-disadvantages.md b/faq/decision-tree-disadvantages.md
new file mode 100644
index 00000000..8ce01e61
--- /dev/null
+++ b/faq/decision-tree-disadvantages.md
@@ -0,0 +1,19 @@
+# What are the disadvantages of using classic decision tree algorithm for a large dataset?
+
+
+
+### The computational efficiency perspective
+
+It's a combinatorial search problem: at each split, we want to find the features that give us "the best bang for the buck" (maximizing information gain). If we choose a"brute" force approach, our computational complexity is O(m^2), where m is the number of features in our training set, and O(n^2) for the number of n training cases (I think it can be O(n log(n) if you are lucky).
+
+Let's take a look at a simple dataset, Iris (150 flowers, 3 classes, 4 continuous features). At each split, we have to re-evaluate all 4 features, and for each feature we have to find the optimal value to split on, e.g,. sepal length <3.4 cm (this is for a binary split). Computational complexity is one of the reasons why people implement *binary* decision trees most of the time.
+
+### The predictive performance perspective
+
+An unpruned model is much more likely to overfit as a consequence of the curse of dimensionality. However, instead of pruning a single decision tree, it often a better idea to use ensemble methods. We could
+
+- combine decision tree stumps that learn from each other by focusing on samples that are hard to classify (AdaBoost)
+- create an ensemble of unpruned decision trees; draw bootstrap samples, and do random feature selection (random forests)
+- forget about bagging and use all training samples as input for your unpruned trees; choose both the splitting feature and splitting value at random (= Extremely randomized trees)
+
+(Related topic: [How does the random forest model work? How is it different from bagging and boosting in ensemble models?](../bagging-boosting-rf.md))
diff --git a/faq/decisiontree-error-vs-entropy.md b/faq/decisiontree-error-vs-entropy.md
new file mode 100644
index 00000000..b9c8294e
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+++ b/faq/decisiontree-error-vs-entropy.md
@@ -0,0 +1,75 @@
+# Why are we growing decision trees via entropy instead of the classification error?
+
+Before we get to the main question -- the real interesting part -- let's take a look at some of the (classification) decision tree basics to make sure that we are on the same page.
+
+##### The Basic Algorithm
+
+1. Start at the root node as parent node
+2. Split the parent node at the feature *xi* to minimize the sum of the child node impurities (maximize information gain)
+3. Assign training samples to new child nodes
+4. Stop if leave nodes are pure or early stopping criteria is satisfied, else repeat steps 1 and 2 for each new child node
+
+##### Stopping Rules
+
+1. The leaf nodes are pure
+2. A maximal node depth is reached
+3. Splitting a note does not lead to an information gain*
+
+\* This is the important part as we will see later.
+
+
+##### Impurity Metrics and Information Gain
+
+Formally, we can write the "Information Gain" as
+
+
+
+
+
+(Note that since the parent impurity is a constant, we could also simply compute the average child node impurities, which would have the same effect.)
+For simplicity, we will only compare the "Entropy" criterion to the classification error; however, the same concepts apply to the Gini index as well.
+
+We write the Entropy equation as
+
+
+
+
+for all non-empty classed *p(i | t)* ≠ 0, where *p(i | t)* is the proportion (or frequency or probability) of the samples that belong to class *i* for a particular node *t*; *C* is the number of unique class labels.
+
+
+
+
+
+
+Although we are all very familiar with the classification error, we write it down for completeness:
+
+
+
+
+
+
+
+### Classification Error vs. Entropy
+
+
+Here comes the more interesting part, just as promised. Let's consider the following binary tree starting with a training set of 40 "positive" training samples (y=1) and 80 training samples from the "negative" class (y=0). Further, let's assume that it is possible to come up with 3 splitting criteria (based on 3 binary features x1, x2, and x3) that can separate the training samples perfectly:
+
+
+
+Now, is it possible to learn this hypothesis (i.e., tree model) by minimizing the classification error as a criterion function? Let's do the math:
+
+
+
+As we can see, the Information Gain after the first split is exactly 0, since average classification error of the 2 child nodes is exactly the same as the classification error of the parent node (40/120 = 0.3333333). In this case, splitting the initial training set wouldn't yield any improvement in terms of our classification error criterion, and thus, the tree algorithm would stop at this point (for this statement to be true, we have to make the assumption that neither splitting on feature x2 nor x3 would lead to an Information gain as well).
+
+Next, let's see what happens if we use Entropy as an impurity metric:
+
+
+
+In contrast to the average classification error, the average child node entropy is **not** equal to the entropy of the parent node. Thus, the splitting rule would continue until the child nodes are pure (after the next 2 splits). So, why is this happening? For an intuitive explanation, let's zoom in into the Entropy plot:
+
+
+
+The green square-shapes are the Entropy values for p(28/70) and (12/50) of the first two child nodes in the decision tree model above, connected by a green (dashed) line. To recapitulate: the decision tree algorithm aims to find the feature and splitting value that leads to a maximum decrease of the average child node impurities over the parent node.
+So, if we have 2 entropy values (left and right child node), the average will fall onto the straight, connecting line.
+However **-- and this is the important part --** we can see that the Entropy is always larger than the averaged Entropy due to its "bell shape," which is why we keep continuing to split the nodes in contrast to the classification error.
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diff --git a/faq/decisiontree-error-vs-entropy/decisiontree-error-vs-entropy.ipynb b/faq/decisiontree-error-vs-entropy/decisiontree-error-vs-entropy.ipynb
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+++ b/faq/decisiontree-error-vs-entropy/decisiontree-error-vs-entropy.ipynb
@@ -0,0 +1,2488 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Why don't we use the classification error as a metric to grow a decision tree?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
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+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
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+ " this.image_mode = 'full';\n",
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+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
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+ " $(parent_element).append(this.root);\n",
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+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
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+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
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+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
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+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
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+ " this.root.append(titlebar);\n",
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+ "\n",
+ "\n",
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+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
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+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var rubberband = $('');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width);\n",
+ " canvas.attr('height', height);\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('');\n",
+ "\n",
+ " var fmt_picker = $('');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'];\n",
+ " var y0 = fig.canvas.height - msg['y0'];\n",
+ " var x1 = msg['x1'];\n",
+ " var y1 = fig.canvas.height - msg['y1'];\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x;\n",
+ " var y = canvas_pos.y;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('');\n",
+ " var button = $('');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib notebook\n",
+ "\n",
+ "\n",
+ "def entropy(p):\n",
+ " return - p*np.log2(p) - (1 - p)*np.log2((1 - p))\n",
+ "x = np.arange(0.0, 1.0, 0.01)\n",
+ "ent = [entropy(p) if p != 0 else None for p in x]\n",
+ "plt.plot(x, ent)\n",
+ "plt.ylim([0,1.1])\n",
+ "plt.xlabel('p(i=1)')\n",
+ "plt.axhline(y=1.0, linewidth=1, color='k', linestyle='--')\n",
+ "plt.ylabel('Entropy')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " this.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '');\n",
+ " var titletext = $(\n",
+ " '');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var rubberband = $('');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width);\n",
+ " canvas.attr('height', height);\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('');\n",
+ "\n",
+ " var fmt_picker = $('');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'];\n",
+ " var y0 = fig.canvas.height - msg['y0'];\n",
+ " var x1 = msg['x1'];\n",
+ " var y1 = fig.canvas.height - msg['y1'];\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x;\n",
+ " var y = canvas_pos.y;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('');\n",
+ " var button = $('');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "\n",
+ "def entropy(p):\n",
+ " return - p*np.log2(p) - (1 - p)*np.log2((1 - p))\n",
+ "x = np.arange(0.0, 1.0, 0.01)\n",
+ "ent = [entropy(p) if p != 0 else None for p in x]\n",
+ "plt.plot(x, ent)\n",
+ "plt.ylim([0,1.1])\n",
+ "plt.xlabel('p(i=1)')\n",
+ "e0 = entropy(40/120)\n",
+ "e1 = entropy(28/70)\n",
+ "e2 = entropy(12/50)\n",
+ "plt.plot([28/70, 12/50], [e1, e2], marker='s', linestyle='--', markersize=10)\n",
+ "plt.plot([(40/120)], [e0 ], marker='^', markersize=7)\n",
+ "plt.plot([(70/120 * 28/70 + 50/120 * 12/50)], [(70/120 * e1 + 50/120 * e2) ], marker='o', markersize=7) \n",
+ "plt.axhline(y=1.0, linewidth=1, color='k', linestyle='--')\n",
+ "plt.ylabel('Entropy')\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " this.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '');\n",
+ " var titletext = $(\n",
+ " '');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var rubberband = $('');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width);\n",
+ " canvas.attr('height', height);\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('');\n",
+ "\n",
+ " var fmt_picker = $('');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'];\n",
+ " var y0 = fig.canvas.height - msg['y0'];\n",
+ " var x1 = msg['x1'];\n",
+ " var y1 = fig.canvas.height - msg['y1'];\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x;\n",
+ " var y = canvas_pos.y;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('');\n",
+ " var button = $('');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def error(p):\n",
+ " return 1 - np.max([p, 1-p])\n",
+ "\n",
+ "x = np.arange(0.0, 1.0, 0.01)\n",
+ "err = [error(i) for i in x]\n",
+ "plt.plot(x, err)\n",
+ "plt.ylim([0,0.55])\n",
+ "plt.xlabel('p(i=1)')\n",
+ "plt.axhline(y=0.5, linewidth=1, color='k', linestyle='--')\n",
+ "plt.ylabel('Classification Error')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$IG(D_{p}, x_i) = I(D_{p}) - \\frac{N_{left}}{N_p} I(D_{left}) - \\frac{N_{right}}{N_p} I(D_{right})$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- $IG$: Information Gain\n",
+ "- $x_i$: feature to perform the split\n",
+ "- $N_p$: number of samples in the parent node\n",
+ "- $N_{left}$: number of samples in the left child node\n",
+ "- $N_{right}$: number of samples in the right child node\n",
+ "- $I$: impurity\n",
+ "- $D_p$: training subset of the parent node\n",
+ "- $D_{left}$: training subset of the left child node\n",
+ "- $D_right$: training subset of the right child node"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$I_H(t) = - \\sum_{i =1}^{C} p(i \\mid t) \\;log_2 \\,p(i \\mid t)$$\n",
+ "\n",
+ "for all non-empty classed $p(i \\mid t) \\neq 0$, where $p(i \\mid t)$ is the proportion (or frequency or probability) of the samples that belong to class *i* for a particular node *t*; *C* is the number of unique class labels."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$I_E(t) = 1 - max\\{{p_i}\\}$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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diff --git a/faq/deep-learning-resources.md b/faq/deep-learning-resources.md
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+# What are some good books/papers for learning deep learning?
+
+
+A good overview and introduction is the recent deep learning review published in Nature (http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html); it references a lot of useful literature to follow up on
+
+- LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature521.7553 (2015): 436-444.
+
+
+As a good textbook resource, I would like to recommend Yoshua Bengio's upcoming "Deep Learning" book. The book is freely accessible at: http://goodfeli.github.io/dlbook/
+
+- Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. "Deep learning." An MIT Press book in preparation. Draft chapters available at http://www. iro. umontreal. ca/∼ bengioy/dlbook (2014).
diff --git a/faq/deeplearn-vs-svm-randomforest.md b/faq/deeplearn-vs-svm-randomforest.md
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+# How can I know if Deep Learning works better for a specific problem than SVM or random forest?
+
+If we tackle a supervised learning problem, my advice is to start with the simplest hypothesis space first. I.e., try a linear model such as logistic regression. If this doesn't work "well" (i.e., it doesn't meet our expectation or performance criterion that we defined earlier), I would move on to the next experiment.
+
+
+### Random Forests vs. SVMs
+
+I would say that random forests are probably THE "worry-free" approach - if such a thing exists in ML: There are no real hyperparameters to tune (maybe except for the number of trees; typically, the more trees we have the better). On the contrary, there are a lot of knobs to be turned in SVMs: Choosing the "right" kernel, regularization penalties, the slack variable, ...
+
+Both random forests and SVMs are non-parametric models (i.e., the complexity grows as the number of training samples increases). Training a non-parametric model can thus be more expensive, computationally, compared to a generalized linear model, for example. The more trees we have, the more expensive it is to build a random forest. Also, we can end up with a lot of support vectors in SVMs; in the worst-case scenario, we have as many support vectors as we have samples in the training set. Although, there are multi-class SVMs, the typical implementation for mult-class classification is One-vs.-All; thus, we have to train an SVM for each class -- in contrast, decision trees or random forests, which can handle multiple classes out of the box.
+
+To summarize, random forests are much simpler to train for a practitioner; it's easier to find a good, robust model. The complexity of a random forest grows with the number of trees in the forest, and the number of training samples we have. In SVMs, we typically need to do a fair amount of parameter tuning, and in addition to that, the computational cost grows linearly with the number of classes as well.
+
+### Deep Learning
+
+As a rule of thumb, I'd say that SVMs are great for relatively small data sets with fewer outliers. Random forests may require more data but they almost always come up with a pretty robust model. And deep learning algorithms... well, they require "relatively" large datasets to work well, and you also need the infrastructure to train them in reasonable time. Also, deep learning algorithms require much more experience: Setting up a neural network using deep learning algorithms is much more tedious than using an off-the-shelf classifiers such as random forests and SVMs.
+On the other hand, deep learning really shines when it comes to complex problems such as image classification, natural language processing, and speech recognition. Another advantage is that you have to worry less about the feature engineering part. Again, in practice, the decision which classifier to choose really depends on your dataset and the general complexity of the problem -- that's where your experience as machine learning practitioner kicks in.
+
+If it comes to predictive performance, there are cases where SVMs do better than random forests and vice versa:
+
+- Caruana, Rich, and Alexandru Niculescu-Mizil. "[An empirical comparison of supervised learning algorithms.](https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf)" Proceedings of the 23rd international conference on Machine learning. ACM, 2006.
+
+The same is true for deep learning algorithms if you look at the MNIST benchmarks ([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)):
+The best-performing model in this set is a committee consisting of 35 ConvNets, which were reported to have a 0.23% test error; the best SVM model has a test error of 0.56%. The ConvNet ensemble may reach a better accuracy (for the sake of this ensemble, let's pretend that these are totally unbiased estimates), but without a question, I'd say that the 35 ConvNet committee is far more expensive (computationally). So, if you make that decision: Is a 0.33% improvement worth it? In some cases, it's maybe worth it (e.g., in the financial sector for non-real time predictions), in other cases it perhaps won't be worth it, though.
+
+
+
+So, my practical advice is:
+- Define a performance metric to evaluate your model
+- Ask yourself: What performance score is desired, what hardware is required, what is the project deadline
+- Start with the simplest model
+- If you don't meet your expected goal, try more complex models (if possible)
diff --git a/faq/deeplearning-criticism.md b/faq/deeplearning-criticism.md
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+++ b/faq/deeplearning-criticism.md
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+# Why do some people hate neural networks/deep learning?
+
+I also know many people who make disrespectful remarks about neural networks in genaral. Personally, I find recurrent and convolutional neural networks truly beautiful. However, there is this popular saying: "that if al that you have is a hammer everything starts to look like a nail"
+
+The math behind neural nets is probably a bit harder to understand, but I don't think they are really black boxes. I think a neural net is not more of a black box than standard techniques like kernel SVMs of random forests. Actually, I think it is easier to explain backpropagation than kernel methods.
+
+However, I think people in the biosciences prefer "interpretable" results, e.g., decision trees where they can follow the "reasoning" step by step. Unarguably, random forests are better at solving the prediction task since you don't have to worry so much about overfitting or pruning your tree; at the same time, we lose some of this "interpretability." Although, I think this is not really true. Feature importance computed from e.g., extremely randomized trees might be even more useful than looking at a single decision tree.
+
+Please note that I don't blame the bio-research field for this kind of thinking, they really try to solve different problems.
+Assuming biologists want to know which functional groups of a ligand are "interacting" with residues in the protein binding site. Of course, the primary goal is often to a good agonist or antagonist (inhibitor or drug) in a million-compound database to solve a particular problem; in addition, they are also trying to "understand" and "explain" the results.
diff --git a/faq/definition_data-science.md b/faq/definition_data-science.md
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+++ b/faq/definition_data-science.md
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+# What is the Definition of Data Science?
+
+That's a tricky question. It's particularly challenging because there is no "definition." Unfortunately, this rather novel term has been used ambiguously, and there are many subjective definitions. Anyway, I think the field of Data Science is highly interdisciplinary and influenced by many many other fields ...
+
+Okay, before I start, I’d say data science is mainly about extracting knowledge from data (and I think that the terms “data mining” or “Knowledge Discovery in Databases” are highly related). So, data science can be about analyzing trends, building predictive models, … etc.
+
+It’s say data science is an agglomerate of data collection, data modeling and analysis, decision making, and everything you need to know to accomplish your goals with respect to the aforementioned sub-tasks. Eventually, it boils down to the following fields/skills:
+
+There’s computer science:
+
+- algorithms
+- programming (patterns, languages etc.),
+- understanding hardware & operating systems
+- high-performance computing'
+
+And the mathematical aspects:
+
+- linear algebra
+- differential equations for optimization problems
+- statistics
+
+Plus others:
+
+- machine learning of course (somewhere between the technical and mathematical skills)
+- domain knowledge
+- and very important: the data visualization & communication skills
diff --git a/faq/diff-perceptron-adaline-neuralnet.md b/faq/diff-perceptron-adaline-neuralnet.md
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+# What is the difference between a Perceptron, Adaline, and neural network model?
+
+Both Adaline and the Perceptron are (single-layer) neural network models.
+The Perceptron is one of the oldest and simplest learning algorithms out there, and I would consider Adaline as an improvement over the Perceptron.
+
+
+### What Adaline and the Perceptron have in common
+
+- they are classifiers for binary classification
+- both have a linear decision boundary
+- both can learn iteratively, sample by sample (the Perceptron naturally, and Adaline via stochastic gradient descent)
+- both use a threshold function
+
+Before we talk about the differences, let's talk about the inputs first. The first step in the two algorithms is to compute the so-called net input *z* as the linear combination of our feature variables *x* and the model weights *w*.
+
+
+
+
+Then, in the Perceptron and Adaline, we define a threshold function to make a prediction. I.e., if *z* is greater than a threshold theta, we predict class 1, and 0 otherwise:
+
+
+
+
+
+### The differences between the Perceptron and Adaline
+
+- the Perceptron uses the class labels to learn model coefficients
+- Adaline uses continuous predicted values (from the net input) to learn the model coefficients, which is more "powerful" since it tells us by "how much" we were right or wrong
+
+So, in the perceptron, as illustrated below, we simply use the predicted class labels to update the weights, and in Adaline, we use a continuous response:
+
+
+
+(Note that I inserted the "activation function" in Adaline just for illustrative purposes; here, this activation function is simply the identity function)
+Both learning algorithms can actually be summarized by 4 simple steps -- given that we use stochastic gradient descent for Adaline:
+
+1. Initialize the weights to 0 or small random numbers.
+2. For each training sample:
+ 1. Calculate the output value.
+ 2. Update the weights.
+
+We write the weight update in each iteration as:
+
+
+
+where
+
+
+
+
+Again, the "output" is the continuous net input value in Adaline and the predicted class label in case of the perceptron; eta is the learning rate.
+(In case you are interested: This weight update in Adaline is basically just taking the "opposite step" in direction of the sum-of-squared error cost gradient. I've a more detailed walkthrough [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/linear-gradient-derivative/) on deriving the cost gradient.
+
+
+### Multi-layer neural networks
+
+Although you haven't asked about multi-layer neural networks specifically, let me add a few sentences about one of the oldest and most popular multi-layer neural network architectures: the Multi-Layer Perceptron (MLP). The term "Perceptron" is a little bit unfortunate in this context, since it really doesn't have much to do with Rosenblatt's Perceptron algorithm.
+
+
+
+MLPs can basically be understood as a network of multiple artificial neurons over multiple layers. Here, the activation function is not linear (like in Adaline), but we use a non-linear activation function like the logistic sigmoid (the one that we use in logistic regression) or the hyperbolic tangent, or a piecewise-linear activation function such as the rectifier linear unit (ReLU). In addition, we often use a softmax function (a generalization of the logistic sigmoid for multi-class problems) in the output layer, and a threshold function to turn the predicted probabilities (by the softmax) into class labels.
+
+
+So, what the advantage of the MLP over the classic Perceptron and Adaline? By connecting the artificial neurons in this network through non-linear activation functions, we can create complex, non-linear decision boundaries that allow us to tackle problems where the different classes are not linearly separable.
+
+Let me show you an example :)
+
+
+
+
+Here's the Python code if you want to reproduce these plots:
+
+```Python
+from mlxtend.plotting import plot_decision_regions
+from mlxtend.classifier import Perceptron
+from mlxtend.classifier import Adaline
+from mlxtend.classifier import MultiLayerPerceptron
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_moons
+import matplotlib.gridspec as gridspec
+import itertools
+
+gs = gridspec.GridSpec(2, 2)xw
+X, y = make_moons(n_samples=100, random_state=123)
+fig = plt.figure(figsize=(10,8))
+
+ppn = Perceptron(epochs=50, eta=0.05, random_seed=0)
+ppn.fit(X, y)
+ada = Adaline(epochs=50, eta=0.05, random_seed=0)
+ada.fit(X, y)
+
+mlp = MultiLayerPerceptron(n_output=len(np.unique(y)),
+ n_features=X.shape[1],
+ n_hidden=150,
+ l2=0.0,
+ l1=0.0,
+ epochs=500,
+ eta=0.01,
+ alpha=0.0,
+ decrease_const=0.0,
+ minibatches=1,
+ shuffle_init=False,
+ shuffle_epoch=False,
+ random_seed=0)
+
+mlp = mlp.fit(X, y)
+
+
+for clf, lab, grd in zip([ppn, ppn, mlp],
+ ['Perceptron', 'Adaline', 'MLP (logistic sigmoid)'],
+ itertools.product([0, 1], repeat=2)):
+
+ clf.fit(X, y)
+ ax = plt.subplot(gs[grd[0], grd[1]])
+ fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
+ plt.title(lab)
+
+plt.show()
+```
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diff --git a/faq/difference-deep-and-normal-learning.md b/faq/difference-deep-and-normal-learning.md
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+# What is the difference between deep learning and usual machine learning?
+
+That's an interesting question, and I try to answer this in a very general way.
+
+In essence, deep learning offers a set of techniques and algorithms that help us to parameterize deep neural network structures -- artificial neural networks with many hidden layers and parameters.
+One of the key ideas behind deep learning is to extract high level features from the given dataset. Thereby, deep learning aims to overcome the challenge of the often tedious feature engineering task and helps with parameterizing traditional neural networks with many layers.
+
+
+Now, to introduce deep learning, let us take a look at a more concrete example involving multi-layer perceptrons (MLPs).
+
+
+On a tangent: The term "perceptron" in MLPs may be a bit confusing since we don't really want only linear neurons in our network. Using MLPs, we want to learn complex functions to solve non-linear problems. Thus, our network is conventionally composed of one or multiple "hidden" layers that connect the input and output layer. Those hidden layers normally have some sort of sigmoid activation function (log-sigmoid or the hyperbolic tangent etc.). For example, think of a log-sigmoid unit in our network as a logistic regression unit that returns continuous values outputs in the range 0-1. A simple MLP could look like this
+
+
+
+
+where y_hat is the final class label that we return as the prediction based on the inputs (x) if this are classification tasks. The "a"s are our activated neurons and the "w"s are the weight coefficients.
+Now, if we add multiple hidden layers to this MLP, we'd also call the network "deep." The problem with such "deep" networks is that it becomes tougher and tougher to learn "good" weights for this network. When we start training our network, we typically assign random values as initial weights, which can be terribly off from the "optimal" solution we want to find. During training, we then use the popular backpropagation algorithm (think of it as reverse-mode auto-differentiation) to propagate the "errors" from right to left and calculate the partial derivatives with respect to each weight to take a step into the opposite direction of the cost (or "error") gradient. **Now, the problem with deep neural networks is the so-called "vanishing gradient" -- the more layers we add, the harder it becomes to "update" our weights because the signal becomes weaker and weaker. Since our network's weights can be terribly off in the beginning (random initialization) it can become almost impossible to parameterize a "deep" neural network with backpropagation.**
+
+**Deep Learning**
+
+Now, this is where "deep learning" comes into play. Roughly speaking, we can think of deep learning as "clever" tricks or algorithms that can help us with the training of such "deep" neural network structures. There are many, many different neural network architectures, but to continue with the example of the MLP, let me introduce the idea of convolutional neural networks (ConvNets). We can think of those as an "add-on" to our MLP that helps us to detect features as "good" inputs for our MLP.
+
+In applications of "usual" machine learning, there is typically a strong focus on the feature engineering part; the model learned by an algorithm can only be so good as its input data. Of course, there must be sufficient discriminatory information in our dataset, however, the performance of machine learning algorithms can suffer substantially when the information is buried in meaningless features. The goal behind deep learning is to automatically learn the features from (somewhat) noisy data; it's about algorithms that do the feature engineering for us to provide deep neural network structures with meaningful information so that it can learn more effectively. **We can think of deep learning as algorithms for automatic "feature engineering," or we could simply call them "feature detectors," which help us to overcome the vanishing gradient challenge and facilitate the learning in neural networks with many layers.**
+
+
+
+Let's consider a ConvNet in context of image classification.
+Here, we use so-called "receptive fields" (think of them as "windows") that slide over our image. We then connect those "receptive fields" (for example of the size of 5x5 pixel) with 1 unit in the next layer, this is the so-called "feature map." After this mapping, we have constructed a so-called convolutional layer. Note that our feature detectors are basically replicates of one another -- they share the same weights. The idea is that if a feature detector is useful in one part of the image it is likely that it is useful somewhere else, but at the same time it allows each patch of image to be represented in several ways.
+
+
+
+
+Next, we have a "pooling" layer, where we reduce neighboring features from our feature map into single units (by taking the max feature or by averaging them, for example). We do this over many rounds and eventually arrive at an almost scale invariant representation of our image (the exact term is "equivariant"). This is very powerful since we can detect objects in an image no matter where they are located.
+
+
+
+
+In essence, the "convolutional" add-on that acts as a feature extractor or filter to our MLP. Via the convolutional layers we aim to extract the useful features from the images, and via the pooling layers, we aim to make the features somewhat equivariant to scale and translation.
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diff --git a/faq/difference_classifier_model.md b/faq/difference_classifier_model.md
new file mode 100644
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+++ b/faq/difference_classifier_model.md
@@ -0,0 +1,17 @@
+# What is the difference between a classifier and a model?
+
+Essentially, the terms "classifier" and "model" are synonymous in certain contexts; however, sometimes people refer to "classifier" as the learning algorithm that learns the model from the training data. To makes things more tractable, let's define some of the key terminology:
+
+- ***Training sample:*** A training sample is a data point *x* in an available training set that we use for tackling a predictive modeling task. For example, if we are interested in classifying emails, one email in our dataset would be one training sample. Sometimes, people also use the synonymous terms *training instance* or *training example*.
+
+- ***Target function:*** In predictive modeling, we are typically interested in modeling a particular process; we want to learn or approximate a particular function that, for example, let's us distinguish spam from non-spam email. The ***target function*** *f(x) = y* is the **true** function *f* that we want to model.
+
+- ***Hypothesis:*** A hypothesis is a certain function that we believe (or hope) is similar to the true function, the *target function* that we want to model. In context of email spam classification, it would be the *rule* we came up with that allows us to separate spam from non-spam emails.
+
+- ***Model:*** In machine learning field, the terms *hypothesis* and *model* are often used interchangeably. In other sciences, they can have different meanings, i.e., the hypothesis would be the "educated guess" by the scientist, and the *model* would be the manifestation of this *guess* that can be used to test the hypothesis.
+
+- ***Learning algorithm:*** Again, our goal is to find or approximate the ***target function***, and the learning algorithm is a set of instructions that tries to *model* the target function using our training dataset. A learning algorithm comes with a ***hypothesis space***, the set of possible hypotheses it can come up with in order to model the unknown target function by formulating the *final hypothesis*
+
+- ***Classifier:*** A classifier is a special case of a *hypothesis* (nowadays, often learned by a machine learning algorithm). A *classifier* is a *hypothesis* or *discrete-valued function* that is used to assign (categorical) class labels to particular data points. In the email classification example, this classifier could be a hypothesis for labeling emails as spam or non-spam. However, a *hypothesis* must not necessarily be synonymous to a *classifier*. In a different application, our *hypothesis* could be a function for mapping study time and educational backgrounds of students to their future SAT scores.
+
+So, we can say that a *classifier* is a special case of a *hypothesis* or *model*: a classifier is a function that assigns a class label to a data point.
diff --git a/faq/different.md b/faq/different.md
index ba3db0a1..83ff1030 100644
--- a/faq/different.md
+++ b/faq/different.md
@@ -11,5 +11,5 @@ This is not yet just another "this is how scikit-learn works" book. I aim to exp
-Sure, this book will also contain a decent amount of "math & equations," and in my opinion, there is now way around it if we want to get away from the "black box thinking." However, I hope I managed to make it really easy to follow so that it can be read by a person who doesn't have a strong math background. Many parts of this book will provide examples in scikit-learn, in my opinion the most beautiful and practical machine learning library. However, we will also implement certain algorithms, which are not part of scikit-learn yet, by ourselves to boost our learning experience, for example, adaptive linear neurons, sequential feature selection algorithms, and multilayer artificial neural networks with backpropagation.
+Sure, this book will also contain a decent amount of "math & equations," and in my opinion, there is no way around it if we want to get away from the "black box thinking." However, I hope I managed to make it really easy to follow so that it can be read by a person who doesn't have a strong math background. Many parts of this book will provide examples in scikit-learn, in my opinion the most beautiful and practical machine learning library. However, we will also implement certain algorithms, which are not part of scikit-learn yet, by ourselves to boost our learning experience, for example, adaptive linear neurons, sequential feature selection algorithms, and multilayer artificial neural networks with backpropagation.
However, this book is not only about learning algorithms, but I also have a strong emphasis on "best practices" and everything that comes before and after the "model learning" in a machine learning application pipeline. Dealing with missing data, transforming categorical data into proper formats, extracting features, evaluating models via cross-validation, comparing algorithms with nested cross-validation, plotting receiver operator characteristics just to name a few ... All in all, this (350 page) short book provides you with everything you need to apply machine learning to real-world problem solving!
diff --git a/faq/dimensionality-reduction.md b/faq/dimensionality-reduction.md
new file mode 100644
index 00000000..763e9abc
--- /dev/null
+++ b/faq/dimensionality-reduction.md
@@ -0,0 +1,31 @@
+# What are the different dimensionality reduction methods in machine learning?
+
+Since there are so many different approaches, let's break it down to "feature selection" and "feature extraction."
+
+Some examples of feature selection:
+
+- L1 regularization (e.g., Logistic regression) and sparsity
+- variance thresholds
+- recursive feature elimination based on the weights of linear models
+- random forests / extra trees and feature importance (calculated as average information gain)
+- sequential forward/backward selection
+- genetic algorithms
+- exhaustive search
+
+Some examples of feature extraction:
+
+- Principal Component Analysis (PCA), unsupervised, returns axes of maximal variance given the constraint that those axes are orthogonal to each other
+- Linear Discriminant Analysis (LDA; not to be confused with Latent Dirichlett Allocation), supervised, returns axes that maximizes class separability (same constraint that axes are also orthogonal); and another article: Linear Discriminant Analysis bit by bit
+- kernel PCA: uses kernel trick to transform non-linear data to a feature space were samples may be linearly separable (in contrast, LDA and PCA are linear transformation techniques
+- supervised PCA
+- and many more non-linear transformation techniques, which you can find nicely summarized here: [Nonlinear dimensionality reduction](https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction)
+
+** So, which technique should we use? **
+
+This also follows the "No Lunch Theorem" principle in some sense: there is no method that is always superior; it depends on your dataset. Intuitively, LDA would make more sense than PCA if you have a linear classification task, but empirical studies showed that it is not always the case. Although kernel PCA can separate concentric circles, it fails to unfold the Swiss Rroll, for example; here, locally linear embedding (LLE) would be more appropriate.
+
+
+
+
+
+
diff --git a/faq/dimensionality-reduction/lle.png b/faq/dimensionality-reduction/lle.png
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diff --git a/faq/dimensionality-reduction/rbf-kpca.png b/faq/dimensionality-reduction/rbf-kpca.png
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diff --git a/faq/dimensionality-reduction/swiss-roll.png b/faq/dimensionality-reduction/swiss-roll.png
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diff --git a/faq/dropout.md b/faq/dropout.md
new file mode 100644
index 00000000..a999e63f
--- /dev/null
+++ b/faq/dropout.md
@@ -0,0 +1,10 @@
+# What is the basic idea behind the dropout technique?
+
+Dropout is a regularization technique, which aims to reduce the complexity of the model with the goal to prevent overfitting.
+
+Using “dropout", you randomly deactivate certain units (neurons) in a layer with a certain probability p from a Bernoulli distribution (typically 50%, but this yet another hyperparameter to be tuned). So, if you set half of the activations of a layer to zero, the neural network won’t be able to rely on particular activations in a given feed-forward pass during training. As a consequence, the neural network will learn different, redundant representations; the network can’t rely on the particular neurons and the combination (or interaction) of these to be present. Another nice side effect is that training will be faster.
+
+Additional technical notes: Dropout is only applied during training, and you need to rescale the remaining neuron activations. E.g., if you set 50% of the activations in a given layer to zero, you need to scale up the remaining ones by a factor of 2. Finally, if the training has finished, you’d use the complete network for testing (or in other words, you set the dropout probability to 0).
+
+For more details, I recommend the original paper: Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). *Dropout: A simple way to prevent neural networks from overfitting.* The Journal of Machine Learning Research, 15(1), 1929-1958.
+(http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)
diff --git a/faq/euclidean-distance.md b/faq/euclidean-distance.md
new file mode 100644
index 00000000..1631fc47
--- /dev/null
+++ b/faq/euclidean-distance.md
@@ -0,0 +1,12 @@
+# What is Euclidean distance in terms of machine learning?
+
+
+It is just a distance measure between a pair of samples *p* and *q* in an *n*-dimensional feature space:
+
+
+
+For example, picture it as a "straight, connecting" line in a 2D feature space:
+
+
+
+The Euclidean is often the "default" distance used in e.g., K-nearest neighbors (classification) or K-means (clustering) to find the "k closest points" of a particular sample point. Another prominent example is hierarchical clustering, agglomerative clustering (complete and single linkage) where you want to find the distance between clusters.
diff --git a/faq/euclidean-distance/eucl-1.png b/faq/euclidean-distance/eucl-1.png
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diff --git a/faq/euclidean-distance/eucl-2.png b/faq/euclidean-distance/eucl-2.png
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diff --git a/faq/evaluate-a-model.md b/faq/evaluate-a-model.md
new file mode 100644
index 00000000..1d7d634b
--- /dev/null
+++ b/faq/evaluate-a-model.md
@@ -0,0 +1,50 @@
+# How do I evaluate a model?
+
+The short answer is to keep an independent test set for your final model -- this has to be data that your model hasn't seen before.
+
+
+However, it all depends on your goal & approach.
+
+
+### Scenario 1:
+
+
+- Just train a simple model.
+
+
+Split the dataset into a separate test and training set. Train the model on the former, evaluate the model on the latter (by "evaluate" I mean calculating performance metrics such as the error, precision, recall, ROC auc, etc.)
+
+
+### Scenario 2:
+
+
+- Train a model and tune (optimize) its hyperparameters.
+
+
+Split the dataset into a separate test and training set. Use techniques such as k-fold cross-validation on the training set to find the "optimal" set of hyperparameters for your model. If you are done with hyperparameter tuning, use the independent test set to get an unbiased estimate of its performance.
+Below I inserted a figure to illustrate the difference:
+
+
+
+
+The first row refers to "Scenario 1", and the 3rd row describes a more "classic" approach where you further split your training data into a training subset and a validation set. Then, you train your model on the training subset and evaluate in on the validation set to optimize its hyperparameters, for example. Eventually, you test it on the independent test set. The fourth row describes the "superior" (more unbiased) approach using k-fold cross-validation as described in "Scenario 2."
+
+
+Also, let me attach an overview of k-fold cross validation in case you are not familiar with it, yet:
+
+
+
+
+
+(Here: E = prediction error, but you can also substitute it by precision, recall, f1-score, ROC auc or whatever metric you prefer for the given task.)
+
+### Scenario 3:
+
+
+- Build different models and compare different algorithms (e.g., SVM vs. logistic regression vs. Random Forests, etc.).
+
+
+Here, we'd want to use nested cross-validation. In nested cross-validation, we have an outer k-fold cross-validation loop to split the data into training and test folds, and an inner loop is used to select the model via k-fold cross-validation on the training fold. After model selection, the test fold is then used to evaluate the model performance. After we have identified our "favorite" algorithm, we can follow-up with a "regular" k-fold cross-validation approach (on the complete training set) to find its "optimal" hyperparameters and evaluate it on the independent test set. Let's consider a logistic regression model to make this clearer:
+Using nested cross-validation you will train *m* different logistic regression models, 1 for each of the *m* outer folds, and the inner folds are used to optimize the hyperparameters of each model (e.g., using gridsearch in combination with k-fold cross-validation. If your model is stable, these *m* models should all have the same hyperparameter values, and you report the average performance of this model based on the outer test folds. Then, you proceed with the next algorithm, e.g., an SVM etc.
+
+
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diff --git a/faq/evaluate-a-model/evaluate_overview.pdf b/faq/evaluate-a-model/evaluate_overview.pdf
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diff --git a/faq/feature_sele_categories.md b/faq/feature_sele_categories.md
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--- /dev/null
+++ b/faq/feature_sele_categories.md
@@ -0,0 +1,54 @@
+# What is the difference between filter, wrapper, and embedded methods for feature selection?
+
+
+Wrapper methods measure the "usefulness" of features based on the classifier performance. In contrast, the filter methods pick up the intrinsic properties of the features (i.e., the "relevance" of the features) measured via univariate statistics instead of cross-validation performance. So, wrapper methods are essentially solving the "real" problem (optimizing the classifier performance), but they are also computationally more expensive compared to filter methods due to the repeated learning steps and cross-validation.
+The third class, embedded methods, are quite similar to wrapper methods since they are also used to optimize the objective function or performance of a learning algorithm or model. The difference to wrapper methods is that an intrinsic model building metric is used during learning.
+Let me give you a -- off the top of my head -- list of examples from these three categories.
+
+
+#### Filter methods:
+- information gain
+- chi-square test
+- fisher score
+- correlation coefficient
+- variance threshold
+
+
+#### Wrapper methods:
+- recursive feature elimination
+- sequential feature selection algorithms
+- genetic algorithms
+
+
+#### Embedded methods:
+- L1 (LASSO) regularization
+- decision tree
+
+
+(Note that I would count transformation and projection techniques such as Principal Component Analysis as a feature *extraction* approach, since we are projecting the data into a new feature space.)
+To give you a more hands-on illustration, let me pick one algorithm from each category and explain w
+
+
+**1). A Filter method Example: Variance Thresholds**
+
+
+Here, we simply compute the variance of each feature, and we select the subset of features based on a user-specified threshold. E.g., "keep all features that have a variance greater or equal to *x*" or "keep the the top *k* features with the largest variance." We assume that features with a higher variance may contain more useful information, but note that we are not taking the relationship between feature variables or feature and target variables into account, which is one of the drawbacks of filter methods.
+
+
+**2). A Wrapper Method Example: Sequential Feature Selection**
+
+Sequential Forward Selection (SFS), a special case of sequential feature selection, is a greedy search algorithm that attempts to find the "optimal" feature subset by iteratively selecting features based on the classifier performance. We start with an empty feature subset and add one feature at the time in each round; this one feature is selected from the pool of all features that are not in our feature subset, and it is the feature that -- when added -- results in the best classifier performance. Since we have to train and cross-validate our model for each feature subset combination, this approach is much more expensive than a filter approach such as the variance threshold, which we discussed above.
+
+
+**3). An Embedded Method Example: L1 Regularization**
+
+
+L1 (or LASSO) regression for generalized linear models can be understood as adding a penalty against complexity to reduce the degree of overfitting or variance of a model by adding more bias. Here, we add a penalty term directly to the cost function,
+
+ regularized_cost = cost + regularization_penalty
+
+In L1 regularization, the penalty term is
+
+L1 : λ Σki |wi| = λ |**w**|1,
+
+where **w** is our *k-dimensional* feature vector. Through adding the L1 term, our objective function now becomes the minimization of the regularized cost, and since the penalty term grows with the value of the weight parameters (λ is just a free parameter to fine-tune the regularization strength), we can induce sparsity through this L1 vector norm, which can be considered as an intrinsic way of feature selection that is part of the model training step.
diff --git a/faq/implementing-from-scratch.md b/faq/implementing-from-scratch.md
new file mode 100644
index 00000000..80872c18
--- /dev/null
+++ b/faq/implementing-from-scratch.md
@@ -0,0 +1,18 @@
+# Why do you and other people sometimes implement machine learning algorithms from scratch?
+There are several different reasons why implementing algorithms from scratch can be useful:
+
+1. it can help us to understand the inner works of an algorithm
+2. we could try to implement an algorithm more efficiently
+3. we can add new features to an algorithm or experiment with different variations of the core idea
+4. we circumvent licensing issues (e.g., Linux vs. Unix) or platform restrictions
+5. we want to invent new algorithms or implement algorithms no one has implemented/shared yet
+6. we are not satisfied with the API and/or we want to integrate it more "naturally" into an existing software library
+
+Let us narrow down the phrase "implementing from scratch" a bit further in context of the 6 points I mentioned above. When we talk about "implementing from scratch," we need to narrow down the scope to make this question really tangible. Let's talk about a particular algorithm, simple logistic regression, to address the different points using concrete examples. I'd claim that logistic regression has been implemented more than thousand times.
+
+One reason why we'd still want to implement logistic regression from scratch could be that we don't have the impression that we fully understand how it works; we read a bunch of papers, and kind of understood the core concept though. Using a programming language for prototyping (e.g., Python, MATLAB, R, and so forth), we could take the ideas from paper and try to express them in code -- step by step. An established library, such as scikit-learn, can help us than double-check the results and to see if our implementation -- our idea of how the algorithm is supposed to work -- is correct. Here, we don't really care about efficiency; although we spend so much time to implement the algorithm, we probably want to use an established library if we want to perform some serious analysis in our research lab and/or company. Established libraries are typically more trustworthy -- they have been battle-tested by many people, people who may have already encountered certain edge cases and made sure that there are no weird surprises. Furthermore, it is also more likely that this code was highly optimized for computational efficiency over time. Here, implementing from scratch simply serves the purpose of self-assessment. Reading about a concept is one thing, but putting it to action is a
+whole other level of understanding -- and being able to explain it to others is the icing on the cake.
+
+Another reason why we want to re-implement logistic regression from scratch may be that we are not satisfied with the "features" of other implementations. Let's us naively assume that other implementations don't have regularization parameters, or it doesn't support multi-class settings (i.e., via One-vs-All, One-vs-One, or softmax). Or if computational (or predictive) efficiency is an issue, maybe we want to implement it with another solver (e.g., Newton vs. Gradient Descent vs. Stochastic Gradient Descent, etc.). But improvements concerning computational efficiency does not necessarily need to be in terms of modifications of the algorithms, but we could use lower-level programming languages, for example, Scala instead of Python, or Fortran instead of Scala, ... this can go all down to assembly or machine code, or designing a chip that is optimized for running such kind of analysis. However, if you are a machine learning (or "data science") practitioner or researcher, this is probably something you should delegate to the software engineering team.
+
+To come back to the main question: Different people implement algorithms from scratch for various reasons. Personally, when I implement algorithms from scratch, I do it because of the learning experience.
diff --git a/faq/inventing-deeplearning.md b/faq/inventing-deeplearning.md
new file mode 100644
index 00000000..ac16452a
--- /dev/null
+++ b/faq/inventing-deeplearning.md
@@ -0,0 +1,14 @@
+# Why did it take so long for deep networks to be invented?
