Home > gmmbayestb-v1.0 > gmmb_generatehist.m

gmmb_generatehist

PURPOSE ^

GMMB_GENERATEHIST Create histS structure for PDF-value - density quantile mapping.

SYNOPSIS ^

function histS = gmmb_generatehist(bayesS, N);

DESCRIPTION ^

GMMB_GENERATEHIST Create histS structure for PDF-value - density quantile mapping.

    histS = GMMB_GENERATEHIST(bayesS, N)

    bayesS   parameters for the distributions to be used
    N        number of approximation points per class

    This function creates ordered lists of PDF-values of
    random points generated by given distributions (bayesS).
    These lists can be used to evaluate distribution density quantiles.

    See gmmb_hist, gmmb_lhood2frac, gmmb_frac2lhood, gmmb_fracthresh

 References:
   [1] Paalanen, P., Kamarainen, J.-K., Ilonen, J., Kälviäinen, H.,
    Feature Representation and Discrimination Based on Gaussian Mixture Model
    Probability Densities - Practices and Algorithms, Research Report 95,
    Lappeenranta University of Technology, Department of Information
    Technology, 2005.

 Author(s):
    Pekka Paalanen <pekka.paalanen@lut.fi>
    Jarmo Ilonen <jarmo.ilonen@lut.fi>
    Joni Kamarainen <Joni.Kamarainen@lut.fi>

 Copyright:

   Bayesian Classifier with Gaussian Mixture Model Pdf
   functionality is Copyright (C) 2004 by Pekka Paalanen and
   Joni-Kristian Kamarainen.

   $Name:  $ $Revision: 1.2 $  $Date: 2005/04/14 10:33:34 $

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 %GMMB_GENERATEHIST Create histS structure for PDF-value - density quantile mapping.
0002 %
0003 %    histS = GMMB_GENERATEHIST(bayesS, N)
0004 %
0005 %    bayesS   parameters for the distributions to be used
0006 %    N        number of approximation points per class
0007 %
0008 %    This function creates ordered lists of PDF-values of
0009 %    random points generated by given distributions (bayesS).
0010 %    These lists can be used to evaluate distribution density quantiles.
0011 %
0012 %    See gmmb_hist, gmmb_lhood2frac, gmmb_frac2lhood, gmmb_fracthresh
0013 %
0014 % References:
0015 %   [1] Paalanen, P., Kamarainen, J.-K., Ilonen, J., Kälviäinen, H.,
0016 %    Feature Representation and Discrimination Based on Gaussian Mixture Model
0017 %    Probability Densities - Practices and Algorithms, Research Report 95,
0018 %    Lappeenranta University of Technology, Department of Information
0019 %    Technology, 2005.
0020 %
0021 % Author(s):
0022 %    Pekka Paalanen <pekka.paalanen@lut.fi>
0023 %    Jarmo Ilonen <jarmo.ilonen@lut.fi>
0024 %    Joni Kamarainen <Joni.Kamarainen@lut.fi>
0025 %
0026 % Copyright:
0027 %
0028 %   Bayesian Classifier with Gaussian Mixture Model Pdf
0029 %   functionality is Copyright (C) 2004 by Pekka Paalanen and
0030 %   Joni-Kristian Kamarainen.
0031 %
0032 %   $Name:  $ $Revision: 1.2 $  $Date: 2005/04/14 10:33:34 $
0033 %
0034 
0035 function histS = gmmb_generatehist(bayesS, N);
0036 
0037 K = size(bayesS,2);
0038 
0039 histS = {};
0040 
0041 for k = 1:K
0042     samples = [];
0043     Mu = bayesS(k).mu;
0044 
0045     for c = 1:size(Mu, 2)
0046         n = ceil(N*bayesS(k).weight(c));
0047         if ~isreal(Mu)
0048             samples = [ samples; ...
0049                gmmb_mkcplx(Mu(:,c).', bayesS(k).sigma(:,:,c), n) ];
0050         else
0051             samples = [ samples; ...
0052                mvnrnd(Mu(:,c).', bayesS(k).sigma(:,:,c), n) ];
0053         end
0054     end
0055 
0056     dens = gmmb_pdf( samples, bayesS(k) );
0057     histS(k) = {sort(dens)};
0058 end

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