MachineLearning/AutoNormal/AutoNorm.py at master · datascienceife/MachineLearning · GitHub

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from __future__ import division
def GetAverage(mat):

    n=len(mat)
    m= width(mat)
    num = [0]*m
    for j in range(0,m):
           for i in mat:
              num[j]=num[j]+i[j]
           num[j]=num[j]/n
    return num

def width(lst):
    i=0
    for j in lst[0]:
       i=i+1
    return i

def GetVar(average,mat):
    ListMat=[]
    for i in mat:
        ListMat.append(list(map(lambda x: x[0]-x[1], zip(average, i))))

    n=len(ListMat)
    m= width(ListMat)
    num = [0]*m
    for j in range(0,m):
        for i in ListMat:
                  num[j]=num[j]+(i[j]*i[j])
        num[j]=num[j]/n
    return num

def DenoisMat(mat):
    average=GetAverage(mat)
    variance=GetVar(average,mat)
    section=list(map(lambda x: x[0]+x[1], zip(average, variance)))

    n=len(mat)
    m= width(mat)
    num = [0]*m
    denoisMat=[]
    for i in mat:
        for j in range(0,m):
               if i[j]>section[j]:
                     i[j]=section[j]
        denoisMat.append(i)
    return denoisMat

def AutoNorm(mat):
    n=len(mat)
    m= width(mat)
    MinNum=[9999999999]*m
    MaxNum = [0]*m
    for i in mat:
        for j in range(0,m):
            if i[j]>MaxNum[j]:
                MaxNum[j]=i[j]

    for p in mat:
        for q in range(0,m):
            if p[q]<=MinNum[q]:
                    MinNum[q]=p[q]

    section=list(map(lambda x: x[0]-x[1], zip(MaxNum, MinNum)))
    print section
    NormMat=[]

    for k in mat:

          distance=list(map(lambda x: x[0]-x[1], zip(k, MinNum)))
          value=list(map(lambda x: x[0]/x[1], zip(distance,section)))
          NormMat.append(value)
    return NormMat

if __name__=='__main__':
    mat=[[1,42,512],[4,5,6],[7,8,9],[2,2,2],[2,10,5]]
    a=GetAverage(mat)
    b=GetVar(a,mat)
    print a,
    print DenoisMat(mat)

#     print list(map(lambda x: x[0]-x[1], zip(v2, v1)))
    print AutoNorm(mat)