[PCA]

- minor improvements

git-svn-id: http://moon:8086/svn/matlab/trunk@95 801c6759-fa7c-4059-a304-17956f83a07c

RBM/pca.m

@@ -5,8 +5,9 @@
 %  the raw image data from the kth 12x12 image patch sampled.
 %  You do not need to change the code below.
 
-function [xHat, k, xZCAWhite] = pca(x, retained_variance_target)
- 
+function [xHat, k, xZCAWhite, xPCAWhite] = pca(x, retained_variance_target)
+epsilon = 1e-1; 
+
 %%================================================================
 %% Step 1a: Implement PCA to obtain xRot
 %  Implement PCA to obtain xRot, the matrix in which the data is expressed
@@ -19,8 +20,8 @@ fprintf('Perform Singular Value Decomposition\n');
 [U,S,V] = svd(sigma);
 
 fprintf('Perform PCA transformation\n');
-xrot = U' * x;
-
+wpca = U' * diag(sqrt(1./(diag(S) + epsilon)));
+wzca = U * wpca;
 %%================================================================
 %% Step 2: Find k, the number of components to retain
 %  Write code to determine k, the number of components to retain in order
@@ -51,6 +52,7 @@ k = k
 %  correspond to dimensions with low variation.
 
 fprintf('Calculate xHat\n');
+xrot = U' * x;
 xHat = U(:, 1:k) * xrot(1:k, :);
 
 %%================================================================
@@ -58,10 +60,9 @@ xHat = U(:, 1:k) * xrot(1:k, :);
 %  Implement PCA with whitening and regularisation to produce the matrix
 %  xPCAWhite. 
 
-epsilon = 1e-1; 
 %%% YOUR CODE HERE %%%
 fprintf('Calculate xPCAWhite\n');
-xPCAWhite = diag(sqrt(1./(diag(S) + epsilon))) * xrot;
+xPCAWhite = wpca * x;
 
 %%================================================================
 %% Step 5: Implement ZCA whitening
@@ -71,5 +72,5 @@ xPCAWhite = diag(sqrt(1./(diag(S) + epsilon))) * xrot;
 
 %%% YOUR CODE HERE %%%
 fprintf('Calculate xZCAWhite\n');
-xZCAWhite = U * xPCAWhite;
+xZCAWhite = wzca * x;