whitenv.m

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

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 G鋠ert

% ========================================================
% 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 ~isreal(newVectors)
  error ('Whitened vectors have imaginary values.');
end

% Print some information to user
if b_verbose
  fprintf ('Check: covariance differs from identity by [ %g ].\n', ...
    max (max (abs (cov (newVectors', 1) - eye (size (newVectors, 1))))));
end