Python处理脑电数据：PCA数据降维

pca.py

#!-coding:UTF-8-
from numpy import *
import numpy as np


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 percentage2n(eigVals,percentage):
    sortArray=np.sort(eigVals)   #升序
    sortArray=sortArray[-1::-1]  #逆转，即降序
    arraySum=sum(sortArray)
    tmpSum=0
    num=0
    for i in sortArray:
        tmpSum+=i
        num+=1
        if tmpSum>=arraySum*percentage:
            return num

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
    lowData_N = meanRemoved * redEigVects#transform data into new dimensions
    reconMat_N = (lowData_N * redEigVects.T) + meanVals
    return lowData_N,reconMat_N

def pcaPerc(dataMat, percentage=1):
    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
    n=percentage2n(eigVals,percentage)
    n_eigValIndice=eigValInd[-1:-(n+1):-1]
    n_eigVect=eigVects[:,n_eigValIndice]
    lowData_P=meanRemoved*n_eigVect
    reconMat_P = (lowData_P * n_eigVect.T) + meanVals
    return lowData_P,reconMat_P

readData.py

import matplotlib.pyplot as plt
from pylab import *
import numpy as np
import scipy.io as sio
def loadData(filename,mName):
    load_fn = filename
    load_data = sio.loadmat(load_fn)
    load_matrix = load_data[mName]
    #load_matrix_row = load_matrix[0]

    #figure(mName)
    #plot(load_matrix,'r-')
    #show()

    #print type(load_data)
    #print type(load_matrix)
    #print load_matrix_row
    return load_matrix

main.py

#!-coding:UTF-8
import matplotlib.pyplot as plt
from pylab import *
import numpy as np
import scipy.io as sio
import pca
from numpy import mat,matrix
import scipy as sp
import readData
import pca

if __name__ == '__main__':
    A1=readData.loadData('6electrodes.mat','A1')
    lowData_N, reconMat_N= pca.pca(A1,30)
    lowData_P, reconMat_P = pca.pcaPerc(A1,0.95)
    #print lowDMat
    #print reconMat
    print shape(lowData_N)
    print shape(reconMat_N)
    print shape(lowData_P)
    print shape(reconMat_P)