6_PSO+PCA.py 文件源码

def pca(dataMat,n):
    print "Start to do PCA..."
    newData,meanVal=zeroMean(dataMat)

#    covMat=np.cov(newData,rowvar=0)
#    eigVals,eigVects=np.linalg.eig(np.mat(covMat))
#    joblib.dump(eigVals,'./features/PCA/eigVals_train_%s.eig' %m,compress=3)
#    joblib.dump(eigVects,'./features/PCA/eigVects_train_%s.eig' %m,compress=3)

    eigVals = joblib.load('./features/PCA/eigVals_train_%s.eig' %m)
    eigVects = joblib.load('./features/PCA/eigVects_train_%s.eig' %m)

    eigValIndice=np.argsort(eigVals)
    n_eigValIndice=eigValIndice[-1:-(n+1):-1]
    n_eigVect=eigVects[:,n_eigValIndice]
#    joblib.dump(n_eigVect,'./features/PCA/n_eigVects_train_%s_%s.eig' %(m,n))
    lowDDataMat=newData*n_eigVect
    return lowDDataMat