/*
* <moeoHyperVolumeDifferenceMetric.h>
* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007
* (C) OPAC Team, LIFL, 2002-2007
*
* Jeremie Humeau
* Arnaud Liefooghe
*
* This software is governed by the CeCILL license under French law and
* abiding by the rules of distribution of free software.  You can  use,
* modify and/ or redistribute the software under the terms of the CeCILL
* license as circulated by CEA, CNRS and INRIA at the following URL
* "http://www.cecill.info".
*
* As a counterpart to the access to the source code and  rights to copy,
* modify and redistribute granted by the license, users are provided only
* with a limited warranty  and the software's author,  the holder of the
* economic rights,  and the successive licensors  have only  limited liability.
*
* In this respect, the user's attention is drawn to the risks associated
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* software by the user in light of its specific status of free software,
* that may mean  that it is complicated to manipulate,  and  that  also
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*
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*/
//-----------------------------------------------------------------------------

#ifndef MOEOHYPERVOLUMEDIFFERENCEMETRIC_H_
#define MOEOHYPERVOLUMEDIFFERENCEMETRIC_H_

#include <metric/moeoMetric.h>
#include <metric/moeoHyperVolumeMetric.h>

/**
 * The contribution metric evaluates the proportion of non-dominated solutions given by a Pareto set relatively to another Pareto set
 * (Meunier, Talbi, Reininger: 'A multiobjective genetic algorithm for radio network optimization', in Proc. of the 2000 Congress on Evolutionary Computation, IEEE Press, pp. 317-324)
 */
template < class ObjectiveVector >
class moeoHyperVolumeDifferenceMetric : public moeoVectorVsVectorBinaryMetric < ObjectiveVector, double >
  {
	public:

    /**
     * Constructor with a coefficient (rho)
     * @param _normalize allow to normalize data (default true)
     * @param _rho coefficient to determine the reference point.
     */
    moeoHyperVolumeDifferenceMetric(bool _normalize=true, double _rho=1.1): normalize(_normalize), rho(_rho), ref_point(NULL){
        bounds.resize(ObjectiveVector::Traits::nObjectives());
        // initialize bounds in case someone does not want to use them
        for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
        {
            bounds[i] = eoRealInterval(0,1);
        }
    }

    /**
     * Constructor with a reference point
     * @param _normalize allow to normalize data (default true)
     * @param _ref_point the reference point
     */
    moeoHyperVolumeDifferenceMetric(bool _normalize=true, ObjectiveVector& _ref_point=NULL): normalize(_normalize), rho(0.0), ref_point(_ref_point){
        bounds.resize(ObjectiveVector::Traits::nObjectives());
        // initialize bounds in case someone does not want to use them
        for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
        {
            bounds[i] = eoRealInterval(0,1);
        }
    }

    /**
     * calculates and returns the HyperVolume value of a pareto front
     * @param _set1 the vector contains all objective Vector of the first pareto front
     * @param _set2 the vector contains all objective Vector of the second pareto front
     */
    double operator()(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2)
    {
    	double hypervolume_set1;
    	double hypervolume_set2;

    	if(rho >= 1.0){
		//determine bounds
		setup(_set1, _set2);
		//determine reference point
   		for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++){
   			if(normalize){
   				if (ObjectiveVector::Traits::minimizing(i))
   					ref_point[i]= rho;
   				else
   					ref_point[i]= 1-rho;
   			}
   			else{
   				if (ObjectiveVector::Traits::minimizing(i))
   					ref_point[i]= bounds[i].maximum() * rho;
   				else
   					ref_point[i]= bounds[i].maximum() * (1-rho);
   			}
    	}
    	//if no normalization, reinit bounds to O..1 for
    	if(!normalize)
     		for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
        		bounds[i] = eoRealInterval(0,1);

    	}
    	else if(normalize)
    		setup(_set1, _set2);

	   	moeoHyperVolumeMetric <ObjectiveVector> unaryMetric(ref_point, bounds);
	   	hypervolume_set1 = unaryMetric(_set1);
	   	hypervolume_set2 = unaryMetric(_set2);

    	return hypervolume_set1 - hypervolume_set2;
    }

    /**
     * getter on bounds
     * @return bounds
     */
    std::vector < eoRealInterval > getBounds(){
        return bounds;
    }

    /**
     * method caclulate bounds for the normalization
     * @param _set1 the vector contains all objective Vector of the first pareto front
     * @param _set2 the vector contains all objective Vector of the second pareto front
     */
    void setup(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2){
    	if(_set1.size() < 1 || _set2.size() < 1)
    		throw("Error in moeoHyperVolumeUnaryMetric::setup -> argument1: vector<ObjectiveVector> size must be greater than 0");
    	else{
	        double min, max;
	        unsigned int nbObj=ObjectiveVector::Traits::nObjectives();
	        bounds.resize(nbObj);
	        for (unsigned int i=0; i<nbObj; i++){
	            min = _set1[0][i];
	            max = _set1[0][i];
	            for (unsigned int j=1; j<_set1.size(); j++){
	                min = std::min(min, _set1[j][i]);
	                max = std::max(max, _set1[j][i]);
	            }
	            for (unsigned int j=0; j<_set2.size(); j++){
	                min = std::min(min, _set2[j][i]);
	                max = std::max(max, _set2[j][i]);
	            }
	            bounds[i] = eoRealInterval(min, max);
	        }
    	}
    }

  	private:

    /*boolean indicates if data must be normalized or not*/
    bool normalize;

    double rho;

    /*vectors contains bounds for normalization*/
    std::vector < eoRealInterval > bounds;

    ObjectiveVector ref_point;

  };

#endif /*MOEOHYPERVOLUMEMETRIC_H_*/