#coding=utf-8
'''
'''
from math import log
import operator

def createDataSet():
    dataSet =[[1,1,'yes'],
              [1,1,'yes'],
              [1,0,'no'],
              [0,1,'no'],
              [0,1,'no']]
    labels = ['no surfacing','flippers'] #分类的属性
    return dataSet,labels
    
#计算给定数据的香农熵
def calcShannonEnt(dataSet):
    numEntries = len(dataSet)
    labelCounts = {}
    for featVec in dataSet:
        currentLabel = featVec[-1] #获得标签
        #构造存放标签的字典
        if currentLabel not in labelCounts.keys():
            labelCounts[currentLabel]=0
        labelCounts[currentLabel]+=1 #对应的标签数目+1
    #计算香农熵
    shannonEnt = 0.0
    for key in labelCounts:
        prob = float(labelCounts[key])/numEntries
        shannonEnt -=prob*log(prob,2)
    return shannonEnt

#划分数据集,三个参数为带划分的数据集，划分数据集的特征，特征的返回值
def splitDataSet(dataSet,axis,value):  
    retDataSet = []
    for featVec in dataSet:
        if featVec[axis] ==value:
            #将相同数据集特征的抽取出来
            reducedFeatVec = featVec[:axis]
            reducedFeatVec.extend(featVec[axis+1:])
            retDataSet.append(reducedFeatVec)
    return retDataSet #返回一个列表
        
#选择最好的数据集划分方式
def chooseBestFeatureToSplit(dataSet):
    numFeature = len(dataSet[0])-1
    baseEntropy = calcShannonEnt(dataSet)
    bestInfoGain = 0.0
    beatFeature = -1
    for i in range(numFeature):
        featureList = [example[i] for example in dataSet] #获取第i个特征所有的可能取值
        uniqueVals = set(featureList)  #从列表中创建集合，得到不重复的所有可能取值ֵ
        newEntropy = 0.0
        for value in uniqueVals:
            subDataSet = splitDataSet(dataSet,i,value)   #以i为数据集特征，value为返回值，划分数据集
            prob = len(subDataSet)/float(len(dataSet))   #数据集特征为i的所占的比例
            newEntropy +=prob * calcShannonEnt(subDataSet)   #计算每种数据集的信息熵
        infoGain = baseEntropy- newEntropy
        #计算最好的信息增益，增益越大说明所占决策权越大
        if (infoGain > bestInfoGain):
            bestInfoGain = infoGain
            bestFeature = i
    return bestFeature

#递归构建决策树
def majorityCnt(classList):      
    classCount = {}
    for vote in classList:
        if vote not in classCount.keys():
            classCount[vote]=0
        classCount[vote]+=1
    sortedClassCount = sorted(classCount.iteritems(),key =operator.itemgetter(1),reverse=True)#排序，True升序
    return sortedClassCount[0][0]  #返回出现次数最多的

 #创建树的函数代码
def createTree(dataSet,labels):     
    classList = [example[-1]  for example in dataSet]
    if classList.count(classList[0])==len(classList):#类别完全相同则停止划分
        return classList[0]
    if len(dataSet[0]) ==1:             #遍历完所有特征值时返回出现次数最多的
        return majorityCnt(classList)
    bestFeat = chooseBestFeatureToSplit(dataSet)   #选择最好的数据集划分方式
    bestFeatLabel = labels[bestFeat]   #得到对应的标签值
    myTree = {bestFeatLabel:{}}
    del(labels[bestFeat])      #清空labels[bestFeat],在下一次使用时清零
    featValues = [example[bestFeat] for example in dataSet] 
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels =labels[:]
        #递归调用创建决策树函数
        myTree[bestFeatLabel][value]=createTree(splitDataSet(dataSet,bestFeat,value),subLabels)
    return myTree  

if __name__=="__main__":
    dataSet,labels = createDataSet()
    print createTree(dataSet,labels)