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1 function [evals, evec] = eigdec(x, N)
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2 %EIGDEC Sorted eigendecomposition
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3 %
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4 % Description
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5 % EVALS = EIGDEC(X, N computes the largest N eigenvalues of the
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6 % matrix X in descending order. [EVALS, EVEC] = EIGDEC(X, N) also
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7 % computes the corresponding eigenvectors.
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8 %
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9 % See also
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10 % PCA, PPCA
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11 %
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12
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13 % Copyright (c) Ian T Nabney (1996-2001)
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14
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15 if nargout == 1
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16 evals_only = logical(1);
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17 else
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18 evals_only = logical(0);
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19 end
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20
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21 if N ~= round(N) | N < 1 | N > size(x, 2)
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22 error('Number of PCs must be integer, >0, < dim');
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23 end
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24
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25 % Find the eigenvalues of the data covariance matrix
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26 if evals_only
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27 % Use eig function as always more efficient than eigs here
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28 temp_evals = eig(x);
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29 else
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30 % Use eig function unless fraction of eigenvalues required is tiny
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31 if (N/size(x, 2)) > 0.04
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32 fprintf('netlab pca: using eig\n');
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33 [temp_evec, temp_evals] = eig(x);
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34 else
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35 options.disp = 0;
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36 fprintf('netlab pca: using eigs\n');
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37 [temp_evec, temp_evals] = eigs(x, N, 'LM', options);
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38 end
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39 temp_evals = diag(temp_evals);
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40 end
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41
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42 % Eigenvalues nearly always returned in descending order, but just
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43 % to make sure.....
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44 [evals perm] = sort(-temp_evals);
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45 evals = -evals(1:N);
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46 %evec=temp_evec(:,1:N);
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47 if ~evals_only
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48 if evals == temp_evals(1:N)
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49 % Originals were in order
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50 evec = temp_evec(:, 1:N);
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51 return
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52 else
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53 fprintf('netlab pca: sorting evec\n');
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54 % Need to reorder the eigenvectors
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55 for i=1:N
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56 evec(:,i) = temp_evec(:,perm(i));
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57 end
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58 end
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59 end
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