Python处理脑电数据：PCA数据降维

2019-04-13 12:56发布

站内文章 / 模拟电子
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pca.py #!-coding:UTF-8- from numpy import * import numpy as np def loadDataSet(fileName, delim=' '): 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)

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