摘自《机器学习实践》第13章：

1 from numpy import * 2 import matplotlib 3 import matplotlib.pyplot as plt 4  5 def loadDataSet(fileName, delim='\t'): 6     fr = open(fileName) 7     stringArr = [line.strip().split(delim) for line in fr.readlines()] 8     datArr = [map(float, line) for line in stringArr] 9     return mat(datArr)10 11 def pca(dataMat, topNfeat = 999999):12     meanVals = mean(dataMat, axis = 0)13     meanRemoved = dataMat - meanVals14     covMat = cov(meanRemoved, rowvar = 0)15     eigVals, eigVects = linalg.eig(mat(covMat))16     eigValInd = argsort(eigVals)17     eigValInd = eigValInd[: - (topNfeat + 1): -1]18     redEigVects = eigVects[:, eigValInd]19     lowDDataMat = meanRemoved * redEigVects20     reconMat = (lowDDataMat * redEigVects.T) + meanVals21     return lowDDataMat, reconMat22 23 if __name__ == "__main__":24     dataMat = loadDataSet('testSet.txt')25     lowDMat, reconMat = pca(dataMat, 1)26     fig = plt.figure()27     ax = fig.add_subplot(111)28     ax.scatter(dataMat[:, 0].flatten().A[0], dataMat[:, 1].flatten().A[0], marker='^', s = 90)29     ax.scatter(reconMat[:, 0].flatten().A[0], reconMat[:, 1].flatten().A[0], marker='o', s = 50, c='red')30     plt.show()

 

输入数据格式： 文本文件，每行是两个以TAB键分隔的浮点数。

附运行结果：