import numpy as np

#（1）零均值化 def zeroMean(dataMat):     meanVal=np.mean(dataMat,axis=0)     newData =dataMat -meanVal     return newData, meanVal #3、选择主成分个数 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 #pca算法 def pca(dataMat,percentage=0.99):     # （1）零均值化     newData, meanVal = zeroMean(dataMat)     # 求协方差矩阵     covMat = np.cov(newData, rowvar=0)     # （3）求特征值、特征矩阵     eigVals, eigVects = np.linalg.eig(np.mat(covMat))     n =percentage2n(eigVals,percentage)     # eigVals 特征值和eigVects特征向量     eigValIndice = np.argsort(eigVals)     #所以eigValIndice[-1:-(n+1):-1]就取出这个n个特征值对应的下标。【python里面，list[a:b:c]代表从下标a开始到b，步长为c。】     n_eigValIndice = eigValIndice[-1:-(n + 1):-1]  # 最大的n个特征值的下标     n_eigVect = eigVects[:, n_eigValIndice]  # 最大的n个特征值对应的特征向量     lowDDataMat = newData * n_eigVect  # 低维特征空间的数据     reconMat = (lowDDataMat * n_eigVect.T) + meanVal  # 重构数据     return lowDDataMat, reconMat def main():     data = [[10.2352,11.322],             [10.1223,11.811],             [9.1902,8.9049],             [9.3064,9.8474],             [8.3301,8.3404],             [10.1528,10.1235],             [10.4085,10.822],             [9.0036,10.0392],             [9.5349,10.097],             [9.4982,10.8254]]     lowDDataMat, reconMat = pca(data,0.9)     print(lowDDataMat) if __name__=="__main__":     main()