// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-

//-----------------------------------------------------------------------------
// moeoIndicatorBasedFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
    This library...

    Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
 */
//-----------------------------------------------------------------------------

#ifndef MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
#define MOEOINDICATORBASEDFITNESSASSIGNMENT_H_

#include <math.h>
#include <eoPop.h>
#include <moeoConvertPopToObjectiveVectors.h>
#include <moeoFitnessAssignment.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>

/**
 * Fitness assignment sheme based an Indicator proposed in:
 * E. Zitzler, S. Künzli, "Indicator-Based Selection in Multiobjective Search", Proc. 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), pp. 832-842, Birmingham, UK (2004).
 * This strategy is, for instance, used in IBEA.
 */
template < class MOEOT >
class moeoIndicatorBasedFitnessAssignment : public moeoParetoBasedFitnessAssignment < MOEOT >
{
public:

    /** The type of objective vector */
    typedef typename MOEOT::ObjectiveVector ObjectiveVector;


    /**
     * Ctor.
     * @param _metric the quality indicator
     * @param _kappa the scaling factor
     */
    moeoIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > * _metric, const double _kappa) : metric(_metric), kappa(_kappa)
    {}


    /**
     * Sets the fitness values for every solution contained in the population _pop
     * @param _pop the population
     */
    void operator()(eoPop < MOEOT > & _pop)
    {
        // 1 - setting of the bounds
        setup(_pop);
        // 2 - computing every indicator values
        computeValues(_pop);
        // 3 - setting fitnesses
        setFitnesses(_pop);
    }


    /**
     * Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
     * @param _pop the population
     * @param _objVec the objective vector
     */
    void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
    {
        vector < double > v;
        v.resize(_pop.size());
        for (unsigned i=0; i<_pop.size(); i++)
        {
            v[i] = (*metric)(_objVec, _pop[i].objectiveVector());
        }
        for (unsigned i=0; i<_pop.size(); i++)
        {
            _pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
        }
    }


    /**
     * Updates the fitness values of the whole population _pop by taking the adding of the objective vector _objVec into account
     * and returns the fitness value of _objVec.
     * @param _pop the population
     * @param _objVec the objective vector
     */
    double updateByAdding(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
    {
        vector < double > v;
        // update every fitness values to take the new individual into account
        v.resize(_pop.size());
        for (unsigned i=0; i<_pop.size(); i++)
        {
            v[i] = (*metric)(_objVec, _pop[i].objectiveVector());
        }
        for (unsigned i=0; i<_pop.size(); i++)
        {
            _pop[i].fitness( _pop[i].fitness() - exp(-v[i]/kappa) );
        }
        // compute the fitness of the new individual
        v.clear();
        v.resize(_pop.size());
        for (unsigned i=0; i<_pop.size(); i++)
        {
            v[i] = (*metric)(_pop[i].objectiveVector(), _objVec);
        }
        double result = 0;
        for (unsigned i=0; i<v.size(); i++)
        {
            result -= exp(-v[i]/kappa);
        }
        return result;
    }


protected:

    /** the quality indicator */
    moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > * metric;
    /** the scaling factor */
    double kappa;
    /** the computed indicator values */
    std::vector < std::vector<double> > values;


    /**
     * Sets the bounds for every objective using the min and the max value for every objective vector of _pop
     * @param _pop the population
     */
    void setup(const eoPop < MOEOT > & _pop)
    {
        double min, max;
        for (unsigned i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
        {
            min = _pop[0].objectiveVector()[i];
            max = _pop[0].objectiveVector()[i];
            for (unsigned j=1; j<_pop.size(); j++)
            {
                min = std::min(min, _pop[j].objectiveVector()[i]);
                max = std::max(max, _pop[j].objectiveVector()[i]);
            }
            // setting of the bounds for the objective i
            (*metric).setup(min, max, i);
        }
    }


    /**
     * Compute every indicator value in values (values[i] = I(_v[i], _o))
     * @param _pop the population
     */
    void computeValues(const eoPop < MOEOT > & _pop)
    {
        values.clear();
        values.resize(_pop.size());
        for (unsigned i=0; i<_pop.size(); i++)
        {
            values[i].resize(_pop.size());
            for (unsigned j=0; j<_pop.size(); j++)
            {
                if (i != j)
                {
                    values[i][j] = (*metric)(_pop[i].objectiveVector(), _pop[j].objectiveVector());
                }
            }
        }
    }


    /**
     * Sets the fitness value of the whple population
     * @param _pop the population
     */
    void setFitnesses(eoPop < MOEOT > & _pop)
    {
        for (unsigned i=0; i<_pop.size(); i++)
        {
            _pop[i].fitness(computeFitness(i));
        }
    }


    /**
     * Returns the fitness value of the _idx th individual of the population
     * @param _idx the index
     */
    double computeFitness(const unsigned _idx)
    {
        double result = 0;
        for (unsigned i=0; i<values.size(); i++)
        {
            if (i != _idx)
            {
                result -= exp(-values[i][_idx]/kappa);
            }
        }
        return result;
    }

};

#endif /*MOEOINDICATORBASEDFITNESSASSIGNMENT_H_*/