ExampleLDA.cpp

example for linear discriminant analysis on a set of classes of vectors

Author

MIP

/* This file is part of the BIAS library (Basic ImageAlgorithmS).

Copyright (C) 2003-2009    (see file CONTACT for details)
  Multimediale Systeme der Informationsverarbeitung
  Institut fuer Informatik
  Christian-Albrechts-Universitaet Kiel


BIAS is free software; you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation; either version 2.1 of the License, or
(at your option) any later version.

BIAS is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License
along with BIAS; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
*/


/** @example ExampleLDA.cpp
    @relates LDA
    @ingroup g_examples
    @brief  example for linear discriminant analysis on a set of classes of vectors
    @author MIP
*/

#include <bias_config.h>

#include <MathAlgo/LDA.hh>
#include <Base/Math/Random.hh>

using namespace BIAS;
using namespace std;

int main(int args, char **arg)
{
  LDA myLDA(true);//equalweights for all classes ?
  myLDA.SetDebugLevel(1+2);

  std::vector<std::vector<BIAS::Vector<LDAType> > > vec; 

  
  int reductionSize = 2;
  std::vector<BIAS::Matrix<LDAType> > *covs = NULL;
  unsigned int classes = 3;
  unsigned int numsamples = 1000;
  unsigned int dim = 2;
  BIAS::Matrix<LDAType> matrix(reductionSize,dim,MatrixZero);
  Vector<double> sigma(dim);
  sigma[0] = 1.1;
  sigma[1] = 0.01;
  for (unsigned int i=2; i<dim; i++) sigma[i] = 0.1;

  Random R;

 
  vector<Vector<LDAType> > means;
  for (unsigned int c=0; c<classes; c++){
    Vector<LDAType> curMean(dim), estMean(dim);
    means.push_back(curMean);
    switch (c) {
    case 0: curMean[0] = -1;curMean[1] = 0;break;
    case 1: curMean[0] = 1;curMean[1] = 0;break;
    case 2: curMean[0] = 0.1f;curMean[1] = -0.1f;break;
    default: {
      for (unsigned int d=0; d<dim; d++) {
        curMean[d] = (float) R.GetUniformDistributed(-1.0*double(d),
                                                     double(d));
        estMean[d] = 0;
      }
     }
    }
    cout<<"mean "<<c<<" is "<<curMean<<endl;
    std::vector<BIAS::Vector<LDAType> > curvec;
    for (unsigned int i=0; i<numsamples; i++) {
      Vector<LDAType> sample(dim);
      for (unsigned int d=0; d<dim; d++) {
        sample[d] = (float) R.GetNormalDistributed(curMean[d], sigma[d]);
        estMean[d] += sample[d];
      }
      curvec.push_back(sample);
    }
    for (unsigned int d=0; d<dim; d++) {
      estMean[d] /= float(numsamples);
    }
    cout<<"estimated mean is "<<estMean<<endl;
    vec.push_back(curvec);
  }

    /*
GenerateRandomTestData(int vectorSize, int numberOfClasses, 
      int classSizeMin, int classSizeMax, int vectorEntryMin, int vectorEntryMax,
      double stdDeviation, double mainDirectionStdDeviation, 
      std::vector<std::vector<BIAS::Vector<LDAType> > > &vec,
      std::vector<BIAS::Vector<LDAType> > &means, bool Normalize);

    */

  myLDA.ComputeReductionMatrix(vec, matrix, reductionSize, covs);
 cout<<"Reduction matrix is "<<matrix<<endl;
  return 0;
}