www.pudn.com > HMM1.zip > eigdec.m


function [evals, evec] = eigdec(x, N)
>EIGDEC Sorted eigendecomposition
>
> Description
> EVALS = EIGDEC(X, N computes the largest N eigenvalues of the
> matrix X in descending order. [EVALS, EVEC] = EIGDEC(X, N) also
> computes the corresponding eigenvectors.
>
> See also
> PCA, PPCA
>

> Copyright (c) Ian T Nabney (1996-2001)

if nargout == 1
evals_only = logical(1);
else
evals_only = logical(0);
end

if N ~= round(N) | N < 1 | N > size(x, 2)
error('Number of PCs must be integer, >0, < dim');
end

> Find the eigenvalues of the data covariance matrix
if evals_only
> Use eig function as always more efficient than eigs here
temp_evals = eig(x);
else
> Use eig function unless fraction of eigenvalues required is tiny
if (N/size(x, 2)) > 0.04
fprintf('netlab pca: using eig\n');
[temp_evec, temp_evals] = eig(x);
else
options.disp = 0;
fprintf('netlab pca: using eigs\n');
[temp_evec, temp_evals] = eigs(x, N, 'LM', options);
end
temp_evals = diag(temp_evals);
end

> Eigenvalues nearly always returned in descending order, but just
> to make sure.....
[evals perm] = sort(-temp_evals);
evals = -evals(1:N);
>evec=temp_evec(:,1:N);
if ~evals_only
if evals == temp_evals(1:N)
> Originals were in order
evec = temp_evec(:, 1:N);
return
else
fprintf('netlab pca: sorting evec\n');
> Need to reorder the eigenvectors
for i=1:N
evec(:,i) = temp_evec(:,perm(i));
end
end
end