+
+It's not that "deep networks" haven't been around in the 1960s, but the problem was how to train them. In the 1970s, backpropagation was "invented" or re-discovered -- I don't want to quote a single resource here not to offend any of the parties involved since this is a sensitive topic those days ... In any case, the problem was the "vanishing gradient," when gradient-based methods were used for learning the weights. It was observed that there was no gain going beyond 1-3 hidden layers.
+
+
+So back to the question: Deep network structures existed, but it was hard/impossible to train them appropriately. I'd say the 2 main reasons why this field experienced such a leap in the recent years
+are
+
+
+1. availability of computing resources
+2. clever ideas to pre-train a neural network
+
+
+The second point is what deep learning is all about; in a nutshell, we pre-train our deep neural networks using unsupervised learning, but this goes beyond the scope of the question ...
diff --git a/faq/issues-with-clustering.md b/faq/issues-with-clustering.md
new file mode 100644
index 00000000..f0b6038e
--- /dev/null
+++ b/faq/issues-with-clustering.md
@@ -0,0 +1,29 @@
+# What are some of the issues with Clustering?
+
+I wouldn't necessarily call most of them "issues" but rather "challenges". For example, *k*-means:
+
+
+- The different results via *k*-means with distinct random initializations are definitely a problem. However, we could use *k*-means++ as an alternative, and if it's computationally feasible, we want to run your algorithm multiple times with different seeds and pick the one with e.g., lowest within cluster SSE (sum of squared errors)
+
+
+- The number of clusters is (typically) not known a priori (that's basically the characteristic of unsupervised learning problems), but there are a few "performance" or "evaluation metrics one can use to infer a "satisfying" grouping against the value of K; this is also called the elbow method:
+
+
+
+Here, it seems that k=3 would be a good pick. Let's have a look at the accompanying 2D dataset that I used to train the *k*-means algorithm and see if our intuition agrees:
+
+
+
+
+
+I'd say k=3 is definitely a reasonable pick. However, note that the "elbow" is typically not as clear as shown above. Moreover, note that in practice we normally work with higher-dimensional datasets so that we can't simply plot our data and double-check visually. (We could use unsupervised dimensionality reduction techniques though such as PCA). In fact, if we already knew that the 3 clusters belong to three different groups, this would be a classification task.
+
+
+Anyway, there are other useful evaluation metrics such as the silhouette coefficient, which gives us some idea of the cluster sizes and shapes. Using the same dataset, let me give you a "good" silhouette plot (with k=3) and a not so decent one (k=2)
+
+
+
+
+
+
+I would say that the biggest "shortcoming" in *k*-means may be that we assume that the groups come in spherical or globular shapes, which is rarely the case with "real-world" data. In contrast, I could think of choosing the "optimal" *k* as just another hyperparameter optimization procedure, which is also necessary for almost every supervised learning algorithm.
diff --git a/faq/issues-with-clustering/clusters_kmeans.png b/faq/issues-with-clustering/clusters_kmeans.png
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diff --git a/faq/issues-with-clustering/elbow.png b/faq/issues-with-clustering/elbow.png
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diff --git a/faq/issues-with-clustering/silhouette_bad.png b/faq/issues-with-clustering/silhouette_bad.png
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diff --git a/faq/issues-with-clustering/silhouette_good.png b/faq/issues-with-clustering/silhouette_good.png
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diff --git a/faq/large-num-features.md b/faq/large-num-features.md
new file mode 100644
index 00000000..de44ce00
--- /dev/null
+++ b/faq/large-num-features.md
@@ -0,0 +1,46 @@
+# How do you attack a machine learning problem with a large number of features?
+
+
+There are 3 main strategies to reduce the number of features if necessary to avoid overfitting (due to the curse of dimensionality) and/or reduce the computational complexity (i.e., increase the computational efficiency).
+
+
+**1) Regularization and Sparsity**
+
+- If supported by the model, I would recommend L1 or ElasticNet regularization to zero-out some features.
+
+
+**2) Feature Selection**
+
+- We could try various different feature selection algorithms (e.g., selecting by variance or by greedy search: sequential backward/forward selection, genetic algorithms, etc.)
+
+
+**3) Feature Extraction**
+
+- We could compress your feature space via transformation onto a lower-dimensional subspace. One popular example would be Principal Component Analysis. But we have to keep in mind that PCA is a linear transformation technique, which may be problematic in non-linear problems. For example, let's consider a simple "concentric circles" dataset:
+
+
+
+
+Let's assume the blue samples belong to one class, and the red circles belong to a second class. Our goal is to train a model for classification. Furthermore, we assume that this dataset has too many dimensions (okay, we only have 2 features here, but we need to keep it "simple" for visualization purposes). Now, we want to compress the data onto a lower-dimensional subspace, here: 1 dimension.
+Let's start with "standard" PCA. Can you spot the problem?
+
+
+
+
+
+The two classes are not separable anymore...
+Let's use kernel PCA:
+
+
+
+This is much better; we can now train a linear classifier to separate those two classes. However, the problem is that we introduce an additional hyperparameter (gamma) that needs to be tuned. Also, this "kernel trick" does not work for any dataset, and there are also many more manifold learning techniques that are "more powerful"/appropriate than kernel PCA.
+For example, locally linear embedding (LLE) to unfold the famous Swiss Roll:
+
+
+
+
+
+
+
+
+
diff --git a/faq/large-num-features/concentric-circles.png b/faq/large-num-features/concentric-circles.png
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diff --git a/faq/lazy-knn.md b/faq/lazy-knn.md
new file mode 100644
index 00000000..0d68fef3
--- /dev/null
+++ b/faq/lazy-knn.md
@@ -0,0 +1,7 @@
+# Why is Nearest Neighbor a Lazy Algorithm?
+
+Although, Nearest neighbor algorithms, for instance, the K-Nearest Neighbors (K-NN) for classification, are very "simple" algorithms, that's not why they are called *lazy* ;). K-NN is a lazy learner because it doesn't learn a discriminative function from the training data but "memorizes" the training dataset instead.
+
+For example, the logistic regression algorithm learns its model weights (parameters) during training time. In contrast, there is no training time in K-NN. Although this may sound very convenient, this property doesn't come without a cost: The "prediction" step in K-NN is relatively expensive! Each time we want to make a prediction, K-NN is searching for the nearest neighbor(s) in the entire training set! (Note that there are certain tricks such as BallTrees and KDtrees to speed this up a bit.)
+
+To summarize: An eager learner has a model fitting or training step. A lazy learner does not have a training phase.
diff --git a/faq/lda-vs-pca.md b/faq/lda-vs-pca.md
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+# What is the difference between LDA and PCA for dimensionality reduction?
+
+Both LDA and PCA are linear transformation techniques: LDA is a supervised whereas PCA is unsupervised -- PCA ignores class labels.
+
+We can picture PCA as a technique that finds the directions of maximal variance:
+
+
+
+In contrast to PCA, LDA attempts to find a feature subspace that maximizes class separability (note that LD 2 would be a very bad linear discriminant in the figure above).
+
+
+
+
+Remember that LDA makes assumptions about normally distributed classes and equal class covariances.
+If you are interested in an empirical comparison: A. M. Martinez and A. C. Kak. PCA versus LDA. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 23(2):228–233, 2001). (PCA tends to result in better classification results in an image recognition task if the number of samples for a given class was relatively small.)
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diff --git a/faq/linear-gradient-derivative.md b/faq/linear-gradient-derivative.md
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+
+
+
+# How do you derive the Gradient Descent rule for Linear Regression and Adaline?
+
+Linear Regression and Adaptive Linear Neurons (Adalines) are closely related to each other. In fact, the Adaline algorithm is a identical to linear regression except for a threshold function  that converts the continuous output into a categorical class label
+
+
+
+where $z$ is the net input, which is computed as the sum of the input features **x** multiplied by the model weights **w**:
+
+
+
+(Note that  refers to the bias unit so that .)
+
+In the case of linear regression and Adaline, the activation function  is simply the identity function so that .
+
+
+
+Now, in order to learn the optimal model weights **w**, we need to define a cost function that we can optimize. Here, our cost function  is the sum of squared errors (SSE), which we multiply by  to make the derivation easier:
+
+
+
+where  is the label or target label of the *i*th training point .
+
+(Note that the SSE cost function is convex and therefore differentiable.)
+
+In simple words, we can summarize the gradient descent learning as follows:
+
+1. Initialize the weights to 0 or small random numbers.
+2. For *k* epochs (passes over the training set)
+ 3. For each training sample 
+ - Compute the predicted output value 
+ - Compare  to the actual output  and Compute the "weight update" value
+ - Update the "weight update" value
+ 4. Update the weight coefficients by the accumulated "weight update" values
+
+Which we can translate into a more mathematical notation:
+
+1. Initialize the weights to 0 or small random numbers.
+2. For *k* epochs
+ 3. For each training sample 
+ - 
+ -  (where *η* is the learning rate);
+ - 
+ 3. 
+
+Performing this global weight update
+
+,
+
+can be understood as "updating the model weights by taking an opposite step towards the cost gradient scaled by the learning rate *η*"
+
+
+
+where the partial derivative with respect to each  can be written as
+
+
+
+
+
+To summarize: in order to use gradient descent to learn the model coefficients, we simply update the weights **w** by taking a step into the opposite direction of the gradient for each pass over the training set -- that's basically it. But how do we get to the equation
+
+
+
+Let's walk through the derivation step by step.
+
+
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diff --git a/faq/logistic-analytical.md b/faq/logistic-analytical.md
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+# Is there an analytical solution to Logistic Regression similar to the Normal Equation for Linear Regression?
+
+Unfortunately, there is no closed-form solution for maximizing the log-likelihood (or minimizing the inverse, the logistic cost function); at least it has not been found, yet.
+
+There's the exception where you only have 2 obervations, and there is this paper
+
+*Lipovetsky, Stan. ["Analytical closed-form solution for binary logit regression by categorical predictors."](http://www.tandfonline.com/doi/abs/10.1080/02664763.2014.932760) Journal of Applied Statistics 42.1 (2015): 37-49. (Analytical closed-form solution for binary logit regression by categorical predictors)*
+
+which "shows that for categorical explanatory variables, it is possible to present the solution in the analytical closed-form formulae."
+The problem is that the logistic sigmoid function is non-linear -- in case of linear regression, you are assuming independent Gaussian noise.
diff --git a/faq/logistic-boosting.md b/faq/logistic-boosting.md
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+# Do bagging and boosting can be used with logistic regression?
+
+I am not sure if bagging would make much sense for logistic regression -- in bagging, we reduce the variance of the deep decision tree models that overfit the training data, which wouldn't really apply to logistic regression.
+
+Boosting could work though, however, I think that "stacking" would be a better approach here. Stacking would be more "powerful" since we don't use a pre-specified equation to adjust the weight, rather, we train a meta-classifier to learn the optimal weights to combine the models.
+
+
+Here's one of the many interesting, related papers, I recommend you to check out :)
+
+- "Is Combining Classifiers with Stacking Better than Selecting the Best One?"
diff --git a/faq/logistic-why-sigmoid.md b/faq/logistic-why-sigmoid.md
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+# Logistic Regression: Why sigmoid function?
+
+So, one of the nice properties of logistic regression is that the sigmoid function outputs the conditional probabilities of the prediction, the class probabilities. How does it work?
+Let's start with the so-called "odds ratio" *p / (1 - p)*, which describes the ratio between the probability that a certain, positive, event occurs and the probability that it doesn't occur -- where positive refers to the "event that we want to predict", i.e., *p(y=1 | x)*.
+
+(Note that logistic regression a special kind of sigmoid function, the logistic sigmoid; other sigmoid functions exist, for example, the hyperbolic tangent).
+
+
+
+So, the more likely it is that the positive event occurs, the larger the odds' ratio.
+Now, if we take the natural log of this odds' ratio, the log-odds or logit function, we get the following
+
+
+
+
+
+Next, let's use this *log transformation* to model the relationship between our explanatory variables and the target variable:
+
+
+
+Now, keep it mind that we are not trying to predict the right part of the equation above, since *p(y=1|x)* is what we are really interested in. So, let's take the inverse of this logit function ... et viola, we get the logistic sigmoid:
+
+
+
+which returns the class probabilities *p(y=1|x)* from the inputs
+
+
+
+
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diff --git a/faq/logistic_regression_linear.md b/faq/logistic_regression_linear.md
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+# Why is logistic regression considered a linear model?
+
+
+The short answer is: Logistic regression is considered a generalized linear model because the outcome **always** depends on the **sum** of the inputs and parameters. Or in other words, the output cannot depend on the product (or quotient, etc.) of its parameters!
+
+So, why is that? Let’s recapitulate the basics of logistic regression first, which hopefully makes things more clear. Logistic regression is an algorithm that learns a model for binary classification. A nice side-effect is that it gives us the *probability* that a sample belongs to class 1 (or vice versa: class 0). Our objective function is to minimize the so-called logistic function Φ (a certain kind of sigmoid function); it looks like this:
+
+
+
+Now, if *φ(z)* is larger than *0.5* (alternatively: if *z* is larger than *0*), we classify an input as class 1 (and class 0, otherwise). Although logistic regression produces a linear decision surface (see the classification example in the figure below) this logistic (activation) function doesn't look very linear at all, right!?doesn't look very linear at all, right!?
+
+
+
+
+So, let's dig a bit deeper and take a look at the equation we use to compute *z* -- the net input function!
+
+
+
+The net input function is simply the dot product of our input features and the respective model coefficients **w**:
+
+
+
+Here, x0 refers to the weight of the bias unit which is always equal to 1 (a detail we don’t have to worry about here). I know, mathematical equations can be a bit "abstract" at times, so let's look at a concrete example.
+
+Let's assume we have a sample training point **x** consisting of 4 features (e.g., *sepal length*, *sepal width*, *petal length*, and *petal width* in the [*Iris dataset*](https://archive.ics.uci.edu/ml/datasets/Iris)):
+
+ x = [1, 2, 3, 4]
+
+Now, let's assume our weight vector looks like this:
+
+ w = [0.5, 0.5, 0.5, 0.5]
+
+Let's compute *z* now!
+
+z = wTx = 1*0.5 + 2*0.5 + 3*0.5 + 4*0.5 = 5
+
+---
+
+Not that it is important, but we have a 99.3% chance that this sample belongs to class 1:
+
+*Φ(z=148.41) = 1 / (1 + e-5) = 0.993*
+
+---
+
+The key is that our model is ***additive***
+our outcome *z* depends on the additivity of the weight parameter values, e.g., :
+
+*z = w1x1 + w2x2*
+
+There's no interaction between the weight parameter values, nothing like w1x1 * w2x2
+
+ or so, which would make our model non-linear!
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diff --git a/faq/logisticregr-neuralnet.md b/faq/logisticregr-neuralnet.md
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+# What is the relation between Logistic Regression and Neural Networks and when to use which?
+
+
+The "classic" application of logistic regression model is binary classification. However, we can also use "flavors" of logistic to tackle multi-class classification problems, e.g., using the One-vs-All or One-vs-One approaches, via the related softmax regression / multinomial logistic regression.
+Although there are kernelized variants of logistic regression exist, the standard "model" is a linear classifier. Thus, logistic regression is useful if we are working with a dataset where the classes are more or less "linearly separable." For "relatively" very small dataset sizes, I'd recommend comparing the performance of a discriminative Logistic Regression model to a related Naive Bayes classifier (a generative model) or SVMs, which may be less susceptible to noise and outlier points. Even so, logistic regression is a great, robust model for simple classification tasks; the March Madness prediction contest this year was one by 2 professors using a logistic regression model
+
+
+> Professors Lopez and Matthews didn’t use any of the au courant methods in data science circles, either: no deep learning, no hierarchical clustering, no compressed sensing; just a good old model called logistic regression, which turns a number (like a point spread) into an estimated probability that team A will beat team B.
+([The Math of March Madness](http://www.nytimes.com/2015/03/22/opinion/sunday/making-march-madness-easy.html?_r=0))
+
+
+Neural networks are somewhat related to logistic regression. Basically, we can think of logistic regression as a one layer neural network.
+
+
+
+
+
+In fact, it is very common to use logistic sigmoid functions as activation functions in the hidden layer of a neural network -- like the schematic above but without the threshold function.
+
+
+
+
+
+It's fine to use the threshold function in the output layer if we have a binary classification task (in this case, you'd only have one sigmoid unit in the output layer). In the case of multi-class classification, we can use a generalization of the One-vs-All approach; i.e., we encode your target class labels via one-hot encoding.
+
+
+
+
+
+For example, we would encode the three class labels in the familiar Iris dataset (0=Setosa, 1=Versicolor, 2=Virginica) as follows:
+
+
+
+Then, for the prediction step after learning the model, we just return the "argmax," the index in the output vector with the highest value as the class label. That's fine if we are only interested in the class label prediction. Now, if we want "meaningful" class probabilities, that is, class probabilities that sum up to 1, we could use the softmax function (aka "multinomial logistic regression"). In softmax, the probability of a particular sample with net input *z* belongs to the i th class can be computed with a normalization term in the denominator that is the sum of all *M* linear functions:
+
+
+
+
+
+Although, I mentioned that neural networks (multi-layer perceptrons to be specific) may use logistic activation functions, the hyperbolic tangent (tanh) often tends to work better in practice, since it's not limited to only positive outputs in the hidden layer(s).
+
+
+
+
+Anyway, going back to the logistic sigmoid. One of the nice properties of logistic regression is that the logistic cost function (or max-entropy) is convex, and thus we are guaranteed to find the global cost minimum. But, once we stack logistic activation functions in a multi-layer neural network, we'll lose this convexity. Looking only at a single weight / model coefficient, we can picture the cost function in a multi-layer perceptron as a rugged landscape with multiple local minima that can trap the optimization algorithm:
+
+
+
+
+
+However, in practice, backpropagation works quite well for 1 or 2 layer neural networks (and there are deep learning algos such as autoencoders) to help with deeper architectures. Even if you may likely converge to a local minima, you often still end up with a powerful predictive model.
+So, in summary, I would recommend to approach a classification problem with simple models first (e.g., logistic regression). In some cases, this may already solve your problem sufficiently well. However, if you are not satisfied with it's performance and you have sufficient training data, I'd try to train a computationally more expensive neural network, which has the advantage to learn more complex, non-linear functions.
+
+
+
+
+
+
+
+
+
+
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diff --git a/faq/many-deeplearning-libs.md b/faq/many-deeplearning-libs.md
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+# Why are there so many deep learning libraries?
+
+In my opinion, the main reason is that everything in the Deep Learning field is still highly experimental. The libraries are usually a side-product of someone's own research. In addition, deep learning algorithms are not so general (in terms of writing a general code that can be applied to many different problems) in comparison to other algorithms, e.g., Random forests, logistic regression, SVMs, etc. Thus, everyone has his or her particular idea how a good interface may look like; hence, they may end up developing a new library. Also, they probably want to incorporate their personal research in the respective library and get credit for it -- it's much easier to have your personal library that you can tweak and change as you wish.
+
+However, I also think that it may only seem that there are so many libraries because it is a truly trendy topic at the moment, and we are living in the time and age where open-source and code sharing is (fortunately) very popular. I guess there are 100x more libraries that implement SVMs, logistic regression etc. than deep learning libraries.
+
+
+Anyway, if you are interested in implementing neural networks yourself, have a look at [Theano](http://deeplearning.net/software/theano/) -- NumPy on steroids as how it is commonly called. Not only does it allow you to use numerical expressions more efficiently, but they also implement tensors and let you utilize GPUs. Theano is actually what most of the "many deep learning libraries" in Python are using, e.g,.
+
+- [Lasagne](https://github.com/Lasagne/Lasagne)
+- [Keras](http://keras.io)
+- [PyLearn 2](https://github.com/lisa-lab/pylearn2)
+
+...
diff --git a/faq/median-vs-mean.md b/faq/median-vs-mean.md
new file mode 100644
index 00000000..a1857c09
--- /dev/null
+++ b/faq/median-vs-mean.md
@@ -0,0 +1,17 @@
+# When should one use median, as opposed to the mean or average?
+
+It really depends on the distribution of the data and the question you are trying to address.
+
+Consider a symmetric distribution (here, I've drawn 10,000 random samples from a standard normal distribution):
+
+
+
+In this case, the median and mean will be very similar (mean≈0.00337, median≈0.01690). In fact, the median and mean will be the same if your data sample is perfectly symmetrical distributed, for example, consider [1, 3, 5] or [1, 3, 3, 5].
+
+Now, if you data is skewed, you are likely more interested in computing the median, which is less susceptible to outliers and extreme values. Let's consider a classic examples "salaries." Here, I plotted [FGCU salary dataset from OpenData](https://opendata.socrata.com/dataset/FGCU-salary-dataset/fjqw-ymup):
+
+
+
+As we can see, there is a substantial difference between the mean and median value. Here, the mean picks up the relatively high but infrequent salaries at > 15,000.
+
+Again, it depends on the question you are asking of the data, however, if you question is "what is the average salary" in terms of "what is the typical salary of an employee," then median would be a much better measure than the mean.
diff --git a/faq/median-vs-mean/FGCU_salary_dataset.csv b/faq/median-vs-mean/FGCU_salary_dataset.csv
new file mode 100644
index 00000000..35e53300
--- /dev/null
+++ b/faq/median-vs-mean/FGCU_salary_dataset.csv
@@ -0,0 +1,989 @@
+Last Name,First Name,Annual Salary,FGCU Hire Date,College/Dept,Class Title,Working Title,Employee Class
+Gray-Vickrey,Margaret,$127972.27,07/01/1996,Academic Affairs Administration,Assoc. Vice President/Prof.,Assc. Provost/Assoc. VP C&I,"Faculty Admin 10, 11, 12 mo"
+Rogers,Hudson,$134316.57,08/07/1997,Academic Affairs Administration,Assoc. Vice President/Prof.,Associate VP & Professor,"Faculty Admin 10, 11, 12 mo"
+Hart,Erika,$26650.11,08/03/2001,Academic Affairs Administration,Program Asst,Program Assistant,Support Personnel NonExempt PT
+Deschene,Catherine,$63960.27,01/05/2004,Academic Affairs Administration,Executive Asst,Exec Asst to Provost & VPAA,Administrative/Professional
+Baker,Jennifer,$90000.00,05/26/2009,Academic Affairs Administration,"Dir, Academic Support Services","Dir., Budgets & Management Svs",Administrative/Professional
+Toll,Ronald,$227250.00,06/23/2008,Academic Affairs Administration,Provost & VP Academic Affairs,Provost & VP Academic Affairs,Executive Service
+Jones,Troy,$45000.00,08/07/2009,Academic Foundation Support,Instructor I,Music,Faculty 9 Months
+Fuentes,Barbara,$28280.00,08/27/2007,Adaptive Services,Program Asst,Adaptive Svcs Assistant,SP NonExempt Full Time
+Dingess,Cassandra,$32000.00,02/09/2009,Adaptive Services,Interpreter/Hearing Impaired,Interpreter-Hearing Impairment,SP NonExempt Full Time
+Parker,Beverly,$37605.49,07/01/1997,Admission Operations,Admissions/Registrar Officer,Admissions Officer,SP NonExempt Full Time
+Bannworth,Starr,$44660.26,07/03/1997,Admission Operations,"Coord, Admissions/Registration","Coord, Articulation/Itn'l Adm",Administrative/Professional
+Moore,Whitney,$25250.00,12/03/2007,Admission Operations,Sr Clerk,Senior Clerk,SP NonExempt Full Time
+Szmania,Brandy,$30000.00,08/10/2009,Admission Operations,Admissions/Registrar Officer,Admissions Officer,SP NonExempt Full Time
+Edwards,Kimberly,$27270.00,11/17/2008,Admission Operations,Program Asst,Program Asst,SP NonExempt Full Time
+Elshahawy,Maria,$31613.00,04/30/2007,Admission Operations,Admissions/Registrar Officer,Admissions Officer,SP NonExempt Full Time
+Brundage,Debra,$30000.00,08/10/2009,Admission Operations,Admissions/Registrar Officer,Admissions Officer,SP NonExempt Full Time
+McClean,Meredith,$60000.00,04/01/2005,Admission Operations,"Assoc Dir, Admissions/Regist","Assoc Dir, Admissions Operatio",Administrative/Professional
+Moore,Sandra,$33865.53,10/17/2005,Admission Operations,Admissions/Registrar Officer,"Manager, Admissions Records",SP NonExempt Full Time
+Grier,Joylynn,$30000.00,08/17/2009,Admission Operations,Admissions/Registrar Officer,Admission Officer,SP NonExempt Full Time
+Blood,William,$50500.00,12/01/2003,Alico Arena,"Asst Dir, Intercolleg Athletic",Asst Athletic Dir. for Facilit,Administrative/Professional
+Fairchild,Matthew,$33330.00,07/17/2006,Alico Arena,"Coord., Intercollege. Athletic",Asst Dir for Facilities,Administrative/Professional
+Collver,Matthew,$28562.80,01/29/2007,Alico Arena,Sr Univ. Union Program Spec.,Box Office Supervisor,Support Personnel - Exempt
+Reeners,Emma,$38000.00,05/20/2009,Aquatics Operations,"Coord, Public Functions",Interim Aquatics Facility Coor,Administrative/Professional
+Perchan,Stanley,$92793.40,11/03/2000,Athletic Administration,Assoc Dir Intercolleg Athletic,Sr Assoc Athl Dir/Ext. Affairs,Administrative/Professional
+Kavanagh,Kenneth,$170000.00,06/15/2009,Athletic Administration,"Dir, Intercollegiate Athletics",Dirrector of Athletics,Administrative/Professional
+Smith,Kathryn,$30300.00,09/08/2008,Athletic Administration,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Peterson,Katherine,$66306.50,09/29/1997,Athletic Administration,Assoc Dir Intercolleg Athletic,Assoc Athl Dir Std Athl Svc,Administrative/Professional
+Blankenship,Patricia,$42000.00,08/18/2008,Athletic Administration,Administrative Asst,Administrative Asst,SP NonExempt Full Time
+Horton,J Webb,$57248.01,08/01/2001,Athletic Administration,Head Athletic Coach,Asst Ath Dir/Hd Mns Tennis Cch,Administrative/Professional
+Sorem,Colleen,$60000.00,07/15/2008,Athletic Administration,"Asst Dir, Intercolleg Athletic",Asst Athletic Dir of Business,Administrative/Professional
+Rouse,Jessica,$35000.00,10/05/2009,Athletic Administration,"Coord., Intercollege. Athletic",Director of Compliance,Administrative/Professional
+Popple,Randy,$39273.85,07/01/2007,Athletic Administration,"Coord., Intercollege. Athletic",Strength & Conditioning Coach,Administrative/Professional
+Da Silveira,Denise,$45000.00,11/09/2009,Athletic Marketing,"Coord, Info/Publications Serv",Dir of Corp Sales & Marketing,Administrative/Professional
+Araujo,Elisa,$32522.60,07/01/2007,Athletic Medicine,Athletic Trainer,Assistant Athletic Trainer,Administrative/Professional
+Estes,Michael,$61012.18,07/05/2005,Athletic Medicine,"Asst Dir, Intercolleg Athletic",Asst Athletic Dir/Hd Athl Trnr,Administrative/Professional
+Pitt,William,$40000.00,07/01/2009,Athletics Laundry,"Coord., Intercollege. Athletic",Director of Operations,Administrative/Professional
+Steinberg,Carl,$48480.00,11/13/2007,Auxiliary Bookstore Operating,"Coord, Business/Auxiliary Serv","Coord, Contract Operations",Administrative/Professional
+Prive,Loren,$75000.00,01/13/2006,Auxiliary Duplicating Operating,"Dir, Business/Auxiliary Serv",Dir. Business Operations,Administrative/Professional
+Rosenhauer,Marianne,$43000.00,08/25/2008,AVP Curriculum & Instruction,"Coord, Management Analysis","Coord, Technical Support",Administrative/Professional
+Amundsen,Sherrie,$27899.41,01/14/2000,AVP Curriculum & Instruction,"Coord, Academic Support","Coord, Curriculum/Instruction",Administrative/Professional PT
+Wiggins,Lula,$33000.00,01/18/2008,AVP Curriculum & Instruction,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Duff,Cathy,$112003.75,07/01/1997,AVP Curriculum & Instruction,"Dir, Research Programs/Serv",Acting Assoc. VP for C & I,Administrative/Professional
+Vazquez,David,$102010.00,10/09/1995,Budget Office,"Dir, University Budgets","Dir, University Budgets",Administrative/Professional
+Dorado,Juanita,$44884.40,05/01/2006,Budget Office,"Coord, Budgeting",Budget Analyst,Administrative/Professional
+Phillips,Susan,$37867.19,08/05/1996,Bursar,Accountant,Accountant,SP NonExempt Full Time
+Rusnak,Mark,$60762.26,07/01/2003,Bursar,"Asst Dir, Bus/Auxiliary Serv",Bursar,Administrative/Professional
+Heins,Christina,$25743.24,04/23/2007,Bursar,Sr Fiscal Asst,Cashier,SP NonExempt Full Time
+Sugg,Jennifer,$32000.00,09/08/2008,Bursar,Accountant,Accountant,SP NonExempt Full Time
+Wasson,Lisa,$48854.79,09/17/1998,Bursar C&G P/R,"Coord, Accounting","Coord, Accounting",Administrative/Professional
+Stevens,Joanne,$25488.36,07/09/2007,Bursar F&A Aux Payroll,Sr Fiscal Asst,Cashier,SP NonExempt Full Time
+Moses,Kenneth,$32000.00,11/02/2009,Business Operations,Accountant,Acct for Business Operations,SP NonExempt Full Time
+Danielson,Bruce,$27542.70,10/30/2006,Campus Involvement A&S,Program Asst,Program Asst,SP NonExempt Full Time
+Wang,Xue Qin,$36000.00,07/14/2008,Campus Involvement A&S,"Coord, Student Affairs",Programming Coord,Administrative/Professional
+Schorsch,Matthew,$36000.00,07/24/2008,Campus Involvement A&S,"Coord, Student Affairs",Programming Coord.,Administrative/Professional
+Anderson,Richard,$28973.85,09/21/2005,Campus Police & Safety,Police Communications Operator,Police Communication Officer,SP NonExempt Full Time
+Davidson,Bruce,$39000.00,08/17/2009,Campus Police & Safety,Law Enforcement Officer,Law Enforcement Officer,SP NonExempt Full Time
+Rispoli,Anthony,$45730.99,08/01/2002,Campus Police & Safety,Law Enforcement Sergeant,Sergeant,SP NonExempt Full Time
+Blake,Heidi,$38655.21,12/15/2003,Campus Police & Safety,Law Enforcement Officer,Law Enforcement Officer,SP NonExempt Full Time
+Caudle,Herbert,$66647.64,01/24/1997,Campus Police & Safety,Law Enforcement Captain,Law Enforcement Captain,Support Personnel - Exempt
+Page,Tommie,$41513.21,08/01/2003,Campus Police & Safety,Law Enforcement Officer,Law Enforcement Officer,SP NonExempt Full Time
+McDonald,Veronica,$28229.23,10/31/2006,Campus Police & Safety,Police Communications Operator,Police Communications Operator,SP NonExempt Full Time
+Jones,Brian,$42583.00,12/16/2004,Campus Police & Safety,Law Enforcement Sergeant,Sergeant,SP NonExempt Full Time
+Harris,John,$28229.23,01/02/2007,Campus Police & Safety,Police Communications Operator,Police Communications Operator,SP NonExempt Full Time
+Harbury,Michael,$41513.21,10/17/2003,Campus Police & Safety,Law Enforcement Officer,Law Enforcement Officer,SP NonExempt Full Time
+Halvorsen,Thomas,$45450.00,01/09/2007,Campus Police & Safety,Law Enforcement Sergeant,Sergeant,SP NonExempt Full Time
+Moore,Steven,$91809.00,08/14/2006,Campus Police & Safety,"Dir, Safety And Security",Dir. Public Safety,Administrative/Professional
+Gray,Cindy,$49922.18,06/21/1999,Campus Police & Safety,Law Enforcement Sergeant,Law Enforcement Sergeant,SP NonExempt Full Time
+Germano,Joseph,$40083.67,06/05/2006,Campus Police & Safety,Law Enforcement Officer,Law Enforcement Officer,SP NonExempt Full Time
+Tripp,Robert,$38655.21,09/15/2003,Campus Police & Safety,Law Enforcement Officer,Law Enforcement Officer,SP NonExempt Full Time
+Slapp,James,$39685.93,03/10/2008,Campus Police & Safety,Law Enforcement Officer,Law Enforcement Officer,SP NonExempt Full Time
+Engle,Steven,$44645.46,09/20/2002,Campus Police & Safety,Law Enforcement Officer,Law Enforcement Officer,SP NonExempt Full Time
+Sergeys,Michael,$27949.00,11/02/2009,Campus Police & Safety,Police Communications Operator,Police Communication/Svc Tech,SP NonExempt Full Time
+Sandora,Dianna,$27949.00,01/11/2010,Campus Police & Safety,Police Communications Operator,Police Communications Operator,SP NonExempt Full Time
+Rounsifer,Nancy,$40103.09,06/29/1998,Campus Police & Safety,Office Manager,Office Manager,SP NonExempt Full Time
+Robinson,Stephen,$36000.00,06/01/2009,Campus Rec Operational Account,"Coord, Student Affairs",FGCU Outdoors Coord.,Administrative/Professional
+Schlief,Benjamin,$34000.00,08/03/2009,Campus Rec Operational Account,"Coord, Student Affairs",Sports Club Manager,Administrative/Professional
+Freiburger,Tia,$43706.18,05/24/2004,Campus Recreation,"Coord, Student Affairs","Event, Mrkting & St.Dvpt. Coor",Administrative/Professional
+Swingle,Amy,$73000.00,02/15/2010,Campus Recreation,"Dir, Student Affairs","Director, Campus Recreation",Administrative/Professional
+Davis,Jason,$36360.00,04/14/2008,Campus Recreation,"Coord, Student Affairs","Fitness Specialist, Campus Rec",Administrative/Professional
+Howard,Michael,$34683.40,11/13/2006,Campus Recreation,"Coord, Student Affairs","Intramural Coord., Campus Rec",Administrative/Professional
+Sullivan,Richard,$26650.11,07/10/2006,Campus Recreation,Program Asst,Recreation Office Asst,SP NonExempt Full Time
+Hebert,Marie-Josee,$34275.36,07/02/2007,Campus Reservation Control Account,Sr Univ. Union Program Spec.,Compliance Supervisor & Event,Support Personnel - Exempt
+Rodrigues,Ruth,$94859.55,06/05/2000,Campus Reservations & Record Mgt,"Dir, Business/Auxiliary Serv","Dir., Reservations & Rec Mgt",Administrative/Professional
+Lattanzi,April,$40804.00,12/16/2002,Campus Reservations & Record Mgt,Office Manager,Card Office Manager,SP NonExempt Full Time
+Janutolo,Stella,$29582.90,06/25/2007,Campus Reservations & Record Mgt,Program Asst,Program Asst,SP NonExempt Full Time
+Balmer,Eric,$63960.27,08/01/2005,Campus Reservations & Record Mgt,"Asst Dir, Bus/Auxiliary Serv","Asst Dir, Campus Reservations",Administrative/Professional
+Raynor,Margaret,$45904.50,08/09/2002,CAPP,"Coord, Academic Programs","Coord., Curriculum Systems",Administrative/Professional
+Flick,Alison,$35000.00,01/05/2009,Career Center,"Coord, Career Develop Services","Coord, Student Training & Dev",Administrative/Professional
+Lennertz,Reid,$66392.33,09/25/2000,Career Center,"Dir, Career Development Serv","Dir., Career Development Serv.",Administrative/Professional
+Layton,Elizabeth,$35000.00,07/28/2008,Career Center,"Coord, Student Affairs",Marketing & Events Coord,Administrative/Professional
+West,Michael,$35000.00,08/11/2008,Center for Academic Achievement,"Coord, Academic Programs",Student Instructional Support,Administrative/Professional
+Smith,Lori,$38500.00,11/17/2008,Center for Academic Achievement,"Coord, Academic Programs",Upper-Level Academic Retention,Administrative/Professional
+Parker,Stacey,$38500.00,01/05/2009,Center for Academic Achievement,"Coord, Academic Programs",Lower-Level Academic Retention,Administrative/Professional
+Jones,Francisca,$33046.14,01/09/2006,Center for Academic Achievement,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Clugston,Richard,$42820.44,01/12/2010,Center for Environmental & Sustaina,"Coord, Academic Programs","Project Coord, Earth Charter",Administrative/Professional
+Tomasello,Leslie,$28500.00,08/11/2008,Coastal Watershed Institute,Senior Laboratory Technician,Senior Laboratory Technician,SP NonExempt Full Time
+Tzadik,Katharine,$40000.00,03/15/2010,Coastal Watershed Institute,"Coord, Research Programs",Environmental Project Coord,Administrative/Professional
+Cassani,Mary Kay,$43990.51,08/07/2002,College of Arts & Sciences,Instructor II,Instructor II,Faculty 9 Months
+Walker,Norman,$34000.00,05/11/2009,College of Arts & Sciences,Accountant,Accountant,SP NonExempt Full Time
+Andersen,Stacy,$52281.62,08/07/1997,College of Arts & Sciences,Asst Professor,Assistant Professor,Faculty 9 Months
+Austin,Rebecca,$53236.84,08/07/2004,College of Arts & Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Barreto,Jose,$73931.97,08/07/1998,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Beatty,Thomas,$57080.66,08/07/1999,College of Arts & Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Bledsoe,Carol,$38369.51,08/07/1999,College of Arts & Sciences,Instructor II,Instructor II,Faculty 9 Months
+Smith,Eliane,$30914.13,05/02/2005,College of Arts & Sciences,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Brock,James,$66533.02,08/07/1998,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Robinson,Dewie,$41023.12,02/04/2008,College of Arts & Sciences,Administrative Asst,Administrative Asst,SP NonExempt Full Time
+Rice,Patricia,$31980.14,02/22/2005,College of Arts & Sciences,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Pollock,Tina,$34173.35,07/18/2003,College of Arts & Sciences,Accountant,Accountant & Grants Specialist,SP NonExempt Full Time
+Ceilley,David,$53060.50,02/14/2006,College of Arts & Sciences,Research Associate,Research Associate,Faculty 9 Months
+Chen,Zhao,$56333.73,08/18/2004,College of Arts & Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Niemeyer,Christal,$30450.69,08/11/2003,College of Arts & Sciences,Program Asst,Program Assistant,SP NonExempt Full Time
+Corcoran,Peter,$87159.02,08/07/2002,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Nicholson,Cindo,$25000.00,09/02/2008,College of Arts & Sciences,Biological Scientist,Laboratory-Based Research Tech,SP NonExempt Full Time
+Creagan,Felicidad,$39980.21,08/07/2004,College of Arts & Sciences,Instructor I,Instructor I,Faculty 9 Months
+McGrane,Megan,$30000.00,03/15/2010,College of Arts & Sciences,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Cruz-Alvarez,Marilyn,$55815.32,08/07/2001,College of Arts & Sciences,Assoc Professor,Assoc Professor,Faculty 9 Months
+McElroy,Kathleen,$33046.14,02/28/2006,College of Arts & Sciences,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Demers,Nora,$55724.95,08/07/1997,College of Arts & Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Garcia,Lynne,$34581.09,10/23/2000,College of Arts & Sciences,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Ellis,Jerald,$44848.89,08/07/1999,College of Arts & Sciences,Coordinator/Instructor II,Coordinator/Instructor II,Faculty 9 Months
+Epple,Michael,$53387.90,08/07/2003,College of Arts & Sciences,Asst Professor,Asst Professor,Faculty 9 Months
+Everham,Edwin,$71872.17,08/07/1996,College of Arts & Sciences,Professor,Program Leader/Professor,Faculty 9 Months
+Fay,Patricia,$58512.25,08/07/2000,College of Arts & Sciences,Assoc Professor,Assoc Professor,Faculty 9 Months
+Feng,Peng,$48125.88,08/07/2005,College of Arts & Sciences,Asst Professor,Assistant Professor,Faculty 9 Months
+Fitch,John,$70837.16,12/02/1996,College of Arts & Sciences,Assoc Professor,Assoc Professor,Faculty 9 Months
+Fugate,David,$50407.47,01/03/2006,College of Arts & Sciences,Asst Professor,Asst Professor,Faculty 9 Months
+Dupres,Clareatha,$32229.96,07/06/1998,College of Arts & Sciences,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Goebel,Anna,$49074.60,08/07/2004,College of Arts & Sciences,Asst Professor,Asst Professor,Faculty 9 Months
+Costantino,Nicolette,$32320.00,03/24/2008,College of Arts & Sciences,Assoc Professor,Executive Secretary,SP NonExempt Full Time
+Griffis,John,$60000.00,10/15/2009,College of Arts & Sciences,Assoc Professor,Assoc Professor,Faculty 9 Months
+Campbell,Caroline,$33046.14,08/16/2005,College of Arts & Sciences,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Hair,Thomas,$52159.38,08/07/1998,College of Arts & Sciences,Asst Professor,Assistant Professor,Faculty 9 Months
+Hefner,Ronald,$38146.94,08/07/2003,College of Arts & Sciences,Instructor II,Instructor II,Faculty 9 Months
+Smith,Lacey,$30300.00,06/24/2008,College of Arts & Sciences,Laboratory Manager,Laboratory Manager,Support Personnel - Exempt
+Haynes,Lesli,$33633.88,12/19/2003,College of Arts & Sciences,Laboratory Manager,Laboratory Manager,Support Personnel - Exempt
+Bullens,Ralph,$33579.15,11/30/2005,College of Arts & Sciences,Laboratory Manager,Laboratory Manager,Support Personnel - Exempt
+Isern,Sharon,$58354.14,08/07/2004,College of Arts & Sciences,Assoc Professor,Assc. Professor,Faculty 9 Months
+Jackson,Jerome,$95046.25,05/07/1999,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Jackson,Kimberly,$46194.47,08/07/2004,College of Arts & Sciences,Asst Professor,Asst Professor,Faculty 9 Months
+Kakareka,Joseph,$66056.17,08/07/1997,College of Arts & Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Sturdivant,Anica,$43310.15,08/01/2004,College of Arts & Sciences,"Coord, Academic Programs",Interim Dir of Art Gallery,Administrative/Professional
+Lindsey,Charles,$79148.27,08/07/1997,College of Arts & Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Loh,Ai Ning,$58084.02,01/06/2003,College of Arts & Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Manley,Joan,$49894.29,08/07/2005,College of Arts & Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Marquez,Enrique,$59865.14,08/07/1997,College of Arts & Sciences,Assoc Professor,Assoc Professor,Faculty 9 Months
+Martinez-Rico,Ingrid,$55312.40,08/07/2000,College of Arts & Sciences,Assoc Professor,Assoc. Professor,Faculty 9 Months
+McDonald,Michael,$62737.26,08/07/1997,College of Arts & Sciences,Assoc Professor,Assoc Professor,Faculty 9 Months
+Rodrigues,Raymond,$59696.25,05/15/2006,College of Arts & Sciences,Business Manager,Budget Manager,Administrative/Professional
+Michael,Scott,$72451.96,08/07/2004,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Millner,Jesse,$36172.96,08/07/2002,College of Arts & Sciences,Instructor II,Instructor II,Faculty 9 Months
+Mujtaba,Mustafa,$37142.36,08/07/2005,College of Arts & Sciences,Instructor I,Instructor I,Faculty 9 Months
+Narayanan,Lakshmi,$58048.29,08/07/1997,College of Arts & Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Paine,Morgan,$60500.01,08/07/1997,College of Arts & Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Monty,Jamie,$40571.70,06/02/2008,College of Arts & Sciences,"Coord, Research Programs",FDOU Project Coordinator,Administrative/Professional
+Planas,Juan-Antonio,$35098.92,08/07/2003,College of Arts & Sciences,Instructor I,Instructor I,Faculty 9 Months
+Renk,Clifford,$84891.16,08/07/1997,College of Arts & Sciences,Coordinator/Professor,Program Coordinator/Professor,Faculty 9 Months
+Rosenthal,Martha,$69648.28,08/07/1997,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Smith,Eleanor,$83920.29,10/04/1996,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Southard,Larry,$36723.60,03/21/2005,College of Arts & Sciences,Instructor I,Instructor I,Faculty 9 Months
+Strahorn,Eric,$54456.88,08/07/1997,College of Arts & Sciences,Asst Professor,Asst. Prof.,Faculty 9 Months
+Boykin,Christopher,$41132.67,09/19/2005,College of Arts & Sciences,"Coord, Research Programs","Coord, Research Programs",Administrative/Professional
+Tarnowski,Kenneth,$84923.77,08/07/1997,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Tolchin,Karen,$52275.61,08/07/2004,College of Arts & Sciences,Assoc Professor,Associate Professor - English,Faculty 9 Months
+Tolley,Stephen,$71319.69,08/07/1997,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Totaro,Rebecca,$64618.01,08/07/1999,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Savarese,Michael,$83054.11,08/07/1997,College of Arts & Sciences,Director/Professor,"Dir, Graduate Studies/Prof.",Faculty Administrator 9 mo.
