Mercurial > hg > camir-aes2014
view toolboxes/distance_learning/mlr/mlr_plot.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function mlr_plot(X, Y, W, D) [d, n] = size(X); %%% % Color-coding CODES = { 'b.', 'r.', 'g.', 'c.', 'k.', 'm.', 'y.', ... 'b+', 'r+', 'g+', 'c+', 'k+', 'm+', 'y+', ... 'b^', 'r^', 'g^', 'c^', 'k^', 'm^', 'y^', ... 'bx', 'rx', 'gx', 'cx', 'kx', 'mx', 'yx', ... 'bo', 'ro', 'go', 'co', 'ko', 'mo', 'yo'}; %%% % First, PCA-plot of X figure; z = sum(X,3); subplot(3,2,[1 3]), pcaplot(z, eye(d), Y, CODES), title('Native'); subplot(3,2,[2 4]), pcaplot(X, W, Y, CODES), title('Learned'); [vecs, vals] = eig(z * z'); subplot(3,2,5), bar(sort(real(diag(vals)), 'descend')), title('X*X'' spectrum'), axis tight; if size(X,3) == 1 [vecs, vals] = eig(W); vals = real(diag(vals)); else if size(W,3) == 1 vals = real(W(:)); else vals = []; for i = 1:size(W,3) [vecs, vals2] = eig(W(:,:,i)); vals = [vals ; real(diag(vals2))]; end end end subplot(3,2,6), bar(sort(vals, 'descend')), title('W spectrum'), axis tight; if nargin < 4 return; end %%% % Now show some diagnostics figure; subplot(2,1,1), semilogy(D.f), title('Objective'); subplot(2,1,2), barh([D.time_SO, D.time_solver D.time_total]), ... title('Time% in SO/solver/total'); function pcaplot(X, W, Y, CODES) if size(X,3) == 1 A = X' * W * X; else A = 0; if size(W,3) == 1 for i = 1:size(X,3) A = A + X(:,:,i)' * bsxfun(@times, W(:,i), X(:,:,i)); end else for i = 1:size(X,3) A = A + X(:,:,i)' * W(:,:,i) * X(:,:,i); end end end [v,d] = eigs(A, 3); X2 = d.^0.5 * v'; hold on; for y = 1:max(Y) z = Y == y; scatter3(X2(1, z), X2(2, z), X2(3,z), CODES{y}); end axis equal;