jens
e922e08d0a
git-svn-id: http://moon:8086/svn/matlab/trunk@97 801c6759-fa7c-4059-a304-17956f83a07c
44 lines
1.2 KiB
Matlab
44 lines
1.2 KiB
Matlab
function mnist_gen_pca(retained_variance_target)
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close all;
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addpath(genpath('UFLDL/common'))
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fprintf('Load mnist raw data\n');
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x = loadMNISTImages('UFLDL/common/train-images-idx3-ubyte');
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rbmWrite(x, 'mnist.trainingStates.dat')
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figure('name','Raw images');
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randsel = randi(size(x,2),64,1); % A random selection of samples for visualization
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display_network(x(:,randsel));
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fprintf('Zero mean raw data\n');
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avg = mean(x, 1); % Compute the mean pixel intensity value separately for each patch.
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x = x - repmat(avg, size(x, 1), 1);
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fprintf('Do the PCA whitening\n');
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[xHat, k, xZCAWhite, xPCAWhite] = pca(x, retained_variance_target);
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figure('name',['PCA processed images ',sprintf('(%d / %d dimensions)', k, size(x, 1)),'']);
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display_network(xHat(:,randsel));
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fprintf('Normalize the whitened data\n');
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minZ = min(xZCAWhite(:))
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maxZ = max(xZCAWhite(:))
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xZCAWhite_norm = (xZCAWhite - minZ)/(maxZ-minZ);
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minZ = min(xHat(:))
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maxZ = max(xHat(:))
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xHat_norm = (xHat - minZ)/(maxZ-minZ);
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figure('name','ZCA whitened images');
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display_network(xZCAWhite_norm(:,randsel));
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figure('name','xHat');
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display_network(xHat_norm(:,randsel));
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fprintf('Save data\n');
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rbmWrite(xZCAWhite_norm, 'mnist_zca.trainingStates.dat')
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rbmWrite(xHat_norm, 'mnist_xhat.trainingStates.dat')
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