+Ueda,Takashi,$64243.84,08/07/2001,College of Arts & Sciences,Assoc Professor,Assoc Professor,Faculty 9 Months
+Rowland,Linda,$43709.78,08/07/2001,College of Arts & Sciences,Director/Instructor II,Director of Composition,Faculty Administrator 9 mo.
+Parsons,Michael,$60085.40,01/02/2007,College of Arts & Sciences,Program Director/Assoc Prof,Interim Director/Assc. Prof,Faculty Administrator 9 mo.
+Voytek,Mary,$46843.93,08/07/2004,College of Arts & Sciences,Asst Professor,Asst Professor - Art Sculpture,Faculty 9 Months
+Wohlpart,Alfred,$102297.64,08/07/1997,College of Arts & Sciences,Associate Dean/Professor,Associate Dean/Professor,"Faculty Admin 10, 11, 12 mo"
+Volety,Aswani,$90972.80,08/07/1999,College of Arts & Sciences,Chairperson/Professor,Chairperson & Professor,"Faculty Admin 10, 11, 12 mo"
+Wilkinson,Andrew,$39012.71,08/07/2005,College of Arts & Sciences,Instructor I,Instructor I,Faculty 9 Months
+Wilkinson,Neil,$65946.19,08/07/2000,College of Arts & Sciences,Instructor II,Instructor II,Faculty 9 Months
+Wimberley,Edward,$87231.93,04/17/1995,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Winsboro,Irvin,$84739.40,08/07/1997,College of Arts & Sciences,Professor,Professor,Faculty 9 Months
+Wisdom,Joe,$78190.97,08/07/1997,College of Arts & Sciences,Assoc Professor,Assoc. Professor,Faculty 9 Months
+Barr,Kelli,$37000.00,09/22/2008,College of Arts & Sciences,Research Associate,Research Associate,"Faculty 10,11 or 12 Months"
+Mendible,Myra,$91644.86,08/07/1997,College of Arts & Sciences,Chairperson/Professor,Professor/Chairperson,"Faculty Admin 10, 11, 12 mo"
+Karakas,Scott,$75514.94,08/07/2003,College of Arts & Sciences,Program Director/Assoc Prof,Prog Director/Assoc Professor,"Faculty Admin 10, 11, 12 mo"
+Jackson,Bette,$80460.38,08/07/2000,College of Arts & Sciences,Chairperson/Assoc. Professor,Chairperson/Assoc. Professor,"Faculty Admin 10, 11, 12 mo"
+Hess,Debra,$81977.10,09/01/2000,College of Arts & Sciences,Assoc Dean/Assistant Professor,Associate Dean/Asst. Professor,"Faculty Admin 10, 11, 12 mo"
+Carvajal,Lucero,$40285.30,10/01/2004,College of Arts & Sciences,Academic Advisor II,Academic Advisor II,"Faculty 10,11 or 12 Months"
+Henry,Donna,$131339.63,08/07/1996,College of Arts & Sciences,Dean/Professor,"Dean, College Arts and Science","Faculty Admin 10, 11, 12 mo"
+Costin,Joshua,$41889.75,05/19/2008,College of Arts & Sciences,Research Associate,Research Associate,"Faculty 10,11 or 12 Months"
+Cavin,Barry,$78900.52,08/07/2005,College of Arts & Sciences,Chairperson/Professor,Chairperson/Professor,"Faculty Admin 10, 11, 12 mo"
+Fitch,Laura,$37168.00,07/30/2007,College of Arts & Sciences,Academic Advisor I,Academic Advisor I,"Faculty 10,11 or 12 Months"
+Swanson,Mary,$40285.30,02/02/2004,College of Arts & Sciences,Academic Advisor II,Academic Advisor II,"Faculty 10,11 or 12 Months"
+Stanis,Melanie,$36942.27,02/20/2006,College of Arts & Sciences,Academic Advisor I,Academic Advisor I,"Faculty 10,11 or 12 Months"
+Horton,Justin,$36700.00,07/13/2009,College of Arts & Sciences,Academic Advisor I,Counselor/Advisor,"Faculty 10,11 or 12 Months"
+Kari,Madhavilatha,$30000.00,02/12/2009,College of Arts & Sciences,Research Associate,Research Associate,"Faculty 10,11 or 12 Months"
+Volkan,Ara,$166516.80,08/07/2004,College of Business,Chairperson/Professor,Assoc Dean/Chair of Accounting,"Faculty Admin 10, 11, 12 mo"
+Eastwood,Karen,$104319.88,08/07/1997,College of Business,Professor,Professor,Faculty 9 Months
+Wells,Ludmilla,$79254.72,08/07/1998,College of Business,Assoc Professor,Assoc Professor,Faculty 9 Months
+Kirche,Elias,$91808.50,08/07/2001,College of Business,Assoc Professor,Assc. Professor,Faculty 9 Months
+Kerekes,Carrie,$77500.00,08/07/2008,College of Business,Asst Professor,Asst Professor - Economics,Faculty 9 Months
+Kauanui,Sandra,$111100.00,08/07/2007,College of Business,Assoc Professor,Assoc Professor,Faculty 9 Months
+Jones,Travis,$111427.06,08/07/2005,College of Business,Asst Professor,Assistant Professor - Finance,Faculty 9 Months
+Wright,Gail,$116150.00,08/07/2007,College of Business,Professor,Professor,Faculty 9 Months
+Telep,Daniel,$51005.00,09/23/2003,College of Business,"Coord, Educ/Training Programs","Coord, Educ/Training Programs",Administrative/Professional
+Hobbs,Bradley,$106033.50,08/07/1997,College of Business,Professor,BB&T Professor of Free Enterps,Faculty 9 Months
+Renard,Monika,$86715.12,08/07/1999,College of Business,Assoc Professor,Assoc Professor,Faculty 9 Months
+Weeks,Henry,$135741.08,08/07/1997,College of Business,Professor,Lucas Professor,Faculty 9 Months
+Specht,Suzanne,$55908.77,09/02/1997,College of Business,"Asst Dir, Educ/Training Prog","Asst Dir, Educ/Training Prog",Administrative/Professional
+Haytko,Diana,$120000.00,08/07/2009,College of Business,Assoc Professor,"Marketing, Assoc Professor",Faculty 9 Months
+Yazici,Hulya,$88080.43,08/07/2005,College of Business,Assoc Professor,"Decision Sci, Assoc Prof.",Faculty 9 Months
+Pendergast,Mark,$96508.55,12/22/1998,College of Business,Assoc Professor,Assoc Professor,Faculty 9 Months
+Zalewski,Janusz,$98819.52,08/07/2002,College of Business,Professor,Professor,Faculty 9 Months
+Rottig,Daniel,$103000.00,08/07/2009,College of Business,Asst Professor,Asst Professor,Faculty 9 Months
+Rubens,Arthur,$80993.56,08/30/1996,College of Business,Assoc Professor,Assoc Professor,Faculty 9 Months
+Placid,Raymond,$89120.53,08/07/2002,College of Business,Assoc Professor,Associate Professor,Faculty 9 Months
+Rue,Joseph,$104780.35,08/07/1997,College of Business,Professor,Professor,Faculty 9 Months
+Regelski,Daniel,$86041.70,01/01/1997,College of Business,Director,"Dir, Small Bus Dev Ctr","Faculty Admin 10, 11, 12 mo"
+Gray,Barbara,$61206.00,08/07/1995,College of Business,Business Manager,Dir. of Business Operations,Administrative/Professional
+Pacini,Carl,$115954.09,08/07/2000,College of Business,Professor,Professor,Faculty 9 Months
+Conrecode,Jacqueline,$70925.96,08/07/2001,College of Business,Instructor II,Instructor II,Faculty 9 Months
+Collier,David,$141200.00,08/07/2007,College of Business,Eminent Scholar,"Alico Chair, Operations Mgmt",Faculty 9 Months
+Scheff,Steven,$43023.91,08/07/1999,College of Business,Instructor II,Instructor II,Faculty 9 Months
+Fernandez,Daniel,$67500.00,08/07/2009,College of Business,Asst Professor,"Business Law, Asst Professor",Faculty 9 Months
+Cecil,Howard,$121200.00,08/07/2007,College of Business,Assoc Professor,Associate Professor,Faculty 9 Months
+Segal,Gerald,$88676.53,08/07/1997,College of Business,Assoc Professor,Associate Professor,Faculty 9 Months
+Fornaciari,Charles,$118200.82,08/07/1997,College of Business,Professor,Professor,Faculty 9 Months
+MacDiarmid,Andrew,$41334.20,01/31/2005,College of Business,Academic Advisor II,Academic Advisor II,"Faculty 10,11 or 12 Months"
+Wright-Isak,Christine,$81749.25,08/07/2004,College of Business,Asst Professor,Asst Professor - Marketing,Faculty 9 Months
+Platt,Alan,$78073.22,08/07/2004,College of Business,Asst Professor,Asst Professor - Sports Mgt.,Faculty 9 Months
+Zhao,Fan,$92217.04,08/11/2006,College of Business,Asst Professor,Assistant Professor,Faculty 9 Months
+Burgess,Deanna,$90155.17,08/07/1997,College of Business,Assoc Professor,Assoc Professor,Faculty 9 Months
+Fraser,Steven,$121200.00,08/07/2007,College of Business,Asst Professor,Asst Professor,Faculty 9 Months
+Pegnetter,Richard,$214020.00,07/03/1995,College of Business,Dean/Professor,Dean/Professor,"Faculty Admin 10, 11, 12 mo"
+Srivastava,Rajesh,$101027.20,08/07/2000,College of Business,Professor,Professor,Faculty 9 Months
+Stansel,Dean,$75339.48,08/07/2004,College of Business,Assoc Professor,Associate Professor,Faculty 9 Months
+Borgia,Daniel,$102654.50,08/07/1997,College of Business,Coordinator/Professor,"Dir, Intl Cooperation/Prof.",Faculty 9 Months
+Hernandez,Jennifer,$35452.75,10/31/2001,College of Business,Office Manager,Office Manager,SP NonExempt Full Time
+Swaleheen,Mushfiq,$76507.50,08/07/2006,College of Business,Asst Professor,Asst Professor,Faculty 9 Months
+Sweeney,Carol,$71270.87,07/31/2002,College of Business,Coordinator/Instructor II,Coord. Lucas Instit/Instruc II,Faculty 9 Months
+Wingert,Kay,$30000.00,07/28/2008,College of Business,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Nakatani,Kazuo,$92190.21,08/17/1997,College of Business,Assoc Professor,Associate Professor,Faculty 9 Months
+Ouverson,Marisa,$50375.10,10/24/2005,College of Business,Director & Academic Advisor I,Director Enrollment Management,"Faculty Admin 10, 11, 12 mo"
+Machlin,Paula,$37370.00,02/25/2008,College of Business,Academic Advisor I,Academic Advisor I,"Faculty 10,11 or 12 Months"
+Boggs,Roy,$97106.20,08/07/1997,College of Business,Professor,Professor,Faculty 9 Months
+Davidson,Sigrid,$34694.51,11/21/2005,College of Business,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Finch,James,$147440.89,08/07/2000,College of Business,Assoc Dean/Eminient Scholar,Chair for Economics & Finance,"Faculty Admin 10, 11, 12 mo"
+Schoenfeld,Gerald,$111941.14,08/07/1997,College of Business,Chairperson/Assoc. Professor,Chairperson/Assoc. Professor,"Faculty Admin 10, 11, 12 mo"
+Andrews,Christine,$92524.76,12/28/1998,College of Business,Assoc Professor,Associate Professor,Faculty 9 Months
+Holmes,Ana,$34683.40,03/27/2000,College of Business,Office Manager,Office Manager,SP NonExempt Full Time
+Jones,David,$15000.00,01/02/2007,College of Business,Distinguish. Service Professor,Executive Professor,"Faculty 10,11 or 12 Months"
+Andert,Darlene,$63819.65,08/07/2004,College of Business,Instructor II,Instructor II,Faculty 9 Months
+Kakkuri,David,$111676.39,03/15/2004,College of Business,Director/Asst. Professor,"Dir, for Ldrship & Innovation","Faculty Admin 10, 11, 12 mo"
+Volpe,Karen,$30300.00,01/02/2008,College of Business,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Guo,Dahai,$80587.90,08/07/2006,College of Business,Asst Professor,Assistant Professor,Faculty 9 Months
+Aboulnasr,Khaled,$99969.80,08/07/2006,College of Business,Asst Professor,Asst Professor,Faculty 9 Months
+Meza,Rosemary,$47262.37,11/25/1996,College of Business,"Coord, Academic Programs",Program Coordinator,Administrative/Professional
+Duffus,Lee,$83550.72,08/07/1997,College of Business,Assoc Professor,Associate Professor,Faculty 9 Months
+Rodriguez,Walter,$126671.90,10/28/1996,College of Business,Director/Professor,"Dir. Grants & Research, COB",Faculty Administrator 9 mo.
+Mathews,Charles,$74033.14,08/07/1997,College of Business,Instructor I,Instructor I,Faculty 9 Months
+Jackson,Gary,$69118.79,08/07/2005,College of Business,Director/Asst. Professor,Director/Asst Professor,Faculty Administrator 9 mo.
+Drew,Stephen,$115000.00,08/07/2008,College of Business,Director/Professor,"Director, CLI/Professor",Faculty Administrator 9 mo.
+Van Auken,Stuart,$177069.91,08/07/2000,College of Business,Chairperson/Professor,Chair/Eminent Scholar,"Faculty Admin 10, 11, 12 mo"
+Wynekoop,Judy,$134275.67,08/07/1999,College of Business,Chairperson/Professor,Assoc. Dean/Chair CIS & OM,"Faculty Admin 10, 11, 12 mo"
+Lusht,Kenneth,$45000.00,01/02/2007,College of Business,Professor,Professor,Faculty 9 Months
+Upham,Dayle,$60108.78,08/07/2006,College of Education,Coordinator/Assoc. Professor,Prog. Leader/Assc. Prof,Faculty 9 Months
+McNulty,Jacqueline,$46965.00,09/26/2005,College of Education,Business Manager,Business Manager,Administrative/Professional
+Kohler,Susan,$49474.85,06/04/2007,College of Education,"Coord, Educ/Training Programs",TIP Coordinator,Administrative/Professional
+Rea,Dorothy,$50500.00,08/07/2004,College of Education,Asst Professor,Asst Professor,Faculty 9 Months
+Medina,Gilmaris,$30300.00,06/16/2008,College of Education,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Mendolusky,Jeannine,$33722.94,04/23/2001,College of Education,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Carothers,Douglas,$58080.62,08/07/2003,College of Education,Assoc Professor,Associate Professor,Faculty 9 Months
+Pugh,Penny,$28000.00,04/06/2009,College of Education,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Hung-Simons,Olivia,$37947.72,05/01/2007,College of Education,Academic Advisor I,Academic Advisor I,"Faculty 10,11 or 12 Months"
+Weingartt,Eleanor,$51464.01,08/07/2003,College of Education,Instructor II,Instructor II,"Faculty 10,11 or 12 Months"
+Houston,Sherree,$50014.09,07/03/2000,College of Education,Asst Dean/Academic Advisor II,Asst Dean/Academic Advisor II,"Faculty Admin 10, 11, 12 mo"
+Weser,Stanley,$68716.18,01/08/2001,College of Education,"Coord, Educ/Training Programs","Coord, Educ/Training Programs",Administrative/Professional
+Sullivan,Margaret,$52887.16,05/24/2004,College of Education,"Coord, Academic Programs","Coord, Academic Programs",Administrative/Professional PT
+Greene,Marcia,$134026.99,08/07/1997,College of Education,Dean/Professor,Dean/Professor,"Faculty Admin 10, 11, 12 mo"
+Shaver,Deborah,$76392.54,07/23/2004,College of Education,"Coord, Academic Programs",Project Director,Administrative/Professional
+Searcy,Roshan,$41000.00,10/26/2009,College of Education,"Coord, Management Analysis","Coord, Management Analysis",Administrative/Professional
+Bloomster,Bridget,$39426.13,09/25/1997,College of Education,Administrative Asst,Administrative Assistant,SP NonExempt Full Time
+Wilkerson,Judy,$62510.09,08/07/2006,College of Education,Assoc Professor,Assc. Professor,Faculty 9 Months
+Borrego,Minerva,$31980.14,07/21/2006,College of Education,Program Asst,Program Asst,SP NonExempt Full Time
+Chamberlin,Kandi,$31000.00,05/27/2008,College of Education,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Desmore,Keiana,$36000.00,04/06/2005,College of Education,Academic Advisor I,Academic Advisor I,"Faculty 10,11 or 12 Months"
+Rees,Beverly,$57970.20,10/28/2002,College of Education,"Coord, Academic Programs",ELLM Literacy Coach,Administrative/Professional
+Duda,Susan,$30973.83,01/06/2003,College of Education,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Fragassi,Tracy,$30674.55,10/14/2002,College of Education,Program Asst,Program Asst,SP NonExempt Full Time
+Morrison,Theresa,$60000.00,01/03/2010,College of Health Prof Adjuncts,Instructor I,Instructor I,"Faculty 10,11 or 12 Months"
+St. Hill,Halcyon,$106371.15,06/24/1996,College of Health Professions,Professor,Professor,"Faculty 10,11 or 12 Months"
+Smith,Patricia,$34408.87,06/02/2000,College of Health Professions,Accountant,Accountant,SP NonExempt Full Time
+O'Hare,Lynn,$40540.62,04/01/1997,College of Health Professions,Administrative Asst,Administrative Asst,SP NonExempt Full Time
+Hickox,Lorie,$36865.00,03/03/2008,College of Health Professions,Academic Advisor I,Academic Advisor I,"Faculty 10,11 or 12 Months"
+Millar,Kenneth,$127211.00,07/31/2006,College Professional Studies,Dean/Professor,Dean/Professor,"Faculty Admin 10, 11, 12 mo"
+Jordan,Christina,$36360.00,07/25/2005,College Professional Studies,Academic Advisor I,Academic Advisor I,"Faculty 10,11 or 12 Months"
+Asfour,Paul,$49490.00,08/07/2007,College Professional Studies,Asst Professor,Asst Professor,Faculty 9 Months
+Burnside,Lynde,$37370.00,11/26/2007,College Professional Studies,Academic Advisor I,Academic Advisor I,"Faculty 10,11 or 12 Months"
+Green,Roger,$63317.87,08/07/1997,College Professional Studies,Assoc Professor,Assoc. Professor,Faculty 9 Months
+Brown,Cheryl,$42789.62,01/06/1997,College Professional Studies,Administrative Asst,Administrative Asst,SP NonExempt Full Time
+McGaha,Johnny,$123178.88,11/01/1996,College Professional Studies,Professor,Professor,"Faculty 10,11 or 12 Months"
+Benscoter,Andrea,$44804.23,01/25/1998,College Professional Studies,Computer Support Specialist,Computer Support Specialist,SP NonExempt Full Time
+Brundage,Isaac,$80000.00,08/01/2008,Community Outreach,"Director, University Relations",Director of Community Outreach,Administrative/Professional
+Williams,Carolyn,$39489.37,02/13/1998,Community Outreach,Senior Information Specialist,Senior Info Specialist,Support Personnel - Exempt
+Davis,Bernard,$32320.00,06/16/2008,Computing Services,Computer Support Specialist,Computer Support Specialist,SP NonExempt Full Time
+Maloney,Kevin,$40804.00,09/01/2006,Computing Services,"Coord, Computer System Control","Coord, Computer System Control",Administrative/Professional
+Campbell,Joshua,$50000.00,02/27/2007,Computing Services,"Coord, Computer System Control",Server/Systems Administrator,Administrative/Professional
+Conley,Lorinda,$50488.53,06/23/1997,Computing Services,Sr Computer Support Specialist,Sr Comp Support Spec,SP NonExempt Full Time
+Flechsig,Evan,$32320.00,06/16/2008,Computing Services,Computer Support Specialist,Computer Support Specialist,SP NonExempt Full Time
+Weaver,Charles,$77967.23,06/06/1997,Computing Services,"Asst Dir, Univ Computer Systm","Asst Dir, Campus Network Servs",Administrative/Professional
+Hahues,Sven,$51523.66,06/28/2004,Computing Services,"Coord, Computer System Control","Coord, Computer System Control",Administrative/Professional
+Shannon,Timothy,$44950.56,01/30/2004,Computing Services,Computer Support Specialist,Computer Support Specialist,SP NonExempt Full Time
+Dulle,Kendall,$54540.00,10/29/2007,Computing Services,"Coord, Computer System Control",Network Engineer,Administrative/Professional
+Phillips,Scott,$60600.00,10/29/2007,Computing Services,"Coord, Computer System Control",Server Administrator,Administrative/Professional
+Banks,Mary,$92269.25,05/06/1996,Computing Services,"Dir, University Computer Syst","Director, Computing Serv.",Administrative/Professional
+Gambo,Vincenzo,$42500.00,10/23/2006,Computing Services,"Coord, Computer System Control",Server Administrator,Administrative/Professional
+McCormick,Judson,$40730.75,12/06/2002,Computing Services,Computer Support Specialist,Computer Support Specialist,SP NonExempt Full Time
+McTygue,Diane,$36500.00,08/27/2001,Counseling & Student Health Serv,Medical Records Manager,Medical Records Manager,SP NonExempt Full Time
+Bright,Cori,$44772.19,06/28/2002,Counseling & Student Health Serv,"Assoc Dir, Student Affairs","Director, Disability Svcs",Administrative/Professional
+Brunner,Jon,$85280.36,08/26/1996,Counseling & Student Health Serv,"Asst Dean, Student Affairs","Dir, Counseling & Hlth Svcs",Administrative/Professional
+Dondero,Eileen,$74808.09,10/19/1998,Counseling & Student Health Serv,"Director, Medical/Health Admin","Director, Student Health Serv.",Administrative/Professional
+Gibbons,Judith,$56498.24,11/04/2002,Counseling & Student Health Serv,"Assoc Dir, Student Affairs","Assoc. Dir., Student Affairs",Administrative/Professional
+Isaacson,Jill,$40000.00,09/04/2007,Counseling & Student Health Serv,"Coord, Student Affairs",Counselor,Administrative/Professional
+Mottley,Juanita,$57500.00,02/16/2004,Counseling & Student Health Serv,"Asst. Dir., Medical/Heatlh Adm",Asst. Director of Operations,Administrative/Professional
+Rubin,Kelly,$43000.00,01/04/2010,Counseling & Student Health Serv,"Coord, Student Affairs",Substance Abuse Counselor,Administrative/Professional
+Thomas,Priya,$33663.30,08/22/2005,Counseling & Student Health Serv,"Coord, Student Affairs",Counselor in Residence,Administrative/Professional
+Ullman,Janet,$57564.24,07/01/1997,Counseling & Student Health Serv,"Director, Testing & Evaluation","Director, Testing & Assessment",Administrative/Professional
+Shore,Christie,$62220.74,04/10/2000,Counseling & Student Health Serv,Adv. Reg Nurse Practitioner,Adv. Reg Nurse Practitioner,Support Personnel - Exempt
+Brown,Taneshia,$28280.00,09/10/2007,Counseling & Student Health Serv,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Estrada-Lopez,Jose,$35703.50,03/05/2007,Counseling & Student Health Serv,Computer Support Specialist,Computer Support Specialist,SP NonExempt Full Time
+Gustafson,Mary Ellen,$28562.80,03/12/2007,Counseling & Student Health Serv,Senior Secretary,Receptionist/Patient Schg,SP NonExempt Full Time
+Harper,Stephanie,$29000.00,07/31/2008,Counseling & Student Health Serv,Program Asst,Immunizations Specialist,SP NonExempt Full Time
+Herold,Jill,$29146.67,12/06/1999,Counseling & Student Health Serv,Program Asst,Program Asst,SP NonExempt Full Time
+Koenig,Maria,$36233.23,11/22/1999,Counseling & Student Health Serv,Office Manager,Office Manager,SP NonExempt Full Time
+Ludy,Kiersten,$25000.00,12/22/2008,Counseling & Student Health Serv,Program Asst,Program Asst,SP NonExempt Full Time
+McMasters,Elizabeth,$25502.50,10/13/2006,Counseling & Student Health Serv,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Phillipine,Kristin,$47975.00,08/28/2006,Counseling & Student Health Serv,Senior Registered Nurse,Senior Registered Nurse,SP NonExempt Full Time
+Reardon,Cheryl,$56450.00,05/12/2008,Counseling & Student Health Serv,Senior Registered Nurse,Senior Registered Nurse,SP NonExempt Full Time
+Goldin,Arthur,$36000.00,07/01/2009,Dean of Students Office,"Coord, Student Affairs",Coord. for Leadership Developm,Administrative/Professional
+Listowski,Cindy,$42640.18,07/18/2005,Dean Student Affairs,"Asst Dir, Student Affairs","Asst Dir, Judicial Affairs",Administrative/Professional
+Sullivan,Elaine,$36233.23,10/04/2004,Dean Student Affairs,Administrative Asst,Administrative Assistant,SP NonExempt Full Time
+Yovanovich,Elizabeth,$100169.21,07/22/1996,Dean Student Affairs,"Dean, Student Affairs","Dean, Student Affairs",Administrative/Professional
+Hammerling,Julie,$78824.66,07/01/2006,Department of Health Sciences,Prog Dir/Instructor II,"Prog Dir, CLS & Visit Inst II","Faculty Admin 10, 11, 12 mo"
+Glacken,Joan,$91348.17,12/09/1996,Department of Health Sciences,Assoc Dean/Associate Professor,Assoc Dean/Chair,"Faculty Admin 10, 11, 12 mo"
+Faris,Joan,$56432.40,10/01/2001,Department of Health Sciences,Instructor II,Instructor II,"Faculty 10,11 or 12 Months"
+Heinemann,Denise,$83583.32,07/01/1997,Department of Health Sciences,Assoc Professor,Associate Professor,Faculty 9 Months
+Angeletti,Michelle,$80443.53,08/07/2000,Department of Health Sciences,Assoc Professor,Associate Professor,"Faculty 10,11 or 12 Months"
+Landy,Karen,$56432.41,03/22/2002,Department of Health Sciences,Instructor II,Instructor II,"Faculty 10,11 or 12 Months"
+Burkett,Paul,$62957.39,08/28/1997,Department of Health Sciences,Instructor II,Instructor II,"Faculty 10,11 or 12 Months"
+Baron,Michael,$69693.23,08/07/2006,Department of Music,Professor,Professor,Faculty 9 Months
+Lindsay,Charise,$48000.00,08/07/2008,Department of Music,Asst Professor,Visiting Asst Professor,Faculty 9 Months
+Thurmaier,David,$51510.00,08/07/2007,Department of Music,Asst Professor,Asst Professor,Faculty 9 Months
+Chesnutt,Rodham,$58145.70,08/07/2006,Department of Music,Assoc Professor,Associate Professor,Faculty 9 Months
+Harkins,Pamela,$38237.58,10/11/2004,Department of Music,"Coord, Academic Programs",Music Program Coord,Administrative/Professional
+Darnell,Debra,$56707.36,08/07/2006,Department of Music,Assoc Professor,Associate Professor,Faculty 9 Months
+Doyle,Melinda,$43000.00,08/07/2009,Department of Music,Lecturer,Visiting Lecturer,Faculty 9 Months
+Thayer,Robert,$105000.00,08/01/2009,Department of Music,Director/Professor,Professor,"Faculty Admin 10, 11, 12 mo"
+Wilson,Jo,$96685.08,11/01/1996,Dept. of Biological Sciences,Program Director/Professor,"Director, Professional Program",Faculty Administrator 9 mo.
+LaGier,Adriana,$40000.00,08/07/2009,Dept. of Biological Sciences,Instructor,Instructor,Faculty 9 Months
+Gunnels,Charles,$51500.00,08/07/2007,Dept. of Biological Sciences,Asst Professor,Asst Professor,Faculty 9 Months
+Cross,Randall,$59165.80,08/07/2006,Dept. of Biological Sciences,Coordinator/Assoc. Professor,Associate Professor/Coord.,Faculty 9 Months
+DeJarnette,Jan,$51005.00,08/07/2006,Dept. of Biological Sciences,Asst Professor,"Microbiology, Assist. Prof.",Faculty 9 Months
+Sanders,William,$50500.00,08/07/2007,Dept. of Biological Sciences,Asst Professor,"Botanist, Asst. Professor",Faculty 9 Months
+Humphries,Robert,$44000.00,08/07/2009,Dept. of Biological Sciences,Instructor I,Instructor I,Faculty 9 Months
+Palmtag,Matthew,$40000.00,08/07/2009,Dept. of Biological Sciences,Instructor I,Instructor I,Faculty 9 Months
+Elgart,Alison,$51005.00,08/07/2006,Dept. of Biological Sciences,Asst Professor,Asst Professor,Faculty 9 Months
+LaGier,Michael,$50000.00,08/07/2009,Dept. of Biological Sciences,Asst Professor,"Microbiology, Asst Professor",Faculty 9 Months
+Erdman,Robert,$58145.70,08/07/2006,Dept. of Biological Sciences,Assoc Professor,Assoc Professor,Faculty 9 Months
+Van Horn,Julie,$32320.00,07/16/2007,Dept. of Biological Sciences,Laboratory Manager,Laboratory Manager,Support Personnel - Exempt
+Allman,Phillip,$50500.00,08/07/2007,Dept. of Biological Sciences,Asst Professor,Assistant Professor,Faculty 9 Months
+Whitehouse,Glenn,$79271.10,08/07/1997,Dept. of Communication & Philosophy,Chairperson/Assoc. Professor,Chair/Associate Professor,"Faculty Admin 10, 11, 12 mo"
+Otto,Eric,$48480.00,08/07/2007,Dept. of Communication & Philosophy,Asst Professor,"Envir. Humanities, Asst. Prof",Faculty 9 Months
+Ehman,Mark,$37142.36,08/07/2005,Dept. of Communication & Philosophy,Instructor I,Instructor I,Faculty 9 Months
+Aho,Kevin,$51500.00,08/07/2004,Dept. of Communication & Philosophy,Assoc Professor,Associate Professor,Faculty 9 Months
+Bailey,Terri,$54500.00,08/07/2009,Dept. of Communication & Philosophy,Asst Professor,Asst Professor,Faculty 9 Months
+Moretta,Emily,$31663.50,07/30/2007,Dept. of Communication & Philosophy,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Tankei,Sachiko,$48480.00,08/07/2007,Dept. of Communication & Philosophy,Asst Professor,Assistant Professor,Faculty 9 Months
+Aminian,Farshad,$49490.00,08/07/2007,Dept. of Communication & Philosophy,Asst Professor,Asst. Professor,Faculty 9 Months
+Braddy,Jon,$47736.36,08/07/2003,Dept. of Communication & Philosophy,Asst Professor,Assistant Professor,Faculty 9 Months
+Kelly,Sean,$56664.27,08/07/2004,Dept. of Communication & Philosophy,Program Director/Assoc Prof,"Assoc. Prof., Program Director",Faculty Administrator 9 mo.
+Roca,Maria,$67195.83,08/07/1997,Dept. of Communication & Philosophy,Program Director/Assoc Prof,Program Leader/Assc. Professor,Faculty Administrator 9 mo.
