《机器学习实战》PCA降维

时间：2024-04-28 19:01:03

注释：由于各方面原因，理论部分不做介绍，网上很多自行百度吧！

pca.py

 import numpy as np

 import matplotlib.pyplot as plt

 import math

 def  loadDataSet(filename, delin = '\t'):

     fr = open(filename)

     #读取分割存入数组

     stringArr = [line.strip().split(delin) for line in fr.readlines()]

     dataArr   = [list(map(float,line)) for line in stringArr]

     return np.mat(dataArr)

 def pca(dataMat, topNfeet = 9999999):

     meanVals = np.mean(dataMat,axis=0)#求取平均值

     meanRemoved = dataMat - meanVals

     covMat = np.cov(meanRemoved,rowvar=0)#方差

     eigVals, eigVects= np.linalg.eig(np.mat(covMat))#求解特征向量和特征值

     eigValInd = np.argsort(eigVals)#对特征值进行排序

     eigValInd = eigValInd[:-(topNfeet+1):-1]#最后的-1是防止越界的，当然你可以在前面加一个判断

     redEigVects = eigVects[:,eigValInd]

     lowDDataMat = meanRemoved*redEigVects #

     reconMat = (lowDDataMat * redEigVects.T) + meanVals

     return lowDDataMat, reconMat

main.py

 import PCA

 import matplotlib.pyplot as plt

 if __name__ == "__main__":

     dataMat = PCA.loadDataSet('testSet.txt')

     lowDMat, reconMat = PCA.pca(dataMat,1)

     fig = plt.figure()

     ax  = fig.add_subplot(111)

     ax.scatter(dataMat[:,0].flatten().A[0],dataMat[:,1].flatten().A[0],marker = '^',s=90)

     ax.scatter(reconMat[:,0].flatten().A[0],reconMat[:,1].flatten().A[0],marker = "o",s=50,c='red')

     plt.show()

《机器学习实战》PCA降维

对丢失的值进行替代：

 #零的数据都转化为平均值

 def replaceNanWithMean():

     dataMat = loadDataSet('secom.data',' ')

     numFeat = dataMat.shape[1]

     for i in range(numFeat):

         meanVal = np.mean(dataMat[np.nonzero(~np.isnan(dataMat[:,i].A))[0],i])

         dataMat[np.nonzero(np.isnan(dataMat[:,i].A))[0],i] = meanVal

     return dataMat

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