经典机器学习算法系列8-PCA

PCA是经典的降维手段，降维的代码在《机器学习实战》里面有完整的python代码，现在将代码书写如下

写一个pca.py文件，将下面的代码复制到文件中。

'''
Created on Jun 1, 2011

@author: Peter Harrington
'''
from numpy import *

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=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, reconMat

def replaceNanWithMean():
datMat = loadDataSet('secom.data', ' ')
numFeat = shape(datMat)[1]
for i in range(numFeat):
meanVal = mean(datMat[nonzero(~isnan(datMat[:,i].A))[0],i]) #values that are not NaN (a number)
datMat[nonzero(isnan(datMat[:,i].A))[0],i] = meanVal #set NaN values to mean
return datMat


书写main.py文件，复制如下代码
import pca
lowDMat1,feature1 = pca.pca(feature,200);这样就可以把一个向量降到200维度了