+Cavin,Mary,$63397.94,08/07/2005,Dept. of Communication & Philosophy,Professor,Professor,Faculty 9 Months
+Norris,Kathy,$35000.00,08/07/2008,Dept. of Communication & Philosophy,Instructor I,Instructor I,Faculty 9 Months
+Rhea,Jessica,$38717.69,08/07/2004,Dept. of Communication & Philosophy,Instructor II,Instructor II,Faculty 9 Months
+Millner,Lyn,$48480.00,08/07/2007,Dept. of Communication & Philosophy,Asst Professor,"Journalism, Asst. Prof",Faculty 9 Months
+Walch,Mary,$49680.93,08/07/2003,Dept. of Communication & Philosophy,Asst Professor,Environ Comm Asst. Prof.,Faculty 9 Months
+Moniz,Susan,$35350.00,01/02/2008,Dept. of Communication & Philosophy,Instructor I,Instructor I,Faculty 9 Months
+Hale,Katherine,$70328.18,08/07/2006,Dept. of Communication & Philosophy,Professor,Professor,Faculty 9 Months
+Walch,Samuel,$40293.54,08/07/2004,Dept. of Communication & Philosophy,Instructor II,Instructor II,Faculty 9 Months
+Mancini,Miles,$40293.54,08/07/2004,Dept. of Communication & Philosophy,Instructor II,Instructor II,Faculty 9 Months
+Harrison,Douglas,$44374.35,08/07/2006,Dept of Language & Literature,Asst Professor,Asst Professor,Faculty 9 Months
+Busbee,Mark,$44543.91,08/07/2006,Dept of Language & Literature,Asst Professor,Asst Professor,Faculty 9 Months
+Fulton,Carol,$16500.00,08/07/2009,Dept of Language & Literature,Instructor I,Visiting Instructor I,Faculty 9 Months
+de Armas,Emilio,$43500.00,08/07/2009,Dept of Language & Literature,Asst Professor,Visitng Asst Professor,Faculty 9 Months
+Allen,Jill,$32825.00,08/07/2007,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Allen,Kevin,$32825.00,08/07/2007,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+DeMarchi,Thomas,$36471.36,08/07/2004,Dept of Language & Literature,Instructor II,Instructor II,Faculty 9 Months
+Haney-Withrow,Anna,$33000.00,08/07/2009,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Bolduc-Simpson,Sheila,$32825.00,08/07/2007,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Henshon,Suzanna,$33165.59,08/07/2006,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Vallier,Emily,$33000.00,08/07/2008,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Cox,Diane,$33000.00,08/07/2008,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Peddie,Ian,$45000.00,08/07/2009,Dept of Language & Literature,Asst Professor,Visiting Asst Professor,Faculty 9 Months
+Hill,Nathan,$32825.00,08/07/2007,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Szczesny,Paul,$33000.00,08/07/2008,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Towne,Amy,$33153.25,08/07/2005,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Cornelius,Lori,$32825.00,08/07/2007,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Ramos,Marta,$34146.37,08/07/2005,Dept of Language & Literature,Instructor I,Instructor I,Faculty 9 Months
+Thompson,Anna,$33000.00,08/07/2009,Dept of Language & Literature,Instructor I,Visiting Instructor I,Faculty 9 Months
+Snyder,Scott,$43696.23,08/08/2005,Dept. of Visual & Performing Arts,Asst Professor,Assistant Professor,Faculty 9 Months
+Owen,Andrew,$49000.00,08/07/2008,Dept. of Visual & Performing Arts,Asst Professor,Assistant Professor,Faculty 9 Months
+Bouche,Anne-Marie,$54000.00,08/07/2008,Dept. of Visual & Performing Arts,Assoc Professor,Assoc Professor,Faculty 9 Months
+Carncross,Anne,$46460.00,08/07/2007,Dept. of Visual & Performing Arts,Asst Professor,Asst Professor,Faculty 9 Months
+Courcier,Lisa,$43376.45,08/07/2001,Dept. of Visual & Performing Arts,"Coord, Academic Support","Coord, Academic Support",Administrative/Professional
+Layton,Tyler,$46500.00,08/07/2009,Dept. of Visual & Performing Arts,Asst Professor,"Theatre, Asst. Professor",Faculty 9 Months
+McShane,Megan,$46286.30,08/07/2004,Dept. of Visual & Performing Arts,Asst Professor,Asst Professor - Art History,Faculty 9 Months
+Hayford,Michelle,$46924.60,08/07/2006,Dept. of Visual & Performing Arts,Asst Professor,Asst Professor,Faculty 9 Months
+Fauerbach,Michael,$58017.12,08/18/2000,Division of Ecological Studies,Assoc Professor,Assoc. Professor,Faculty 9 Months
+Panek,Richard,$38000.00,08/07/2009,Division of Ecological Studies,Instructor I,Instructor I,Faculty 9 Months
+Bryan,James,$32500.00,08/11/2008,Division of Ecological Studies,Laboratory Manager,Laboratory Manager,Support Personnel - Exempt
+Gable,Frank,$51510.00,01/02/2008,Division of Ecological Studies,Asst Professor,Asst Professor,Faculty 9 Months
+Rumbold,Darren,$59878.75,08/07/2006,Division of Ecological Studies,Assoc Professor,Assc. Prof/Coordinator,Faculty 9 Months
+Watanabe,Kenji,$51005.00,08/07/2006,Division of Ecological Studies,Asst Professor,Asst Professor,Faculty 9 Months
+Meyer,Angela,$47470.00,08/07/2007,Division of Ecological Studies,Asst Professor,"Physics, Assistant Prof",Faculty 9 Months
+Urakawa,Hidetoshi,$49000.00,01/04/2010,Division of Ecological Studies,Asst Professor,Asst Professor,Faculty 9 Months
+Wohlpart,Sasha,$38380.00,08/07/2007,Division of Ecological Studies,Instructor I,Instructor I,Faculty 9 Months
+Bovard,Brian,$48167.38,08/07/2006,Division of Ecological Studies,Asst Professor,Asst Professor,Faculty 9 Months
+Sakharuk,Alexander,$48964.80,08/07/2006,Division of Ecological Studies,Asst Professor,Asst Professor - Physical Sci.,Faculty 9 Months
+Hartley,Anne,$58347.70,08/07/2007,Division of Ecological Studies,Assoc Professor,Associate Professor,Faculty 9 Months
+Thomas,Serge,$50500.00,01/02/2008,Division of Ecological Studies,Asst Professor,Asst Professor,Faculty 9 Months
+Duke,L. Donald,$65650.00,08/07/2007,Division of Ecological Studies,Assoc Professor,Assoc Professor,Faculty 9 Months
+Forest,Marguerite,$49000.00,11/02/2009,Division of Ecological Studies,Asst Professor,Asst Professor,Faculty 9 Months
+MacDonald,James,$50500.00,08/07/2007,Division of Ecological Studies,Asst Professor,"Geology, Assistant Professor",Faculty 9 Months
+Green,David,$38380.00,08/07/2007,Division of Ecological Studies,Instructor I,Instructor I,Faculty 9 Months
+Finn,Abbe,$76071.09,08/07/2003,Division of Graduate Studies,Assoc Dean/Associate Professor,"Assoc. Dean, Graduate Programs","Faculty Admin 10, 11, 12 mo"
+Kemler,Annette,$31082.75,02/12/2007,Division of Justice Studies,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Steckler,David,$45000.00,08/07/2009,Division of Justice Studies,Instructor I,"Criminal Justice, Instructor I",Faculty 9 Months
+Lipton,Barry,$55000.00,08/07/2007,Division of Justice Studies,Asst Professor,Asst Professor,Faculty 9 Months
+Jinian,Jeffrie,$40177.15,09/01/2000,Division of Justice Studies,Instructor I,Instructor/Coordinator,Faculty 9 Months
+Thomas,David,$49984.90,08/07/2006,Division of Justice Studies,Asst Professor,Assistant Professor,Faculty 9 Months
+Diotalevi,Robert,$61484.20,01/03/2002,Division of Justice Studies,Coordinator/Assoc. Professor,L. S. Prog. Coord./Assc. Prof,Faculty 9 Months
+Mesloh,Charles,$58253.66,08/07/2003,Division of Justice Studies,Assoc Professor,Associate Professor,Faculty 9 Months
+Kleeger,Jeffrey,$49341.86,08/07/2004,Division of Justice Studies,Asst Professor,Assistant Professor,Faculty 9 Months
+Walsh-Haney,Heather,$53019.39,08/07/2005,Division of Justice Studies,Asst Professor,Assistant Professor,Faculty 9 Months
+Dobbert,Duane,$77593.15,08/07/2000,Division of Justice Studies,Coordinator/Professor,Professor,Faculty 9 Months
+Seay,Pamella,$73066.29,01/06/1997,Division of Justice Studies,Professor,Professor,Faculty 9 Months
+Barringer,Tony,$100231.12,12/23/1997,Division of Justice Studies,Associate Dean/Professor,Assoc Dean/Chair/Professor,"Faculty Admin 10, 11, 12 mo"
+Pavelka,Sandra,$63317.87,12/28/2001,Division of Public Affairs,Assoc Professor,Associate Professor,Faculty 9 Months
+Hamley,Melissa,$30300.00,05/19/2008,Division of Public Affairs,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Coughlin,Richard,$62017.90,08/07/1997,Division of Public Affairs,Assoc Professor,Assoc Professor,Faculty 9 Months
+Busson,Terry,$108550.20,08/07/2006,Division of Public Affairs,Chairperson/Professor,Professor,"Faculty Admin 10, 11, 12 mo"
+Smith,Howard,$58550.00,08/07/2007,Division of Public Affairs,Asst Professor,"Public Admin, Asst. Prof.",Faculty 9 Months
+Bergerson,Peter,$84547.32,08/07/2002,Division of Public Affairs,Professor,Professor,Faculty 9 Months
+Prowatzke,Adam,$37875.00,08/07/2007,Division of Science & Math,Instructor I,Instructor I,Faculty 9 Months
+Navaratna,Jayanga Menaka,$49984.90,08/07/2006,Division of Science & Math,Asst Professor,Asst Professor,Faculty 9 Months
+Kern,Daniel,$53500.00,08/07/2009,Division of Science & Math,Asst Professor,"Applied Math, Asst Professor",Faculty 9 Months
+Moore,Mary Ann,$34000.00,08/07/2008,Division of Science & Math,Instructor I,Instructor I,Faculty 9 Months
+Legge,Nicole,$30000.00,09/08/2008,Division of Science & Math,"Coord, Academic Support",Math Lab Assistant,Administrative/Professional
+Avallone,Enrico,$30603.00,03/19/2007,Division of Science & Math,Laboratory Manager,Laboratory Manager,Support Personnel - Exempt
+Dubetz,Terry,$58523.16,08/07/2001,Division of Science & Math,Assoc Professor,Associate Professor,Faculty 9 Months
+Coticone,Sulekha,$52350.00,01/09/2006,Division of Science & Math,Asst Professor,"Chemistry, Asst Professor",Faculty 9 Months
+Huffman,Tanya,$36360.00,08/07/2007,Division of Science & Math,Instructor I,Instructor I,Faculty 9 Months
+Newman,Mary,$39273.85,07/09/1997,Division of Science & Math,Sr Teaching Laboratory Spec.,Sr Teaching Laboratory Spec.,Support Personnel - Exempt
+Benvie,Amy,$36081.14,09/28/2004,Division of Science & Math,Instructor I,Instructor I,Faculty 9 Months
+Mielke,Douglas,$34000.00,08/07/2009,Division of Science & Math,Instructor I,Instructor I,Faculty 9 Months
+McManus,Gregory,$52350.00,08/07/2008,Division of Science & Math,Asst Professor,Asst Professor,Faculty 9 Months
+Williams,Carmeline,$39438.17,01/04/2010,Division of Science & Math,Instructor I,"Chemistry, Instructor I",Faculty 9 Months
+Schnackenberg,F.,$70589.47,08/07/2004,Division of Science & Math,Chairperson/Asst. Professor,Chair/Asst. Professor,"Faculty Admin 10, 11, 12 mo"
+Brant,Jacilynn,$40000.00,07/20/2009,Division of Science & Math,Instructor I,Chemistry Lab Coord/Instructor,"Faculty 10,11 or 12 Months"
+Papkov,Galen,$53500.00,08/07/2009,Division of Science & Math,Asst Professor,"Statistics, Asst Professor",Faculty 9 Months
+Leung,Hoitung,$47000.00,08/07/2009,Division of Science & Math,Asst Professor,Visiting Asst Professor,Faculty 9 Months
+Saha,David,$30300.00,08/13/2007,Division of Science & Math,"Coord, Academic Support",Math Lab Assistant,Administrative/Professional
+Sheng,Yinghong,$52350.00,08/07/2007,Division of Science & Math,Asst Professor,"Physical Chemistry, Asst. Prof",Faculty 9 Months
+Shahul Hameed,Jaffar Ali,$51000.00,08/07/2008,Division of Science & Math,Asst Professor,Asst Professor,Faculty 9 Months
+Gubernat,Jodi,$36000.00,08/07/2009,Division of Science & Math,Instructor I,Instructor I,Faculty 9 Months
+Brown,David,$67830.92,08/24/1998,Division of Science & Math,Assoc Professor,Associate Professor,Faculty 9 Months
+Salapska-Gelleri,Joanna,$47245.59,08/07/2005,Division of Science & Math,Asst Professor,Assoc Professor,Faculty 9 Months
+Maldonado,Sandra,$35072.69,08/07/2006,Division of Science & Math,Instructor I,Instructor I,Faculty 9 Months
+Hart,Mary,$71679.44,08/07/2007,Division of Social Work,Chairperson/Asst. Professor,"Interim Dir., Social Work","Faculty Admin 10, 11, 12 mo"
+Jani,Nairruti,$48000.00,08/07/2009,Division of Social Work,Asst Professor,Assistant Professor,Faculty 9 Months
+Althouse,Laura,$29500.00,11/24/2008,Division of Social Work,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Bruster,Belinda,$52000.00,08/07/2009,Division of Social Work,Asst Professor,Asst Professor,Faculty 9 Months
+Anstadt,Scott,$50000.00,08/07/2008,Division of Social Work,Asst Professor,Asst Professor,Faculty 9 Months
+Heckes,Harvey,$55390.65,08/07/2004,Division of Social Work,Instructor II,Instructor II,"Faculty 10,11 or 12 Months"
+Evans,Amanda,$54121.71,01/03/2000,Division of Social Work,Asst Professor,Assistant Professor,Faculty 9 Months
+Coccoma,Patricia,$48964.80,08/07/2006,Division of Social Work,Asst Professor,"Asst. Professor, Social Work",Faculty 9 Months
+Schmidt,Diane,$60781.96,12/15/1999,Division of Teacher Education,Coordinator/Assoc. Professor,Program Leader/Assoc Professor,Faculty 9 Months
+Slick,Susan,$65620.00,08/07/2007,Division of Teacher Education,Coordinator/Assoc. Professor,"C&I, Langage Arts, Assoc. Prof",Faculty 9 Months
+Szecsi,Tunde,$58633.78,08/07/2003,Division of Teacher Education,Assoc Professor,Associate Professor,Faculty 9 Months
+Gilbert,Shelby,$50500.00,08/07/2008,Division of Teacher Education,Asst Professor,Asst Professor,Faculty 9 Months
+Christensen,Lois,$76071.40,08/07/1998,Division of Teacher Education,Assoc Dean/Associate Professor,Assoc. Dean/Assoc. Professor,"Faculty Admin 10, 11, 12 mo"
+Simpson,Mark,$52943.19,08/07/2006,Division of Teacher Education,Asst Professor,"C&I, ESOL, Assist. Professor",Faculty 9 Months
+Nikolov,Darina,$27775.00,06/24/2008,Division of Teacher Education,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Triscari,Robert,$53000.00,08/07/2009,Division of Teacher Education,Asst Professor,Asst Professor,Faculty 9 Months
+Sabella,Russell,$73872.90,08/07/1999,Division of Teacher Education,Coordinator/Professor,Program Leader/Professor,Faculty 9 Months
+Hibbard,Susan,$46000.00,08/18/2008,Division of Teacher Education,Lecturer,Lecturer,Faculty 9 Months
+Valesky,Thomas,$84816.76,08/07/1997,Division of Teacher Education,Coordinator/Professor,Prog. Leader/Professor,Faculty 9 Months
+Vazquez-Montilla,Elia,$71565.64,08/07/1997,Division of Teacher Education,Professor,Professor,Faculty 9 Months
+Gischel,Carolynne,$51051.46,08/07/2006,Division of Teacher Education,Asst Professor,"Spec. Education, Asst. Prof",Faculty 9 Months
+Elliott,Elizabeth,$58159.90,08/07/2001,Division of Teacher Education,Assoc Professor,Assoc. Professor,Faculty 9 Months
+Ray,Linda,$76347.46,08/07/1997,Division of Teacher Education,Coordinator/Professor,Program Leader/Professor,Faculty 9 Months
+Miranda,Helena,$52000.00,08/07/2008,Division of Teacher Education,Asst Professor,Asst Professor,Faculty 9 Months
+Wilder,Lynn,$63301.75,08/07/2007,Division of Teacher Education,Assoc Professor,Assoc. Professor,Faculty 9 Months
+Isaacs,Madelyn,$83184.85,08/07/1997,Division of Teacher Education,Professor,Professor,Faculty 9 Months
+Mayberry,Sara,$77863.24,08/07/1997,Division of Teacher Education,Professor,Professor,Faculty 9 Months
+Bogan,Margaret,$62575.82,08/07/2000,Division of Teacher Education,Assoc Professor,Assoc Professor,Faculty 9 Months
+Cooper,Susan,$51051.46,08/07/2007,Division of Teacher Education,Asst Professor,"C&I, Assistant Professor",Faculty 9 Months
+Giambo,Debra,$58159.90,08/07/2000,Division of Teacher Education,Assoc Professor,Associate Professor,Faculty 9 Months
+Crayton,Lisa,$48267.10,08/07/2005,Division of Teacher Education,Asst Professor,Asst Professor,Faculty 9 Months
+Kratt,Diane,$40500.00,08/18/2008,Division of Teacher Education,Instructor I,Clinical Instructor,Faculty 9 Months
+Paschall,Edward,$52812.27,08/07/2005,Division of Teacher Education,Asst Professor,Asst. Prof,Faculty 9 Months
+Greene,Jackie,$41314.05,08/07/2006,Division of Teacher Education,Instructor I,Instructor I,Faculty 9 Months
+Carter,Cecil,$93614.51,01/07/1997,Division of Teacher Education,Assoc Professor,Assoc Professor,"Faculty 10,11 or 12 Months"
+Kimbler,Kristopher,$51000.00,08/07/2009,Div of Social & Behavioral Sci,Asst Professor,Developmental Psychology,Faculty 9 Months
+Foote,Nicola,$46162.64,08/19/2005,Div of Social & Behavioral Sci,Asst Professor,"History, Asst. Professor",Faculty 9 Months
+Cudjoe,Joseph,$79625.82,08/07/1997,Div of Social & Behavioral Sci,Chairperson/Assoc. Professor,Chair/Assoc. Prof,"Faculty Admin 10, 11, 12 mo"
+Hatz,Jessica,$45000.00,08/07/2009,Div of Social & Behavioral Sci,Asst Professor,Visiting Asst Professor,Faculty 9 Months
+Thompson,Glenn,$58580.00,08/07/2007,Div of Social & Behavioral Sci,Assoc Professor,Assoc Professor,Faculty 9 Months
+DeWees,Mari,$50000.00,08/07/2008,Div of Social & Behavioral Sci,Asst Professor,Assistant Professor,Faculty 9 Months
+Bourgeois,Martin,$62736.15,08/07/2006,Div of Social & Behavioral Sci,Assoc Professor,Assoc Professor,Faculty 9 Months
+Gogate,Lakshmi,$58347.70,08/07/2007,Div of Social & Behavioral Sci,Assoc Professor,Associate Professor,Faculty 9 Months
+De Welde,Kristine,$52520.00,08/07/2007,Div of Social & Behavioral Sci,Asst Professor,Asst Professor,Faculty 9 Months
+Carlson,Erik,$54540.00,08/07/2007,Div of Social & Behavioral Sci,Assoc Professor,"Public History,Assoc Professor",Faculty 9 Months
+Cole,Michael,$46516.56,08/07/2005,Div of Social & Behavioral Sci,Asst Professor,"History, Assistant Professor",Faculty 9 Months
+Popov-Reynolds,Nadejda,$46000.00,08/07/2009,Div of Social & Behavioral Sci,Asst Professor,History,Faculty 9 Months
+Cox,John,$45904.50,08/07/2006,Div of Social & Behavioral Sci,Asst Professor,Asst Professor,Faculty 9 Months
+O'Neil,Kevin,$52000.00,08/07/2008,Div of Social & Behavioral Sci,Asst Professor,Asst Professor,Faculty 9 Months
+McGrath,Lindsay,$34340.00,06/25/2007,E&G Campus Reservations,"Coord, Public Functions","Coord., Campus Reservations",Administrative/Professional
+Pacheco,Amber,$28782.12,06/05/2006,E&G Campus Reservations,Sr Univ. Union Program Spec.,Campus Reservations Event Plnr,Support Personnel - Exempt
+Manjerovic,Kristen,$33330.00,02/15/2010,Enrollment Marketing & Recruiting,"Coord, HS/Comm College Rel","Coord, Undergraduate Relations",Administrative/Professional
+Marchante,Tiziana,$34302.13,04/14/2008,Enrollment Marketing & Recruiting,"Coord, HS/Comm College Rel","Recruitment Coord, Gifted Stdt",Administrative/Professional
+Mayeron,Jessica,$33330.00,11/26/2007,Enrollment Marketing & Recruiting,"Coord, HS/Comm College Rel","Coord, Undergraduate Relations",Administrative/Professional
+Friedman,Lisa,$57564.24,03/17/2003,Enrollment Marketing & Recruiting,"Assoc Dir, Admissions/Regist",Assoc. Dir Enrollment Market,Administrative/Professional
+Morin,Donna,$36377.41,11/01/2004,Enrollment Marketing & Recruiting,"Coord, HS/Comm College Rel",Admissions Rep & Advisor,Administrative/Professional
+Hyatt,Anthony,$33330.00,04/14/2008,Enrollment Marketing & Recruiting,"Coord, HS/Comm College Rel","Coord, Undergraduate Relations",Administrative/Professional
+Burzio,Chrysten,$33330.00,11/05/2007,Enrollment Marketing & Recruiting,"Coord, HS/Comm College Rel",Recruitment Coordinator,Administrative/Professional
+Johnson,Lewis,$88145.25,01/10/2005,"Environmental, Health & Safety","Dir, Environ Health/Safety","Dir, Environ Health/Safety",Administrative/Professional
+Baca,Willie,$60038.44,01/02/1998,"Environmental, Health & Safety","Asst Dir, Envir Health/Safety",Asst Dir. Envir. Health & Saf,Administrative/Professional
+Crawford,Kathleen,$41932.17,10/08/2001,"Environmental, Health & Safety","Coord, Environ Health & Safety","Coord, Env. Sustainability",Administrative/Professional
+Holtzclaw,Rhonda,$55595.45,07/09/1997,"Environmental, Health & Safety","Coord, Environ Health & Safety","Coord, Environ Health & Safety",Administrative/Professional
+Genson,Barrett,$95000.00,06/30/2003,Facilities Planning,"Dir, Facilities Planning","Dir, Facilities Planning",Administrative/Professional
+Mankiewicz,Gary,$47011.69,01/31/2000,Facilities Planning,Engineering Technican/Designer,CAD Operator,SP NonExempt Full Time
+Anderson,Edward,$63041.47,06/06/2003,Facilities Planning,"Coord., Construction Projects","Coord., Building Codes",Administrative/Professional
+Hernandez,Lidia,$38000.00,03/15/2010,Facilities Planning,Administrative Asst,Administrative Assistant,SP NonExempt Full Time
+Sands,Kimberly,$21750.00,07/27/2009,Family Dev Resource Center,Child Care Group Leader,Assistant Teacher,SP NonExempt Full Time
+Pugh,Kendra,$18083.26,01/14/2010,Family Dev Resource Center,Child Care Group Leader,Assistant Teacher,SP NonExempt Full Time
+Layden,Margaret,$31447.14,07/29/2005,Family Dev Resource Center,"Coord, Educ/Training Programs",Lead Teacher,Administrative/Professional
+Faulkner,Doreen,$30000.00,03/30/2009,Family Dev Resource Center,"Coord, Educ/Training Programs",Lead Teacher,Administrative/Professional
+Altomere,Joseph,$21967.50,01/07/2008,Family Dev Resource Center,Child Care Group Leader,Assistant Teacher,SP NonExempt Full Time
+Nyholm,Trisha,$24482.40,05/29/2007,Family Dev Resource Center,Program Asst,Program Asst,SP NonExempt Full Time
+Piscitelli,Janette,$46579.15,11/22/2004,Family Dev Resource Center,"Asst Dir, Educ/Training Prog","Director, FRC",Administrative/Professional
+Shelkofsky,Tracey,$22187.18,05/21/2007,Family Dev Resource Center,Child Care Group Leader,Assistant Teacher,SP NonExempt Full Time
+Saxby,Hilda,$30437.84,03/06/2005,FGCU Charlotte Center,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Popovich,CarolAnn,$56105.50,11/17/2005,FGCU Collegiate HS,"Director, Academic Programs",Dir. Collegiate HS and CAA,Administrative/Professional
+Vida,Thomas,$43484.98,09/12/2003,Field Maintenance,Maintenance Specialist,Athletic Maint./Grounskeeper,SP NonExempt Full Time
+Dillingham,Elizabeth,$45000.00,01/06/2009,Finance & Accounting Aux P/R,Administrative Asst,Administrative Assistant,SP NonExempt Full Time
+Mort,Peggy,$60600.00,02/20/2007,Finance & Accounting Aux P/R,"Asst Dir, Bus/Auxiliary Serv","Asst Dir., Finance & Acctg",Administrative/Professional
+Valentin,Enereida,$35113.90,03/21/2001,Florida Institute of Government,"Coord, Educ/Training Programs","Coord, Educ/Training Programs",Administrative/Professional
+Staub,Nancy,$26522.60,01/02/2007,Florida Institute of Government,Program Asst,Program Assistant,SP NonExempt Full Time
+Hartke,Joanne,$82268.90,07/16/1999,Florida Institute of Government,"Director, Continuing Education","Director, FL Inst. of Gov't.",Administrative/Professional
+Pittman,Ashley,$35000.00,11/10/2008,Freshman Advising Services,"Coord, Student Affairs",First Year Academic Counselor,Administrative/Professional
+Bacigalupi,Allison,$35350.00,05/22/2008,Freshman Advising Services,"Coord, Student Affairs",Academic Counselor,Administrative/Professional
+Casanova,Sonia,$35178.15,07/02/2001,Freshman Advising Services,Office Manager,Office Manager,SP NonExempt Full Time
+Cinoman,Andrew,$82000.00,01/19/2010,Freshman Advising Services,"Dir, Academic Support Services",Dir. of New Student Programs,Administrative/Professional
+Graceffo,Frank,$37843.16,03/06/2006,Freshman Advising Services,"Coord, Student Affairs","Coord, of New Stdt Prgms",Administrative/Professional
+Majure,James,$42420.00,06/16/2008,Freshman Advising Services,"Asst Dir, Student Affairs","Asst Dir, Eagle View Orientati",Administrative/Professional
+Anderson,David,$35000.00,10/05/2009,Freshman Advising Services,"Coord, Student Affairs",Academic Counselor,Administrative/Professional
+Pawliczak,Jillian,$28000.00,05/26/2009,Freshman Advising Services,Program Asst,Prgm Asst for New Stdt Prgm,SP NonExempt Full Time
+Musolino,Adam,$56615.55,12/26/2000,Freshman Advising Services,"Assoc Dir, Student Affairs",Assoc. Dir. 1st year Advising,Administrative/Professional
+Greenbaum,David,$93000.00,11/09/2009,General Counsel - Office,Asst General Counsel,Asst General Counsel,Administrative/Professional
+Leonard,Vee,$151485.00,06/13/2005,General Counsel - Office,General Counsel,General Counsel,Executive Service
+St. John,Diane,$43000.00,04/14/2008,General Counsel - Office,Administrative Asst,Administrative Asst,SP NonExempt Full Time
+Cournoyer,Vanessa,$39000.00,02/08/2010,General Counsel - Office,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Haring,Debora,$33330.00,08/19/2005,Graduate Admissions,"Coord, HS/Comm College Rel","Coordinator, Graduate Studies",Administrative/Professional
+Hill,Ana,$33758.62,07/01/2004,Graduate Admissions,Admissions/Registrar Officer,Grad Studies Adm Officer,SP NonExempt Full Time
+Johnston,Jennifer,$43706.18,10/13/2004,Graduate Studies,"Asst Dir, Admissions/Regist","Asst. Director, Grad Studies",Administrative/Professional
+Fells,Carey,$28785.00,10/22/2007,Honors Program,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Farrara,Margaret,$34000.00,04/13/2009,Housing - Central Office Operations,Accountant,Student Accounts Specialist,SP NonExempt Full Time
+Moschella,Jameson,$64500.00,10/23/2006,Housing - Central Office Operations,"Assoc Dir., Univ. Housing","Assoc Dir, Residence Education",Administrative/Professional
+Fisher,Brian,$95000.00,03/13/2006,Housing - Central Office Operations,"Dir, University Housing","Dir, University Housing",Administrative/Professional
+Cepeda,Amelia,$63500.00,11/02/2009,Housing - Central Office Operations,"Assoc Dir., Univ. Housing",Assoc Dir. for Business Operat,Administrative/Professional
+Brawley,Beverly,$31113.05,09/12/2005,Housing - Central Office Operations,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Gifford,Randall,$37000.00,07/06/2009,Housing - South Village Operations,Maintenance Mechanic,Maintenance Mechanic,SP NonExempt Full Time
+Kroeger,Rebecca,$32320.00,06/01/2005,Housing - South Village Operations,"Coord, University Housing",Resident Director,Administrative/Professional
+Messinger,Alex,$37370.00,07/01/2008,Housing - South Village Operations,Maintenance Mechanic,Maintenance Mechanic,SP NonExempt Full Time
+Abbate,Lynne,$25000.00,07/17/2008,Housing - South Village Operations,Program Asst,Program Asst,SP NonExempt Full Time
+Belcher,Steven,$107666.45,09/26/1994,Human Resources,"Dir, Human Resources","Dir, Human Resources",Administrative/Professional
+Scott,Nancy,$37310.16,02/27/2006,Human Resources,Personnel Representative,Employment Specialist,SP NonExempt Full Time
+Heuer,Julie,$57047.61,07/28/1995,Human Resources,"Coord, Human Resources",Coordinator,Administrative/Professional
+Hill,Elizabeth,$47490.50,07/28/2004,Human Resources,"Coord, Human Resources","Coor,HR Records Administration",Administrative/Professional
+Congdon,Lynn,$43430.00,08/06/2007,Human Resources,"Coord, Management Analysis","Coord, HRIS",Administrative/Professional
+Holzem,Madeline,$81975.75,07/01/1994,Human Resources,"Assoc Dir, Human Resources","Assoc Dir, Human Resources",Administrative/Professional
+Lennox,Deborah,$37310.16,07/01/2006,Human Resources,Personnel Representative,"HR Rep, Records Administration",SP NonExempt Full Time
+Stuart,Randy,$39975.17,10/24/2003,Human Resources,Accountant,Accountant,SP NonExempt Full Time
+Valentin,Noyra,$67904.49,06/15/2000,Human Resources,"Asst Dir, Human Resources","Asst Dir, Benefits & Payroll",Administrative/Professional
+Voss,Sherry,$60600.00,11/14/2005,Human Resources,"Asst Dir, Human Resources","Asst. Dir., Employee Relations",Administrative/Professional
+Baurer,Susan,$67904.49,07/21/1995,Human Resources,"Asst Dir, Human Resources","Asst Dir, Class. & Employment",Administrative/Professional
+Ausby,Andrea,$63315.26,11/12/2002,Human Resources,"Manager, Human Resources",Payroll Manager,Administrative/Professional
+Naughton,Mary,$37310.16,11/09/2005,Human Resources,Personnel Representative,Employment Specialist,SP NonExempt Full Time
+Zablackas,Sandra,$37000.00,01/27/2010,Human Resources,Personnel Representative,"HR Rep, Records Administration",SP NonExempt Full Time
+McNeal,Deborah,$44688.19,04/13/2001,Human Resources,Sr Personnel Representative,Sr. Classification Specialist,SP NonExempt Full Time
+Bryan,Deborah,$44688.19,06/07/2002,Human Resources,Sr Personnel Representative,Sr. Benefits Specialist,SP NonExempt Full Time
+Cloxton,Barbara,$27000.00,12/01/2008,Human Resources,Senior Secretary,HR Assistant,SP NonExempt Full Time
+Scudder,Cheryl,$43430.00,03/03/2008,Human Resources,"Coord, Human Resources",Assistant Payroll Manager,Administrative/Professional
+Weisberg,Marvin,$37000.00,08/25/2008,Information Systems,"Coord, Computer Applications",Information Systems Support Co,Administrative/Professional
+Symonds,Bobbie,$32000.00,03/02/2009,Information Systems,Office Manager,Office Manager,SP NonExempt Full Time
+Dhar,Partha,$53300.23,11/08/2004,Information Systems,"Spec, Computer Applications",Team Leader,Administrative/Professional
+Rider,Thomas,$58097.25,04/01/2005,Information Systems,"Spec, Computer Applications","Specialist, Computer App.",Administrative/Professional
+Schwarz,Isaac,$34000.00,05/11/2009,Information Systems,"Coord, Computer Applications","Coordinator, Computer Apps.",Administrative/Professional
+Hill,Jacqui,$40804.00,03/19/2007,Information Systems,"Coord, Computer Applications","Coord, Computer Applications",Administrative/Professional
+Cutler,Cynthia,$39390.00,06/30/2008,Information Systems,"Coord, Computer Applications",Information Systems Support Co,Administrative/Professional
+Del Rosario,Waner,$35350.00,06/30/2008,Information Systems,"Coord, Computer Applications",Information Systems Support Co,Administrative/Professional
+Grande,Ricardo,$33330.00,07/30/2007,Information Systems,Computer Programmer-Analyst,Computer Programmer Analyst,SP NonExempt Full Time
+Allen,Paul,$79950.34,01/06/2003,Information Systems,"Asst Dir, Univ Computer Systm","Asst. Dir., Information System",Administrative/Professional
+Baker,Gary,$53300.30,01/25/2010,Information Systems,"Spec, Computer Applications","Spec, Computer Applications",Administrative/Professional
+Evans,Brent,$53300.23,02/20/2004,Information Systems Operations,"Spec, Computer Applications",Team Leader,Administrative/Professional
+Campbell,William,$79950.34,05/05/2003,Information Systems Operations,"Asst Dir, Univ Computer Systm","Asst. Dir., Information Sys",Administrative/Professional
+Johnson,Rick,$116000.00,10/20/2008,Instr Technology & Broadcast Serv,"Dir, Radio/Television Station",WGCU General Manager,Administrative/Professional
+Olsen,Muriel,$44772.19,07/18/1995,Instr Technology & Broadcast Serv,Administrative Asst,Administrative Asst,SP NonExempt Full Time
+McEwan,Deborah,$55085.40,06/18/2007,Internal Audit,Internal Auditor/Investigator,Internal Auditor/Investigator,Administrative/Professional
+Slade,Carol,$99138.42,02/17/2003,Internal Audit,Inspector General,Director Internal Audit,Administrative/Professional
+Hozdik,Elaine,$74620.32,08/12/1996,International Services,"Dir, Academic Support Services","Dir, International Services",Administrative/Professional
+Lindauer,Joan,$31000.00,08/05/2008,International Services,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Gjini,Timothy,$43706.18,08/16/2004,International Services,"Asst Dir, Academic Support","Asst. Dir., International Serv",Administrative/Professional
+St. Laurent,Pamela,$38763.80,08/07/2005,Kleist Health Education Ctr,"Coord, Educ/Training Programs",Health Educator,Administrative/Professional
+Metzger,Nancy,$35101.56,10/01/2001,Kleist Health Education Ctr,Program Asst,Program Asst,Support Personnel NonExempt PT
+McFarland,Renee,$51005.00,08/28/2006,Kleist Health Education Ctr,"Coord, Educ/Training Programs","Dir, Kleist Health Educ Center",Administrative/Professional
+Castro,Lourdes,$50848.92,06/12/1995,Library Services,Business Manager,Member Svcs Coord,Administrative/Professional
+Gardiner,Catherine,$48999.07,10/11/1999,Library Services,Asst University Librarian,Tech Srvs Supervisor,"Faculty 10,11 or 12 Months"
+Mehl,Lorrie,$30089.27,07/07/1997,Library Services,Senior Library Technical Asst,Sr Library Technical Assistant,SP NonExempt Full Time
+Cicinelli,Guy,$37000.00,08/17/2009,Library Services,Computer Support Specialist,Computer Support Specialist,SP NonExempt Full Time
+LeBlanc,Lee,$38380.00,01/18/2005,Library Services,"Coord, Computer Applications","Coord., Computer Applications",Administrative/Professional
+O'Connell,Patrick,$27212.21,02/02/2004,Library Services,Senior Library Technical Asst,Senior Library Technical Asst,SP NonExempt Full Time
+English,Michael,$11000.00,10/20/2008,Library Services,Library Technical Asst,Library Technical Asst,Support Personnel NonExempt PT
+Collins,Geraldine,$64913.22,02/03/2003,Library Services,Dept. Head/Univ. Librarian,"Head of Ref, Rsch & Instr","Faculty Admin 10, 11, 12 mo"
+Peguese,Diana,$25688.05,06/04/2007,Library Services,Senior Library Technical Asst,Sr. Library Technical Asst,SP NonExempt Full Time
+Pendenque,Joslyn,$30603.00,01/12/2004,Library Services,Senior Library Technical Asst,Technical Services Assistant,SP NonExempt Full Time
+Donlan,Rebecca,$80239.32,05/01/2000,Library Services,"Asst. Dir., Library/Librarian","Asst Dir., Collections Mgmt","Faculty Admin 10, 11, 12 mo"
+Budd,Raymond,$22000.00,09/11/2009,Library Services,Library Technical Asst,Library Technical Asst,SP NonExempt Full Time
+Cooke,Rachel,$52275.59,08/02/2004,Library Services,Assoc University Librarian,Humanities Librarian,"Faculty 10,11 or 12 Months"
+Bryan,Charles,$49984.90,07/08/2002,Library Services,Sr Computer Support Specialist,Sr.Computer Support Specialist,SP NonExempt Full Time
+Carlin,Anna,$42420.00,08/18/2004,Library Services,Asst University Librarian,Public Svcs Librarian,"Faculty 10,11 or 12 Months"
+Kenny,Michele,$36987.94,07/17/2000,Library Services,Office Manager,Office Manager,SP NonExempt Full Time
+Fedor,Evelyn,$32322.43,09/06/1996,Library Services,Senior Library Technical Asst,Senior Library Technical Asst,SP NonExempt Full Time
+Reycraft,Kimberly,$30300.00,02/12/2007,Library Services,Senior Library Technical Asst,Reference Assistant,SP NonExempt Full Time
+Bhatt,Anjana,$61817.49,06/10/1997,Library Services,University Librarian,E Resources Librarian,"Faculty 10,11 or 12 Months"
+Russell,Roberta,$33902.70,05/01/2006,Library Services,Senior Library Technical Asst,Senior Library Technical Asst,SP NonExempt Full Time
+Saint,Charles,$50193.24,09/03/1997,Library Services,Sr Computer Support Specialist,Sr Computer Support Specialist,SP NonExempt Full Time
+Miller,Kathleen,$125695.09,08/07/1997,Library Services,"Dir., Libraries/Univ Librarian",Dean of Library Services,"Faculty Admin 10, 11, 12 mo"
+Rosenthal,Danielle,$58791.81,05/05/2003,Library Services,Assoc University Librarian,Associate Univ. Librarian,"Faculty 10,11 or 12 Months"
+Oistad,Kay,$50500.00,07/09/2007,Library Services,Assoc University Librarian,Business Librarian,"Faculty 10,11 or 12 Months"
+Stites,Barbara,$87568.19,03/01/1999,Library Services,"Assoc Dir, Library/Assoc Libr.","Asst Dir, Library/Assoc. Libr.","Faculty Admin 10, 11, 12 mo"
+Fritcha,Jenny,$28572.81,07/20/2001,Library Services,Senior Library Technical Asst,Senior Library Technical Asst,SP NonExempt Full Time
+Snyder,Kevin,$27542.70,03/19/2007,Library Services,Senior Library Technical Asst,Senior Library Technical Asst,SP NonExempt Full Time
+Verbesey,J.,$85000.00,10/01/2009,Library Services,Director,Executive Director,"Faculty Admin 10, 11, 12 mo"
+Hartung,Mary Kay,$67627.00,10/31/1995,Library Services,University Librarian,University Librarian,"Faculty 10,11 or 12 Months"
+Vazquez,Donna,$58630.25,02/22/1999,Library Services,"Asst Dir, Academic Support","Asst Dir, Academic Support",Administrative/Professional
+Maksian,Carol,$50189.93,08/18/2004,Library Services,Assoc University Librarian,Assoc Univ Librarian,"Faculty 10,11 or 12 Months"
+Tait,Mary Marguerite,$26522.60,03/06/2006,Library Services,Senior Library Technical Asst,Senior Library Technical Asst,SP NonExempt Full Time
+Daneri,Ircania,$30808.52,02/19/2002,Library Services,Sr Fiscal Asst,Sr Fiscal Asst,SP NonExempt Full Time
+Bernardo,Mario,$63000.00,06/25/2002,Library Services,"Asst Dir, Univ Computer Systm","Asst Dir, Lib Comp & Tech Sys",Administrative/Professional
+Tollett,David,$76500.00,07/01/2002,Men's Baseball,Head Athletic Coach,Head Athletic Coach,Administrative/Professional
+McKee,Robert,$36546.52,08/16/2004,Men's Baseball,Asst Athletic Coach,Asst Baseball Coach,Administrative/Professional
+Bennett,Nicholas,$33562.80,07/01/2007,Men's Basketball,Asst Athletic Coach,Asst Men's Basketball Coach,Administrative/Professional
+Norwood,Kunta-Kinte,$42000.00,07/01/2009,Men's Basketball,Asst Athletic Coach,Asst Men's Basketball Coach,Administrative/Professional
+Balza,David,$95000.00,06/01/2001,Men's Basketball,Head Athletic Coach,Head Men's Basketball Coach,Administrative/Professional
+Goodson,Cassandra,$30000.00,08/04/2008,Men's Cross Country,Head Athletic Coach,Hd Men/Wmn's Cross Ctny Coach,Administrative/Professional
+Suttie,James,$25250.00,09/08/2000,Men's Golf,Head Athletic Coach,Head Athletic Coach,Administrative/Professional PT
+Aldaz,Alejandro,$29582.90,07/01/2007,Men's Soccer,Asst Athletic Coach,Asst Men's Soccer Coach,Administrative/Professional
+Butehorn,Robert,$56233.01,07/01/2006,Men's Soccer,Head Athletic Coach,Head Men's Soccer Coach,Administrative/Professional
+Rodgers,Marianne,$98839.50,08/07/2007,Nursing,Director/Professor,"Director, School Of Nursing","Faculty Admin 10, 11, 12 mo"
+Hobart,Kimberly,$33774.40,10/13/2004,Nursing,Office Manager,Office Manager,SP NonExempt Full Time
+Stecher,Jo,$68122.49,01/03/2003,Nursing,Instructor II,Instructor II,"Faculty 10,11 or 12 Months"
+Ritrosky,Zulay,$122412.00,11/01/2006,Nursing,Coordinator/Instructor I,Asst. Progr Dir/Instructor I,"Faculty 10,11 or 12 Months"
+Murray,Elizabeth,$68334.84,06/30/2000,Nursing,Asst Professor,Assistant Professor,"Faculty 10,11 or 12 Months"
+Young,Anne,$40400.00,02/14/2005,Nursing,Academic Advisor I,Academic Advisor I,"Faculty 10,11 or 12 Months"
+Lavenia,Ryann,$122400.00,08/07/2009,Nursing,Instructor I,Instructor I,"Faculty 10,11 or 12 Months"
+Wolf,Donna,$80199.09,08/07/2001,Nursing,Instructor II,Instructor II,"Faculty 10,11 or 12 Months"
+Gross,Rosalyn,$87611.61,08/07/2002,Nursing,Instructor II,Instructor II,"Faculty 10,11 or 12 Months"
+Trail,Elisabeta,$28500.00,05/18/2009,Nursing,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Ruder,Shirley,$71146.96,08/07/2002,Nursing,Assoc Professor,Associate Professor,Faculty 9 Months
+Nolan,Anne,$80718.05,08/07/1999,Nursing,Assoc Professor,Associate Professor,"Faculty 10,11 or 12 Months"
+Hagman,Lynda,$53060.50,08/07/2005,Nursing,Asst Professor,Assistant Professor,Faculty 9 Months
+Lupe,Lori,$65413.66,01/07/2008,Nursing,Instructor I,Instructor I,"Faculty 10,11 or 12 Months"
+Ali,Rebecca,$59234.08,04/05/1999,Nursing,Coordinator/Instructor II,Instructor II,Faculty 9 Months
+Chapa,Deborah,$77527.60,08/07/2006,Nursing,Asst Professor,Asst. Professor,"Faculty 10,11 or 12 Months"
+Ellis,Tina,$53703.87,07/02/2001,Nursing,Instructor I,Instructor I,Faculty 9 Months
+Downes,Loureen,$65503.68,08/07/2006,Nursing,Asst Professor,Asst. Professor,Faculty 9 Months
+Bogar,Catherine,$62000.00,08/07/2009,Nursing,Instructor I,Instructor I,"Faculty 10,11 or 12 Months"
+Kirsner,Kenneth,$186550.79,06/01/2006,Nursing,Director/Assoc. Professor,Program Director/Assoc Prof,"Faculty Admin 10, 11, 12 mo"
+Fitzgerald,Leslie,$28280.00,06/09/2008,Nursing,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Polk,Marydelle,$101663.68,08/16/1999,Nursing,Program Director/Professor,Prog. Dir./Professor,"Faculty Admin 10, 11, 12 mo"
+Mock,Karen,$70542.75,10/02/1997,Occupational Therapy,Asst Professor,Asst Professor,"Faculty 10,11 or 12 Months"
+Morris,Douglas,$71599.01,05/03/1999,Occupational Therapy,Asst Professor,Asst Professor,"Faculty 10,11 or 12 Months"
+Martin,Linda,$97179.54,08/07/2001,Occupational Therapy,Chairperson/Professor,Chair/Professor,"Faculty Admin 10, 11, 12 mo"
+Gelpi,Tina,$73712.99,08/07/2001,Occupational Therapy,Coordinator/Asst. Professor,Coordinator/Asst. Professor,"Faculty 10,11 or 12 Months"
+Gregitis,Susan,$69970.70,08/07/2004,Occupational Therapy,Asst Professor,Asst Professor,"Faculty 10,11 or 12 Months"
+Smith,Wanda,$34408.87,12/06/1999,Occupational Therapy,Office Manager,Office Manager,SP NonExempt Full Time
+Krupp,Constance,$35374.00,07/11/2000,Occupational Therapy,Asst In,Asst In,"Faculty 10,11 or 12 Months"
+Guerra,John,$71407.00,04/16/2007,Office of Extend Prog & Renaiss Aca,"Director, Continuing Education",Naples Center Director,Administrative/Professional
+Bloomberg,Steven,$86658.00,01/17/2007,Office of Extend Prog & Renaiss Aca,Executive Director,Exec. Dir for Off Campus Prog.,Administrative/Professional
+Tucker,Pamela,$43000.00,12/08/2008,Office of Extend Prog & Renaiss Aca,Business Manager,Business Manager,Administrative/Professional
+Miller,Erin,$37310.16,01/30/2006,Office of Government Relations,Administrative Asst,Administrative Asst,SP NonExempt Full Time
+Goen,Jennifer,$96909.50,08/29/2005,Office of Government Relations,"Dir, Governmental Relations",Dir. of Government Relations,Executive Service
+Forsyth,Veronica,$52607.32,08/07/2002,Office of the President,Sr Administrative Asst,Sr. Administrative Assistant,Support Personnel - Exempt
+Krell,Barbara,$84747.36,07/01/1993,Office of the President,Executive Asst,Executive Asst,Administrative/Professional
+Reed,Mary,$55432.23,10/16/1996,Office of the President,Business Manager,Business Manager,Administrative/Professional
+Williams,Samantha,$28782.12,05/08/2006,Office of the President,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Evans,Susan,$163216.00,10/01/1993,Office of the President,"Assoc VP, Univ Relations",Chief of Staff/Univ Spokespers,Executive Service
+Bradshaw,Wilson,$325500.00,11/11/2007,Office of the President,University President,University President,Executive Service
+Bottoms,Kathleen,$49036.21,07/19/1999,Office of the President,Administrative Asst,Administrative Assistant,SP NonExempt Full Time
+Kalies,Heather,$25000.00,11/03/2008,Office of the Registrar,Program Asst,Program Asst,SP NonExempt Full Time
+Montalvo,Karina,$38380.00,09/07/2004,Office of the Registrar,"Coord, Admissions/Registration",Coord for Transcripts & Record,Administrative/Professional
+Regelski,Eileen,$48482.65,10/17/1994,Office of the Registrar,Administrative Asst,Administrative Assistant,SP NonExempt Full Time
+Keutgen,Xiomara,$35000.00,09/21/2008,Office of the Registrar,Sr. Admissions/Registrar Off,Sr. Officer for Registration,SP NonExempt Full Time
+Hepner,Duska,$25000.00,07/27/2009,Office of the Registrar,Office Asst,Registration Assistant,SP NonExempt Full Time
+Pottle,Carol,$25250.00,08/06/2007,Office of the Registrar,Office Asst,Office Assistant,SP NonExempt Full Time
+Snauwaert,Susan,$36046.04,03/06/2006,Office of the Registrar,Admissions/Registrar Officer,Registration Officer of Sched.,SP NonExempt Full Time
+Byars,Susan,$75960.27,07/01/1997,Office of the Registrar,Assoc University Registrar,Interim University Registrar,Administrative/Professional
+Hintz,Gail,$25250.00,08/17/2007,Office of the Registrar,Office Asst,Registration Assistant,SP NonExempt Full Time
+Domingues,Jucimara,$51510.00,06/05/2005,Office of the Registrar,"Coord, Management Analysis","Coord., Technical Support",Administrative/Professional
+Seals-Gonzalez,Cheryl,$83648.20,05/15/2006,OIEC,"Dir, Univ Equal Oppty Programs","Dir, Equity and Compliance",Administrative/Professional
+Rispoli,Nancy,$30695.61,08/21/2006,Parking System Operations,Program Asst,Program Asst,SP NonExempt Full Time
+Green,Robert,$25584.11,09/01/2006,Parking System Operations,Sr Clerk,Parking Customer Svc Rep,SP NonExempt Full Time
+Craddock,Jason,$64910.91,08/07/2002,Physical Therapy,Coordinator/Instructor II,Coordinator/Instructor II,"Faculty 10,11 or 12 Months"
+Lee,Kathleen,$70635.60,07/07/2003,Physical Therapy,Instructor II,Instructor II,"Faculty 10,11 or 12 Months"
+Williamson,Ellen,$90980.10,04/01/1996,Physical Therapy,Asst Professor,Assistant Professor,"Faculty 10,11 or 12 Months"
+McAloose,Barbara,$46764.19,08/07/2005,Physical Therapy,Instructor I,Instructor I,Faculty 9 Months
+Lopez-Rosado,Roberto,$64482.64,10/28/2002,Physical Therapy,Instructor I,Instructor I,"Faculty 10,11 or 12 Months"
+van Duijn,Arie,$82774.38,09/12/2005,Physical Therapy,Asst Professor,Assistant Prof,"Faculty 10,11 or 12 Months"
+Bevins,Sharon,$95056.02,08/01/1996,Physical Therapy,Chairperson/Assoc. Professor,Chair/Associate Professor,"Faculty Admin 10, 11, 12 mo"
+van Duijn,Jacqueline,$69690.00,08/07/2007,Physical Therapy,Instructor I,Instructor I,"Faculty 10,11 or 12 Months"
+Black,Stephen,$83000.00,01/04/2010,Physical Therapy,Asst Professor,Assistant Professor,"Faculty 10,11 or 12 Months"
+Coffey,Melinda,$32230.12,11/13/1998,Physical Therapy,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Hunt,Dennis,$63562.37,07/06/2001,Physical Therapy,Asst Professor,Asst Professor,Faculty 9 Months
+Bevins,Thomas,$77834.98,08/01/1996,Physical Therapy,Asst Professor,Asst Professor,"Faculty 10,11 or 12 Months"
+Felton,Shawn,$50407.47,09/06/2005,Physical Therapy,Instructor I,Instructor I,"Faculty 10,11 or 12 Months"
+Venglar,Mollie,$84000.00,08/07/2008,Physical Therapy,Asst Professor,Asst Professor,"Faculty 10,11 or 12 Months"
+Benefield,Lenore,$57864.56,08/03/2001,Planning Institutional Performance,"Asst Dir, Academic Support",Asst Director of Assessment,Administrative/Professional
+Kulmacz,Matthew,$35040.44,01/03/2006,Planning Institutional Performance,Computer Support Specialist,Computer Support Specialist,SP NonExempt Full Time
+Snyder,Paul,$137920.54,07/16/2003,Planning Institutional Performance,"Assoc VP,Planning/Policy Analy",AVP Planning/Inst. Performance,Administrative/Professional
+Vines,Robert,$82039.86,04/14/2000,Planning Institutional Performance,"Dir, Univ. Planning & Analysis","Dir, Inst. Research & Analysis",Administrative/Professional
+Banks,Lisa,$51005.00,03/31/2000,Planning Institutional Performance,"Asst Dir, Instit. Research","Asst Dir, Mgmt Info Resources",Administrative/Professional
+Toth,Jacqueline,$35194.72,12/20/2004,Planning Institutional Performance,Office Manager,Office Manager,SP NonExempt Full Time
+Alexander,George,$92745.49,08/07/1997,Planning Institutional Performance,Assoc Dean/Associate Professor,Assoc Dean/Associate Professor,"Faculty Admin 10, 11, 12 mo"
+Reaves,Jean-Paul,$33971.83,12/27/2000,Plant Operations & Maintenance,Stores/Receiving Supervisor,University Postal Supervisor,SP NonExempt Full Time
+Bielen,Alan,$79003.86,01/01/1998,Plant Operations & Maintenance,"Asst Dir, Physical Plant","Asst Dir, Physical Plant",Administrative/Professional
+Grasso,Mark,$42107.18,10/25/2004,Plant Operations & Maintenance,Refrigeration Mechanic,Refrigeration Mechanic,SP NonExempt Full Time
+McMillan,Jennifer,$26650.18,02/27/2006,Plant Operations & Maintenance,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Lee,James,$55801.81,03/06/2000,Plant Operations & Maintenance,Senior Maintenance Supt.,Senior Maintenance Supt.,Support Personnel - Exempt
+Szmania,Kevin,$36000.00,06/01/2009,Plant Operations & Maintenance,Maintenance Mechanic,Maintenance Mechanic,SP NonExempt Full Time
+Niarchos,Philip,$46647.10,01/31/1997,Plant Operations & Maintenance,Stores/Receiving Manager,Stores/Receiving Manager,Support Personnel - Exempt
+de Mercado,Kenneth,$36360.00,04/14/2008,Plant Operations & Maintenance,Maintenance Mechanic,Maintenance Mechanic,SP NonExempt Full Time
+Gonzalez,Jose,$25250.00,02/11/2008,Plant Operations & Maintenance,Sr Clerk,Sr Clerk,SP NonExempt Full Time
+West,Eddie,$31692.32,07/17/1998,Plant Operations & Maintenance,Maintenance Support Worker,Maintenance Support Worker,SP NonExempt Full Time
+Sweeney,Dominic,$39655.37,08/23/2004,Plant Operations & Maintenance,Carpenter,Carpenter,SP NonExempt Full Time
+Strykowski,Christopher,$39000.00,09/15/2008,Plant Operations & Maintenance,Refrigeration Mechanic,Refrigeration Mechanic,SP NonExempt Full Time
+Harvey,Danny,$40508.17,10/03/2005,Plant Operations & Maintenance,Electrician,Electrician/Maint Mechanic,SP NonExempt Full Time
+Spriggs,Edward,$36723.60,10/30/2006,Plant Operations & Maintenance,Locksmith,Locksmith,SP NonExempt Full Time
+Hicks,Alvin,$35801.76,03/19/2001,Plant Operations & Maintenance,Landscaping/Grounds Supv.,Landscaping/Grounds Supv.,SP NonExempt Full Time
+Africain,Samuel,$30805.00,11/21/2003,Plant Operations & Maintenance,Program Asst,Program Assistant,SP NonExempt Full Time
+Smedley,George,$41574.18,04/06/2005,Plant Operations & Maintenance,Refrigeration Mechanic,Refrigeration Mechanic,SP NonExempt Full Time
+Skotnicki,David,$28816.16,06/17/2002,Plant Operations & Maintenance,Sr Clerk,Sr Clerk,SP NonExempt Full Time
+Sheppard,William,$29991.49,10/15/1999,Plant Operations & Maintenance,Sr Clerk,Sr Clerk,SP NonExempt Full Time
+Sanchez,Irma,$36262.48,05/03/2002,Plant Operations & Maintenance,Office Manager,Office Manager,SP NonExempt Full Time
+Brown,George,$44081.41,08/15/1997,Plant Operations & Maintenance,Landscaping/Grounds Supt,Landscaping/Grounds Supt,Support Personnel - Exempt
+Brown,Loretta,$30415.35,08/04/2000,Plant Operations & Maintenance,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Hehl,James,$102123.92,06/30/1997,Plant Operations & Maintenance,"Dir, Physical Plant","Dir, Physical Plant",Administrative/Professional
+Bryant,Derick,$37443.42,08/30/2004,Plant Operations & Maintenance,Signmaker,Signmaker,SP NonExempt Full Time
+Pendleton,Bruce,$40000.00,07/21/2008,Plant Operations & Maintenance,Refrigeration Mechanic,Refrigeration Mechanic,SP NonExempt Full Time
+Camargo,Oscar,$28086.37,02/09/2004,Plant Operations & Maintenance,Sr Clerk,Sr Clerk,SP NonExempt Full Time
+Villarreal,Alejandro,$42640.18,11/09/2001,Plant Operations & Maintenance,Refrigeration Mechanic,Refrigeration Mechanic,SP NonExempt Full Time
+Payne,John,$47970.20,08/16/2002,Plant Operations & Maintenance,Senior Refrigeration Mechanic,Senior Refrigeration Mechanic,SP NonExempt Full Time
+Kelly,Troy,$76422.00,08/11/1997,Plant Operations & Maintenance,"Asst Dir, Physical Plant",Asst. Dir. Maint & Operations,Administrative/Professional
+Nickelson,Amos,$40508.17,06/26/2003,Plant Operations & Maintenance,Maintenance Mechanic,Plumber,SP NonExempt Full Time
+McConnell,Victoria,$66092.28,05/06/1996,Plant Operations & Maintenance,"Asst Dir, Physical Plant","Asst Dir, Finance & Bus. Oper.",Administrative/Professional
+Marshall,Joe,$47970.20,09/16/2003,Plant Operations & Maintenance,Senior Electrician,Senior Electrician,SP NonExempt Full Time
+Constantine,Michael,$51168.22,06/16/1997,Plant Operations & Maintenance,Senior Utilities Supervisor,Senior Utilities Supervisor,SP NonExempt Full Time
+Egan,Maryan,$79950.34,02/10/1997,Procurement Services,"Dir, Purchasing",Director Procurement Services,Administrative/Professional
+Crabill,Jennifer,$60762.26,05/26/2000,Procurement Services,"Asst Dir, Purchasing","Asst Dir, Accounts Payable",Administrative/Professional
+Foley,John,$39975.17,04/10/2006,Procurement Services,"Coord, Purchasing",Purchasing Specialist,Administrative/Professional
+Ludington,Karen,$39000.00,08/07/2006,Procurement Services,"Coord, Accounting",Procurement Card Administrator,Administrative/Professional
+Pence,Richard,$61206.00,10/24/2005,Procurement Services,"Asst Dir, Purchasing","Asst. Dir, Procurement Svcs",Administrative/Professional
+Frederick,Michelle,$33366.47,05/24/2002,Procurement Services,Accountant,Accountant,SP NonExempt Full Time
+Nelson,Barbara,$36262.45,12/24/1998,Procurement Services,Office Manager,Office Manager,SP NonExempt Full Time
+Smith,Christine,$26303.53,09/12/2005,Procurement Services,Sr Fiscal Asst,Sr. Fiscal Assistant,SP NonExempt Full Time
+Holmes,John,$43621.32,08/30/2002,Procurement Services Auxiliary P/R,"Coord, Purchasing",Purchasing Coordinator,Administrative/Professional
+Hall,Martha,$50500.00,10/29/2007,Professional Golf Management,"Coord, Academic Programs",Internship Coordinator,Administrative/Professional
+Tanous,Deborah,$31363.13,10/10/2005,Professional Golf Management,Executive Secretary,Exe. Sec.- Golf Managment Prog,SP NonExempt Full Time
+Abbate,Allison,$26260.00,06/09/2008,Purchasing F&A Auxiliary P/R,Sr Fiscal Asst,Account Payable Sr Fiscal Asst,SP NonExempt Full Time
+Zager,Mary Ann,$69820.31,08/07/1997,QEP,Program Director/Assoc Prof,QEP Prgram Dir./Assoc. Prof.,Faculty Administrator 9 mo.
