close all
%%================================================================
%% Step : Load data
% We have provided the code to load data from pcaData.txt into x.
% x is a * matrix, where the kth column x(:,k) corresponds to
% the kth data point.Here we provide the code to load natural image data into x.
% You do not need to change the code below.
x = load('pcaData.txt','-ascii');
figure();
scatter(x(, :), x(, :));
title('Raw data');
%%================================================================
%% Step a: Implement PCA to obtain U
% Implement PCA to obtain the rotation matrix U, which is the eigenbasis
% sigma.
% -------------------- YOUR CODE HERE --------------------
u = zeros(size(x, )); % You need to compute this
%x = bsxfun(@minus, x, mean(x, ));
sigma = x * x' / size(x, );
[u,s,v] = svd(sigma);
% --------------------------------------------------------
hold on
plot([ u(,)], [ u(,)]);
plot([ u(,)], [ u(,)]);
scatter(x(, :), x(, :));
hold off
%%================================================================
%% Step b: Compute xRot, the projection on to the eigenbasis
% Now, compute xRot by projecting the data on to the basis defined
% by U. Visualize the points by performing a scatter plot.
% -------------------- YOUR CODE HERE --------------------
xRot = zeros(size(x)); % You need to compute this
xRot = u' * x;
% --------------------------------------------------------
% Visualise the covariance matrix. You should see a line across the
% diagonal against a blue background.
figure();
scatter(xRot(, :), xRot(, :));
title('xRot');
%%================================================================
%% Step : Reduce the number of dimensions from to
% Compute xRot again (this time projecting to dimension).
% Then, compute xHat by projecting the xRot back onto the original axes
% to see the effect of dimension reduction
% -------------------- YOUR CODE HERE --------------------
k = ; % Use k = and project the data onto the first eigenbasis
xHat = zeros(size(x)); % You need to compute this
tx = xRot;
tx(k+:size(xRot,),:) = zeros(size(xRot,)-k,size(xRot,));
xHat = u * tx;
% --------------------------------------------------------
figure();
scatter(xHat(, :), xHat(, :));
title('xHat');
%%================================================================
%% Step : PCA Whitening
% Complute xPCAWhite and plot the results.
epsilon = ;
% -------------------- YOUR CODE HERE --------------------
xPCAWhite = zeros(size(x)); % You need to compute this
xPCAWhite = diag(/sqrt(diag(s)+epsilon)) * xRot;
% --------------------------------------------------------
figure();
scatter(xPCAWhite(, :), xPCAWhite(, :));
title('xPCAWhite');
%%================================================================
%% Step : ZCA Whitening
% Complute xZCAWhite and plot the results.
% -------------------- YOUR CODE HERE --------------------
xZCAWhite = zeros(size(x)); % You need to compute this
xZCAWhite = u * xPCAWhite;
% --------------------------------------------------------
figure();
scatter(xZCAWhite(, :), xZCAWhite(, :));
title('xZCAWhite');
%% Congratulations! When you have reached this point, you are done!
% You can now move onto the next PCA exercise. :)