Ajouter 'octave/whitenv.m'

octave/whitenv.m

@@ -0,0 +1,67 @@
+function [newVectors, whiteningMatrix, dewhiteningMatrix] = whitenv ...
+    (vectors, E, D, s_verbose);
+%WHITENV - Whitenv vectors.
+%
+% [newVectors, whiteningMatrix, dewhiteningMatrix] = ...
+%                               whitenv(vectors, E, D, verbose);
+%
+% Whitens the data (row vectors) and reduces dimension. Returns
+% the whitened vectors (row vectors), whitening and dewhitening matrices.
+%
+% ARGUMENTS
+%
+% vectors       Data in row vectors.
+% E             Eigenvector matrix from function 'pcamat'
+% D             Diagonal eigenvalue matrix from function 'pcamat'
+% verbose       Optional. Default is 'on'
+%
+% EXAMPLE
+%       [E, D] = pcamat(vectors);
+%       [nv, wm, dwm] = whitenv(vectors, E, D);
+%
+%
+% This function is needed by FASTICA and FASTICAG
+%
+%   See also PCAMAT
+
+% 24.8.1998
+% Hugo Gävert
+
+% ========================================================
+% Default value for 'verbose'
+if nargin < 4, s_verbose = 'on'; end
+
+% Check the optional parameter verbose;
+switch lower(s_verbose)
+ case 'on'
+  b_verbose = 1;
+ case 'off'
+  b_verbose = 0;
+ otherwise
+  error(sprintf('Illegal value [ %s ] for parameter: ''verbose''\n', s_verbose));
+end
+
+% ========================================================
+% Calculate the whitening and dewhitening matrices (these handle
+% dimensionality simultaneously).
+whiteningMatrix = inv (sqrt (D)) * E';
+dewhiteningMatrix = E * sqrt (D);
+
+% Project to the eigenvectors of the covariance matrix.
+% Whiten the samples and reduce dimension simultaneously.
+if b_verbose, fprintf ('Whitening...\n'); end
+newVectors =  whiteningMatrix * vectors;
+
+% ========================================================
+% Just some security...
+if max (max (imag (newVectors))) ~= 0,
+  error ('Whitened vectors have imaginary values.');
+end
+
+% Print some information to user
+if b_verbose
+  fprintf ('Check: covariance differs from identity by [ %g ].\n', ...
+% Added by DR for "Octave" compatibility:
+% Normalize the covariance matrix by N, not by N-1:
+max (max (abs (cov(newVectors')*((size(newVectors',1)-1)/size(newVectors',1)) - eye (size (newVectors, 1))))));
+end