PCA python代码实现
程序员文章站
2023-01-28 21:16:01
PCA python代码实现
#coding:utf-8
from numpy import *
import matplotlib
import matplotlib...
PCA python代码实现
#coding:utf-8 from numpy import * import matplotlib import matplotlib.pyplot as plt def 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
上一篇: php 数组随机取值的简单实例