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'''@author: Garvin'''from numpy import *import 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=9999999):  meanVals = mean(dataMat, axis=0)  meanRemoved = dataMat - meanVals #remove mean  covMat = cov(meanRemoved, rowvar=0)  eigVals,eigVects = linalg.eig(mat(covMat))  eigValInd = argsort(eigVals)          #sort, sort goes smallest to largest  eigValInd = eigValInd[:-(topNfeat+1):-1]  #cut off unwanted dimensions  redEigVects = eigVects[:,eigValInd]      #reorganize eig vects largest to smallest  lowDDataMat = meanRemoved * redEigVects#transform data into new dimensions  reconMat = (lowDDataMat * redEigVects.T) + meanVals  return lowDDataMat, reconMatdef plotBestFit(dataSet1,dataSet2):     dataArr1 = array(dataSet1)  dataArr2 = array(dataSet2)  n = shape(dataArr1)[0]   n1=shape(dataArr2)[0]  xcord1 = []; ycord1 = []  xcord2 = []; ycord2 = []  xcord3=[];ycord3=[]  j=0  for i in range(n):      xcord1.append(dataArr1[i,0]); ycord1.append(dataArr1[i,1])      xcord2.append(dataArr2[i,0]); ycord2.append(dataArr2[i,1])                 fig = plt.figure()  ax = fig.add_subplot(111)  ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')  ax.scatter(xcord2, ycord2, s=30, c='green')  plt.xlabel('X1'); plt.ylabel('X2');  plt.show()    if __name__=='__main__':   mata=loadDataSet('/Users/hakuri/Desktop/testSet.txt')     a,b= pca(mata, 2)