[RBM]

- added

git-svn-id: http://moon:8086/svn/matlab/trunk@93 801c6759-fa7c-4059-a304-17956f83a07c

RBM/mnist_gen_pca.m

@@ -0,0 +1,64 @@
+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);