function mnist_gen_pca(retained_variance_target)

close all;

addpath(genpath('UFLDL/common'))
fprintf('Load mnist raw data\n');

x = loadMNISTImages('UFLDL/common/train-images-idx3-ubyte');

rbmWrite(x, 'mnist.trainingStates.dat')
figure('name','Raw images');
randsel = randi(size(x,2),64,1); % A random selection of samples for visualization
display_network(x(:,randsel));

fprintf('Zero mean raw data\n');
avg = mean(x, 1);     % Compute the mean pixel intensity value separately for each patch. 
x = x - repmat(avg, size(x, 1), 1);

fprintf('Do the PCA whitening\n');
[xHat, k, xZCAWhite] = pca(x, retained_variance_target);

figure('name',['PCA processed images ',sprintf('(%d / %d dimensions)', k, size(x, 1)),'']);
display_network(xHat(:,randsel));

fprintf('Normalize the whitened data\n');
minZ = min(xZCAWhite(:))
maxZ = max(xZCAWhite(:))
xZCAWhite_norm = (xZCAWhite - minZ)/(maxZ-minZ);

minZ = min(xHat(:))
maxZ = max(xHat(:))
xHat_norm = (xHat - minZ)/(maxZ-minZ);

figure('name','ZCA whitened images');
display_network(xZCAWhite_norm(:,randsel));

figure('name','xHat');
display_network(xHat_norm(:,randsel));

fprintf('Save data\n');
rbmWrite(xZCAWhite_norm, 'mnist_zca.trainingStates.dat')
rbmWrite(xHat_norm, 'mnist_xhat.trainingStates.dat')


function rbmWrite(data, name)
[numPixel, numTrain] = size(data)
nx = sqrt(numPixel)
ny = nx

data = reshape(data, nx, ny, numTrain);

fid = fopen(name, 'w');
fprintf(fid, '%d\n', numTrain);
fprintf(fid, '%d\n', numPixel);

for m=1:numTrain,
	d = data(:,:,m)';
	d = d(:);
	for n=1:numPixel,
		fprintf(fid, '%f\n', d(n));
	end
end

fclose(fid);