+Davis,Sarah,$38000.00,08/07/2009,QEP,Instructor I,Visiting Instructor I,Faculty 9 Months
+Snapp,Annette,$46436.00,08/07/2008,QEP,Instructor I,Visiting Instructor I,"Faculty 10,11 or 12 Months"
+Knibbs,Locksley,$34112.14,09/11/2006,QEP,Office Manager,Office Manager,SP NonExempt Full Time
+Terranova,Sandra,$40000.00,03/17/2010,Research & Sponsored Programs,"Coord, Research Programs",Compliance Coordinator,Administrative/Professional
+Rieger,Elizabeth,$36244.15,08/22/2006,Research & Sponsored Programs,Grants Specialist,Grants Specialist,SP NonExempt Full Time
+Stremke,Donna,$51646.51,09/29/1997,Research & Sponsored Programs,Grant Specialist Supervisor,Grants Specialist Supervisor,Support Personnel - Exempt
+Hubbard,Hazel,$37310.16,08/23/2004,Research & Sponsored Programs,Senior Grants Specialist,Compliance Monitor Specialist,SP NonExempt Full Time
+Soria,Ana,$31082.37,10/03/2003,Research & Sponsored Programs,Grants Asst,Grants Assistant,SP NonExempt Full Time
+Kirk,Lou,$62054.26,01/29/2004,Research & Sponsored Programs,"Asst Dir, Research Programs","Assistant Director, ORSP",Administrative/Professional
+Roberts,Thomas,$127920.54,06/15/1998,Research & Sponsored Programs,Assoc VP Research/Asst. Prof,Assoc VP of Research/Asst Prof,"Faculty Admin 10, 11, 12 mo"
+Wisnom,Mary,$83962.40,08/07/2006,Resort & Hospitality Managment,Assoc Professor,Assoc Professor,Faculty 9 Months
+Alexakis,George,$75750.00,01/05/2008,Resort & Hospitality Managment,Asst Professor,Asst Professor,Faculty 9 Months
+McGurk,Jennifer,$49500.00,11/03/2008,Resort & Hospitality Managment,"Coord, Academic Programs",RHM Internship Coord,Administrative/Professional
+Brezina,Sherie,$110073.07,05/01/2003,Resort & Hospitality Managment,Director/Assoc. Professor,Director/Assoc Professor,"Faculty Admin 10, 11, 12 mo"
+Lee,Scott,$74500.00,08/07/2009,Resort & Hospitality Managment,Asst Professor,Asst Professor,Faculty 9 Months
+Royal,Karen,$33240.60,02/22/2002,Resort & Hospitality Managment,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Blanchard,Susan,$156702.66,02/01/2005,School of Engineering,Director/Professor,Director of Engineering,"Faculty Admin 10, 11, 12 mo"
+Bondehagen,Diane,$69366.80,08/07/2006,School of Engineering,Asst Professor,Asst Professor,Faculty 9 Months
+Sweeney,James,$129552.70,08/07/2006,School of Engineering,Chairperson/Professor,Chair/Professor,"Faculty Admin 10, 11, 12 mo"
+See,Linda,$38056.37,11/01/2004,School of Engineering,Office Manager,Office Manager,SP NonExempt Full Time
+Orndoff,Cynthia,$85922.72,08/07/2006,School of Engineering,Assoc Professor,Assoc Professor,Faculty 9 Months
+Stoppiello,Diana,$37921.28,04/24/2006,School of Engineering,Academic Advisor I,Academic Advisor I,"Faculty 10,11 or 12 Months"
+Villiers,Claude,$71407.00,08/07/2006,School of Engineering,Asst Professor,Asst Professor- Civil Engineer,Faculty 9 Months
+O'Neill,Robert,$129311.50,08/07/2006,School of Engineering,Chairperson/Professor,Chairperson/Professor,"Faculty Admin 10, 11, 12 mo"
+Komisar,Simeon,$95000.00,08/07/2008,School of Engineering,Program Director/Assoc Prof,Env Eng Prog Dir/Assoc Prof,Faculty Administrator 9 mo.
+Zidek,Lisa,$86708.50,01/02/2007,School of Engineering,Program Director/Assoc Prof,Grad Program Dir./Assc. Prof.,Faculty Administrator 9 mo.
+Geiger,Robert,$76507.50,01/02/2007,School of Engineering,Asst Professor,Asst Professor,Faculty 9 Months
+Kim,Jong-Yeop,$71000.00,01/19/2009,School of Engineering,Asst Professor,Asst Professor,Faculty 9 Months
+Koufakou,Anna,$80000.00,08/07/2009,School of Engineering,Asst Professor,Asst Professor,Faculty 9 Months
+Kunberger,Tanya,$70700.00,08/07/2007,School of Engineering,Asst Professor,Geotechnical-Asst. Prof.,Faculty 9 Months
+Csavina,Kristine,$75750.00,08/07/2007,School of Engineering,Asst Professor,Asst Professor,Faculty 9 Months
+Torres,Jorge,$86860.00,08/07/2007,School of Engineering,Assoc Professor,Assoc Professor,Faculty 9 Months
+Badir,Ashraf,$90000.00,08/07/2008,School of Engineering,Assoc Professor,Assoc Professor,Faculty 9 Months
+Chew,Mark,$54000.00,01/20/2009,School of Engineering,"Coord, Academic Support",Engineering Lab Manager,Administrative/Professional
+Paige,Lisa,$38394.75,01/21/1997,Service Learning,Office Manager,Office Manager,SP NonExempt Full Time
+Boucher,Jena,$34000.00,06/15/2009,SGA Executive Branch,Accountant,Accountant,SP NonExempt Full Time
+Hopkins,Stacy,$32598.09,12/18/2000,SGA Executive Branch,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Hill,Michael,$38640.00,07/23/2007,Sports Information,Coordinator/Sports Information,Interim Sports Information Dir,Administrative/Professional
+Reynolds,Tiffany,$30300.00,02/25/2008,Student Financial Aid,Financial Aid Officer,Financial Aid Officer,SP NonExempt Full Time
+Mendez,Victoria,$36390.08,01/24/2005,Student Financial Aid,Senior Financial Aid Officer,Senior Financial Aid Officer,SP NonExempt Full Time
+Murphy,Sandra,$28206.48,02/27/2006,Student Financial Aid,Program Asst,Program Asst,SP NonExempt Full Time
+Casey,Brian,$70700.00,10/01/2007,Student Financial Aid,"Assoc Dir, Student Fin. Aid","Assoc Dir, Stdt Fin. Svcs",Administrative/Professional
+Lopez-Rosado,Jorge,$81608.00,01/30/2003,Student Financial Aid,"Dir, Student Financial Aid","Dir., Student Financial Svcs",Administrative/Professional
+Lewis,Vanessa,$30300.00,02/25/2008,Student Financial Aid,Financial Aid Officer,Financial Aid Officer,SP NonExempt Full Time
+Font,Barbara,$40400.00,12/03/2007,Student Financial Aid,"Coord, Student Financial Aid","Coord, Fin Aid & Scholarships",Administrative/Professional
+Peterson,Barbara,$40804.00,02/01/2006,Student Financial Aid,"Coord, Student Financial Aid","Coord, Student Financial Aid",Administrative/Professional
+Bullock,Holly,$56105.50,02/14/2003,Student Financial Aid,"Asst Dir, Student Finl Aid","Asst. Dir, Stdt Financial Aid",Administrative/Professional
+Baurer,Andrew,$30000.00,11/24/2008,Student Financial Aid,Financial Aid Officer,Financial Aid Officer,SP NonExempt Full Time
+Miller,Timothy,$40804.00,01/29/2007,Student Housing Syste Operating,Refrigeration Mechanic,Refrigeration Mechanic,SP NonExempt Full Time
+Bramlett,John,$41041.18,11/03/2003,Student Housing Syste Operating,Maintenance Mechanic,Maintenance Mechanic,SP NonExempt Full Time
+Lopez,Niurka,$37233.65,06/18/2007,Student Housing Syste Operating,"Coord, University Housing",Assignments Coordinator,Administrative/Professional
+Stone,Jeffrey,$51168.22,05/23/2006,Student Housing Syste Operating,Maintenance Superintendent,Housing Maintenance Supt,Support Personnel - Exempt
+Whitaker,Valerie,$25000.00,07/13/2009,Student Housing Syste Operating,Program Asst,Program Asst,SP NonExempt Full Time
+Ananda,Pradip,$28000.00,07/07/2008,Student Housing Syste Operating,"Coord, University Housing","Coord, Residential Judicial",Administrative/Professional
+Thomas,Susan,$42309.72,03/05/2004,Student Housing Syste Operating,"Coord, University Housing","Coord, Summer Conf & Marketing",Administrative/Professional
+Caraway,Marcus,$28052.75,08/21/2006,Student Housing Syste Operating,"Coord, University Housing",Resident Director,Administrative/Professional
+Spohr,Anita,$35970.96,06/17/2002,Student Housing Syste Operating,Administrative Asst,Office Manager,SP NonExempt Full Time
+Jackson,Kesha,$25250.00,09/26/2007,Student Housing Syste Operating,Executive Secretary,Senior Secretary,SP NonExempt Full Time
+Chilcutt,Amy,$28785.00,07/01/2008,Student Housing Syste Operating,"Coord, University Housing",Resident Director,Administrative/Professional
+Keating,Denise,$25502.50,11/27/2006,Student Housing Syste Operating,Program Asst,Program Asst,SP NonExempt Full Time
+De La Rosa,Elizabeth,$44000.00,07/06/2009,Student Housing Syste Operating,"Asst Dir., Univ. Housing",NLV Area Director,Administrative/Professional
+Dimitriadis,Anastasios,$29000.00,07/01/2009,Student Housing Syste Operating,"Coord, University Housing",Resident Director,Administrative/Professional
+Potter,Arthur,$37743.70,10/31/2006,Student Housing Syste Operating,Maintenance Mechanic,Maintenance Mechanic,SP NonExempt Full Time
+Montalbano,Anthony,$28785.00,12/03/2007,Student Housing Syste Operating,"Coord, University Housing",Resident Director,Administrative/Professional
+Melious,Norman,$41041.18,08/19/2002,Student Housing Syste Operating,Maintenance Mechanic,Maintenance Mechanic,SP NonExempt Full Time
+Dean,Randy,$44950.22,02/07/2003,Technology Support Services,Computer Support Specialist,Acad & Event Tech Spec,SP NonExempt Full Time
+Rowland,Robert,$35000.00,09/22/2008,Technology Support Services,Computer Support Specialist,Academic Support Technician,SP NonExempt Full Time
+Santiago,Richard,$41574.18,09/25/2006,Technology Support Services,Computer Support Specialist,Computer Support Specialist,SP NonExempt Full Time
+McMillan,Jason,$45904.50,06/06/2003,Technology Support Services,"Coord, Computer System Control","Coord, Academic Computing Supp",Administrative/Professional
+Colvin,Richard,$40806.48,10/11/2004,Technology Support Services,Computer Support Specialist,Academic & Event Tech Spec,SP NonExempt Full Time
+Ceron,Luis,$74106.65,09/02/1997,Technology Support Services,"Asst Dir, Univ Computer Systm","Asst. Dir, Acad/Evt Tech Coord",Administrative/Professional
+McCarthy,Tim,$54614.52,08/24/1998,Technology Support Services,Sr Computer Support Specialist,"Coord, Comp Clasrm & Stdt Labs",SP NonExempt Full Time
+Wilson,Minor,$39783.90,12/05/2006,Technology Support Services,Computer Support Specialist,Computer Support Specialist,SP NonExempt Full Time
+Wilson,John,$54614.52,07/07/1997,Technology Support Services,Sr Computer Support Specialist,Sr Computer Support Specialist,SP NonExempt Full Time
+McFarlane,Carlin,$41573.16,09/12/2005,Technology Support Services,Computer Support Specialist,Academic Support Technician,SP NonExempt Full Time
+O'Connor-Benson,Patricia,$92258.69,10/07/2002,Technology Support Services,"Dir, University Computer Syst","Dir, Acad & Event Tech Svcs",Administrative/Professional
+Snyder,Neal,$90945.14,12/11/1995,Telecommunications Services,"Director, Telecommunications","Dir, Univ. Telecommunications",Administrative/Professional
+Boyd,Michael,$35970.00,07/09/2007,Telecommunications Services,Telephone System Manager,Telephone Sys & Chargeback Mgr,SP NonExempt Full Time
+Donnelly,Sarah,$28239.35,12/04/1998,Telecommunications Services,Telephone System Operator,Telephone System Operator,SP NonExempt Full Time
+Stiles,Frederick,$53300.23,12/01/2003,Telecommunications Services,"Coord., Telecommunications","Coord., Telecommunications",Administrative/Professional
+Duck,Monette,$36360.00,07/05/2005,Undergraduate Admissions,Office Manager,Admissions Officer Manager,SP NonExempt Full Time
+Allen,Carmen,$79950.34,10/10/1997,Undergraduate Admissions,"Asst Dir, Univ Computer Systm",Asst. Dir. Information Resourc,Administrative/Professional
+Laviolette,Richard,$90130.68,02/16/2004,Undergraduate Admissions,"Dir, Admissions/Registration",Director of Admissions,Administrative/Professional
+Schexnayder,Kenneth,$115000.00,09/14/2009,Univ Adv Communications,"Asst VP, Univ Rel/Public Rel","Asst VP, Comm Rel. & Markt.",Administrative/Professional
+Feldman,Karen,$55085.40,09/05/2006,Univ Adv Communications,"Asst Dir, Info/Publ Services",Editor/Grant Writer,Administrative/Professional
+Meyer,Laureen,$35703.50,12/04/2006,Univ Adv Communications,Administrative Asst,Administrative Assistant,SP NonExempt Full Time
+Kemler,John,$48454.75,09/05/2006,Univ Adv Communications,"Coord, Info/Publications Serv",Sr. Graphic Designer/Art Dir,Administrative/Professional
+Pagan,Lillian,$45845.43,12/18/2000,University Advancement,"Coord, University Relations",Program Coordinator,Administrative/Professional
+Catton,Brianna,$33000.00,03/29/2010,University Advancement,Executive Secretary,Executive Secretary,SP NonExempt Full Time
+Reyff,Mary,$40000.00,08/14/2006,University Advancement,Senior Information Specialist,Events & Special Projects,Support Personnel - Exempt
+DeLuccia,Carolyn,$58445.61,05/08/1995,University Advancement,Sr Administrative Asst,Executive Assistant,Support Personnel - Exempt
+Williams,Kimberly,$34000.00,05/18/2009,University Advancement,"Coord, Adv/Alumni Affairs",Coord of Alumni Programming,Administrative/Professional
+Touchette,Lindsey,$48480.00,02/28/2006,University Advancement,"Coord, Adv/Alumni Affairs",Dir. of Alumni Relations,Administrative/Professional
+McCloud,Darlene,$71407.00,10/24/2006,University Advancement,"Assoc Dir, Adv/Alumni Affairs","Dir, Major Gifts",Administrative/Professional
+McCarthy,Kelly,$64821.35,02/09/2001,University Advancement,"Assoc Dir, Adv/Alumni Affairs",Dir. of Communications,Administrative/Professional
+Mayo,Sara,$69290.29,09/29/1997,University Advancement,"Asst Dir, Adv/Alumni Affairs","Asst Dir, of Finance",Administrative/Professional
+Lehtomaa,Linda,$103544.58,05/15/2000,University Advancement,"Dir, Adv/Alumni Affairs","Dir, of Advancement",Administrative/Professional
+Lefferts,Peter,$85280.36,07/01/2004,University Advancement,"Dir, Adv/Alumni Affairs","Dir, Adv Alum/Ann Gvg/Pln Gvg",Administrative/Professional
+Kroffke,Michele,$58000.00,04/16/2001,University Advancement,"Asst Dir, Adv/Alumni Affairs",Dir. Events & Special Projects,Administrative/Professional
+Pettis,Constance,$27982.62,05/22/2005,University Advancement,Senior Secretary,Senior Secretary,SP NonExempt Full Time
+Schmitt,Nicole,$32000.00,09/29/2008,University Advancement,Program Asst,Events & Special Proj. Creativ,SP NonExempt Full Time
+Seilberger,Karie,$35000.00,10/26/2009,University Advancement,Senior Information Specialist,Senior Information Specialist,SP NonExempt Full Time
+Cassidy,Judith,$103546.54,08/29/1997,University Advancement,"Dir, Adv/Alumni Affairs","Dir, of Advancement",Administrative/Professional
+Carrington,Gerard,$98072.41,01/08/1999,University Advancement,"Dir, Adv/Alumni Affairs",Chief Financial Officer,Administrative/Professional
+Callaghan,Keith,$37000.00,12/07/2009,University Advancement,"Coord, Adv/Alumni Affairs",Dir of Annual Giving,Administrative/Professional
+Magiera,Steve,$206656.96,10/30/1995,University Advancement,"VP, Adv/Alumni Affairs","VP, Advancement",Executive Service
+Cornellier,Barbara,$37310.16,04/11/2000,University Advancement,Computer Support Specialist,Database Analyst,SP NonExempt Full Time
+Heins,Rebecca,$43000.00,08/23/2009,University Controller,"Coord, Accounting",Coordinator of Accounting,Administrative/Professional
+Bacheler,Linda,$117597.99,09/06/1994,University Controller,"Asst VP, Admin Affairs",Asst. VP Admin Serv/Controller,Administrative/Professional
+Carncross,Rose,$41000.00,09/08/2009,University Controller,"Coord, Accounting",Property Coordinator,Administrative/Professional
+Stewart,Barbara,$41574.18,10/01/2001,University Controller,"Coord, Accounting","Coord, Auxiliary Accounting",Administrative/Professional
+Hope,Nicole,$31815.00,03/17/2008,University Controller,Accountant,General Ledger & Treasury Acct,SP NonExempt Full Time
+Bailey-Hayden,Lorraine,$33046.14,07/18/2003,University Controller,Accountant,Construction & Property Acct,SP NonExempt Full Time
+Christian,Rachel,$41410.00,02/11/2008,University Controller,"Coord, Accounting",Treasury Coordinator,Administrative/Professional
+Gutknecht,June,$86708.50,09/18/2006,University Controller,"Dir, Business/Auxiliary Serv","Director, Finance & Acctg",Administrative/Professional
+Sundhagen,Amy,$40400.00,01/15/2008,University Controller,"Coord, Management Analysis","Coord, Operational Support",Administrative/Professional
+Gulati,Raminder,$61206.00,09/01/2005,University Controller,"Asst Dir, Bus/Auxiliary Serv","Asst. Director, Business Svcs",Administrative/Professional
+Garcia,Jeana,$71407.00,03/14/2007,University Controller,"Asst Dir, Bus/Auxiliary Serv","Asst Dir, Finance & Accounting",Administrative/Professional
+Vanderlinde,Denise,$37000.00,11/10/2008,University Controller,Computer Programmer-Analyst,Reporting & Support Analyst,SP NonExempt Full Time
+Cento,Linda,$42202.56,06/12/2006,University Ombudsman,Administrative Asst,Administrative Asst,SP NonExempt Full Time
+Cameron,Susan,$48964.80,12/04/2006,Vice President Student Affairs,Business Manager,Business Manager,Administrative/Professional
+Rose,Julie,$38993.32,04/23/2007,Vice President Student Affairs,"Coord, Student Affairs",Outreach Program Coord,Administrative/Professional
+Gawor,Michele,$27270.00,01/28/2008,Vice President Student Affairs,Program Asst,Outreach Program Asst,SP NonExempt Full Time
+McCaslin,Tammy,$42000.00,06/22/2009,Vice President Student Affairs,Sr Administrative Asst,Sr. Administrative Asst,Support Personnel - Exempt
+Rollo,James,$173417.00,07/01/2006,Vice President Student Affairs,"Vice Pres, Student Affairs","VP, Student Affairs",Executive Service
+Thompson,Kris,$47500.00,09/14/2009,Vice President Student Affairs,Senior Registered Nurse,Senior Registered Nurse,SP NonExempt Full Time
+Doyle,Catherine,$61206.00,01/02/2007,Vice President Student Affairs,"Dir, Student Affairs","Director, Outreach Programs",Administrative/Professional
+Gleason,Julie,$55954.00,06/25/2007,Vice President Student Affairs,"Dir, Student Affairs",Dir. Campus Involvement,Administrative/Professional
+Ghali,Michael,$50000.00,08/17/2009,Vice President Student Affairs,Psychologist,Staff Psychologist,Administrative/Professional
+Genson,Susan,$44474.57,07/01/2004,Vice President Student Affairs,"Asst Dir, Student Affairs",Asst. Dir. Stdt Support Svcs,Administrative/Professional
+Collins,Kevin,$127600.00,09/08/2009,Vice President Student Affairs,Physician,Medical Dir of Clinc Svcs,Administrative/Professional
+Scott,Darlyn,$34340.00,06/29/2007,Vice President Student Affairs,"Coord, Student Affairs",CROP/Scholars Coord,Administrative/Professional
+Jenny,Maureen,$58630.25,03/14/2002,VP Administrative Services,Executive Asst,"Exec Asst, to VP Admin Svcs",Administrative/Professional
+Pasden,Patricia,$54426.34,05/25/2001,VP Administrative Services,"Coord, Administrative Services","Coordinator, Admin. Services",Administrative/Professional
+Shepard,Joseph,$212561.30,10/09/1995,VP Administrative Services,"Vice Pres, Admin Affairs","VP, Administration and Finance",Executive Service
+McBride,Charles,$137586.11,11/07/1994,VP Administrative Services,"Assoc VP, Admin Affairs","Assoc VP, Admin Services",Administrative/Professional
+Jordanek,Christopher,$52000.00,08/31/2009,Web E-Learning & Publications,"Coord, Academic Programs",E-Learning Designer,Administrative/Professional
+Klein,Robert,$46810.66,03/13/2000,Web E-Learning & Publications,"Coord, Educational Media/Comm",Graphic Designer,Administrative/Professional
+deMoya,David,$49984.90,10/25/2006,Web E-Learning & Publications,"Coord, Computer Applications",Senior Website Developer,Administrative/Professional
+Greco,James,$47305.31,03/02/2001,Web E-Learning & Publications,"Coord, Computer Applications",Website Developer,Administrative/Professional
+Jaeger,David,$80402.74,09/01/1999,Web E-Learning & Publications,"Dir, Instructional Services","Dir, Web, E-Lrng & Pubc Svcs",Administrative/Professional
+McCulloch,Elspeth,$50000.00,09/02/2008,Web E-Learning & Publications,"Coord, Academic Programs",E-Learning Designer,Administrative/Professional
+Thompson,Arlene,$48470.31,06/01/1998,Web E-Learning & Publications,"Coord, Computer Applications",Senior Website Designer,Administrative/Professional
+Chevli,Neela,$52343.17,05/15/2000,Web E-Learning & Publications,"Coord, Computer Applications",Course Management Sys. Admin.,Administrative/Professional
+Tassler,Tamara,$56412.95,08/20/2001,Welcome Center,"Assoc Dir, Admissions/Regist","Assoc Dir, Admissions",Administrative/Professional
+Kazor,Rachel,$28280.00,04/07/2008,Welcome Center,Program Asst,Welcome Center Specialist,SP NonExempt Full Time
+Wesley,Brooke,$32990.14,08/07/2002,Welcome Center,Program Asst,Program Assistant,SP NonExempt Full Time
+Hernandez,Luis,$42844.20,04/02/2007,WGCU-FM Payroll,"Coord, Broadcasting",Host/News Reporter,Administrative/Professional
+Cooper,Valerie,$43603.09,07/01/1996,WGCU-FM Payroll,"Coord, Broadcasting",Host & News Reporter,Administrative/Professional
+Sabatka,Glenn,$44597.47,07/01/1996,WGCU-FM Payroll,"Coord, Broadcasting","Coord, Broadcasting",Administrative/Professional
+Martin,Luc,$37310.16,08/07/2006,WGCU-FM Payroll,Broadcast Prod./Program Asst,WGCU-FM Traffic Mgr,SP NonExempt Full Time
+Kiniry,Michael,$45904.50,06/04/2006,WGCU-FM Payroll,"Coord, Broadcasting",Assistant News Director,Administrative/Professional
+Davis,John,$39000.00,11/16/2009,WGCU-FM Payroll,"Coord, Broadcasting",Reporter,Administrative/Professional
+Tardif,Amy,$57564.24,07/01/1996,WGCU-FM Payroll,"Asst Dir, Radio/TV Station",Station Mngr/News Dir.,Administrative/Professional
+Chin Quee,Richard,$38376.16,06/04/2006,WGCU-FM Payroll,Broadcast Prod./Program Asst,Sound Engineer/Producer,SP NonExempt Full Time
+Carroll,Rickie,$78780.00,03/06/2006,WGCU-TV Payroll,"Asst Dir, Radio/TV Station",WGCU - Engineering Director,Administrative/Professional
+Carder,Dorrena,$38376.16,07/10/2006,WGCU-TV Payroll,Office Manager,Office Manager,SP NonExempt Full Time
+Guerrine,Linda,$62500.00,05/23/2005,WGCU-TV Payroll,"Asst Dir, Radio/TV Station",Dir. Donor Development,Administrative/Professional
+Gulnac,Michael,$42844.20,07/24/2006,WGCU-TV Payroll,"Coord, Broadcasting",Ed/Videographer,Administrative/Professional
+Brennen,Terry,$57380.99,05/18/2001,WGCU-TV Payroll,"Asst Dir, Adv/Alumni Affairs","Dir, Corp Underwriting",Administrative/Professional
+Taylor,Bradley,$37275.42,02/03/2000,WGCU-TV Payroll,Broadcast Eng. Technologist,Broadcast Engineer,SP NonExempt Full Time
+Lehtomaa,Andrew,$40400.00,04/07/2008,WGCU-TV Payroll,"Coord, Computer System Control",Broadcast Computing Specialist,Administrative/Professional
+LaRochelle,Diane,$29000.00,06/22/2009,WGCU-TV Payroll,Program Asst,Program Assistant,SP NonExempt Full Time
+Santiago-Suarez,Noelia,$44772.19,06/05/2006,WGCU-TV Payroll,Senior Accountant,Senior Accountant,SP NonExempt Full Time
+Price,Brian,$42015.57,05/24/2000,WGCU-TV Payroll,"Coord, Broadcasting",Director/Editor,Administrative/Professional
+Lott,Rebecca,$37500.00,09/21/2009,WGCU-TV Payroll,Senior Broadcast Specialist,Traffic Manager,SP NonExempt Full Time
+Rose,Colleen,$29000.00,04/19/2009,WGCU-TV Payroll,Broadcast Eng. Technician,Master Control Operator,SP NonExempt Full Time
+Barnes,Kerry,$29029.17,04/04/2004,WGCU-TV Payroll,Broadcast Eng. Technician,Master Control Operator,SP NonExempt Full Time
+Kenney,Timothy,$39819.48,03/12/2001,WGCU-TV Payroll,Cinematographer/Videographer,Videographer,SP NonExempt Full Time
+Shelton,Michael,$29606.94,02/03/2000,WGCU-TV Payroll,Broadcast Eng. Technician,Broadcast Eng. Technician,SP NonExempt Full Time
+Sklodowski,Paula,$44534.32,04/17/2000,WGCU-TV Payroll,"Coord, University Relations","Coord, University Relations",Administrative/Professional
+Kaylor,Tom,$29315.13,07/31/2005,WGCU-TV Payroll,Broadcast Eng. Technician,Master Control Operator,SP NonExempt Full Time
+Cooke,Toby,$53524.15,07/01/1996,WGCU-TV Payroll,"Asst Dir, Radio/TV Station","Dir, TV Prgm & Promotions",Administrative/Professional
+Rodriguez,Antonio,$49984.90,07/01/1996,WGCU-TV Payroll,Senior Broadcast Specialist,Senior Broadcast Specialist,SP NonExempt Full Time
+Coleman,Sheri,$58505.54,07/01/1996,WGCU-TV Payroll,"Asst Dir, Radio/TV Station","Dir, TV Production",Administrative/Professional
+Gerstle,Iris,$56805.81,04/18/1997,WGCU-TV Payroll,"Asst Dir, Radio/TV Station","Director, Finance",Administrative/Professional
+Smith-Wohlpart,Susanna,$47970.20,08/15/2003,WGCU-TV Payroll,"Coord, Info/Publications Serv","Coord, Info/Publications Serv",Administrative/Professional
+Steinhoff,Barbara,$53530.00,08/22/2007,WGCU-TV Payroll,"Asst Dir, Radio/TV Station",Marketing Director,Administrative/Professional
+Gill,Martha,$42844.20,09/18/2006,WGCU-TV Payroll,"Coord, Adv/Alumni Affairs",WGCU Pub Media/Undwtg Acct Exc,Administrative/Professional
+Williams,Mark,$43474.39,12/10/2001,WGCU-TV Payroll,Sr Broadcast Eng. Technologist,Sr Broadcast Eng. Technologist,SP NonExempt Full Time
+Stepp,Michael,$38376.16,02/03/2000,WGCU-TV Payroll,Broadcast Eng. Technician Supv,Broadcast Eng. Technician Supv,Support Personnel - Exempt
+Esmon,Dwight,$44786.39,06/25/2001,WGCU-TV Payroll,"Coord, Adv/Alumni Affairs","WGCU/TV, Underwrtg Acct Exec",Administrative/Professional
+Linstrom,Barbara,$56105.50,07/10/2006,WGCU-TV Payroll,"Assoc Dir, Radio/TV Station","Exc Producer, TV & New Media",Administrative/Professional
+Spencer,Douglas,$85000.00,09/21/2009,Whitaker Science Center,Director,Director Whitaker Center,"Faculty Admin 10, 11, 12 mo"
+Fohs,Susan,$38056.37,10/31/1994,Whitaker Science Center,Administrative Asst,Administrative Asst,SP NonExempt Full Time
+Wasno,Robert,$47470.00,01/28/2008,Whitaker Science Center,"Coord, Research Programs","Coord, Marine Edu & Outreach",Administrative/Professional
+Pires,Ricky,$35703.50,08/27/2006,Wings of Hope,"Coord, Educ/Training Programs",Dir Wings of Hope Prgm,Administrative/Professional
+Daume,Nathan,$38380.00,08/01/2007,Women's Basketball,Asst Athletic Coach,Women's Dir. of Basketball Ope,Administrative/Professional
+Schrader,Katie,$24000.00,08/24/2009,Women's Basketball,Asst Athletic Coach,Asst. Women's Basketball Coach,Administrative/Professional
+Boldon,Robert,$45000.00,08/17/2009,Women's Basketball,Asst Athletic Coach,Asst Women's Basketball Coach,Administrative/Professional
+Smesko,Karl,$120000.00,06/01/2001,Women's Basketball,Head Athletic Coach,Head Athletic Coach,Administrative/Professional
+Bertilson,Brittany,$25000.00,07/28/2008,Women's Golf,Head Athletic Coach,Head Women's Golf Coach,Administrative/Professional
+Blankenship,James,$56233.01,07/01/2006,Women's Soccer,Head Athletic Coach,Head Women's Soccer Coach,Administrative/Professional
+Zabel,Jessica,$32000.00,12/02/2009,Women's Soccer,Asst Athletic Coach,Asst Women's Soccer Coach,Administrative/Professional
+Jones,Jean,$27775.00,07/01/2008,Women's Softball,Asst Athletic Coach,Asst Softball Coach,Administrative/Professional
+Deiros,David,$71000.00,07/01/2002,Women's Softball,Head Athletic Coach,Head Softball Coach,Administrative/Professional
+Studd,Neal,$56233.01,07/01/2006,Women's Swimming/Diving,Head Athletic Coach,Head Women's Swim/Diving Coach,Administrative/Professional
+Lykins,Elizabeth,$31623.10,07/01/2007,Women's Swimming/Diving,Asst Athletic Coach,Asst Women's Swim/Diving Coach,Administrative/Professional
+Gabou,Jennifer,$40400.00,06/16/2008,Women's Tennis,Head Athletic Coach,Head Women Tennis Coach,Administrative/Professional
+Nichols,David,$63000.00,05/12/2008,Women Volleyball,Head Athletic Coach,Head Women's Volleyball Coach,Administrative/Professional
+Nelson,Fernanda,$32000.00,07/15/2008,Women Volleyball,Asst Athletic Coach,Asst. Women's Volleyball Coach,Administrative/Professional
diff --git a/faq/median-vs-mean/mean-vs-median.ipynb b/faq/median-vs-mean/mean-vs-median.ipynb
new file mode 100644
index 00000000..e5933821
--- /dev/null
+++ b/faq/median-vs-mean/mean-vs-median.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "data source: https://opendata.socrata.com/dataset/FGCU-salary-dataset/fjqw-ymup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Last Name
\n",
+ "
First Name
\n",
+ "
Annual Salary
\n",
+ "
FGCU Hire Date
\n",
+ "
College/Dept
\n",
+ "
Class Title
\n",
+ "
Working Title
\n",
+ "
Employee Class
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
Gray-Vickrey
\n",
+ "
Margaret
\n",
+ "
$127972.27
\n",
+ "
07/01/1996
\n",
+ "
Academic Affairs Administration
\n",
+ "
Assoc. Vice President/Prof.
\n",
+ "
Assc. Provost/Assoc. VP C&I
\n",
+ "
Faculty Admin 10, 11, 12 mo
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
Rogers
\n",
+ "
Hudson
\n",
+ "
$134316.57
\n",
+ "
08/07/1997
\n",
+ "
Academic Affairs Administration
\n",
+ "
Assoc. Vice President/Prof.
