PCA python代码实现

#coding:utf-8from numpy import *import matplotlibimport matplotlib.pyplot as pltdef loadDataSet(fileName, delim='\t'):    fr = open(fileName)    stringArr = [line.strip().split(delim) for line in fr.readlines()]    datArr = [map(float,line) for line in stringArr]    return mat(datArr)def pca(dataMat, topNfeat=999999):    meanVals = mean(dataMat, axis=0)    DataAdjust = dataMat - meanVals           #减去平均值    covMat = cov(DataAdjust, rowvar=0)    eigVals,eigVects = linalg.eig(mat(covMat)) #计算特征值和特征向量    #print eigVals    eigValInd = argsort(eigVals)    eigValInd = eigValInd[:-(topNfeat+1):-1]   #保留最大的前K个特征值    redEigVects = eigVects[:,eigValInd]        #对应的特征向量    lowDDataMat = DataAdjust * redEigVects     #将数据转换到低维新空间    reconMat = (lowDDataMat * redEigVects.T) + meanVals   #重构数据，用于调试    return lowDDataMat, reconMat# 　　测试数据testSet.txt由1000个数据点组成。下面对数据进行降维，并用matplotlib模块将降维后的数据和原始数据一起绘制出来。dataMat = loadDataSet('./testSet.txt')lowDMat, reconMat = pca(dataMat,1)print "shape(lowDMat): ",shape(lowDMat)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()

结果如下图