HypervolumeContribution2D.h

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/*!
 *
 * \author      O.Krause, T. Glasmachers
 * \date        2014-2016
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 * 
 * Shark 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 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark 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 Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
#ifndef SHARK_ALGORITHMS_DIRECTSEARCH_HYPERVOLUME_CONTRIBUTION_2D_H
#define SHARK_ALGORITHMS_DIRECTSEARCH_HYPERVOLUME_CONTRIBUTION_2D_H
 
#include <shark/LinAlg/Base.h>
#include <shark/Core/utility/KeyValuePair.h>
 
#include <algorithm>
#include <vector>
 
namespace shark{
/// \brief Finds the smallest/largest Contributors given 2D points
///
/// The algorithm sorts the points by their first coordinate and then 
/// calculates the volume of the hypervolume dominated by each Pointseparately
/// returning the index of the elements with the minimum/maximum contribution.
struct HypervolumeContribution2D{
private:
    struct Point{
        Point(){}
 
        Point(double f1, double f2, std::size_t index)
        : f1(f1)
        , f2(f2)
        , index(index)
        {}
 
        bool operator<(Point const& rhs) const{//for lexicographic sorting
            if (f1 < rhs.f1) return true;
            if (f1 > rhs.f1) return false;
            return (f2 < rhs.f2);
        }
 
        double f1;
        double f2;
        std::size_t index;
    };
    
    ///\brief Stores the k elements with the best contribution(highest,lowest) as indicated by comparator
    ///
    /// The algorithm returns the indizes of the front front[i].index in order of contribution.
    /// The input is a set of points sorted by x-value. the edge-points are never selected.
    ///
    /// This is implemented by using a min-heap that stores the k best elements,
    /// but having the smallest element on top so that we can quickly decide which
    /// element to remove.
    template<class Comparator>
    std::vector<KeyValuePair<double,std::size_t> > bestContributors( std::vector<Point> const& front, std::size_t k, Comparator comp)const{
        
        std::vector<KeyValuePair<double,std::size_t> > bestK(k+1);
        auto heapStart = bestK.begin();
        auto heapEnd = bestK.begin();
        
        auto pointComp = [&](KeyValuePair<double,std::size_t> const& lhs, KeyValuePair<double,std::size_t> const& rhs){return comp(lhs.key,rhs.key);};
        
        //compute the hypervalue contribution for each Pointexcept the endpoints;
        for(std::size_t i = 1; i < front.size()-1;++i){
            double contribution = (front[i+1].f1 - front[i].f1)*(front[i-1].f2 - front[i].f2);
            *heapEnd = makeKeyValuePair(contribution,front[i].index);
            ++heapEnd;
            std::push_heap(heapStart,heapEnd,pointComp);
            
            if(heapEnd == bestK.end()){
                std::pop_heap(heapStart,heapEnd,pointComp);
                --heapEnd;
            }
        }
        std::sort_heap(heapStart,heapEnd,pointComp);
        bestK.pop_back();//remove the k+1th element
        return bestK;
    }
public:
    /// \brief Returns the index of the points with smallest contribution.
    ///
    /// \param [in] points The set \f$S\f$ of points from which to select the smallest contributor.
    /// \param [in] k The number of points to select.
    /// \param [in] referencePoint The reference Point\f$\vec{r} \in \mathbb{R}^2\f$ for the hypervolume calculation, needs to fulfill: \f$ \forall s \in S: s \preceq \vec{r}\f$.
    template<typename Set, typename VectorType>
    std::vector<KeyValuePair<double,std::size_t> > smallest( Set const& points, std::size_t k, VectorType const& referencePoint)const{
        SHARK_RUNTIME_CHECK(points.size() >= k, "There must be at least k points in the set");
        std::vector<Point> front;
        front.emplace_back(0,referencePoint[1],points.size()+1);//add reference point
        for(std::size_t i  = 0; i != points.size(); ++i)
            front.emplace_back(points[i][0],points[i][1],i);
        front.emplace_back(referencePoint[0],0,points.size()+1);//add reference point
        std::sort(front.begin()+1,front.end()-1);
        
        return bestContributors(front,k,[](double con1, double con2){return con1 < con2;});
    }
    
    /// \brief Returns the index of the points with smallest contribution.
    ///
    /// As no reference point is given, the edge points can not be computed and are not selected.
    ///
    /// \param [in] points The set \f$S\f$ of points from which to select the smallest contributor.
    /// \param [in] k The number of points to select.
    template<typename Set>
    std::vector<KeyValuePair<double,std::size_t> > smallest( Set const& points, std::size_t k)const{
        SHARK_RUNTIME_CHECK(points.size() >= k, "There must be at least k points in the set");
        std::vector<Point> front;
        for(std::size_t i  = 0; i != points.size(); ++i)
            front.emplace_back(points[i][0],points[i][1],i);
        std::sort(front.begin(),front.end());
        
        return bestContributors(front,k,[](double con1, double con2){return con1 < con2;});
    }
    
    /// \brief Returns the index of the points with largest contribution.
    ///
    /// \param [in] points The set \f$S\f$ of points from which to select the smallest contributor.
    /// \param [in] k Number of points to select.
    /// \param [in] referencePoint The reference Point\f$\vec{r} \in \mathbb{R}^2\f$ for the hypervolume calculation, needs to fulfill: \f$ \forall s \in S: s \preceq \vec{r}\f$.
    template<typename Set, typename VectorType>
    std::vector<KeyValuePair<double,std::size_t> > largest( Set const& points, std::size_t k, VectorType const& referencePoint)const{
        SHARK_RUNTIME_CHECK(points.size() >= k, "There must be at least k points in the set");
        std::vector<Point> front;
        front.emplace_back(0,referencePoint[1],points.size()+1);//add reference point
        for(std::size_t i  = 0; i != points.size(); ++i)
            front.emplace_back(points[i][0],points[i][1],i);
        front.emplace_back(referencePoint[0],0,points.size()+1);//add reference point
        std::sort(front.begin()+1,front.end()-1);
        
        return bestContributors(front,k,[](double con1, double con2){return con1 > con2;});
    }
    
    /// \brief Returns the index of the points with largest contribution.
    ///
    /// As no reference point is given, the edge points can not be computed and are not selected.
    ///
    /// \param [in] points The set \f$S\f$ of points from which to select the smallest contributor.
    /// \param [in] k The number of points to select.
    template<typename Set>
    std::vector<KeyValuePair<double,std::size_t> > largest( Set const& points, std::size_t k)const{
        SHARK_RUNTIME_CHECK(points.size() >= k, "There must be at least k points in the set");
        std::vector<Point> front;
        for(std::size_t i  = 0; i != points.size(); ++i)
            front.emplace_back(points[i][0],points[i][1],i);
        std::sort(front.begin(),front.end());
        
        return bestContributors(front,k,[](double con1, double con2){return con1 > con2;});
    }
 
};
 
}
#endif