\n",
+ "
Associate VP & Professor
\n",
+ "
Faculty Admin 10, 11, 12 mo
\n",
+ "
\n",
+ "
\n",
+ "
2
\n",
+ "
Hart
\n",
+ "
Erika
\n",
+ "
$26650.11
\n",
+ "
08/03/2001
\n",
+ "
Academic Affairs Administration
\n",
+ "
Program Asst
\n",
+ "
Program Assistant
\n",
+ "
Support Personnel NonExempt PT
\n",
+ "
\n",
+ "
\n",
+ "
3
\n",
+ "
Deschene
\n",
+ "
Catherine
\n",
+ "
$63960.27
\n",
+ "
01/05/2004
\n",
+ "
Academic Affairs Administration
\n",
+ "
Executive Asst
\n",
+ "
Exec Asst to Provost & VPAA
\n",
+ "
Administrative/Professional
\n",
+ "
\n",
+ "
\n",
+ "
4
\n",
+ "
Baker
\n",
+ "
Jennifer
\n",
+ "
$90000.00
\n",
+ "
05/26/2009
\n",
+ "
Academic Affairs Administration
\n",
+ "
Dir, Academic Support Services
\n",
+ "
Dir., Budgets & Management Svs
\n",
+ "
Administrative/Professional
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Last Name First Name Annual Salary FGCU Hire Date \\\n",
+ "0 Gray-Vickrey Margaret $127972.27 07/01/1996 \n",
+ "1 Rogers Hudson $134316.57 08/07/1997 \n",
+ "2 Hart Erika $26650.11 08/03/2001 \n",
+ "3 Deschene Catherine $63960.27 01/05/2004 \n",
+ "4 Baker Jennifer $90000.00 05/26/2009 \n",
+ "\n",
+ " College/Dept Class Title \\\n",
+ "0 Academic Affairs Administration Assoc. Vice President/Prof. \n",
+ "1 Academic Affairs Administration Assoc. Vice President/Prof. \n",
+ "2 Academic Affairs Administration Program Asst \n",
+ "3 Academic Affairs Administration Executive Asst \n",
+ "4 Academic Affairs Administration Dir, Academic Support Services \n",
+ "\n",
+ " Working Title Employee Class \n",
+ "0 Assc. Provost/Assoc. VP C&I Faculty Admin 10, 11, 12 mo \n",
+ "1 Associate VP & Professor Faculty Admin 10, 11, 12 mo \n",
+ "2 Program Assistant Support Personnel NonExempt PT \n",
+ "3 Exec Asst to Provost & VPAA Administrative/Professional \n",
+ "4 Dir., Budgets & Management Svs Administrative/Professional "
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "df = pd.read_csv('./FGCU_salary_dataset.csv')\n",
+ "df_m = df\n",
+ "d = df_m['Annual Salary'].apply(lambda x: x.lstrip('$'))\n",
+ "d = d.astype('float64')\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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CLpdr7Mv169cRHh6O5s2bw9nZGRMmTKhRk191XnnpwYMH+PXXXyGTydCjRw9IJJLKiIu0\nwLWCiWq+skHLu+++C09PT0RHR+PYsWNYvnw5rK2tsX37dnTu3BnR0dHYtWsXoqKi4Ofnh65duwIA\nhg8fjpSUFIwaNQqenp5IS0vDhg0bkJaWhj179iiP8+GHH2L37t0YOnQo2rdvjx9++AFvvfWW2qBJ\n0z3Wffv2IS8vD6NHj0aTJk1w6dIlbNmyBVevXsXRo0fV+jJmzBi8/PLLmDt3Ls6fP48tW7ZAIpEg\nKiqqUn6GVU2nxLp06VIsW7YMjx49giAI2Lt3LyQSCe7fv4+WLVti4cKFyhefExEZovIeUSvvD1Vd\n21eWtm3bYuXKlQCAUaNGoVWrVpg7dy5mz56NSZMmAQAGDx4Mb29vbNu2DV27dkVCQgJOnDiBgwcP\nomPHjsp9tWnTBuPGjcPJkyfRo0cPXLx4EQkJCXjvvfewePFiAI8T+fjx43H58uVnxhYdHQ0LCwuV\nsldeeQXjxo1DamoqOnTooFLn5+eHVatWKT/fv38fW7durTGJVetLwZs2bcKCBQswZMgQ/O9//1NZ\necnW1hYDBgzAvn37KiVIIqLaTBAEhIWFKT+bmJjAz88PoihixIgRyvJGjRrBzc0Nf/31F4DHI0k3\nNzd4enoiOztb+VWWZE+fPg0AOHbsGARBwNixY1WOO378eJXf9eV5Mqnm5eUhOzsbr7zyCkRRxLlz\n59T6MnLkSJWyjh07Ijs7Gw8fPtTmx2HwtB6xrl+/HoMGDcLKlSuRnZ2tVt+qVSvExcXpNTgiIn3T\ndaRpKLdcnJycVD5bWVnB3NwcdnZ2auX37t0DAPzxxx9IT0+Hq6ur2v4EQYBMJgMA3Lp1C4IgqK1F\noGk7TTIzMzFnzhx8++23yMvLUznGgwcPntmXspe3yOVyNGjQQKtjGjKtE2tGRgbCw8PLrbe2ttbp\nheVERKQ9U1NTtTITE80XHctGmQqFAl5eXoiNjdU48nRwcHjhuBQKBYKCgpCdnY0pU6bA3d0dlpaW\nUCgUGDx4MBQKhdo2mvryZNzGTuvEam1trfzrRpMrV67A3t5eL0EREdGLe/nll3H+/Hl069atwnYv\nvfQSRFHEjRs34OHhoSy/fv36M49x6dIlpKenY926dQgJCVGW37hx4/kDN3Ja32Pt06cPNm/erHFU\nevHiRWzZsgUDBgzQa3BUMa4VTEQVCQoKQlZWFjZu3KhWV1RUpLyn+dprr0EURXzxxRcqbdavX//M\nRynLRp9Pj0xXrVpVax/D1HrEOnv2bCQlJaFjx47o06cPBEHA9u3bsXnzZhw6dAjNmjXD9OnTdQ4g\nOTkZq1evxvnz53Hnzh2sXbtWuTRiSUkJ5s+fj2+//RYZGRlo2LAhunbtiqioKJVr9EVFRZg1axYS\nExNRUFCAbt26YenSpWjWrJnO8RAR1RQhISHYv38/pk2bhh9//BEBAQEQRRHp6enYt28fNm/ejM6d\nO8PX1xdDhgzBxo0bkZubiw4dOuD06dP4448/nnl51sPDA66urpg1axYyMzNhY2OD48eP486dOzXm\n0q6utB6x2tvb4+TJk+jbty8OHDgAURSRkJCAb7/9FkOHDsXx48fRuHFjnQPIz8+Hj48PYmNjUb9+\nfZW6R48e4cKFC5g+fTpOnTqFHTt24NatWxg6dKjKX0eRkZE4dOgQNm3ahMOHDyMvLw8hISG19qQS\nUe1Q3oiwrLxsADRv3jykpaUhKioKsbGx+PXXXzF27Fj4+Pgot1mzZg3ef/99nDhxAnPnzkVxcTF2\n7dr1zLWBzczMEB8fD39/f6xevRoLFy6ElZUV9uzZU2vXFRbkcvlzZZ979+5BoVCgSZMm5d5A15WT\nkxMWL15c4WL+aWlpCAgIQHJyMry9vfHgwQO4ubkhLi4OwcHBAB7PUPP19cWePXvQs2dPvcSmTxl5\nxbhwv0RjnYOlgDv56qfE19YMLg3NVcrKLgOXzVpMT0+Hu7v7E/X/zrQzVk/3qaZgv4hqLq0z4v/+\n9z+VX9BNmjSBRCLRW1LV1oMHDyAIgjJpnDt3DiUlJSoJ1NHREZ6enkhNTa3S2IiIiLTOipMnT4aX\nlxdGjRqFb775BiUlmkdclam4uBizZ89G//79ldPEpVIpTE1N1S5D29nZQSqVVnmMRERUu2k9een0\n6dPYtWsXEhMT8fXXX6Nx48YYPHgwQkJC0K5du8qMEQBQWlqKsWPHIi8vD/Hx8ZV+PGNgKA+uExHR\nv7ROrC1btkTLli0RHR2NU6dOYdeuXYiPj8fGjRvxn//8ByEhIRg6dChcXFz0HmRpaSnGjBmDq1ev\n4tChQ8rLwAAgkUhQWlqK7OxslVGrTCZDp06dKtxvenq63mPVRjYsIM0p1VhX36YepDn/qJVnFZui\n+O6z3/6gqU/V1U99Mfb4y8N+6Yejo6PaxEeqmR49eoTMzMxy6w3l/v5zT14CgMLCQhw+fBhbt25F\nUlISAKBDhw4IDQ3FW2+9pbYo87NomrxUUlKC0aNHIy0tDYcOHVJbvquiyUuJiYno0aPH83av0uhr\n8tLTOHnJeLBfRDXXC808+vXXX5GUlIRffvkFoiiiRYsWKCwsxMSJE+Hn54cff/zxmfvIz8/HhQsX\n8Pvvv0OhUODWrVu4cOECbt26hdLSUowcORK//fYbNmzYAFEUIZVKIZVKle/us7KyQlhYGKKiovD9\n99/j/PnzGD9+PHx9fdG9e/cX6R4REZHOdH4f67Vr1xAfH4+EhATcunULTZo0wYgRIzBs2DD4+voC\nAH7//XdMmDABkydPfubM3LNnz+L1119XPusUExODmJgYhIaGIiIiAocPH4YgCGojzzVr1ihHtrGx\nsTAzM8OYMWNQUFCA7t27a7ViCBERkb5pnVjXrl2LXbt24ffff0edOnXQv39/LF68GK+99pragsqt\nWrVCeHg4Pvroo2fut0uXLhUu3q/Nwv7m5uZYtGgRFi1a9OyOEBERVSKtE+usWbPQvn17LF26FEFB\nQSoTiDRp06YNpk2b9sIBUvmeXiCCiIiqn9aJ9ddff1V7V19FvL294e3t/VxBEREZikePHtXIWcc1\ntV+GQOvJS08m1Xv37uG3337Db7/9pnyhLhFRTVTR4x3GrKb2yxDoNHkpJSUFs2bNwrlz51TK27Zt\niwULFiAgIECvwRERERkbrRNrSkoKBg0ahAYNGmDChAnKl+Feu3YNO3fuxJtvvon9+/czuRIRUa2m\ndWJduHAhnJ2dcfToUbV1eSdPnow+ffpg4cKFOHDggN6DJCIiMhZa32M9e/YsRo4cqfGdqzY2Nhg5\nciTOnj2r1+CoYnJ5LmcEExEZGK0Tq6mpKYqKisqtLywsrPJXyBERERkarTNhhw4dsGHDBmRkZKjV\nZWRkYMOGDejYsaM+YyMiIjI6Wt9jjYqKQv/+/dGhQwf0798fbm5uAB4vun3kyBHUrVsX//3vfyst\n0NquoERERl6xxrpGdUxgU9dUYx0REVUtnV4b991332HevHk4fvw49u/fDwCoX78++vbti9mzZytn\nCpP+PSxRID1X84uIfG3NmFiJiAyETs+xenh4YNu2bVAoFMqFIZo0acJ7q9WsbDSbDYtyR7VERFQ1\ndH67DQCYmJhAIpHoOxbSUZhfUwDAt+lZSM8VIc0phdRc83teiYioapSbWHfs2PFcO3zyJeVERES1\nTbmJ9YMPPtB5Z4IgMLESEVGtVm5iPX/+fFXGQUREVCOUm1idnZ2rMg4iIqIa4bkmL128eBE3b94E\n8DgBt2zZUq9BERERGSudEuu+ffswZ84c5Xv8RFGEIAho1qwZ5s2bh8GDB1dKkKTZ1nN3/+87oVrj\nICKif2n9AOrOnTsxevRo1KtXD9HR0di+fTu++uorREdHo169enjvvfewc+dOnQNITk5GaGgoWrRo\nARsbG42zkWNiYuDt7Q0HBwcEBgbi6tWrKvVFRUWYNm0aXF1d4ejoiNDQUNy+fVvnWIiIiF6U1ol1\nyZIl8Pf3x6lTp/DRRx+hf//+6N+/Pz766COcPn0afn5+WLJkic4B5Ofnw8fHB7Gxsahfv75a/YoV\nKxAXF4fFixcjKSkJdnZ2CAoKQn5+vrJNZGQkDh06hE2bNuHw4cPIy8tDSEgIRFHzSkVERESVRevE\neuvWLQwdOhQWFhZqdRYWFggJCVFeItZF7969MXv2bLzxxhsQBPVLmuvWrcOkSZMQGBgILy8vxMXF\n4eHDh9i9ezcA4MGDB9i2bRvmz5+P7t27o1WrVli/fj0uXbqEkydP6hwPERHRi9A6sXp5eeHOnTvl\n1t++fRuenp56CapMRkYGsrKy0LNnT2WZhYUFOnXqhNTUVACP3xNbUlKi0sbR0RGenp7KNkRERFVF\n68Q6b948bN68GXv37lWr27NnD7Zs2YL58+frNTipVApBEGBnZ6dSbmdnB6lUCgCQyWQwNTVVewH7\nk22IiIiqitazglevXg1bW1u8++67iIyMxMsvvwwA+PPPPyGTyeDq6opVq1Zh1apVym0EQcCuXbv0\nH7WepKenV8txs2EBaU6pxrr6NvUgzflHq/IpfVoDAPb8fE1ZJ5VmqW1bXf3UF2OPvzzsl/GoiX0C\nal6/3N3dqzsEADok1qtXr0IQBDg5OQGActZt3bp14eTkhMLCQqSlpalso+meqS4kEglEUYRMJoOj\no6OyXCaTKV8CIJFIUFpaiuzsbJVRq0wmQ6dOnSrcf3WdhIy84nIXy29kKUBibqV1OQA0sm4EibkV\npNIsSCT2avWG8o/teaSnpxt1/OVhv4xHTewTUHP7ZQi0TqwXLlyozDg0cnFxgb29PZKSkuDn5wcA\nKCgoQEpKChYsWAAA8PPzg5mZGZKSkhAcHAwAyMzMRFpaGgICAqo8ZiIiqt2ea+UlfcrPz8eNGzcg\niiIUCgVu3bqFCxcuwMbGBk5OTggPD8eyZcvg5uYGV1dXLFmyBA0aNFAmUSsrK4SFhSEqKgpNmjSB\ntbU1Zs+eDV9fX3Tv3r2ae0dERLWNzom1sLAQmZmZkMvlGp8T9ff312l/Z8+exeuvv668bBwTE4OY\nmBiEhoZizZo1mDhxIgoKCjB9+nTI5XL4+/sjMTERlpaWyn3ExsbCzMwMY8aMQUFBAbp3747169e/\n8KVoIiIiXWmdWHNycjBz5kwkJiaiuLhYrb5secPs7GydAujSpQtycnIqbBMREYGIiIhy683NzbFo\n0SIsWrRIp2MTERHpm9aJ9YMPPsDRo0cRHBwMf39/WFlpnkhDVYdrBRMRGR6tE+vJkycxfvx4fPrp\np5UZDxERkVHTeoEIW1tb5bOrREREpJnWiXX06NHYvXs3Sks1L2xAREREOlwKnjJlCgoLC9G1a1cM\nGzYMzZo1g6mpqVq7oKAgvQZIRERkTLROrLdu3cJ3332HK1euICoqSmMbQRCYWImIqFbTOrF++OGH\nuHjxIiZPnsxZwQYizK8pAODbdPX1gYmIqHponVjPnDmDiRMnYubMmZUZDxERkVHTevKSRCKBtbV1\nZcZCRERk9LROrB999BG2bNmCvLy8yoyHiIjIqGl9Kfjhw4cwNzdHmzZt8Oabb8LR0VFtVrAgCPj4\n44/1HiQREZGx0Dqxzp07V/n9pk2bNLZhYiUiotpO68R6/vz5yoyDngPXCiYiMjxaJ1ZnZ+fKjIOI\niKhGqHDy0vXr1/Hw4UOtdnTnzh0cO3ZML0EREREZqwoTa/v27XH48GHlZ7lcDi8vL/z0009qbU+f\nPo1hw4bpP0IiIiIjUmFiFUVR5bNCoUBWVhYKCwsrNSgiIiJjpfU9VjJeGXnFGssb1TGBTV31FykQ\nEdHzY2I1YtquFXzhfonGcl9bMyZWIiI903rlpeqiUCiwYMECtG7dGk2bNkXr1q2xYMECKBQKlXYx\nMTHw9vaGg4MDAgMDcfXq1WqKmIiIarNnjljz8vIgk8kAANnZ2QCA3NxcZVmZBw8eVEJ4wPLly7Fp\n0yasW7cO3t7euHTpEsLDw2FhYYGpU6cCAFasWIG4uDisXbsWbm5uWLRoEYKCgvDLL7/A0tKyUuIi\nIiLS5JmJderUqcoEVuadd95RayeKIgRB/wsVnDlzBv369UOfPn0AAC+99BL69euHX375Rdlm3bp1\nmDRpEgJYZB7MAAAgAElEQVQDAwEAcXFxcHd3x+7duzFq1Ci9x0RERFSeChNrREREVcVRro4dO2Lj\nxo1IT0+Hu7s7rl69itOnT2PKlCkAgIyMDGRlZaFnz57KbSwsLNCpUyekpqYysRIRUZWqMLFGRkZW\nVRzl+uSTT/Dw4UN06NABpqamKC0txZQpUzB69GgAgFQqhSAIsLOzU9nOzs4Od+/e1bRLIiKiSmPw\ns4L37NmDnTt3YtOmTfD09MSFCxcQERGB5s2bY8SIES+07/T0dD1FqZtsWECaU6qxrr5NPUhz/tGq\nfOmxx7OBc+X/1kml6jOENZUBQFaxKYrvFugUe3WorvNU2dgv41ET+wTUvH65u7tXdwgAjCCxRkVF\n4eOPP8agQYMAAN7e3rh58yaWL1+OESNGQCKRQBRFyGQyODo6KreTyWSQSCQV7ru6TkJGXjGk5pof\ngWlkKUBibqV1+ZN1UmkWJBJ7tXpNZQBgb2sGl4bmOkRe9cpuAdQ07JfxqIl9AmpuvwyBwT9u8+jR\nI5iYqIZpYmKifNzGxcUF9vb2SEpKUtYXFBQgJSUFAQEBVRorERGRwY9Y+/XrhxUrVsDZ2RleXl44\nf/481q5di7ffflvZJjw8HMuWLYObmxtcXV2xZMkSNGjQAMHBwdUYORER1UYGn1gXL16MhQsXYurU\nqbh37x7s7e3xzjvvYPr06co2EydOREFBAaZPnw65XA5/f38kJibyGVYiIqpy5SbWbt264b///S9e\ne+01AMCOHTvQqVMnNG/evMqCAwBLS0t8+umn+PTTTytsFxERYRCPBxERUe1WbmK9dOkS7t27p/w8\nYcIErF+/vsoTq7HKKSxFbpFCY92jYlFjua60XSuYiIiqTrmJ1dnZGSdOnEBgYCAaNGhQaSsr1VS5\nRYpyF793sOTPkYiopip3VvC4ceOQkJAAZ2dnNG7cGIIgYNy4cWjcuHG5X7a2tlUZOxERkcEpd8Qa\nHh6ONm3a4IcffoBUKsWGDRvQo0cPuLq6VmV8RERERqXCWcEBAQHKZ0G/+OILhIaGYujQoVUSGBER\nkTHS+nGbnJycyoyDiIioRtD5OdZjx47h2LFjuHnzJoDHk5z69eunfCyHqs7Wc2UvGeBkKCIiQ6F1\nYi0oKMCoUaNw/PhxmJiYoGnTx496nDhxAps2bULv3r2xZcsW1K1bt9KCJSIiMnRarxUcExODY8eO\nYfr06bhx4wYuXryIixcv4s8//0RkZCSOHz+O2NjYyoyViIjI4GmdWPfs2YMRI0YgMjISVlb/vmWl\nYcOGmD59OoYPH46EhIRKCZKIiMhYaJ1YZTIZ2rRpU269n58fZDKZXoIiIiIyVlonVkdHR5w6darc\n+lOnTqm8D5WIiKg20jqxvv3229i/fz8++ugjXLlyBcXFxSguLsaVK1fw8ccf48CBAxgxYkRlxkpP\nCfNrqlwvmIiIDIPWs4InT56Mv/76C9u2bcP27duV6waLoghRFBEWFoZJkyZVWqBERETGQOvEamJi\ngtWrV2P8+PE4duwY/v77bwDASy+9hD59+sDHx6fSgiQiIjIWOi8Q4ePjwyRKRERUDq3vsRIREdGz\nMbESERHpkc6XgslwcK1gIiLDYxQj1qysLISHh8PNzQ1NmzZFx44dkZycrNImJiYG3t7ecHBwQGBg\nIK5evVpN0RIRUW1m8Ik1NzcXffv2hSAI2L17N86cOYNFixbBzs5O2WbFihWIi4vD4sWLkZSUBDs7\nOwQFBSE/P78aIyciotpIq8T66NEjNG7cGEuWLKnseNSsXLkSDg4OWLt2Lfz8/ODs7Ixu3brB3d1d\n2WbdunWYNGkSAgMD4eXlhbi4ODx8+BC7d++u8niJiKh20yqx1q9fH02aNFFZfL+qfPPNN/D398eY\nMWPg7u6Orl274osvvlDWZ2RkICsrCz179lSWWVhYoFOnTkhNTa3yeImIqHbT+lLwoEGDsHfvXigU\nisqMR01GRgY2btyIl19+GYmJiQgPD0d0dDQ2bNgAAJBKpRAEQeXSMADY2dlBKpVWaaxERERazwoO\nDAzE6dOn0a9fP4wcORIuLi6oV6+eWjt/f3+9BqhQKODv7485c+YAAHx9ffHHH39gw4YNeO+9915o\n3+np6foIUaNsWECaU6qxrr5NPUhz/tGpTlP5lD6tAQB7fr6mrJNKs9S21VQGAFnFpii+W1B+JwxE\nZZ6n6sR+GY+a2Ceg5vXryVuE1UnrxPrmm28qv//555+VawWXEUURgiAgOztbf9EBsLe3h4eHh0qZ\nh4cH1q9fDwCQSCQQRREymUzl7ToymQwSiaTCfVfmScjIK4bUvERjXSNLARJzzZfVy6urcBvrRpCY\nW0EqzYJEYq9Wr6kMAOxtzeDS0Ly8LhiE9PR0g/nPok/sl/GoiX0Cam6/DIHWiXXNmjWVGUe5AgIC\n1P6qSk9Px0svvQQAcHFxgb29PZKSkuDn5wcAKCgoQEpKChYsWFDl8RIRUe2mdWJ9++23KzOOcn3w\nwQfo27cvli5disGDB+P8+fP4/PPPMXfuXGWb8PBwLFu2DG5ubnB1dcWSJUvQoEEDBAcHV0vMRERU\nez3Xykt//PEHZDIZvL290ahRI33HpKJNmzbYvn07oqOjsWTJEjg5OWHOnDkYM2aMss3EiRNRUFCA\n6dOnQy6Xw9/fH4mJibC0tKzU2IiIiJ6mU2JNSEhAdHQ0bt++DQDYu3cvunfvjvv376NPnz6YPXs2\ngoKC9B5k79690bt37wrbREREICIiQu/HJiIi0oXWj9vs378f48aNg4eHB+bNmwdRFJV1tra28PDw\nwM6dOyslSNJs67m7T6wXTEREhkDrxLp06VL06NEDiYmJGu+3tmvXDhcvXtRrcERERMZG68R67do1\nBAYGlltvZ2eHe/fu6SUoIiIiY6V1Yq1fv36Fi9r/+eefsLW11UtQRERExkrrxNqtWzd89dVXKCoq\nUqu7c+cONm/ejFdffVWvwRERERkbrWcFz5kzB7169UKPHj0waNAgCIKA48ePIykpCZs3b4apqSln\n5RIRUa2n9YjV1dUVR48ehb29PWJjYyGKItasWYOVK1fC19cXR44cUa6GRFUjzK8pwvyaVncYRET0\nBJ2eY/X09MTevXshl8tx48YNKBQKuLi4oEmTJpUVHxERkVF5rpWXrK2t0bZtW33HQkREZPR0Sqxy\nuRxr1qzB0aNHcfPmTQCAs7Mz+vbtiwkTJsDa2rpSgiQiIjIWWt9jvXHjBrp06YIlS5agpKQEXbt2\nRdeuXVFSUoIlS5agc+fO+OOPPyozViIiIoOn9Yh12rRpePDgAfbv349u3bqp1H3//fcICwtDREQE\ndu/erfcgiYiIjIXWiTUlJQUffvihWlIFgO7du+P999+vtne21lb/rhMsVNiOiIiqjtaXghs1alTh\nPVRra+tKf4UcERGRodM6sYaFhWHbtm3Iy8tTq8vNzcW2bdswcuRIvQZHRERkbMq9FLx3716Vzx4e\nHhAEAe3atUNoaCj+85//AHj80vOdO3fCzs4O7u7ulRstERGRgSs3sY4ZMwaCICjfu/rk9ytXrlRr\nL5VKMW7cOAwZMqSSQiUiIjJ85SbWAwcOVGUcVA0KSkRk5BWrlTeqYwKbuqbVEBERkfErN7F26dKl\nKuPQ2rJlyzB//nyMHTsWn332mbI8JiYGW7ZsgVwuh7+/P5YsWQIvL69qjLTyla0T/G161nNt/7BE\ngfRcUa3c19aMiZWI6DlpPXnJEPz888/YvHkzWrZsqVK+YsUKxMXFYfHixUhKSoKdnR2CgoIqfH8s\nERFRZdBpScPTp09j27Zt+OuvvyCXy5X3XMsIgoCffvpJrwGWyc3Nxbhx47BmzRrExsaq1K1btw6T\nJk1CYGAgACAuLg7u7u7YvXs3Ro0aVSnxEBERaaJ1Yl29ejWioqJgYWEBNze3Kn+jzSeffIKgoCC1\nS9QZGRnIyspCz549lWUWFhbo1KkTUlNTmVj1LKewFLlFCo11vDdLRKRjYu3QoQN27txZ5QtBbN68\nGRkZGdi4caNanVQqhSAIsLOzUym3s7PD3bt31drTi8ktUuDC/RKNdbw3S0SkQ2ItKCjAW2+9VeVJ\n9fr165g/fz6OHj0KExOjuiVstMqbLQwAj4rVJzsREdG/tE6sXbt2xcWLFyszFo3OnDmD7OxsdOjQ\nQVlWWlqK5ORk/O9//0NKSgpEUYRMJoOjo6OyjUwmg0QiqXDf6enplRZ3NiwgzSnVWFffph6kOf/o\nVKepfOmxx7OBc+X/1kml6jOENZVVeKziesgoJz6XCmLPKjZF8d0CjXUvojLPU3Viv4xHTewTUPP6\nZSiLFGmdWBcvXozBgwdj+fLlGDFihNql18oSGBio9lL1Dz74AG5ubpgyZQrc3Nxgb2+PpKQk+Pn5\nAXg8uk5JScGCBQsq3HdlnoSMvGJIzTVfMm1kKUBibqVTnTbbSKVZkEjs1eo1lb3osTSxtzWDS0Nz\njXXPKz093WD+s+gT+2U8amKfgJrbL0OgdWJt1qwZhg8fjrlz52L+/PkwNzdXuzQrCAJu376t1wCt\nrKxgZaX6i7x+/fqwtraGp6cnACA8PBzLli2Dm5sbXF1dsWTJEjRo0ADBwcF6jYWIiOhZtE6s8+fP\nx/Lly9GsWTP4+fmpJbuqJAiqr0mbOHEiCgoKMH36dOUCEYmJibC0tKymCImIqLbSOrF++eWX6Nu3\nL7Zv317tk4g0LbcYERGBiIiIaoiGiIjoX1on1uLiYvTp06fakyoZropmE/MZVyKqLbROrP369cOP\nP/6I0aNHV2Y8pIMXXStY38pbexjgM65EVHtoPfycOnUq0tLSMHHiRPzyyy+4e/cuZDKZ2hcREVFt\npvWItew50osXL2Lr1q3ltsvOzn7xqIiIiIyU1ol1+vTparNxiYiISJXWiXXGjBmVGQcREVGNwCm+\nREREeqT1iHXRokXPbCMIAqZPn/5CAZH2tp4re3sPL9ETERkKrRPr0y8Xf5IgCBBFkYmViIhqPa0T\na05OjlqZQqHAzZs3sWHDBiQnJ2P37t16DY6IiMjYvNA9VhMTE7i4uGDBggVwdXXlaJXKVbYq09Nf\nOYWaX61HRGSstB6xPkunTp0QFRWlr91RDVPeqkxckYmIahq9zQo+e/Ys1xEmIqJaT+sR644dOzSW\n5+bmIjk5GQcOHMDIkSP1Fhg9m6GtFUxERDok1g8++KDcOltbW0yaNIn3WImIqNbTOrGeP39erUwQ\nBFhbW6Nhw4Z6DYqIiMhYaZ1YnZ2dKzMOIiKiGoGzjYiIiPSowhFr69atddqZIAg4d+7cCwVERERk\nzCpMrF5eXlrt5O+//8aVK1cq5bVyy5Ytw8GDB3H9+nXUqVMH7dq1Q1RUFLy9vVXaxcTEYMuWLZDL\n5fD398eSJUu0jt9Yca1gIiLDU2FijY+Pr3Djv//+G0uWLEFSUhLq1q2LsLAwvQYHAMnJyRg7diza\ntGkDURSxcOFCDBo0CKmpqbC2tgYArFixAnFxcVi7di3c3NywaNEiBAUF4ZdffoGlpaXeYyIiIirP\nc628dOvWLSxduhRfffUVAGDUqFGYNGkSmjVrptfgAKitP7x+/Xo4OzsjNTUVffv2BQCsW7cOkyZN\nQmBgIAAgLi4O7u7u2L17N0aNGqX3mIiIiMqjU2LNzMzE0qVLsX37dgBAWFgYJk+eXCkJtTx5eXlQ\nKBTK0WpGRgaysrLQs2dPZRsLCwt06tQJqampTKxERFSltEqsTyfUESNGYPLkyXB0dKzU4DSJjIxE\n69at0b59ewCAVCqFIAiws7NTaWdnZ4e7d+9q2gUREVGlqTCxZmZmYtmyZdi+fTtEUazWhAoAM2fO\nxJkzZ3DkyJFKmShFRET0oipMrG3btkVxcTF8fX0xefJkODk54e7duxWOBP39/fUeJADMmDED+/bt\nw8GDB1UWq5BIJBBFETKZTCXhy2QySCSSCveZnp5eKbECQDYsIM3R/Eq0+jb1IM35R6c6TeVT+jx+\nHGrPz9eUdVKp+rrBmsp0PVZl1WUVm6L4boHGbcpU5nmqTuyX8aiJfQJqXr/c3d2rOwQAz0isRUVF\nAIDff/8do0ePrnBHoihCEARkZ2frL7r/ExERgf379+PgwYNwdXVVqXNxcYG9vT2SkpLg5+cHACgo\nKEBKSgoWLFhQ4X4r8yRk5BVDal6isa6RpQCJuZVOdRVuY90IEnMrSKVZkEjs1eo1lT33sfRcZ29r\nBpeG5hq3AR7/xzeU/yz6xH4Zj5rYJ6Dm9ssQVJhY16xZU1VxlGvq1KnYtWsXtm/fDisrK0ilUgCA\npaWl8lGa8PBwLFu2DG5ubnB1dcWSJUvQoEEDBAcHV2foRERUC1WYWN9+++2qiqNcGzduhCAIePPN\nN1XKIyIiEBERAQCYOHEiCgoKMH36dOUCEYmJiXyG1QgUlIjIyCvWWNeoDlfcJCLj81zPsValnJwc\nrdo9mWjJeDwsUSA9V9RY52tr8P88iYjUcEhARESkRxwSGLGavlZwQYmIXFhovFTcqI4JbOqaVkNU\nREQVY2Ilg/WwRIGLOaUaZ1f72poxsRKRQeKlYCIiIj1iYiUiItIjJlYiIiI9YmIlIiLSI05eMmJh\nfk0BAN+ma14LmIiIqh5HrERERHrExEpERKRHTKxERER6xHusVOPkFJYit0ihVs7VmoioKjCxUo2T\nW6TAhftcrYmIqgcTqxGr6WsF61tFr6gzBVBaznYc6RKRLphYyShVlCQfFWt+DV1Fr6hzsBRwJ7/8\n19cxsRKRtphYySg9K0kSEVUXzgomIiLSI45YX0B5s0+B8i9HkvGp6LIz778S0dOYWF9AebNPAV6O\nrEkquuzM+69E9LQadSl4w4YNaN26NZo2bYoePXogJSWlukOqVGF+TZXrBRMRkWGoMSPWxMREzJgx\nA8uWLUNAQAC++OILDB06FKmpqXB0dHzu/coLS5GZr/lBDI5JqbzLxLxETFR71ZjEunbtWowYMQJh\nYWEAgM8++wzfffcdNm3ahDlz5jz3fgtLRdx4oDmxvmRZowb89BzKu0zMS8REtVeNSKzFxcU4d+4c\nPvroI5XyV199FampqdUUFZF+lTdZztgXt6hoEqAxxE/0tBqRWO/fv4/S0lJIJBKVcjs7O3z//ffV\nFBXVZs9a5SkbFhrrK0qSj4pF/KHh6klFi1u4NzItN2lVZUJ+1gx6Tf0Cyh/5MxkbJq7T/Zggl8uN\n/rmQu3fvwtvbG9988w06duyoLP/ss8+we/dunDlzphqjIyKi2qRG3CS0tbWFqakppFKpSrlMJlMb\nxRIREVWmGpFYzc3N4efnh5MnT6qUJyUlISAgoHqCIiKiWqlG3GMFgAkTJmD8+PFo06YNAgICsHHj\nRmRlZeGdd96p7tCIiKgWqTGJNSgoCDk5OVi6dCmysrLg7e2NhIQEODk5VXdoRERUi9SIyUtERESG\nokbcY9WWoSx5GBsbCxsbG5UvLy8vlTYxMTHw9vaGg4MDAgMDcfXqVZX6oqIiTJs2Da6urnB0dERo\naChu376t0kYul2PcuHFwdnaGs7Mz3n//feTm5qq0uXXrFkJCQuDo6AhXV1dERESgpETz+sdPS05O\nRmhoKFq0aAEbGxvs2LFDrY0h9ePy5csYOHAgHBwc4OPjg88+++y5+vXBBx+onb8+ffoYdL+WLVuG\nV199Fc7OznBzc8OwYcNw5coVtXbGdr606Zexna8NGzagc+fOyuP06dMHx44dU2ljbOdJm34Z23mq\nSK1JrGVLHk6dOhWnT59G+/btMXToUGRmZlZLPB4eHkhPT8e1a9dw7do1JCcnK+tWrFiBuLg4LF68\nGElJSbCzs0NQUBDy8/OVbSIjI3Ho0CFs2rQJhw8fRl5eHkJCQiCK/16AeO+993Dx4kXs3bsXiYmJ\n+P333zF+/HhlvUKhwFtvvYVHjx7hyJEj2LRpE77++mvMmjVLqz7k5+fDx8cHsbGxqF+/vlq9IfUj\nLy8PQUFBaNq0KU6ePImYmBisXr0aa9as0blfANCzZ0+V87dr1y6VekPrV3JyMsaOHYtjx47hwIED\nMDMzw6BBgyCXy436fGnTL2M7X46Ojpg3bx5OnTqFkydPolu3bhg+fDguX75stOdJm34Z23mqkFwu\nF2vDV7t27cTRo0erlLm6uopTpkyp8lgiIyPFFi1alFvftGlTMSoqSvn57t27YsOGDcWVK1eKcrlc\nvHnzplinTh1x48aNyjaXLl0STUxMxL1794pyuVxMTU0VBUEQjx8/rmxz5MgRURAE8ddffxXlcrmY\nkJAgmpqaileuXFG2+fzzz8V69eqJt27d0qlPDRo0EOPi4gy2H0uXLhWtrKxEqVSqbDN79mzR0dFR\n5369/fbbYr9+/crdxhj6lZmZKZqamorx8fE16nxp6ldNOF82NjbK81ATzpOmftWE81T2VStGrGVL\nHvbo0UOlvDqXPPzrr7/g7e2N1q1b491330VGRgYAICMjA1lZWejZs6eyrYWFBTp16qSM9ezZsygp\nKVFp4+joCE9PT2Wbn3/+GQ0bNsQrr7yibBMQEABLS0uVNp6ennBwcFC26dWrFwoKCnDu3LkX6p+h\n9ePnn39Gx44dUadOHZU2d+7cwc2bN3Xu308//QR3d3e0a9cOEydOxL1795R1586dM/h+5eXlQaFQ\nwNraGkDNOV9P96uMsZ4vhUKBPXv24NGjR+jQoUONOU9P96uMsZ6np9WKxFrRkodPLypRFV555RWs\nXbsWe/bswapVq5CVlYV+/fpBLpdDKpVCEATY2dmVG6tMJoOpqSkaN25cbhupVApbW1u1Yzdp0kSl\nzdPHKW+xDV0ZWj+kUqnG8y+Kos597d27N9atW4evv/4aCxcuxK+//oo33ngDxcXFymMZer8iIyPR\nunVrtG/fXrmfmnC+nu4XYJzn6/Lly3BycoJEIsGUKVOwbds2eHl5Gf15Kq9fgHGep/LUmMdtjEmv\nXr1UPr/yyito3bo1vvrqK7Rr166aoiJtBQUFKb8vu+rg6+uLo0ePIjAwsBoj087MmTNx5swZHDly\nBIJQc15+WF6/jPF8eXh44IcffkBubi6+/vprjB8/HocOHarusF5Yef3y8vIyyvNUnloxYjX0JQ/r\n168PLy8v3LhxAxKJBKIoQiaTqbR5MlaJRILS0lJkZ2dX2Ob+/ftqx7p3755Km6ePU97oXleG0g97\ne3tlG03nXxCEF+5r06ZN0axZM9y4ccPg+zVjxgzs3bsXBw4cgLOzs7Lc2M9Xef3SxBjOl5mZGVxc\nXNC6dWvMmTMHvr6+WLt2rdGfp/L6pYkxnKfy1IrEauhLHhYUFCA9PR1NmzaFi4sL7O3tkZSUpFKf\nkpKijNXPzw9mZmYqbTIzM5GWlqZs0759ezx8+BA///yzsk1qaqrKPY327dsjLS0Nd+7cUbY5ceIE\nLCws4Ofn90J9MpR+tG7dWtkmJSUFRUVFKm0cHBye+Yv4We7du4c7d+4o/1Maar8iIiKUycfV1VWl\nzpjPV0X90sRYzteTFAoFCgsLjfo8VdQvTYzxPJUxjYyMnPvMVjVAw4YNERMTA3t7e9SrVw+fffYZ\nfvrpJ/y///f/YGVlVaWxzJkzB3Xr1oUoirh+/TqmTZuGP//8E8uXL4eVlRVKS0uxfPlyuLm5obS0\nFLNmzYJUKsXy5ctRp04d1K1bF3fv3sWGDRvg4+OD3NxcTJ48GdbW1pg7dy4EQYCtrS1++eUXJCQk\noFWrVsjMzMSkSZPQrl07jB07FsDjX6YHDhzAiRMn4OPjgytXrmDatGkICQnBgAEDntmP/Px8pKWl\nISsrC1u3boWPjw+srKxQXFxscP1wdXXFl19+iQsXLsDd3R0pKSmIiorC5MmTVSY6PKtfpqammD9/\nPho2bIjS0lL8/vvvmDhxIhQKBRYvXmyw/Zo6dSri4+Px5ZdfwtHREfn5+crHM8omaBjj+XpWv/Lz\n843ufEVHRyt/P2RmZmLt2rXYvXs3oqOj8fLLLxvleXpWvyQSidGdpwppM3W4pnwtW7ZMbN68uWhh\nYSG2adNGPHLkSLXEERwcLDZr1kysW7eu6OjoKL755pvimTNnVNrMmDFDdHBwEOvVqyd26dJF/Omn\nn1TqZTKZ+P7774u2traipaWlOGDAAPHy5csqbf766y8xJCREtLKyEq2srMRhw4aJN2/eVGlz6dIl\nsV+/fqKlpaVoa2srhoeHizKZTKt+HDx4UBQEQTQxMVH5Gj58uEH2IyUlRezcubNYr1490cHBQZw1\na5bO/bp7967Yq1cvUSKRiHXr1hWdnZ3FESNGqMVsaP3S1B8TExNxxowZBvvvTh/9Msbz9fbbb4vO\nzs6ihYWFKJFIxJ49e4r79u0z6vP0rH4Z43mq6ItLGhIREelRrbjHSkREVFWYWImIiPSIiZWIiEiP\nmFiJiIj0iImViIhIj5hYiYiI9IiJlYiISI+YWIlqmJs3b8LGxgY7duyoVccmMhRMrET/Z+vWrbCx\nsVF55VhN9+jRIyxevBidO3eGk5MTXFxc0KlTJ0yaNAnXr1+v7vCIjBJfG0f0fxISEtC8eXNcv34d\n586de+EXERi6kpISDBgwAGlpaQgJCcHYsWPxzz//ID09HceOHUP79u3h5uZW3WESGR0mViIAt2/f\nxo8//oiNGzdi9uzZiI+Pr/GJ9eDBgzh//jzi4uIwbNgwlTqFQoEHDx5UU2T/Ki4uhomJCUxNTas7\nFCKt8VIwER6PVi0tLdG/f38EBQVh7969EEX1ZbRtbGwwZcoUHDp0CJ06dYK9vT06duyI7777TqVd\nTEwMbGxscP36dYSHh6N58+ZwdnbGhAkTUFBQoGxX0T1JGxsbLFq0SPn577//xtSpU9GhQwc0a9YM\nzZs3R0hICC5fvvxcfc7IyIAgCBpfnWhiYgJra2u9HFvbbX/44QfY2NggISEBMTExaNmyJRwcHHD1\n6gGin0cAAAYHSURBVFU0a9YMkZGRavvOycmBRCJBdHT0c/wEiCoHEysRgF27dqF///6oW7cugoOD\nkZWVpfLexyelpqZi+vTpCA4Oxrx581BYWIhRo0ZBLpcr2wiCAAAYM2YMHj16hLlz52Lw4MHYsWOH\nSrLUxW+//YaUlBS8+eabiI2NxYQJE3DhwgUEBgaqvZRZG87OzhBFETt37qzUY+u67bJly3Dw4EGE\nh4cjOjoazZo1w8CBA7F3714oFAqVtnv27EFJSQlCQ0N16zxRJeKlYKr1Ll68iMuXL2Pu3LkAHr9Q\n2dXVFfHx8Xj11VfV2qenpyM1NRUuLi4AgC5duqBLly7YvXs33nvvPZW2fn5+WLVqlfLz/fv3sXXr\nVkRFRekcZ79+/fDmm2+qlIWEhKBDhw7YunUrpkyZotP+Bg4cCA8PDyxatAjbtm1Dly5d0LFjR/Tp\n0wcODg56O7au25a9qNrCwkJZNmzYMOzZswcnTpzAa6+9piwve++mh4eHTn0nqkwcsVKtt2vXLjRu\n3FgliQYHB+Obb77BP//8o9a+W7duyqQKAD4+PmjYsCEyMjJU2gmCgJEjR6qUdezYEdnZ2Xj48KHO\ncdatW1f5/T///IOcnBw0aNAAbm5uOHfu3HPt7+jRo/j4448BPP45TJo0CT4+Phg3bpxKjC9ybF23\nDQ0NVUmqANCzZ0/Y29sjPj5eWZaRkYEzZ86o3R8mqm4csVKtJooiEhMT0blzZ9y8eVNZ3rZtWzx8\n+BCHDh3CkCFDVLZxdHRU24+1tbXKpeAyTk5Oau0AQC6Xo0GDBjrFWlhYiIULFyIhIQF3795VlguC\nAFtbW5329WQ80dHRiI6Oxp07d5CcnIx169YhISEBpqamiIuLe+Fj67rtk3+0lDExMcFbb72FjRs3\nIj8/H5aWloiPj4eZmRmCg4Ofq+9ElYWJlWq106dPIzMzE7dv38aBAwdU6gRBwK5du9QSa3kzVDVN\ndnpW27J7sU97+l4iAEybNg1fffUV3n//fbRv3x6NGjWCIAiYMWOGxva6cnBwQHBwMN544w0EBAQg\nMTERa9asgYmJyQsdW9dt69Wrp3E/w4YNw6pVq3DgwAEMGzYMu3fvRs+ePWFnZ/fCfSfSJyZWqtXi\n4+PRpEkTLFu2TC0xfvfdd9ixYwfu37//3CPCZykbwebm5qqUPzl6LrN//36EhoZi4cKFKuVyuVyv\n8Zmbm6Nly5b4888/cf/+fdjZ2b3QsfUVt7e3N1q3bo34+Hh4eHjg+vXrmDFjhvYdI6oivMdKtVZh\nYSEOHDiAPn364PXXX8cbb7yh8vXhhx+iuLgYe/bsqbQYGjZsCFtbWyQnJ6uUb9iwQW00a2pqqjbC\n2717N+7cufNcx7548SLu37+vVi6Xy3HmzBnY2NigSZMmL3xsfcYdGhqKU6dOYeXKlWjYsCEGDhyo\n8z6IKhtHrFRrffPNN8jLy0P//v011ru7u8PV1RW7du3CuHHjKi2OkSNHYvny5fj444/Rpk0bJCcn\n448//lAbQffv3x/x8fFo0KABWrT4/+3cIYsCURSG4U8EsWixWhTTFjH4AwQRsVjEYhGMdtvUAbGJ\n4B/QoMgwYtA2STDYNAuCRUSxWbctiCu77F5wln2feBnmnEnfcM+dedNms5HjOEokEj+q63mebNtW\nsVhUNptVNBrV4XDQaDTS8XhUp9P5CPff1DbZd6VSkWVZms1mqtVqdwejAL8gWPFvjcdjhcNh5XK5\np9eUSiX1ej3tdjslk0kFAoFP56LP1r+j1WrpfD5rOp3KdV0VCgVNJhOlUqm7e7bbbYVCIbmuq+Fw\nqEwmI8dxZFnWQ+3v9FIul3W73eR5nrrdri6XiyKRiNLptGzbvnvh+E1tk33HYjHl83ktFgtVq9Uv\nnxF4hcD1en08cQEAPlWv17Ver7Xdbl/dCvApZqwA/ozT6aT5fM63q/A1toIB+N5+v9dqtdJgMFAw\nGFSj0Xh1S8BTBCsA31sul2o2m4rH4+r3+w+/XAT8hBkrAAAGMWMFAMAgghUAAIMIVgAADCJYAQAw\niGAFAMAgghUAAIPeASF6+FkA2AEhAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "with plt.style.context('fivethirtyeight'):\n",
+ " plt.hist(d.values, bins=50, alpha=0.4)\n",
+ " plt.axvline(np.mean(d.values), ymin=0.0, ymax=600, linewidth=2, color='k', label='mean')\n",
+ " plt.axvline(x=np.median(d.values), ymin=0.0, ymax=600, linewidth=2, color='b', linestyle='--', label='median')\n",
+ " plt.legend(loc='upper right')\n",
+ " plt.ylabel('Number of Employees')\n",
+ " plt.xlabel('Annual Salary')\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "data = np.random.normal(loc=0.0, scale=1.0, size=10000)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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SZrMZS5YssbSPGzcOGo0G33//PYDLZ1wajQahoaFobGy0fPUG3yeffAIAOHr0KGQyGZYt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\nEZHkMeyIiEjyGHZERCR5DDsiIpK8/wf9gf6FpJtA2wAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "with plt.style.context('fivethirtyeight'):\n",
+ " plt.hist(data, bins=50, alpha=0.4)\n",
+ " plt.axvline(np.mean(data), ymin=0.0, ymax=600, linewidth=2, color='k', label='mean')\n",
+ " plt.axvline(x=np.median(data), ymin=0.0, ymax=600, linewidth=2, color='b', linestyle='--', label='median')\n",
+ " plt.legend(loc='upper right')\n",
+ " plt.ylabel('Count')\n",
+ " plt.xlabel('x')\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0033737113299363549"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.mean(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.016896725981360865"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.median(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/faq/median-vs-mean/salary.png b/faq/median-vs-mean/salary.png
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diff --git a/faq/median-vs-mean/std-normal.png b/faq/median-vs-mean/std-normal.png
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diff --git a/faq/mentor.md b/faq/mentor.md
new file mode 100644
index 00000000..cb0e2dd9
--- /dev/null
+++ b/faq/mentor.md
@@ -0,0 +1,3 @@
+# How important do you think having a mentor is to the learning process?
