Classifier_ncc.hpp

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// Copyright (C) 2011 the authors listed below
// http://ecocpak.sourceforge.net
//
// Authors:
// - Dimitrios Bouzas (bouzas at ieee dot org)
// - Nikolaos Arvanitopoulos (niarvani at ieee dot org)
// - Anastasios Tefas (tefas at aiia dot csd dot auth dot gr)
//
// This file is part of the ECOC PAK C++ library. It is
// provided without any warranty of fitness for any purpose.
//
// You can redistribute this file and/or modify it under
// the terms of the GNU Lesser General Public License (LGPL)
// as published by the Free Software Foundation, either
// version 3 of the License or (at your option) any later
// version.
// (see http://www.opensource.org/licenses for more info)




#ifndef _CLASSIFIER_NCC_H_
#define _CLASSIFIER_NCC_H_



#include "Classifier.hpp"



class Classifier_ncc : public Classifier
  {
  public:

  // ---------------------------------------------------------------- //
  // ------------------------ Constructors -------------------------- //
  // ---------------------------------------------------------------- //

  // Copy ctor -- Overloaded
  Classifier_ncc
    (
    const Classifier_ncc& c
    );

  // User defined ctor NCC -- Overloaded
  Classifier_ncc
    (
    const mat& A,
    const mat& B
    );

  // ---------------------------------------------------------------- //
  // ---------------------- Member Functions ------------------------ //
  // ---------------------------------------------------------------- //

  // return prediction value of classifier for input feature vector
  double predict(const rowvec& t) const;

  // ---------------------------------------------------------------- //
  // -------------------------- Attributes -------------------------- //
  // ---------------------------------------------------------------- //

  // centroids for ncc
  mat centroids;

  // centroids middle
  rowvec middle;
  };



Classifier_ncc::Classifier_ncc
  (
  const Classifier_ncc& c
  )
  {
  // update centers attribute
  centroids = c.centroids;

  // update middle attribute
  middle = c.middle;

  // update the plus and minus classes
  pos = c.pos;
  neg = c.neg;

  // update number of possitive and negative samples
  n_pos = c.n_pos;
  n_neg = c.n_neg;

  // update training error
  training_error = c.training_error;
  }



Classifier_ncc::Classifier_ncc
  (
  const mat& A,
  const mat& B
  )
  {
  // allocate centroids
  centroids = zeros<mat>(2, A.n_cols);

  // find centroid for first class
  centroids.row(0) = mean(A);

  // find centroid for second class
  centroids.row(1) = mean(B);

  // find out middle
  middle = sum(centroids) / 2.0;

  // initialize number of possitive and negative samples
  n_pos = 0;
  n_neg = 0;
  }



inline
double
Classifier_ncc::predict(const rowvec& t) const
  {
  // return prediction
  return accu((t- middle) % (centroids.row(0) - middle));
  }



#endif