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Diffstat (limited to 'sourcecodes/bnt-master/KPMtools/pca_netlab.m')
| -rw-r--r-- | sourcecodes/bnt-master/KPMtools/pca_netlab.m | 42 |
1 files changed, 42 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/KPMtools/pca_netlab.m b/sourcecodes/bnt-master/KPMtools/pca_netlab.m new file mode 100644 index 00000000..4b7063d6 --- /dev/null +++ b/sourcecodes/bnt-master/KPMtools/pca_netlab.m @@ -0,0 +1,42 @@ +function [PCcoeff, PCvec] = pca(data, N) +%PCA Principal Components Analysis +% +% Description +% PCCOEFF = PCA(DATA) computes the eigenvalues of the covariance +% matrix of the dataset DATA and returns them as PCCOEFF. These +% coefficients give the variance of DATA along the corresponding +% principal components. +% +% PCCOEFF = PCA(DATA, N) returns the largest N eigenvalues. +% +% [PCCOEFF, PCVEC] = PCA(DATA) returns the principal components as well +% as the coefficients. This is considerably more computationally +% demanding than just computing the eigenvalues. +% +% See also +% EIGDEC, GTMINIT, PPCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin == 1 + N = size(data, 2); +end + +if nargout == 1 + evals_only = logical(1); +else + evals_only = logical(0); +end + +if N ~= round(N) | N < 1 | N > size(data, 2) + error('Number of PCs must be integer, >0, < dim'); +end + +% Find the sorted eigenvalues of the data covariance matrix +if evals_only + PCcoeff = eigdec(cov(data), N); +else + [PCcoeff, PCvec] = eigdec(cov(data), N); +end + |