+
+I guess it really depends on what type of learner you are. From what I understand, a mentor is like a "personal trainer," someone who gives you homework, tells you what books to read, etc. (in contrast to a teacher or professor who teaches a whole class at once). I think it is important to have friends who share similar interests to bounce of ideas, and I think it is important to know someone more knowledgeable than you who you could ask questions for your personal growth (e.g., a Prof who supervises your Ph.D. thesis would be such a person). However, I am not sure how important a "mentor" is for technical questions -- I think that's the 1-person equivalent of an answer poll via cross-validated or Quora. Anyways, I think a mentor may be a nice thing to have for certain people, but I wouldn't say everyone needs a dedicated "mentor."
diff --git a/faq/missing-data.md b/faq/missing-data.md
new file mode 100644
index 00000000..412ed06f
--- /dev/null
+++ b/faq/missing-data.md
@@ -0,0 +1,17 @@
+# What are some common approaches for dealing with missing data?
+
+Many different approaches exist for dealing with missing values; I'd roughly categorize our options into a) deletion and b) imputation techniques.
+
+## a) Deletion
+
+1) We have a lot of training samples and can afford deleting some of those. Here, we can simply remove samples with missing feature values from the dataset entirely.
+
+2) We have a large number of feature columns and some of them are redundant. Relatively many samples have a missing feature value in a certain column. In this scenario, it may be a good idea to remove these feature columns with missing values entirely.
+
+## b) Imputation
+
+If we can't afford deleting data points, we could use imputation techniques to "guess" placeholder values from the remaining data points.
+
+1) The simplest imputation technique may be the replacement of a missing feature value by its feature column's mean (median or mode).
+
+2) Instead of replacing a feature value by its column mean, we can only consider the k-nearest neighbors of this datapoint for computing the mean (median or mode) -- we identify the neighbors based on the remaining feature columns that don't have missing values.
diff --git a/faq/ml-curriculum.md b/faq/ml-curriculum.md
new file mode 100644
index 00000000..4de1df83
--- /dev/null
+++ b/faq/ml-curriculum.md
@@ -0,0 +1,40 @@
+# How would your curriculum for a machine learning beginner look like?
+If I had to put together a study plan for a beginner, I would probably start with an easy-going intro course such as
+
+- Andrew Ng's [Machine Learning course on Coursera](https://class.coursera.org/ml-005/lecture)
+
+
+
+Next, I would recommend a good intro book on 'Data Mining' (data mining is essentially about extracting knowledge from data, mainly using machine learning algorithms). I can highly recommend the following book written by one of my former professors:
+
+- P.-N. Tan, M. Steinbach, and V. Kumar. [Introduction to Data Mining](http://www-users.cs.umn.edu/~kumar/dmbook/index.php), (First Edition). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2005.
+
+
+
+This book will provide you with a great overview of what's currently out there; you will not only learn about different machine learning techniques, but also learn how to "understand" and "handle" and interpret data -- remember; without "good," informative data, a machine learning algorithm is practically worthless. Additionally, you will learn about alternative techniques since machine learning is not always the only and best solution to a problem
+
+> if all you have is a hammer, everything looks like a nail ...
+
+Now, After completing the Coursera course, you will have a basic understanding of ML and broadened your understanding via the Data Mining book.
+I don't want to self-advertise here, but I think my book would be a good follow-up to learn ML in more depth, understand the algorithms, learn about different data processing pipelines and evaluation techniques, best practices, and learn how to put in into action using Python, NumPy, scikit-learn, and Theano so that you can start working on your personal projects.
+
+
+
+While you work on your individual projects, I would maybe deepen your (statistical learning) knowledge via one of the three below:
+
+
+- T. Hastie, R. Tibshirani, J. Friedman, T. Hastie, J. Friedman, and R. Tibshirani. [The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/), volume 2. Springer, 2009.
+- C. M. Bishop et al. [Pattern recognition and machine learning](http://www.springer.com/us/book/9780387310732), volume 1. springer New York, 2006.
+- Duda, Richard O., Peter E. Hart, and David G. Stork. [Pattern classification](http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471056693.html). John Wiley & Sons, 2012.
+
+
+
+When you are through all of that and still hungry to learn more, I recommend
+
+- [the Deep Learning book](http://www.iro.umontreal.ca/~bengioy/dlbook/) by Yoshua Bengio, Ian Goodfellow, and Aaron Courville. The release date is set around 2016, but the 613-page manuscript is already available as as of today (online and for free).
+
+
+
+- And in-between, if you are looking for a less technical yet very inspirational free-time read, I highly recommend [Pedro Domingo's The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World](https://homes.cs.washington.edu/~pedrod/)
+
+
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diff --git a/faq/ml-examples.md b/faq/ml-examples.md
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+# What are some real-world examples of applications of machine learning in the field?
+
+In our time and age, it is really hard to find a problem where machine learning is not already applied -- machine learning is practically everywhere, in business applications and science. Below is a short list of the maybe most common and intuitive examples:
+
+### Computational Biology & Drug Discovery/Design
+
+
+
+- screening large molecule databases and identify which (drug-like) molecules are likely binding to a particular receptor protein
+- predict the potency of a receptor agonist or antagonist
+
+
+
+(In the figure above, I rendered a crystal structure HIV protease and some potential inhibitors, PDB Code: 4TVH)
+
+Some interesting papers if you want to read more:
+
+- Tarca, Adi L., et al. "Machine learning and its applications to biology." PLoS Comput Biol 3.6 (2007): e116.
+(http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.0030116)
+
+- Lavecchia, Antonio. "Machine-learning approaches in drug discovery: methods and applications." Drug discovery today 20.3 (2015): 318-331.
+(http://www.sciencedirect.com/science/article/pii/S1359644614004176)
+
+
+
+
+### Web Search and Recommendation Engines:
+
+- find recognize input, find relevant searches, predict which results are most relevant to us, return a ranked output
+- recommend similar products (e.g., Netflix, Amazon, etc.)
+
+
+
+
+
+### Finance
+
+- predict if an applicant is credit-worthy
+
+- detect credit card fraud
+- find promising trends on the stock market
+
+
+
+
+
+
+### Text and Speech Recognition
+
+- handwritten digit and letter recognition at the post office
+- voice assistants (Siri)
+- language translation services
+
+
+
+(Source: https://en.wikipedia.org/wiki/Handwriting_recognition)
+
+### Space, Astronomy, and Robotics
+
+
+
+- autonomous Mars robots
+- identification of relevant information (objects) in large amounts of Astronomy data
+
+
+
+(Source: https://en.wikipedia.org/wiki/Star)
+
+### Social Networks and Advertisement
+
+- data mining of personal information
+- selecting relevant ads to show
+
+
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diff --git a/faq/ml-origins.md b/faq/ml-origins.md
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+# What are the origins of machine learning?
+
+I think that it all started with the McCulloch-Pitt (MCP) Neuron, a first model of how a neuron in a mammal's brain could work:
+W. S. McCulloch and W. Pitts. [A logical calculus of the ideas immanent in nervous activity](http://link.springer.com/article/10.1007/BF02459570). The bulletin of mathematical biophysics, 5(4):115–133, 1943.
+
+Note that other methods like linear regression were already invented (F. Galton. [Regression towards mediocrity in hereditary stature](http://www.jstor.org/stable/2841583). Journal of the Anthropological Institute of Great Britain and Ireland, pages 246–263, 1886.). Here, I would like to make the distinction between ML and statistics in terms of how ML evolved. I see ML as a field that emerged from artificial intelligence research, hence, the MCP neuron.
+However, ML is deeply intertwined with statistics. For example, I would describe a linear regression analysis based on the closed-form solution (normal equation) primarily as a technique that came from the statistics field, and I would associate linear regression with stochastic gradient descent learning as an ML technique. I think the early goal in ML was how the algorithm can "learn" a function by itself rather than solving an equation mathematically.
+
+So, I would say that the first ML algorithm really was the perceptron (F. Rosenblatt. The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory, 1957.) What followed was the gradient descent algorithm used in adaptive linear neurons (B. Widrow. Adaptive ”Adaline” neuron using chemical ”memistors”. Number Technical Report 1553-2. Stanford Electron. Labs., Stanford, CA, October 1960.). Those single learning units were then connected to multi-layer architectures, and what followed was the multi-layer perceptron also around the first half of the 20th century.
+
+Note that I would put, for example, Fisher's Linear Discriminant Analysis (R. A. Fisher. [The use of multiple measurements in taxonomic problems](http://onlinelibrary.wiley.com/store/10.1111/j.1469-1809.1936.tb02137.x/asset/j.1469-1809.1936.tb02137.x.pdf;jsessionid=F0E7EFF219B2C2D94FF2B2981D3533E8.f04t03?v=1&t=igrkwkvk&s=631d3f737becda820356e6862bffc239e9b1f2d6). Annals of eugenics, 7(2):179–188, 1936) into developments in the statistics department -- note that this was before the term ML was coined.
diff --git a/faq/ml-python-communities.md b/faq/ml-python-communities.md
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+# Where are the best online communities centered around data science/machine learning or python?
+
+I think [Twitter](https://twitter.com/rasbt) is great to stay in touch and see what other people are up to, and to share news of course. [Cross-validated](http://stats.stackexchange.com) & [Quora](https://www.quora.com/topic/Machine-Learning) are good websites to ask specific questions and collect multiple opinions about a topic. There's also a [machine learning group on Google+](https://plus.google.com/communities/107785538899595981479) that has a lot of members, but is not that active anymore I fear. The [MachineLearning subreddit](https://www.reddit.com/r/MachineLearning/) is another nice place to discuss certain news and articles.
diff --git a/faq/ml-solvable.md b/faq/ml-solvable.md
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+# How do I know if the problem is solvable through machine learning?
+
+
+In general, most ML algorithm assume that our training samples are i.i.d. Since we want your training set to be representative of the population, we need to divide it into training and test sets randomly.
+
+Also, we want to use our test set only once; we don't want retrain your model and evaluate it on the random test set over and over again, or our estimate will be hugely overoptimistic otherwise. We should use k-fold cross-validation or nested cross-validation instead.
+
+There are many different reasons why our performance may be not satisfactory:
+
+- our data is skewed
+- there is a lot of noise
+- there are many outliers
+- our features are not informative enough
+- we don't have enough training samples
+
+In brief: our algorithm suffers from high variance (overfitting) or high bias (underfitting).
+
+
+
+
+
+It may help to get a better grasp of our problem by plotting "learning curves"
+
+For example, here I plotted the average accuracies of a model (using 10-fold cross validation). The blue line (training accuracy) shows the average accuracy on the training folds and the green line shows the average accuracy on the test fold for different sizes of the initial training set.
+
+
+
+
+
+Similarly, we can evaluate a the model performance for a particular tuning parameter (here, I plotted different values for C, the inverse regularization parameter of an SVM).
+
+
diff --git a/faq/ml-solvable/bias-variance.png b/faq/ml-solvable/bias-variance.png
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diff --git a/faq/ml-to-a-programmer.md b/faq/ml-to-a-programmer.md
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+# How would you explain machine learning to a software engineer?
+
+
+Software engineering is about developing programs or tools to automate tasks. Instead of "doing things manually," we write programs; a program is basically just a machine-readable set of instructions that can be executed by a computer.
+Let's consider a classic example: e-mail spam filtering. Assuming that we have access to the source code of our e-mail client and know how to handle it, we could come up with an instinctive set of rules that may help us with our spam problem.
+
+
+For example:
+if not "sender in contacts":
+if "subject line contains BUY!:
+e-mail spam folder:"
+else if ...
+
+
+It is intuitive to say that coming up with these rules is a pretty tedious task. Needless to say that we have to test our spam filter on real-world data, evaluate and improve it constantly, change and update rules, and so forth. Again, our goal is automation: we want to write a set of instructions that automatically filters out spam e-mails so that we don't have to "manually" delete them from our e-mail inbox.
+
+Now, **Machine learning is all about automating automation**! Instead of coming up with the rules to automate a task such as e-mail spam filtering ourselves, we **feed data to a machine learning algorithm, which figures out these rules all by itself.** . In this context, "data" shall be representative sample of the problem we want to solve -- for example, a set of spam and non-spam e-mails so that the machine learning algorithm can "learn from experience."
+
+
+In "conventional" programming, we code up a set of rules, feed it to the computer together with the data, and hope that it produces the desired results.
+
+
+**traditional programming:**
+
+
+- set of rules + data -> computer -> results
+
+
+In machine learning, we take data (e.g., e-mails), provide information about the desired results (spam and non-spam labels for these e-mails), and feed it to a learning algorithm, which in turn executed by a computer. The computer then *learns* a set of rules that we can use to automate (solve) our problem task.
+
+
+**machine learning:**
+
+- results + data -> machine learning algorithm + computer -> set of rules
+
+
+**Or in other words, machine learning is about finding the optimal instructions to automate a task. Machine learning algorithms are instructions for computers to learn other instructions automatically from data or experience. Therefore, machine learning is the automation of automation.**
diff --git a/faq/model-selection-in-datascience.md b/faq/model-selection-in-datascience.md
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+# How do Data Scientists perform model selection? Is it different from Kaggle?
+
+Although, I agree that Kaggle may be a nice playground for experiments, it typically doesn't come even close to a "real-world" application :). (Remember the NetFlix Prize? ["Netflix Never Used Its $1 Million Algorithm Due To Engineering Costs"](http://www.wired.com/2012/04/netflix-prize-costs/).)
+So, I'd say you really want to define a metric for "success!" It's very important to be clear about your goal before you do any kind of modeling. In practice, it often boils down to finding the sweet spot between
+meeting the project deadline
+high predictive performance
+high computational efficiency
+good interpretability
+These aspects differ from project to project, and it is really important to be clear about what you are trying to achieve beforehand. For example, let's say you managed to come up with a model scoring a 0.95 on your favorite performance metric scale from 0-1. Is it worth spending a couple of more days, weeks, months, or years to squeeze out another 0.05 improvement? It depends on your date of delivery. It depends on the available computing hardware. Eventually, it may also be important to know what's going on under the hood (would your collaborators be happy with another one of these multi-layer ensemble XGBoost frankensteins?)
+
+When it comes to choosing between particular algorithms, I'd typically approach a new problem starting with a very simple hypothesis space -- for example, simple logistic regression or softmax regression. (Of course, this comes all after exploring and getting familiar with the dataset.) I'd use my initial model as a benchmark and try another bunch of simple classifiers with piece-wise or non-linear hypothesis spaces like decision trees, random forests, and (Rbf kernel) SVMs. If these don't cut it, I'd explore further options including MLPs, RNNs, and ConvNets if appropriate.
+
+To provide you with a real-world example at this point: I recently ended up using a simple decision tree for a recent project for the sake of interpretability :). I was collaborating with experimental biologists who provided me with hundreds of experimental measurements for chemical molecules that they tested on a particular system. Eventually, they wanted me to tell them which particular atoms at which positions are most important to trigger that response in order to design more effective molecules. Here, I ended up using tree-based methods, since a decision tree was something that I could easily explain to a non-machine learning person. E.g., "If you have a keto-group at this position and a nitrogen group at this position, then ..., etc."
diff --git a/faq/multiclass-metric.md b/faq/multiclass-metric.md
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+# What is the best validation metric for multi-class classification?
+
+It really depends on our "goal" and our dataset. Classification Accuracy (or misclassification error) makes sense if our class labels are uniformly distributed. Even better, we can compute the ROC area under the curve (even for multi-class sytems), e.g., have a look at the nice [ICML'04 tutorial](http://www.cs.bris.ac.uk/~flach/ICML04tutorial/) on ROC analysis.
+Similarly, we can generalize all the binary performance metrics such as precision, recall, and F1-score etc. to multi-class settings. In the binary case, we have
+
+
+
+
+
+
+
+
+
+
+(PRE=precision, REC=recall, F1=F1-Score, MCC=Matthew's Correlation Coefficient)
+And to generalize this to multi-class, assuming we have a One-vs-All (OvA) classifier, we can either go with the "micro" average or the "macro" average. In "micro averaging," we'd calculate the performance, e.g., precision, from the individual true positives, true negatives, false positives, and false negatives of the the k-class model:
+
+
+
+And in macro-averaging, we average the performances of each individual class:
+
+
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diff --git a/faq/naive-bayes-boundary.md b/faq/naive-bayes-boundary.md
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+# What is the decision boundary for Naive Bayes?
+
+
+It's a (piecewise) quadratic decision boundary for the Gaussian model. The multinomial model has a linear boundary.
+Below, I plotted some examples:
+
+1) UCI Wine Dataset
+
+
+
+2) An XOR toy dataset
+
+
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diff --git a/faq/naive-bayes-vartypes.md b/faq/naive-bayes-vartypes.md
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+# Is it possible to mix different variable types in Naive Bayes, for example, binary and continues features?
+
+
+Yes, this is definitely possible.
+
+
+Let's briefly recapitulate the concept behind Naive Bayes: Our objective function is to maximize the posterior probability given the training data:
+
+
+
+
+
+
+
+Let
+
+
+
+
+And based on the objective function, we can formulate the decision rule as:
+
+
+
+
+
+In the context of this question, let's not worry about the priors for now; these are typically computed via Maximum Likelihood Estimation (MLE), for instance, the class frequency Nωj/N (number of samples in class ωj divided by the number of all samples in the training set).
+
+A short note about the evidence term: I wrote it for completeness, but we can simple cancel it from the decision function, because it is a constant term for all classes.
+Now, the class-conditional probabilities, let's call them likelihoods, are computed as the product of the likelihoods of the individual features *d*:
+
+
+
+
+
+Here, we make the "naive" conditional independence assumption, which states that features are independent of each other -- that's how Naive Bayes got its name. What we are basically saying is "The probability of observing this combination of features is equal to the product of observing each feature separately."
+
+Another assumption that we make is that *p(xi = b | ωj )* is drawn from a particular distribution -- that's why Naive Bayes is also called a "generative model."
+To come back to the original question, let us consider the multi-variate Bernoulli model for binary features and the Gaussian Naive Bayes model for continuous features.
+
+
+#### The (Multi-variate) Bernoulli Model
+
+
+We use the Bernoulli distribution to compute the likelihood of a binary variable.
+For example, we could estimate P(xk=1 | ωj) via MLE as the frequency of occurrences in the training set:
+θ = P̂(xk=1 | ωj) = Nxk, ωj / N ωj
+which reads "number of training samples in class ωj that have the property xk=1 (Nxk, ωj) divided by by all training samples in ωj (N ωj)." In context of text classification, this is basically the set of documents in class ωj that contain a particular word divided by all documents in ωj.
+Now, we can compute the likelihood of the binary feature vector **x** given class ωj as
+
+
+
+#### The Gaussian Model
+
+Typically, we use the Gaussian Naive Bayes model for variables on a continuous scale -- assuming that our variables are normal distributed.
+
+
+
+In the equation above, we have 2 parameters we need to estimate, the mean μ of the samples associated with class ωj and the variance σ2 associated with class ωj, respectively. This should be straight-forward, so let's skip the details here. After we plugged the estimated parameters into the equation, we can compute the likelihood of a continuous feature vector as (similar to the Bernoulli model above).
+
+
+
+Since we have the conditional independence assumption in Naive Bayes, we see that mixing variables is not a problem. We can compute the likelihoods of binary variables via Bernoulli Bayes and compute the likelihoods of the continuous variables via the Gaussian model. To compute the class-conditional probability of a sample, we can simply form the product of the likelihoods from the different feature subsets:
+
+
+
+(The same concept applies to Multinomial Naive Bayes and other models.)
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diff --git a/faq/naive-bayes-vs-logistic-regression.md b/faq/naive-bayes-vs-logistic-regression.md
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+# What is the major difference between naive Bayes and logistic regression?
+
+On a high-level, I would describe it as "generative vs. discriminative" models.
+
+- Generative classifiers learn a model of joint probabilities p(x, y) and use Bayes rule to calculate p(x|y) to make a prediction
+- Discriminative models learn the posterior probability p(x|y) "directly"
+
+You can think of discriminative models as "distinguishing between people that speak different languages without actually learning the language".
+
+In discriminative models, you have "less assumptions", e.g,. in naive Bayes and classification, you assume that your p(x|y) follows (typically) a Gaussian, Bernoulli, or Multinomial distribution, and you even violate the assumption of conditional independence of the features. In favor of discriminative models, Vapnik wrote once "one should solve the classification problem directly and never solve a more general problem as an intermediate step".
+(Vapnik, Vladimir Naumovich, and Vlamimir Vapnik. Statistical learning theory. Vol. 1. New York: Wiley, 1998.)
+
+I think it really depends on your problem though which method to prefer. I can't find a reference now, but e.g. in classification, naive Bayes converges quicker but has typically a higher error than logistic regression. On small datasets you'd might want to try out naive Bayes, but as your training set size grows, you likely get better results with logistic regression.
diff --git a/faq/naive-naive-bayes.md b/faq/naive-naive-bayes.md
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+# Why is the Naive Bayes Classifier naive?
+
+Let's start by taking a quick look at the Bayes' Theorem:
+
+
+
+In context of pattern classification, we can express it as
+
+
+
+
+
+If we use the Bayes Theorem in classification, our goal (or objective function) is to maximize the posterior probability
+
+
+
+Now, let's talk a bit more about the individual components. The priors are representing our expert (or any other prior) knowledge; in practice, the priors are often estimated via MLE (computed as class frequencies). The evidence term cancels because it is constant for all classes.
+
+Moving on to the "naive" part in the Naive Bayes Classifier: What makes it "naive" is that we compute the conditional probability (sometimes also called likelihoods) as the product of the individual probabilities for each feature:
+
+
+
+Since this assumption (the absolute independence of features) is probably never met in practice, it's the truly "naive" part in naive Bayes.
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diff --git a/faq/neuralnet-error.md b/faq/neuralnet-error.md
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+# What is wrong when my neural network's error increases?
+
+There are many possible reasons that could explain this problem. There could be a technical explanation -- we implemented backpropagation incorrectly -- or, we chose a learning rate that was too high, which in turn let to the problem that we were overshooting the local minima of the cost function.
+
+#### Gradient Checking
+
+The first thing I would always do is implementing "gradient checking" to make sure that the implementation is correct. Gradient checking is very easy to implement, and it is a good first diagnostic; here, we just compare the analytical solution to a numerically approximated gradient
+
+
+
+(Note that ε is just a small number around 1e-5 or so.)
+
+
+
+Even better yet is to use the 2-point solution with +/- ε
+
+
+
+Then, we compare this numerically approximated gradient to our analytical gradient:
+
+
+
+Depending on the complexity of our network architecture, we could come up with some criteria like this:
+
+- Relative error <= 1e-7: everything is okay!
+- Relative error <= 1e-4: the condition is problematic, and we should
+look into it.
+- Relative error > 1e-4: there is probably something wrong in our code
+
+#### Scaling and Shuffling
+
+Next, we want to check if the data has been scaled appropriately. E.g., if we use stochastic gradient descent and initialized our weights to small random numbers around zero, let's make sure that the features are standardized accordingly (mean = 0 and std deviation=1, which are the properties of a standard normal distribution).
+
+
+
+Also, let's make sure that we are shuffling the training set prior to every pass over the training set to avoid cycles in stochastic gradient descent.
+
+#### Learning Rate
+
+Eventually, we want to look at the learning rate itself. If the calculated cost increases over time, this could simply mean that we are constantly overshooting the local minima. Besides lowering the learning rate, there are a few tricks that I often add to my implementation:
+
+1. a decrease constant d for an adaptive learning rate; in adaptive learning, we shrink learning rate η over time: η / [1 + t * d ], where t is the time step
+2. a momentum factor for faster initial learning based on the previous gradient
+
+
diff --git a/faq/neuralnet-error/approx-grad-1.png b/faq/neuralnet-error/approx-grad-1.png
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diff --git a/faq/nnet-debugging-checklist.md b/faq/nnet-debugging-checklist.md
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+# How do I debug an artificial neural network algorithm?
+
+There are many, many reasons that can explain a unexpected, "bad" performance of neural networks. Let's compile a quick check list that we can process in a somewhat sequential manner to get to the root of that problem
+
+1. Is the data set okay? More concretely: Is there a lot of noise? Are the features "powerful" enough to discriminate between classes? (It's a good idea to try a bunch of off-the-shelf classifiers to get an initial benchmark; classifiers like random forest, softmax regression, or kernel SVM)
+2. Did we forget standardizing the features?
+3. Did we implement and use gradient checking to make sure that our implementation is correct?
+4. Do we use a random weight initialization scheme (e.g., from a random normal distribution multiplied by a small coefficient < 0) vs. initializing the model parameters to all-zero weights?
+5. Did we try to increase or decrease the learning rate?
+6. Have we checked that the cost decreases over time? If yes, have we tried to increase the number of epochs?
+7. Have we tried to modify the learning rate using momentum learning and/or a decrease constant (e.g., AdaGrad)
+8. Have we tried different non-linear activation functions other than the one we are currently using (e.g., logistic sigmoid, tanh, or ReLU)?
+9. When you estimated the performance of our network via cross-validation (for example, via holdout or k-fold), did we notice a large discrepancy between training and validation performance? A substantial difference in performance on training and validation sets may indicate that we are overfitting the training data too much. As a remedy, we could try to
+ 1. collect more training samples if possible
+ 2. decrease the complexity of your network (e.g,. fewer nodes, fewer hidden layers)
+ 3. implement dropout
+ 4. add a penalty against complexity to the cost function (e.g., L2 regularization)
diff --git a/faq/num-support-vectors.md b/faq/num-support-vectors.md
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+++ b/faq/num-support-vectors.md
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+# When training an SVM classifier, is it better to have a large or small number of support vectors?
+
+
+Unfortunately, like so often in machine learning applications, it really depends on the dataset. If we train an RBF kernel SVM, we will typically end up with more support vectors than in a linear model. If our data is linearly separable, the latter may be better, and if that's not the case, the former may be better.
+
+Also, we have to differentiate between computational efficiency and generalization performance. If we increase the number of support vectors, our classification becomes more "expensive", especially in kernel SVM where we have to recalculate the distances between every new sample and the entire training set.
+
+
+I'd say the best way to tackle the predictive performance problem is simply to evaluate the model, plot learning curves, do k-fold and/or cross validation, and see what works best on our given dataset.
diff --git a/faq/number-of-kfolds.md b/faq/number-of-kfolds.md
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+++ b/faq/number-of-kfolds.md
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+# Is it always better to have the largest possible number of folds when performing cross validation?
+
+Let's assume we mean k-fold cross-validation used for hyperparameter tuning of algorithms for classification, and with "better," we mean better at estimating the generalization performance.
+In this case, my answer would be no, otherwise we would always use LOOCV (Leave one out cross validation) instead of k-fold CV. (A useful reference: Shao, Jun. [Linear model selection by cross-validation.](http://www.sciencedirect.com/science/article/pii/S0378375803003719) Journal of the American statistical Association 88.422 (1993): 486-494.).
+
+In practice, I would say most commonly used (default) value is k=10 in k-fold CV, which is often an appropriate a good choice. But if we are working with small(er) training sets, I would increase the number of folds to use more training data in each iteration; this will lower the bias towards estimating the generalization error. On the other hand, it will also increase the run-time and variance of your estimate. The reason for the increasing variance of the estimate is that the overlap between training sets increases with an increasing size of *k* -- note that the test sets never overlap though.
+
+And far as computational efficiency is concerned -- for example, think of training deep neural nets on large(r) datasets including hyperparameter tuning -- I would think carefully about the size of *k*. If our dataset is large, I'd therefore recommend choosing smaller values for *k*, but it is all a balancing act between bias and variance and computational efficiency, and for our final estimate, we still have our independent test set anyway.
diff --git a/faq/open-source.md b/faq/open-source.md
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+++ b/faq/open-source.md
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+# At what point should one start contributing to open source?
+
+Please don't feel pressured to contribute to open source if this is not your thing -- I see it more as an opportunity to learn and "give back" but it is certainly not a "duty" :).
+
+> ... small tasks like fixing typos [...] time could be spent learning instead
+
+I'd say it depends. I think contributing to open-source projects shouldn't be all about personal benefits, and expanding/fixing documentation where it is useful is also valuable for a project. Eventually, it is up to you where you want to be on the spectrum between "how does this benefit me personally" and "would this help the community and the people who put so much effort into the things that I am using daily for free." -- it's certainly not a black or white decision, and there is no right or wrong :)
+
+Btw. depending on your experience level, "fixing typos" can be a valuable learning experience as well. For instance, doing "data science" is not only about analyzing data, but communications skills and team skills are important as well. If you are not familiar with version control, see fixing typos as an opportunity to learn about it or practice working with certain frameworks/workflows.
diff --git a/faq/overfitting.md b/faq/overfitting.md
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+# What is overfitting?
+
+Let’s assume we have a hypothesis or model m that we fit on our training data. In machine learning, the training performance — for example, accuracy — is what we measure and optimize during training time. Let’s call this training accuracy ACCtrain(*m*).
+
+Now, what we really care about in machine learning is to build a model that generalizes well to unseen data, that is, we want to build a model that has a high accuracy on the whole distribution of data; let’s call this ACCpopulation(*m*). (Typically, we use cross-validation techniques and a separate, independent test set to estimate the generalization performance.)
+
+Now, overfitting occurs if there’s an alternative model *m'* from the algorithm's hypothesis space where the training accuracy is better and the generalization performance is worse compared to model *m* -- we say that m overfits the training data.
+
+### Learning Curves
+
+As a rule of thumb, a model is more likely to overfit if it is too complex given a fixed number of training samples. The figure below shows the training and validation accuracies of a SVM model on a certain dataset. Here, I plotted the accuracy as a function of the inverse regularization parameter C -- the larger the value of C the larger the penalty term against complexity.
+
+
+
+We observe a larger difference between training and test accuracy for increasing values of C (more complex models). Based on the plot, we can say that the model at < 10^-1 underfit the training data whereas the models > 10^-1 overfit the training data.
+
+### Remedies
+
+Remedies against overfitting include
+
+1. Choose a simpler model by adding bias and/or reducing the number of parameters
+ 2. Adding regularization penalties
+ 3. Reducing the dimensionality of the feature space
+2. Collecting more training data
\ No newline at end of file
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diff --git a/faq/parametric_vs_nonparametric.md b/faq/parametric_vs_nonparametric.md
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+# What is the difference between a parametric learning algorithm and a nonparametric learning algorithm?
+
+The term "non-parametric" might sound a bit confusing at first: non-parametric does not mean that they have NO parameters! On the contrary, non-parametric models (can) become more and more complex with an increasing amount of data.
+
+So, in a parametric model, we have a finite number of parameters, and in nonparametric models, the number of parameters is (potentially) infinite. Or in other words, in nonparametric models, the complexity of the model grows with the number of training data; in parametric models, we have a fixed number of parameters (or a fixed structure if you will).
+
+Linear models such as linear regression, logistic regression, and linear Support Vector Machines are typical examples of a parametric "learners;" here, we have a fixed size of parameters (the weight coefficient.) In contrast, K-nearest neighbor, decision trees, or RBF kernel SVMs are considered as non-parametric learning algorithms since the number of parameters grows with the size of the training set. -- K-nearest neighbor and decision trees, that makes sense, but why is an RBF kernel SVM non-parametric whereas a linear SVM is parametric? In the RBF kernel SVM, we construct the kernel matrix by computing the pair-wise distances between the training points, which makes it non-parametric.
+
+In the field of statistics, the term parametric is also associated with a specified probability distribution that you "assume" your data follows, and this distribution comes with the finite number of parameters (for example, the mean and standard deviation of a normal distribution); you don't make/have these assumptions
+in non-parametric models. So, in intuitive terms, we can think of a non-parametric model as a "distribution" or (quasi) assumption-free model.
+
+However, keep in mind that the definitions of "parametric" and "non-parametric" are "a bit ambiguous" at best; according to the "The Handbook of Nonparametric Statistics 1 (1962) on p. 2:
+“A precise and universally acceptable definition of the term ‘nonparametric’ is not presently available. The viewpoint adopted in this handbook is that a statistical procedure is of a nonparametric type if it has properties which are satisfied to a reasonable approximation when some assumptions that are at least of a moderately general nature hold.”
diff --git a/faq/pca-scaling.md b/faq/pca-scaling.md
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+i. Does mean centering or feature scaling affect a Principal Component Analysis?
+
+Let us think about whether it matters or not if the variables are centered for applications such as Principal Component Analysis (PCA) if the PCA is calculated from the covariance matrix (i.e., the *k* principal components are the eigenvectors of the covariance matrix that correspond to the *k* largest eigenvalues).
+
+### 1. Mean centering does not affect the covariance matrix
+Here, the rational is: If the covariance is the same whether the variables are centered or not, the result of the PCA will be the same.
+
+Let’s assume we have the 2 variables **x** and **y**. Then the covariance between the attributes is calculated as
+
+
+
+Let us write the centered variables as
+
+
+
+The centered covariance would then be calculated as follows:
+
+
+
+
+But since after centering, x̄'=0 and ȳ'=0 we have
+
+
+
+
+which is our original covariance matrix if we resubstitute back the terms
+
+
+
+Even centering only one variable, e.g., **x** wouldn’t affect the covariance:
+
+
+
+
+### 2. Scaling of variables does affect the covariance matrix
+
+If one variable is scaled, e.g, from pounds into kilogram (1 pound = 0.453592 kg), it does affect the covariance and therefore influences the results of a PCA.
+
+Let *c* be the scaling factor for *x*
+
+Given that the “original” covariance is calculated as
+
+
+
+the covariance after scaling would be calculated as:
+
+
+
+
+Therefore, the covariance after scaling one attribute by the constant *c* will result in a rescaled covariance *cσxy*. So, if we’d scaled x from pounds to kilograms, the covariance between x and y will be 0.453592 times smaller.
+
+### 3. Standardizing affects the covariance
+
+
+Standardization of features will have an effect on the outcome of a PCA (assuming that the variables are originally not standardized). This is because we are scaling the covariance between every pair of variables by the product of the standard deviations of each pair of variables.
+
+The equation for standardization of a variable is written as
+
+
+
+The “original” covariance matrix:
+
+
+
+And after standardizing both variables:
+
+
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diff --git a/faq/pearson-r-vs-linear-regr.md b/faq/pearson-r-vs-linear-regr.md
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@@ -0,0 +1,90 @@
+# What is the difference between Pearson R and Simple Linear Regression?
+
+
+In "simple linear regression" (ordinary least-squares regression with 1 variable), you fit a line
+
+ŷ = a + b * x
+
+in the attempt to predict the target variable *y* using the predictor *x*.
+
+Let’s consider a simple example to illustrate how this is related to the linear correlation coefficient, a measure of how two variables are linearly related (or vary together).
+
+x = [1.0, 1.8, 3.0, 3.7]
+y = [0.5, 2.0, 3.0, 3.9]
+
+
+
+### Linear correlation between variables
+
+
+The Pearson correlation coefficient is computed as:
+
+
+
+As we can see, the correlation coefficient is just the covariance (cov) between 2 features *x* and *y* “standardized” by their standard deviations (σ), where
+
+the standard deviation is computed as
+
+
+
+Similarly, the covariance is computed as
+
+
+
+In our simple example above, we get
+
+- cov(x, y) ≈ 1.3012
+- σ_x ≈ 1.0449
+- σ_y ≈ 1.2620
+- r = 0.9868
+
+### Simple Linear Regression
+
+Now, for simple linear regression, we compute the slope as follows:
+
+
+
+To show how the correlation coefficient r factors in, let's rewrite it as
+
+
+
+where the first term is equal to r, which we defined earlier; we can now see that we could use the “linear correlation coefficient” to compute the slope of the line as
+
+
+
+Continuing with the example from above, we get b ≈ 0.8171.
+
+**So, essentially, the linear correlation coefficient (Pearson’s r) is just the standardized slope of a simple linear regression line (fit).**
+To continue with the example, we can now compute the y-axis intercept as
+
+
+
+a ≈ 0.4298
+
+Now, our linear regression fit would be
+
+ŷ = 0.4298 + 0.8171 * x
+
+
+
+### Standardizing Variables
+
+In practice, we often standardize our input variables:
+
+
+
+After standardization, our variables have the properties of a standard normal distribution with mean=0, and
+standard deviation 1.
+
+
+
+Or in other words, we center our variables at 0 so that we don’t need to compute the y-axis intercept.
+
+ŷ = a * x = r * x
+
+This is also useful if we use optimization algorithms for multiple linear regression, such as gradient descent, instead of the closed-form solution (handy for working with large datasets). Here, we want to standardize the variables so that the gradient descent learning algorithms learns the model coefficients “equally” in multiple linear regression.
+Another advantage of this approach is that the slope is then exactly the same as the correlation coefficient, which saves another computational step.
+
+ŷ = a * x = r * x = a * 0.9868
+
+
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diff --git a/faq/probablistic-logistic-regression.md b/faq/probablistic-logistic-regression.md
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+# What is the probabilistic interpretation of regularized logistic regression?
+
+Let's start directly with the maximum likelihood function:
+
+
+
+where phi is your conditional probability, i.e., sigmoid (logistic) function:
+
+
+
+
+
+and z is simply the *net input* (a scalar):
+
+
+
+
+So, by maximizing the likelihood we maximize the probability. Since we are talking about "cost", lets reverse the likelihood function so that we can minimize a cost function J. First, let's take the log so that we arrive at the equation that most people are familiar with (it's particularly handy to use the "addition trick" in the partial derivative e.g,. if you are using gradient or stochastic gradient descent):
+
+
+
+
+
+Now, imagine we plot the cost as follows, as a function of the 2 weights in a 2D dataset. For the unregularized cost, we would find the global cost minimum (the dot at the center) for a particular w1 and w2 combination. The key idea is that we increase the weights as much as necessary to reach the global cost minimum.
+
+
+
+
+Now, let's add a regularization term, e.g., L2
+
+
+
+This basically means that we will increase the cost by the squared Euclidean norm of your weight vector. Or in other words, we are constraint now, and we can't reach the global minimum anymore due to this increasingly large penalty. Basically, we have to find the sweet spot now: the point that minimizes the cost under the constraint that ywer can't go to far on the w1 and w2 axes, respectively. (In the image below, the size of the sphere depends on an additional hyperparameter, lambda.)
+
+
+
+It is as if we would add a prior to the weights. Instead of maximizing the likelihood (minimizing the cost function) given the training data, we maximize the likelihood given the additional information bias.
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diff --git a/faq/r-in-datascience.md b/faq/r-in-datascience.md
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+# Is R used extensively today in data science?
+
+"Extensively" is a relative term, so let me discuss this in comparison to other languages.
+I would say that R was probably THE language for doing statistics or "data science" work about 5-10 years ago. Today, as the Python sci-stack caught up and keeps growing, it's about as widely used as Python for similar tasks. I can see a shift more towards Python in future though because there seems to be more development going on at the moment towards scalability and computational efficiency. For example,
+
+- Blaze for out-of-core analysis of big datasets
+- Dask for parallel computing on multi-core machines or on a distributed clusters
+- Theano and Tensorflow for the optimization and evaluation of mathematical
+
+expressions involving multi-dimensional arrays utilizing GPUs
+and many, many more. Although R is fine for "small scale" analyses, performance can be (become) a big weakness of R for real-world applications.
+However, keep in mind that Scala is also big on the rise right now, take Spark for instance.
+Eventually, I think it all depends on the task and the problem you'd want to solve. For "smallish" analysis and projects, Python's default sci-stack and R work just fine. For large-scale distributed computing, you'd typically use Spark (written in Scala). For deep learning, you use Theano or Tensorflow (via Python) or Torch (written in Lua).
+
+(If all you have is a hammer, everything looks like a nail :).)
diff --git a/faq/random-forest-perform-terribly.md b/faq/random-forest-perform-terribly.md
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+# When can a random forest perform terribly?
+
+I'd say any time when your classes are linearly separable by a straight line or hyperplane that is not perpendicular to one of the axes (1). Or if you are also interested in predicting values beyond the training dataset window in a regression problem (2).
+
+
+(1) The intuition is that decision trees are piece-wise linear functions that partition the feature space perpendicular to the axes. So, instead of drawing a "straight" diagonal line, we get a zig-zag. The same problem occurs with concentric circles and so forth.
+
+(2) The intuition for the regression window is that in decision tree regression, our predicted target variable is the average of the target variables at a terminal node (these come from the training set). So, if the largest value in your training set is "x," we can never make a prediction that is larger than "x," which may be undesirable in certain situations.
+
+A trivial example: Let's say we want to predict the weight of a person (target variable) based on the person's height (feature). We assume the heaviest person in our training set was 180 lbs with a height of 6 ft; the lightest person was 5 ft tall at 150 lbs. Next, let's assume that there's a perfect correlation between height and weight. Eventually, let us make a prediction for a new data point: We want to predict the weight of a 7 ft person. Using decision tree / random forest regression, your prediction would be max. 180 lbs, which intuitively wouldn't make sense here ...
diff --git a/faq/regularized-logistic-regression-performance.md b/faq/regularized-logistic-regression-performance.md
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--- /dev/null
+++ b/faq/regularized-logistic-regression-performance.md
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+# Does regularization in logistic regression always results in better fit and better generalization?
+
+
+Regularization does NOT improve the performance on the data set that the algorithm used to learn the model parameters (feature weights). However, it **can improve the generalization performance**, i.e., the performance on new, unseen data, which is exactly what we want.
+
+In intuitive terms, we can think of regularization as a penalty against complexity. Increasing the regularization strength penalizes "large" weight coefficients -- our goal is to prevent that our model picks up "peculiarities," "noise," or "imagines a pattern where there is none."
+
+**Again, we don't want the model to memorize the training dataset, we want a model that generalizes well to new, unseen data.**
+
+In more specific terms, we can think of regularization as adding (or increasing the) bias if our model suffers from (high) variance (i.e., it overfits the training data). On the other hand, too much bias will result in underfitting (a characteristic indicator of high bias is that the model shows a "bad" performance for both the training and test dataset).
+We know that our goal in an unregularized model is to minimize the cost function, i.e., we want to find the feature weights that correspond to the global cost minimum (remember that the logistic cost function is convex).
+
+
+
+
+Now, if we regularize the cost function (e.g., via L2 regularization), we add an additional term to our cost function (J) that increases as the value of your parameter weights (w) increase; keep in mind that the regularization we add a new hyperparameter, lambda, to control the regularization strength.
+
+
+
+
+Therefore, our new problem is to minimize the cost function given this added constraint.
