NICE_VisLearning/regression/npregression/RegKNN.cpp

/**
* @file RegKNN.cpp
* @brief Implementation of k-Nearest-Neighbor algorithm for regression purposes
* @author Frank Prüfer
* @date 08/29/2013

*/  
#include <iostream>

#include "vislearning/regression/npregression/RegKNN.h"

#include "vislearning/math/mathbase/FullVector.h"

using namespace OBJREC;

using namespace std;
using namespace NICE;


RegKNN::RegKNN ( const Config *_conf, NICE::VectorDistance<double> *_distancefunc ) : distancefunc (_distancefunc)
{
    K = _conf->gI("RegKNN", "K", 1 );
    if ( _distancefunc == NULL )
		distancefunc = new EuclidianDistance<double>();
}

RegKNN::~RegKNN()
{
}

void RegKNN::teach ( const NICE::VVector & _dataSet, const NICE::Vector & _labelSet)
{
    fprintf (stderr, "teach using all !\n");
    //NOTE this is crucial if we clear _teachSet afterwards!
    //therefore, take care NOT to call _techSet.clear() somewhere out of this method
    this->dataSet = _dataSet;
    this->labelSet = _labelSet;
    
    std::cerr << "number of known training samples: " << this->dataSet.size() << std::endl;   
    
}

// void RegKNN::teach ( const NICE::Vector & x, const double & y )
// {
//     std::cerr << "RegKNN::teach one new example" << std::endl;
//     
//     for ( size_t i = 0 ; i < x.size() ; i++ )
//       if ( isnan(x[i]) ) 
//       {
//           fprintf (stderr, "There is a NAN value in within this vector: x[%d] = %f\n", (int)i, x[i]);
//           cerr << x << endl;
//           exit(-1);
//       }
// 
//     dataSet.push_back ( x );
//     labelSet.push_back ( y );
//     
//     std::cerr << "number of known training samples: " << dataSet.size()<< std::endl;
// }

double RegKNN::predict ( const NICE::Vector & x )
{
    FullVector distances(dataSet.size());

    if ( dataSet.size() <= 0 ) {
		fprintf (stderr, "RegKNN: please train this classifier before classifying\n");
		exit(-1);
    }

    for(uint i = 0; i < dataSet.size(); i++){
    
      double distance = distancefunc->calculate (x,dataSet[i]);
      
      if ( isnan(distance) )
      {
          fprintf (stderr, "RegKNN::classify: NAN value found !!\n");
          cerr << x << endl;
          cerr << dataSet[i] << endl;
      }
      distances[i] = distance;
      
    }
    std::vector<int> ind;
    distances.getSortedIndices(ind);
    
    double response = 0.0;  
    
    for(uint i = 0; i < K; i++){
      response += labelSet[ind[i]];
    }
       
    return (response / (double) K);
}