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#pca svd working def svd(D): import time import numpy # Record starting time timestart = time.time() # Prepare and center the data for PCA # Reshape from 3D array to 2D array # from (pics, height, width) # to (pics, all_pixels) # here: from (2000, 62, 47) to (2000, 2914) # then transpose X = D.reshape(len(D), -1).T X -= X.mean(axis=1, keepdims=True) # Singular Value Decomposition U, L, V = numpy.linalg.svd(X / X.shape[1]**0.5, full_matrices=False) # Print time tfinish = time.time() print('Time in seconds: %.3f' % (tfinish - timestart)) # Visualize first two principal components utils.scatterplot(U[:,0].dot(X), U[:,1].dot(X), xlabel='PCA 1', ylabel='PCA 2') # Render first 60 eigenfaces (principal components) utils.render(U[:,:60].T, 15, 4) # call the function svd(D)
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