+
+
+
+Intuitively, we can think of the "sphere" at the coordinate center in the figure above as our "budget." Now, our objective is still the same: we want to minimize the cost function. However, we are now constrained by the regularization term; we want to get as close as possible to the global minimum while staying within our "budget" (i.e., the sphere).
diff --git a/faq/regularized-logistic-regression-performance/l2-term.png b/faq/regularized-logistic-regression-performance/l2-term.png
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diff --git a/faq/return_self_idiom.md b/faq/return_self_idiom.md
new file mode 100644
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@@ -0,0 +1,25 @@
+# What is the purpose of the `return self` idioms in your code examples?
+
+Many (if not all) of the object-oriented implementations in my code examples return `self` in their respective calls -- and scikit-learn does this, too! So, what is the rational behind a method that returns the the object itself? The answer is a rather simple one: "Chaining," which enables us to concatenate operations more conveniently (and efficiently) by feeding the answer of an operations into the next.
+
+For example, an implementation such as
+
+ class Perceptron(object):
+ def __init__(self, …):
+ …
+
+ def fit(self, …):
+ return self
+
+ def predict(self, …):
+ return self
+
+would allow us to write a compact notation such as
+
+ prediction = Perceptron().fit(X, y).predict(X)
+
+in one line of code instead of
+
+ p = Perceptron()
+ p.fit(X, y)
+ prediction = p.predict(X)
diff --git a/faq/scale-training-test.md b/faq/scale-training-test.md
new file mode 100644
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--- /dev/null
+++ b/faq/scale-training-test.md
@@ -0,0 +1,74 @@
+# Why do we need to re-use training parameters to transform test data?
+
+Many machine learning algorithms require that features are on the same scale; for example, if we compute distances such as in nearest neighbor algorithms. Also, optimization algorithms such as gradient descent work best if our features are centered at mean zero with a standard deviation of one — i.e., the data has the properties of a standard normal distribution. One of the few categories of machine algorithms that are truly scale invariant are the tree-based methods.
+
+Now, a commonly asked question is how we scale our dataset correctly. For simplicity, I will write the examples in pseudo code using the “standardization” procedure. However, note that the same principles apply to other scaling methods such as min-max scaling.
+
+In practice, I’ve seen many ways for scaling a dataset prior to feeding it to a learning algorithm. Can you guess which one is “correct?"
+
+### Scenario 1:
+
+ scaled_dataset = (dataset - dataset_mean) / dataset_std_deviation
+
+ train, test = split(scaled_dataset)
+
+### Scenario 2:
+
+ train, test = split(dataset)
+
+ scaled_train = (train - train_mean) / train_std_deviation
+
+ scaled_test = (test - test_mean) / test_std_deviation
+
+### Scenario 3:
+
+ scaled_train = (train - train_mean) / train_std_deviation
+
+ scaled_test = (test - train_mean) / train_std_deviation
+
+That’s right, the “correct” way is *Scenario 3*. I agree, it may look a bit odd to use the training parameters and re-use them to scale the test dataset. (Note that in practice, if the dataset is sufficiently large, we wouldn’t notice any substantial difference between the scenarios 1-3 because we assume that the samples have all been drawn from the same distribution.)
+
+Again, why *Scenario 3*? The reason is that we want to pretend that the test data is "new, unseen data.” We use the test dataset to get a good estimate of how our model performs on any new data.
+
+Now, in a real application, the new, unseen data could be just 1 data point that we want to classify. (How do we estimate mean and standard deviation if we have only 1 data point?) That's an intuitive case to show why we need to keep and use the training data parameters for scaling the test set.
+
+To recapitulate: If we standardize our training dataset, we need to keep the parameters (*mean* and *standard deviation* for each feature). Then, we'd use these parameters to transform our test data and any future data later on
+
+Let me give a hands-on example why this is important!
+
+Let's imagine we have a simple training set consisting of 3 samples with 1 feature column (let's call the feature column “length in cm"):
+
+- sample1: 10 cm -> class 2
+- sample2: 20 cm -> class 2
+- sample3: 30 cm -> class 1
+
+Given the data above, we compute the following parameters:
+
+- mean: 20
+- standard deviation: 8.2
+
+If we use these parameters to standardize the same dataset, we get the following values:
+
+- sample1: -1.21 -> class 2
+- sample2: 0 -> class 2
+- sample3: 1.21 -> class 1
+
+Now, let's say our model has learned the following hypotheses: It classifies samples with a standardized length value < 0.6 as class 2 (class 1 otherwise). So far so good. Now, let’s imagine we have 3 new unlabeled data points that you want to classify.
+
+- sample4: 5 cm -> class ?
+- sample5: 6 cm -> class ?
+- sample6: 7 cm -> class ?
+
+If we look at the "unstandardized “length in cm" values in our training dataset, it is intuitive to say that all of these samples are likely belonging to class 2. However, if we standardize these by re-computing the *standard deviation* and and *mean* from the new data, we would get similar values as before (i.e., properties of a standard normal distribution) in the training set and our classifier would (probably incorrectly) assign the “class 2” label to the samples 4 and 5.
+
+- sample5: -1.21 -> class 2
+- sample6: 0 -> class 2
+- sample7: 1.21 -> class 1
+
+However, if we use the parameters from your "training set standardization, we will get the following standardized values
+
+- sample5: -18.37
+- sample6: -17.15
+- sample7: -15.92
+
+Note that these values are more negative than the value of sample1 in the original training set, which makes much more sense now!
\ No newline at end of file
diff --git a/faq/select_svm_kernels.md b/faq/select_svm_kernels.md
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+# How do I select SVM kernels?
+
+Given an arbitrary dataset, you typically don't know which kernel may work best. I recommend starting with the simplest hypothesis space first -- given that you don't know much about your data -- and work your way up towards the more complex hypothesis spaces.
+So, the linear kernel works fine if your dataset if linearly separable; however, if your dataset isn't linearly separable, a linear kernel isn't going to cut it (almost in a literal sense ;)).
+For simplicity (and visualization purposes), let's assume our dataset consists of 2 dimensions only. Below, I plotted the decision regions of a linear SVM on 2 features of the iris dataset:
+
+
+
+This works perfectly fine. And here comes the RBF kernel SVM:
+
+
+
+Now, it looks like both linear and RBF kernel SVM would work equally well on this dataset. So, why prefer the simpler, linear hypothesis? Think of Occam's Razor in this particular case. Linear SVM is a parametric model, an RBF kernel SVM isn't, and the complexity of the latter grows with the size of the training set. Not only is it more expensive to train an RBF kernel SVM, but you also have to keep the kernel matrix around, and the projection into this "infinite" higher dimensional space where the data becomes linearly separable is more expensive as well during prediction. Furthermore, you have more hyperparameters to tune, so model selection is more expensive as well! And finally, it's much easier to overfit a complex model!
+Okay, what I've said above sounds all very negative regarding kernel methods, but it really depends on the dataset. E.g., if your data is not linearly separable, it doesn't make sense to use a linear classifier:
+
+
+
+In this case, a RBF kernel would make so much more sense:
+
+
+
+In any case, I wouldn't bother too much about the polynomial kernel. In practice, it is less useful for efficiency (computational as well as predictive) performance reasons. So, the rule of thumb is: use linear SVMs (or logistic regression) for linear problems, and nonlinear kernels such as the Radial Basis Function kernel for non-linear problems.
+
+The RBF kernel SVM decision region is actually also a linear decision region. What RBF kernel SVM actually does is to create non-linear combinations of your features to uplift your samples onto a higher-dimensional feature space where you can use a linear decision boundary to separate your classes:
+
+
+
+Okay, above, I walked you through an intuitive example where we can visualize our data in 2 dimensions ... but what do we do in a real-world problem, i.e., a dataset with more than 2 dimensions? Here, we want to keep an eye on our objective function: minimizing the hinge-loss. We would setup a hyperparameter search (grid search, for example) and compare different kernels to each other. Based on the loss function (or a performance metric such as accuracy, F1, MCC, ROC auc, etc.) we could determine which kernel is "appropriate" for the given task.
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diff --git a/faq/semi-vs-supervised.md b/faq/semi-vs-supervised.md
new file mode 100644
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+++ b/faq/semi-vs-supervised.md
@@ -0,0 +1,11 @@
+# What are the advantages of semi-supervised learning over supervised and unsupervised learning?
+
+Obviously, we are working with a labeled dataset when we are building (typically predictive) models using supervised learning. The goal of unsupervised learning is often of exploratory nature (clustering, compression) while working with unlabeled data.
+
+In semi-supervised learning, we are trying to solve a supervised learning approach using labeled data augmented by unlabeled data; the number of unlabeled or partially labeled samples is often larger than the number of labeled samples, since the former are less expensive and easier to obtain. So, our goal is to overcome one of the problems of supervised learning -- having not enough labeled data. Adding cheap and abundant unlabeled data, we are hoping to build a better model than using supervised learning alone.
+
+Although semi-supervised learning sounds like a powerful approach, we have to be careful. Semi-supervised learning is not always "the hammer to the nail" that we are looking for -- sometimes it works great, sometimes it doesn't. Here's a great paper on this:
+
+Singh, Aarti, Robert Nowak, and Xiaojin Zhu. "[Unlabeled data: Now it helps, now it doesn't.](http://www.cs.cmu.edu/~aarti/pubs/NIPS08_ASingh.pdf)" Advances in Neural Information Processing Systems. 2009.
+
+Also, we have to keep in mind that we need to make certain assumptions (manifold, cluster, or smoothness assumptions; see here for more details: [Semi-supervised learning](https://en.wikipedia.org/wiki/Semi-supervised_learning#Assumptions_used_in_semi-supervised_learning)) when we are using semi-supervised algorithms and have to make sure that they are not violated.
diff --git a/faq/softmax.md b/faq/softmax.md
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+++ b/faq/softmax.md
@@ -0,0 +1,15 @@
+# What exactly is the "softmax and the multinomial logistic loss" in the context of machine learning?
+
+The softmax function is simply a generalization of the logistic function that allows us to compute meaningful class-probabilities in multi-class settings (multinomial logistic regression). In softmax, we compute the probability that a particular sample (with net input z) belongs to the *i*th class using a normalization term in the denominator that is the sum of all *M* linear functions:
+
+
+
+In contrast, the logistic function:
+
+
+
+And for completeness, we define the net input as
+
+
+
+where the weight coefficients of your model are stored as vector "w" and "x" is the feature vector of your sample.
diff --git a/faq/softmax/logistic.png b/faq/softmax/logistic.png
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diff --git a/faq/softmax_regression.md b/faq/softmax_regression.md
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+++ b/faq/softmax_regression.md
@@ -0,0 +1,83 @@
+# What is Softmax regression and how is it related to Logistic regression?
+
+Softmax Regression (synonyms: Multinomial Logistic, Maximum Entropy Classifier, or just Multi-class Logistic Regression) is a generalization of logistic regression that we can use for multi-class classification (under the assumption that the classes are mutually exclusive). In contrast, we use the (standard) Logistic Regression model in binary classification tasks.
+
+Now, let me briefly explain how that works and how softmax regression differs from logistic regression. I have a more detailed explanation on logistic regression here: [LogisticRegression - mlxtend](http://rasbt.github.io/mlxtend/user_guide/classifier/LogisticRegression/) , but let me re-use one of the figures to make things more clear:
+
+
+
+As the name suggests, in softmax regression (SMR), we replace the sigmoid logistic function by the so-called *softmax function* φ:
+
+
+
+where we define the net input z as
+
+
+
+(*w* is the weight vector, *x* is the feature vector of 1 training sample, and *w0* is the bias unit.)
+Now, this softmax function computes the probability that this training sample x(i) belongs to class *j* given the weight and net input z(i). So, we compute the probability *p(y = j | x(i); wj)* for each class label in *j = 1, ..., k*. Note the normalization term in the denominator which causes these class probabilities to sum up to one.
+
+To illustrate the concept of softmax, let us walk through a concrete example. Let's assume we have a training set consisting of 4 samples from 3 different classes (0, 1, and 2).
+
+
+
+First, we want to encode the class labels into a format that we can more easily work with; we apply one-hot encoding:
+
+
+
+A sample that belongs to class 0 (the first row) has a 1 in the first cell, a sample that belongs to class 2 has a 1 in the second cell of its row, and so forth.
+Next, let us define the feature matrix of our 4 training samples. Here, we assume that our dataset consists of 2 features; thus, we create a 4×(2+1) dimensional matrix (+1 one for the bias term).
+
+
+
+Similarly, we created a (2+1)×3 dimensional weight matrix (one row per feature and one column for each class).
+
+
+To compute the net input, we multiply the 4×(2+1) feature matrix **X** with the (2+1)×3 (n_features × n_classes) weight matrix **W**.
+
+**Z = WX**
+
+which yields a 4×3 output matrix (n_samples × n_classes).
+
+
+
+Now, it's time to compute the softmax activation that we discussed earlier:
+
+
+
+
+
+As we can see, the values for each sample (row) nicely sum up to 1 now. E.g., we can say that the first sample
+`[ 0.29450637 0.34216758 0.36332605]` has a 29.45% probability to belong to class 0.
+Now, in order to turn these probabilities back into class labels, we could simply take the argmax-index position of each row:
+
+
+
+As we can see, our predictions are terribly wrong, since the correct class labels are `[0, 1, 2, 2]`. Now, in order to train our logistic model (e.g., via an optimization algorithm such as gradient descent), we need to define a cost function *J* that we want to minimize:
+
+
+
+which is the average of all cross-entropies over our n training samples. The cross-entropy function is defined as
+
+
+
+
+Here the T stands for "target" (the true class labels) and the O stands for output (the computed probability via softmax; ***not*** the predicted class label).
+
+
+
+
+In order to learn our softmax model via gradient descent, we need to compute the derivative
+
+
+
+which we then use to update the weights in opposite direction of the gradient:
+
+ for each class j.
+
+(Note that w_j is the weight vector for the class *y=j*.)
+I don't want to walk through more tedious details here, but this cost derivative turns out to be simply:
+
+
+
+Using this cost gradient, we iteratively update the weight matrix until we reach a specified number of epochs (passes over the training set) or reach the desired cost threshold.
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diff --git a/faq/standardize-param-reuse.md b/faq/standardize-param-reuse.md
new file mode 100644
index 00000000..9ccfe9ac
--- /dev/null
+++ b/faq/standardize-param-reuse.md
@@ -0,0 +1,37 @@
+# Why do we re-use parameters from the training set to standardize the test set and new data?
+
+Let me give you an example to this very common question to show why we don't want to standardize new (or test) data "from scratch."
+
+Let's assume we have a simple training set consisting of 3 samples with 1 feature (let's call this feature "length"):
+
+- train_1: 10 cm -> class_2
+- train_2: 20 cm -> class_2
+- train_3: 30 cm -> class_1
+
+mean: 20, std.: 8.2
+
+After standardization, the transformed feature values are
+
+- train_std_1: -1.21 -> class_2
+- train_std_2: 0 -> class_2
+- train_std_3: 1.21 -> class_1
+
+Next, let's assume our model has learned to classify samples with a standardized length value < 0.6 as class_2 (class_1 otherwise). So far so good. Now, let's say we have 3 unlabeled data points that we want to classify:
+
+- new_4: 5 cm -> class ?
+- new_5: 6 cm -> class ?
+- new_6: 7 cm -> class ?
+
+If we look at the "unstandardized "length" values in our training dataset, it is intuitive to say that all of these samples are likely belonging to class_2. However, if we standardize these by re-computing standard deviation and and mean you would get similar values as before in the training set and your classifier would (probably incorrectly) classify samples 4 and 5 as class 2.
+
+- new_std_4: -1.21 -> class 2
+- new_std_5: 0 -> class 2
+- new_std_6: 1.21 -> class 1
+
+However, if we use the parameters from your "training set standardization," we'd get the values:
+
+- sample5: -18.37 -> class 2
+- sample6: -17.15 -> class 2
+- sample7: -15.92 -> class 2
+
+The values 5 cm, 6 cm, and 7 cm are much lower than anything we have seen in the training set previously. Thus, it only makes sense that the standardized features of the "new samples" are much lower than every standardized feature in the training set.
diff --git a/faq/svm_for_categorical_data.md b/faq/svm_for_categorical_data.md
new file mode 100644
index 00000000..202a47e4
--- /dev/null
+++ b/faq/svm_for_categorical_data.md
@@ -0,0 +1,20 @@
+# How can I apply an SVM to categorical data?
+
+I assume you are asking about categorical features, not the target variable, which is already assumed to be categorical (binary) in SVM classifiers.
+
+First, there are two sub-types of categorical features: Ordinal and nominal features.
+
+Ordinal means that an "order" is implied. For example, a customer satisfaction metric {'satisfied', 'neutral', 'dissatisfied'} is a ordinal variable since we can order it: 'satisfied' > 'neutral' > 'dissatisfied'. Here, we can simply map the 'string' notation into an integer notation, for example 'satisfied'=1, 'neutral' =0, and 'dissatisfied'= -1.
+
+If our variable is *nominal*, an 'order' does not make sense. For example, think of 'color'; there are some cases in image processing where ordering color values makes sense, but for simplicity, we can't say 'red > blue > yellow' or so. To deal with such variables in SVM classification, we typically do a "one-hot" encoding. Here, we create so-called dummy variables that can binary values — we create one dummy variable for each possible value of that nominal feature variable. Say that our color variable can have one of the three values: 'red,' 'blue,' 'yellow.' And Let's say we have the following dataset consisting of 4 training samples:
+
+- sample 1: 'blue'
+- sample 2: 'yellow'
+- sample 3: 'red'
+- sample 4: 'yellow'
+
+Then our one-hot encoding would look like this:
+
+
+
+Note that there's only one "true" value (the integer 1) in each row, which denotes the column for that sample in the training set. Sample 1 is blue; sample 2 is yellow, and so forth.
diff --git a/faq/svm_for_categorical_data/onehot-color.png b/faq/svm_for_categorical_data/onehot-color.png
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diff --git a/faq/tensorflow-vs-scikitlearn.md b/faq/tensorflow-vs-scikitlearn.md
new file mode 100644
index 00000000..57cae08d
--- /dev/null
+++ b/faq/tensorflow-vs-scikitlearn.md
@@ -0,0 +1,145 @@
+# What is the main difference between TensorFlow and scikit-learn?
+
+TensorFlow is more of a low-level library; basically, we can think of TensorFlow as the Lego bricks (similar to NumPy and SciPy) that we can use to implement machine learning algorithms whereas scikit-learn comes with off-the-shelf algorithms, e.g., algorithms for classification such as SVMs, Random Forests, Logistic Regression, and many, many more. TensorFlow really shines if we want to implement deep learning algorithms, since it allows us to take advantage of GPUs for more efficient training.
+To get a better idea of how these two libraries differ, let's fit a softmax regression model on the Iris dataset via scikit-learn:
+
+```python
+from sklearn.datasets import load_iris
+from sklearn.linear_model import LogisticRegression
+import matplotlib.pyplot as plt
+
+# Loading Data
+iris = load_iris()
+X = iris.data[:, [0, 3]] # sepal length and petal width
+y = iris.target
+
+# standardize
+X[:,0] = (X[:,0] - X[:,0].mean()) / X[:,0].std()
+X[:,1] = (X[:,1] - X[:,1].mean()) / X[:,1].std()
+
+lr = LogisticRegression(penalty='l2',
+ dual=False,
+ tol=0.000001,
+ C=10.0,
+ fit_intercept=True,
+ intercept_scaling=1,
+ class_weight=None,
+ random_state=1,
+ solver='newton-cg',
+ max_iter=100,
+ multi_class='multinomial',
+ verbose=0,
+ warm_start=False,
+ n_jobs=1)
+lr.fit(X, y)
+```
+
+In addition, I have a little helper function to plot the 2D decision surface:
+
+```python
+from mlxtend.plotting import plot_decision_regions
+
+plot_decision_regions(X, y, clf=lr)
+plt.title('Softmax Regression in scikit-learn')
+plt.show()
+```
+
+
+
+That was easy, right? :). Now, if we want to fit a Softmax regression model via TensorFlow, however, we have to "build" the algorithm first. But it really sounds more complicated than it really is.
+TensorFlow comes with many "convenience" functions and utilities, for example, if we want to use a gradient descent optimization approach, the core or our implementation could look like this:
+
+```python
+# Construct the Graph
+ g = tf.Graph()
+ with g.as_default():
+
+ if init_weights:
+ self._n_classes = np.max(y) + 1
+ self._n_features = X.shape[1]
+ tf_weights_, tf_biases_ = self._initialize_weights(
+ n_features=self._n_features,
+ n_classes=self._n_classes)
+ self.cost_ = []
+ else:
+ tf_weights_ = tf.Variable(self.weights_)
+ tf_biases_ = tf.Variable(self.biases_)
+
+ # Prepare the training data
+ y_enc = self._one_hot(y, self._n_classes)
+ n_idx = list(range(y.shape[0]))
+ tf_X = tf.convert_to_tensor(value=X, dtype=self.dtype)
+ tf_y = tf.convert_to_tensor(value=y_enc, dtype=self.dtype)
+ tf_idx = tf.placeholder(tf.int32,
+ shape=[int(y.shape[0] / n_batches)])
+ X_batch = tf.gather(params=tf_X, indices=tf_idx)
+ y_batch = tf.gather(params=tf_y, indices=tf_idx)
+
+ # Setup the graph for minimizing cross entropy cost
+ logits = tf.matmul(X_batch, tf_weights_) + tf_biases_
+ cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits,
+ y_batch)
+ cost = tf.reduce_mean(cross_entropy)
+ optimizer = tf.train.GradientDescentOptimizer(
+ learning_rate=self.eta)
+ train = optimizer.minimize(cost)
+
+ # Initializing the variables
+ init = tf.initialize_all_variables()
+```
+
+And we can execute the training as follows:
+
+```python
+# Launch the graph
+with tf.Session(graph=g) as sess:
+ sess.run(init)
+ self.init_time_ = time()
+ for epoch in range(self.epochs):
+ if self.minibatches > 1:
+ n_idx = np.random.permutation(n_idx)
+ minis = np.array_split(n_idx, self.minibatches)
+ costs = []
+ for idx in minis:
+ _, c = sess.run([train, cost], feed_dict={tf_idx: idx})
+ costs.append(c)
+```
+
+```python
+For demonstration purposes, I have implemented Softmax regression via TensorFlow in an object oriented style that is somewhat similar to scikit-learn's implementation. The complete code example can be found here if you are interested: [mlxtend/tf_classifier/TfSoftmax](https://github.com/rasbt/mlxtend/blob/master/mlxtend/tf_classifier/tf_softmax.py).
+```
+
+```python
+from mlxtend.tf_classifier import TfSoftmaxRegression
+
+lr = TfSoftmaxRegression(eta=0.75,
+ epochs=1000,
+ print_progress=True,
+ minibatches=1,
+ random_seed=1)
+
+lr.fit(X, y)
+Epoch: 1000/1000 | Cost 0.12
+
+plt.plot(range(len(lr.cost_)), lr.cost_)
+plt.xlabel('Iterations')
+plt.ylabel('Cost')
+plt.show()
+```
+
+
+
+
+```python
+from mlxtend.plotting import plot_decision_regions
+
+plot_decision_regions(X, y, clf=lr)
+plt.title('Softmax Regression via Gradient Descent in TensorFlow')
+plt.show()
+```
+
+
+
+**Note**
+
+I've removed the TensorFlow code from mlxtend because it became pretty inconvenient to maintain. The original code should still be available through GitHub. E.g., if you install mlxtend 0.5.1, (`pip install mlxtend=0.5.1`) or browse through the files here: https://github.com/rasbt/mlxtend/tree/86e40d5af5222d78acf219cc8188cfd28a972d9e/mlxtend/tf_classifier
diff --git a/faq/tensorflow-vs-scikitlearn/scikit-softmax.png b/faq/tensorflow-vs-scikitlearn/scikit-softmax.png
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diff --git a/faq/underscore_convention.md b/faq/underscore-convention.md
similarity index 75%
rename from faq/underscore_convention.md
rename to faq/underscore-convention.md
index 01307e04..35937b44 100644
--- a/faq/underscore_convention.md
+++ b/faq/underscore-convention.md
@@ -18,14 +18,15 @@ instead of
def sigmoid(self, z):
return 1.0 / (1.0 + np.exp(-z))
-The short answer is, the trailing underscore (`self.gamma_`) in class attributes is a scikit-learn convention to denote "estimated" or "fitted" attributes. The leading underscores are (`_sigmoid(self, z)`) denote private methods that the user shouldn't bother with.
+The short answer is, the trailing underscore (`self.gamma_`) in class attributes is a scikit-learn convention to denote "estimated" or "fitted" attributes.
+The leading underscores are (`_sigmoid(self, z)`) denote private methods that the user should not bother with.
In brief: As a reader, you can safely ignore those underscores, however, if you are curious about their intention, please read on!
## Leading underscores in class methods
-The usage of underscores for naming class methods is a common Python convention to distinguish between private and public methods. Basically, you don't want the user to worry about these private methods, which is why they don't appear in the help menu.
+The usage of underscores for naming class methods is a common Python convention to distinguish between private and public methods. Basically, you do not want the user to worry about these private methods, which is why they do not appear in the help menu.
class MyClass(object):
def __init__(self, param='some_value'):
@@ -67,7 +68,7 @@ The usage of underscores for naming class methods is a common Python convention
* Note that `__init__` is an exception, `__init__` is a special method that is required to initialize a class.
-Let’s initialize a new object and call this “public” class:
+Let us initialize a new object and call this "public" class:
>>> MyObj = MyClass()
>>> MyObj.public()
@@ -81,7 +82,7 @@ The single underscore in `_indicate_private` indicates privacy. Typically, priva
- Please keep in mind that calling private methods is at your own risk; the developers usually take no responsibilities for odd things that may happen if you call private methods as a user.
-The indication of “privacy” is a bit stronger if we use 2 preceding underscores, for example, calling the `__pseudo_private` method directly like a regular method doesn’t work anymore:
+The indication of "privacy" is a bit stronger if we use 2 preceding underscores, for example, calling the `__pseudo_private` method directly like a regular method does not work anymore:
>>> MyObj.__pseudo_private()
---------------------------------------------------------------------------
@@ -91,7 +92,7 @@ The indication of “privacy” is a bit stronger if we use 2 preceding undersco
AttributeError: 'MyClass' object has no attribute '__pseudo_private'
-To call the a private methods that is prefaced with 2 underscores, we need to adhere to the “name mangling” rules; that is, we need to add a `_classname` prefix, to call the method, for example,
+To call the a private methods that is prefaced with 2 underscores, we need to adhere to the "name mangling" rules; that is, we need to add a `_classname` prefix, to call the method, for example,
>>> MyObj._MyClass__pseudo_private()
'really private method'
@@ -100,16 +101,16 @@ To call the a private methods that is prefaced with 2 underscores, we need to ad
## Class attributes with trailing underscores
-In contrast to the leading underscore, the trailing underscores in class attributes don't any "technical" effects. In fact, this is just a convention that I adopted from scikit-learn out of habit.
+In contrast to the leading underscore, the trailing underscores in class attributes do not have any "technical" effects. In fact, this is just a convention that I adopted from scikit-learn out of habit.
Here are two excerpts from the scikit-learn [developer/contributor documentation](http://scikit-learn.org/stable/developers/):
-> Attributes that have been estimated from the data must always have a name ending with trailing underscore, for example, the coefficients of some regression estimator would be stored in a coef_ attribute after fit has been called.
+> Attributes that have been estimated from the data must always have a name ending with trailing underscore `_`, for example, the coefficients of some regression estimator would be stored in a `coef_` attribute after `fit()` has been called.
-> Also it is expected that parameters with trailing _ are not to be set inside the ``__init__`` method. All and only the public attributes set by fit have a trailing _. As a result the existence of parameters with trailing _ is used to check if the estimator has been fitted.
+> Also it is expected that parameters with trailing underscore `_` are not to be set inside the ``__init__`` method. All and only the public attributes set by `fit()` have a trailing `_`. As a result the existence of parameters with trailing `_` is used to check if the estimator has been fitted.
-To see it in action, let's create a primitive `Estimator`:
+To see it in action, let us create a primitive `Estimator`:
class MyEstimator():
def __init__(self):
@@ -118,7 +119,7 @@ To see it in action, let's create a primitive `Estimator`:
def fit(self):
self.fit_param_ = 0.1
-Intuitively, attributes that are in `__init__` are accessible after we initialized a new object
+Intuitively, attributes that are in `__init__` are accessible after we initialized a new object:
>>> est = MyEstimator()
>>> est.param
diff --git a/faq/visual-backpropagation.md b/faq/visual-backpropagation.md
new file mode 100644
index 00000000..6cc168a1
--- /dev/null
+++ b/faq/visual-backpropagation.md
@@ -0,0 +1,27 @@
+# Can you give a visual explanation for the back propagation algorithm for neural networks?
+
+Let's assume we are really into mountain climbing, and to add a little extra challenge, we cover eyes this time so that we can't see where we are and when we accomplished our "objective," that is, reaching the top of the mountain.
+
+Since we can't see the path upfront, we let our intuition guide us: assuming that the mountain top is the "highest" point of the mountain, we think that the steepest path leads us to the top most efficiently.
+We approach this challenge by iteratively "feeling" around you and taking a step into the direction of the steepest ascent -- let's call it "gradient ascent." But what do we do if we reach a point where we can't ascent any further? I.e., each direction leads downwards? At this point, we may have already reached the mountain's top, but we could just have reached a smaller plateau ... we don't know.
+Essentially, this is just an analogy of gradient ascent optimization (basically the counterpart of minimizing a cost function via gradient descent). However, this is not specific to backpropagation but just one way to minimize a convex cost function (if there is only a global minima) or non-convex cost function (which has local minima like the "plateaus" that let us think we reached the mountain's top). Using a little visual aid, we could picture a non-convex cost function with only one parameter (where the blue ball is our current location) as follows:
+
+ 
+
+Now, backpropagation is just back-propagating the cost over multiple "levels" (or layers). E.g., if we have a multi-layer perceptron, we can picture forward propagation (passing the input signal through a network while multiplying it by the respective weights to compute an output) as follows:
+
+
+
+And in backpropagation, we "simply" backpropagate the error (the "cost" that we compute by comparing the calculated output and the known, correct target output, which we then use to update the model parameters):
+
+
+
+
+It may be some time ago since pre-calc, but it's essentially all based on the simple chain-rule that we use for nested functions
+
+
+
+
+
+
+Instead of doing this "manually" we can use computational tools (called "automatic differentiation"), and backpropagation is basically the "reverse" mode of this auto-differentiation. Why reverse and not forward? Because it is computationally cheaper! If we'd do it forward-wise, we'd successively multiply large matrices for each layer until we multiply a large matrix by a vector in the output layer. However, if we start backwards, that is, we start by multiplying a matrix by a vector, we get another vector, and so forth. So, I'd say the beauty in backpropagation is that we are doing more efficient matrix-vector multiplications instead of matrix-matrix multiplications.
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@@ -0,0 +1,23 @@
+# When should I apply data normalization/standardization?
+
+
+The only family of algorithms that I could think of being scale-invariant are tree-based methods. Let's take the general CART decision tree algorithm. Without going into much depth regarding information gain and impurity measures, we can think of the decision as "is feature x_i >= some_val?" Intuitively, we can see that it really doesn't matter on which scale this feature is (centimeters, Fahrenheit, a standardized scale -- it really doesn't matter).
+
+
+Some examples of algorithms where feature scaling matters are:
+
+
+- k-nearest neighbors with an Euclidean distance measure if want all features to contribute equally
+- k-means (see k-nearest neighbors)
+- logistic regression, SVMs, perceptrons, neural networks etc. if you are using gradient descent/ascent-based optimization, otherwise some weights will update much faster than others
+- linear discriminant analysis, principal component analysis, kernel principal component analysis since you want to find directions of maximizing the variance (under the constraints that those directions/eigenvectors/principal components are orthogonal); you want to have features on the same scale since you'd emphasize variables on "larger measurement scales" more.
+
+
+There are many more cases than I can possibly list here ... I always recommend you to think about the algorithm and what it's doing, and then it typically becomes obvious whether we want to scale your features or not.
+
+
+In addition, we'd also want to think about whether we want to "standardize" or "normalize" (here: scaling to [0, 1] range) our data. Some algorithms assume that our data is centered at 0. For example, if we initialize the weights of a small multi-layer perceptron with tanh activation units to 0 or small random values centered around zero, we want to update the model weights "equally."
+As a rule of thumb I'd say: When in doubt, just standardize the data, it shouldn't hurt.
+
+
+
diff --git a/faq/why_python.md b/faq/why-python.md
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+++ b/faq/why-python.md
@@ -1,6 +1,6 @@
# Why did you choose Python for Machine Learning?
-(Edit: This article is now also available in visually more appealing version [here](http://sebastianraschka.com/Articles/2015_why_python.html).)
+(Edit: This article is now also available in visually more appealing version [here](http://sebastianraschka.com/blog/2015/why-python.html).)
Oh god, another one of those subjective, pointedly opinionated click-bait headlines? Yes! Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it."
@@ -32,24 +32,24 @@ By now, you may have already started wondering about this blog. I haven't writte
Maybe I should start with the short answer. You are welcome to stop reading this article below this paragraph because it really nails it. I am a scientist, I like to get my stuff done. I like to have an environment where I can quickly prototype and jot down my models and ideas. I need to solve very particular problems. I analyze given datasets to draw my conclusions. This is what matters most to me: How can I get the job done most productively? What do I mean by "productively"? Well, I typically run an analysis only once (the testing of different ideas and debugging aside); I don't need to repeatedly run a particular piece of code 24/7, I am not developing software applications or web apps for end users. When I *quantify* "productivity," I literally estimate the sum of (1) the time that it takes to get the idea written down in code, (2) debug it, and (3) execute it. To me, "most productively" means "how long does it take to get the results?" Now, over the years, I figured that Python is for me. Not always, but very often. Like everything else in life, Python is not a "silver bullet," it's not the "best" solution to every problem. However, it comes pretty close if you compare programming languages across the spectrum of common and not-so common problem tasks; Python is probably the most versatile and capable all-rounder.
-
+
(Source: [https://xkcd.com/974/](https://xkcd.com/974/))
Remember: "Premature optimization is the root of all evil" (Donald Knuth). If you are part of the software engineering team that wants to optimize the next game-changing high-frequency trading model from your machine learning and data science division, Python is probably not for you (but maybe it was the language of choice by the data science team, so it may still be useful to learn how to read it). So, my little piece of advice is to evaluate your daily problem tasks and needs when you choose a language. "If all that you have is a hammer, everything starts to look like a nail" -- you are too smart to fall for this trap! However, keep in mind that there is a balance. There are occasions where the hammer may be the best choice even if a screwdriver would probably be the "nicer" solution. Again, it comes down to productivity.
**Let me give you an example from personal experience.**
-I needed to develop a bunch of novel algorithms to "screen" 15 million small, chemical compounds with regard to a very problem specific hypothesis. I am an entirely computational person, but I am collaborating with biologists who do non-computational experiments (we call them "wet lab" experiments). The goal was to narrow it down to a list of 100 potential compounds that they could test in their lab. The caveat was that they needed the results quickly, because they only had limited time to conduct the experiments.
+I needed to develop a bunch of novel algorithms to "screen" 15 million small, chemical compounds with regard to a very problem specific hypothesis. I am an entirely computational person, but I am collaborating with biologists who do non-computational experiments (we call them "wet lab" experiments). The goal was to narrow it down to a list of 100 potential compounds that they could test in their lab. The caveat was that they needed the results quickly, because they only had limited time to conduct the experiments.
> You’re bound to be unhappy if you optimize everything. — Donald Knuth
Trust me, time was really "limited:" We just got our grant application accepted and research funded a few weeks before the results had to be collected (our collaborators were doing experiments on larvae of a certain fish species that only spawns in Spring). Therefore, I started thinking "How could I get those results to them as quickly as possible?" Well, I know C++ and FORTRAN, and if I implement those algorithms in the respective languages executing the "screening" run may be faster compared to a Python implementation. This was more of an educated guess, I don't really know if it would have been substantially faster. But there was one thing I knew for sure: If I started developing the code in Python, I could be able to get it to run in a few days -- maybe it would take a week to get the respective C++ versions coded up. I would worry about a more efficient implementation later. At that moment, it was just important to get those results to my collaborators -- "Premature optimization is the root of all evil." On a side node: The same train of thought applies to data storage solutions. Here, I just went with SQLite. CSV didn't make quite sense since I had to annotate and retrieve certain molecules repeatedly. I surely didn't want to scan or rewrite a CSV from start to end every time I wanted to look up a molecule or manipulate its entry -- issues in dealing with memory capacities aside. Maybe MySQL would have been even better but for the reasons mentioned above, I wanted to get the job done quickly, and setting up an additional SQL server ... there was no time for that, SQLite was just fine to get the job done.
-
+
(Source: [https://xkcd.com/1319/](https://xkcd.com/1319/))
The verdict: **Choose the language that satisfies *your* needs!**
-However, there is once little caveat here! How can a beginning programmer possibly know about the advantages and disadvantages of a language before learning it, and how should the programmer know if this language will be useful to her at all? This is what I would do: Just search for particular applications and solutions related to your most common problem tasks on Google and [GitHub](https://github.com). You don't need to read and understand the code. Just look at the end product.
+However, there is once little caveat here! How can a beginning programmer possibly know about the advantages and disadvantages of a language before learning it, and how should the programmer know if this language will be useful to her at all? This is what I would do: Just search for particular applications and solutions related to your most common problem tasks on Google and [GitHub](https://github.com). You don't need to read and understand the code. Just look at the end product.
> In the one and only true way. The object-oriented version of 'Spaghetti code' is, of course, 'Lasagna code'. (Too many layers). — Roberto Waltman.
@@ -68,7 +68,7 @@ If you are interested, those are my favorite and most frequently used Python "to
- [scikit-learn](http://scikit-learn.org/stable/): The most convenient API for the daily, more basic machine learning tasks.
- [matplotlib](http://matplotlib.org): My library of choice when it comes to plotting. Sometimes I also use [seaborn](http://stanford.edu/~mwaskom/software/seaborn/index.html) for particular plots, for example, the heat maps are particularly great!
-[](./images/heatmap.png)
+[](./why-python/heatmap.png)
(Source: [http://stanford.edu/~mwaskom/software/seaborn/examples/structured_heatmap.html](http://stanford.edu/~mwaskom/software/seaborn/examples/structured_heatmap.html))
@@ -77,7 +77,7 @@ If you are interested, those are my favorite and most frequently used Python "to
- [pandas](http://pandas.pydata.org): Working with relatively small datasets, mostly from CSV files.
- [sqlite3](https://docs.python.org/2/library/sqlite3.html): Annotating and querying "medium-sized" datasets.
- [IPython notebooks](http://ipython.org): What can I say, 90% of my research takes place in IPython notebooks. It's just a great environment to have everything in one place: Ideas, code, comments, LaTeX equations, illustrations, plots, outputs, ...
-
+
Note that the IPython Project recently evolved into [Project Jupyter](https://jupyter.org). Now, you can use Jupyter notebook environment not only for Python but R, Julia, and many more.
@@ -92,7 +92,7 @@ prototyping after all! Since it was built with linear algebra in mind (MATLAB fo
However, keep in mind that MATLAB comes with a big
price tag, and I think it is slowly fading from academia as well as industry. Plus, I am a big fan open-source enthusiast after all ;). In addition, its performance is also not that compelling compared to other "productive" languages looking at the benchmarks below:
-
+
(Benchmark times relative to C -- smaller is better, C performance = 1.0; Source: [http://julialang.org/benchmarks/](http://julialang.org/benchmarks/))
@@ -132,7 +132,7 @@ To be honest, I have to admit that I am not necessarily a big fan of the "@" sym
[[back to top](#table-of-contents)]
-I think Julia is a great language, and I would like to recommend it to someone who's getting started with programming and machine learning. I am not sure if I really should though. Why? There is this sad, somewhat paradoxical thing about committing to programming languages. With Julia, we cannot tell if it will become "popular" enough in the next few years.
+I think Julia is a great language, and I would like to recommend it to someone who's getting started with programming and machine learning. I am not sure if I really should though. Why? There is this sad, somewhat paradoxical thing about committing to programming languages. With Julia, we cannot tell if it will become "popular" enough in the next few years.
> There are only two kinds of languages: the ones people complain about and the ones nobody uses — Bjarne Stroustrup
@@ -163,13 +163,13 @@ I just wanted to bring up Theano and computing on GPUs as a big plus for Python,
To take one of my favorite Python quotes out of its original context: "We are all adults here" -- let's not waste our time with language wars. Choose the tool that "clicks" for you. When it comes to perspectives on the job market: There is no right or wrong here either. I don't think a company that wants to hire you as a "data scientist" really bothers about your favorite toolbox -- programming languages are just "tools" after all. The most important skill is to think like a "data scientist," to ask the right questions, to solve a problems. The hard part is the math and machine learning theory, a new programming language can easily be learned. Just think about, you learned how to swing a hammer to drive the nail in, how hard can it possibly be to pick up a hammer from a different manufacturer?
But if you are still interested, look at the Tiobe Index for example, *one* measure of popularity of programming languages:
-
+
(Source: [http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html](http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html))
However, if we look at the [The 2015 Top Ten Programming Languages](http://spectrum.ieee.org/computing/software/the-2015-top-ten-programming-languages) by Spectrum IEEE, the R language is climbing fast (left column: 2015, right column: 2014).
-
+
(Source: [http://spectrum.ieee.org/computing/software/the-2015-top-ten-programming-languages](http://spectrum.ieee.org/computing/software/the–2015-top-ten-programming-languages))
@@ -205,8 +205,6 @@ Speaking of hammers and nails again, Python is extremely versatile, the largest
Well, this is a pretty long answer to a seemingly very simple question. Trust me, I can go on for hours and days. But why complicate things? Let's bring the talks to a conclusion:
-
+
(Source: [https://xkcd.com/353/](https://xkcd.com/353/))
-
-
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@@ -1,195 +1,19 @@
# Image Gallery
-This is a just a selection of plots and figures as they appear in the book to provide you with a quick overview of what to expect from the book. I included higher-resolution images in the IPython notebooks along with the code examples, which can be accessed via the [Table of Contents](https://github.com/rasbt/python-machine-learning-book#table-of-contents-and-code-notebooks).
-
-## Chapter 1
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-## Chapter 2
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-## Chapter 13
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+This previous image gallery have been removed to avoid duplication and to keep the file size of this GitHub repository slim, since the high-res version of these images are now embedded in the Jupyter notebooks themselves in the [code](../../code/) directory.
+
+Below are the links to the respective images by chapter:
+
+- [ch01](../../code/ch01/images) - Machine Learning - Giving Computers the Ability to Learn from Data
+- [ch02](../../code/ch02/images) - Training Machine Learning Algorithms for Classification
+- [ch03](../../code/ch03/images) - A Tour of Machine Learning Classifiers Using Scikit-Learn
+- [ch04](../../code/ch04/images) - Building Good Training Sets - Data Pre-Processing
+- [ch05](../../code/ch05/images) - Compressing Data via Dimensionality Reduction
+- [ch06](../../code/ch06/images) - Learning Best Practices for Model Evaluation and Hyperparameter Optimization
+- [ch07](../../code/ch07/images) - Combining Different Models for Ensemble Learning
+- [ch08](../../code/ch08/images) - Applying Machine Learning to Sentiment Analysis
+- [ch09](../../code/ch09/images) - Embedding a Machine Learning Model into a Web Application
+- [ch10](../../code/ch10/images) - Predicting Continuous Target Variables with Regression Analysis
+- [ch11](../../code/ch11/images) - Working with Unlabeled Data – Clustering Analysis
+- [ch12](../../code/ch12/images) - Training Artificial Neural Networks for Image Recognition
+- [ch13](../../code/ch13/images) - Parallelizing Neural Network Training via Theano
\ No newline at end of file
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