Home > gmmbayestb-v1.0 > gmmb_hist.m

gmmb_hist

PURPOSE ^

GMMB_HIST Create histS structure from data for PDF-value - density quantile mapping

SYNOPSIS ^

function histS = gmmb_hist(data_, type_, bayesS);

DESCRIPTION ^

GMMB_HIST Create histS structure from data for PDF-value - density quantile mapping

    histS = GMMB_HIST(data, type, bayesS)

    data, type    are the training data used to create the bayesS.

    This function creates ordered lists of training sample
    PDF-values for PDF-value - density quantile mapping.

    See gmmb_generatehist, 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_HIST Create histS structure from data for PDF-value - density quantile mapping
0002 %
0003 %    histS = GMMB_HIST(data, type, bayesS)
0004 %
0005 %    data, type    are the training data used to create the bayesS.
0006 %
0007 %    This function creates ordered lists of training sample
0008 %    PDF-values for PDF-value - density quantile mapping.
0009 %
0010 %    See gmmb_generatehist, gmmb_lhood2frac, gmmb_frac2lhood, gmmb_fracthresh
0011 %
0012 % References:
0013 %   [1] Paalanen, P., Kamarainen, J.-K., Ilonen, J., Kälviäinen, H.,
0014 %    Feature Representation and Discrimination Based on Gaussian Mixture Model
0015 %    Probability Densities - Practices and Algorithms, Research Report 95,
0016 %    Lappeenranta University of Technology, Department of Information
0017 %    Technology, 2005.
0018 %
0019 % Author(s):
0020 %    Pekka Paalanen <pekka.paalanen@lut.fi>
0021 %    Jarmo Ilonen <jarmo.ilonen@lut.fi>
0022 %    Joni Kamarainen <Joni.Kamarainen@lut.fi>
0023 %
0024 % Copyright:
0025 %
0026 %   Bayesian Classifier with Gaussian Mixture Model Pdf
0027 %   functionality is Copyright (C) 2004 by Pekka Paalanen and
0028 %   Joni-Kristian Kamarainen.
0029 %
0030 %   $Name:  $ $Revision: 1.2 $  $Date: 2005/04/14 10:33:34 $
0031 %
0032 
0033 function histS = gmmb_hist(data_, type_, bayesS);
0034 
0035 K = size(bayesS,2);
0036 
0037 histS = {};
0038 
0039 for k = 1:K
0040     samples = data_(type_==k, :);
0041     dens = gmmb_pdf( samples, bayesS(k) );
0042     histS(k) = {sort(dens)};
0043 end

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