Function PCA_faceImage

PCA/PCA.py:48–69

()

'''主成分分析_PCA图像数据降维'''    
def PCA_faceImage():
    print (u'加载图像数据.....')
    data_image = spio.loadmat('data_faces.mat')
    X = data_image['X']
    display_imageData(X[0:100,:])
    m = X.shape[0]  # 数据条数
    
    print (u'运行PCA....')
    X_norm,mu,sigma = featureNormalize(X)  # 归一化
    
    Sigma = np.dot(np.transpose(X_norm),X_norm)/m  # 求Sigma
    U,S,V = np.linalg.svd(Sigma)                   # 奇异值分解
    display_imageData(np.transpose(U[:,0:36]))     # 显示U的数据
    
    print (u'对face数据降维.....')
    K = 100     # 降维100维(原先是32*32=1024维的)
    Z = projectData(X_norm, U, K)
    print (u'投影之后Z向量的大小：%d %d' %Z.shape)
    
    print (u'显示降维之后的数据......')
    X_rec = recoverData(Z, U, K) # 恢复数据
    display_imageData(X_rec[0:100,:])