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1 function mlr_plot(X, Y, W, D)
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2
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3 [d, n] = size(X);
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4 %%%
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5 % Color-coding
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6 CODES = { 'b.', 'r.', 'g.', 'c.', 'k.', 'm.', 'y.', ...
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7 'b+', 'r+', 'g+', 'c+', 'k+', 'm+', 'y+', ...
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8 'b^', 'r^', 'g^', 'c^', 'k^', 'm^', 'y^', ...
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9 'bx', 'rx', 'gx', 'cx', 'kx', 'mx', 'yx', ...
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10 'bo', 'ro', 'go', 'co', 'ko', 'mo', 'yo'};
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11
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12 %%%
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13 % First, PCA-plot of X
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14
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15 figure;
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16
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17 z = sum(X,3);
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18 subplot(3,2,[1 3]), pcaplot(z, eye(d), Y, CODES), title('Native');
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19 subplot(3,2,[2 4]), pcaplot(X, W, Y, CODES), title('Learned');
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20
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21 [vecs, vals] = eig(z * z');
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22 subplot(3,2,5), bar(sort(real(diag(vals)), 'descend')), title('X*X'' spectrum'), axis tight;
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23
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24 if size(X,3) == 1
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25 [vecs, vals] = eig(W);
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26 vals = real(diag(vals));
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27 else
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28 if size(W,3) == 1
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29 vals = real(W(:));
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30 else
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31 vals = [];
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32 for i = 1:size(W,3)
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33 [vecs, vals2] = eig(W(:,:,i));
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34 vals = [vals ; real(diag(vals2))];
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35 end
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36 end
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37 end
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38 subplot(3,2,6), bar(sort(vals, 'descend')), title('W spectrum'), axis tight;
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39
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40 if nargin < 4
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41 return;
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42 end
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43
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44 %%%
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45 % Now show some diagnostics
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46
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47 figure;
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48 subplot(2,1,1), semilogy(D.f), title('Objective');
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49 subplot(2,1,2), barh([D.time_SO, D.time_solver D.time_total]), ...
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50 title('Time% in SO/solver/total');
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51
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52
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53 function pcaplot(X, W, Y, CODES)
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54 if size(X,3) == 1
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55 A = X' * W * X;
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56 else
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57 A = 0;
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58 if size(W,3) == 1
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59 for i = 1:size(X,3)
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60 A = A + X(:,:,i)' * bsxfun(@times, W(:,i), X(:,:,i));
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61 end
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62 else
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63 for i = 1:size(X,3)
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64 A = A + X(:,:,i)' * W(:,:,i) * X(:,:,i);
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65 end
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66 end
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67 end
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68 [v,d] = eigs(A, 3);
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69 X2 = d.^0.5 * v';
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70 hold on;
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71 for y = 1:max(Y)
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72 z = Y == y;
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73 scatter3(X2(1, z), X2(2, z), X2(3,z), CODES{y});
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74 end
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75
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76 axis equal;
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