<commit_before>#pragma once
#ifndef NIFTY_GRAPH_OPTIMIZATION_LIFTED_MULTICUT_LEARNABLE_LIFTED_MULTICUT_OBJECTIVE_HXX
#define NIFTY_GRAPH_OPTIMIZATION_LIFTED_MULTICUT_LEARNABLE_LIFTED_MULTICUT_OBJECTIVE_HXX


#include "nifty/graph/optimization/lifted_multicut/lifted_multicut_objective.hxx"
#include "nifty/structured_learning/weight_vector.hxx"
#include "nifty/structured_learning/instances/weighted_edge.hxx"

namespace nifty{
namespace graph{
namespace lifted_multicut{





    /**
     * @brief      Class for learnable lifted multicut objective.
     * 
     *
     * @tparam     GRAPH        The graph class
     * @tparam     WEIGHT_TYPE  float or double
     */
    template<class GRAPH, class WEIGHT_TYPE>   
    class LearnableLiftedMulticutObjective :  
        public LiftedMulticutObjective<GRAPH, WEIGHT_TYPE>
    {   
    public:
        typedef LiftedMulticutObjective<GRAPH, WEIGHT_TYPE> BaseType;
        typedef typename BaseType::GraphType       GraphType;
        typedef typename BaseType::LiftedGraphType LiftedGraphType;
        typedef typename BaseType::WeightType      WeightType;

        LearnableLiftedMulticutObjective(
            const GraphType & graph, 
            const size_t numberOfWeights,
            const int reserveAdditionalEdges = -1
        );


        template<class WEIGHT_INDICES_ITER, class FEATURE_ITER>
        std::pair<bool,uint64_t> addWeightedFeatures(const uint64_t u, const uint64_t v, 
                                 WEIGHT_INDICES_ITER, WEIGHT_INDICES_ITER, 
                                 FEATURE_ITER, const WeightType constTerm=0.0, 
                                 const bool overwriteConstTerm = false);

        template<class WEIGHT_VECTOR>
        inline void changeWeights(const WEIGHT_VECTOR & weightVector);
        
        template<class NODE_LABELS, class GRADIENT_VECTOR>
        inline void getGradient(const NODE_LABELS & ,GRADIENT_VECTOR &)const;

        template<class NODE_LABELS, class GRADIENT_VECTOR>
        inline void addGradient(const NODE_LABELS & ,GRADIENT_VECTOR &)const;

        template<class NODE_LABELS, class GRADIENT_VECTOR>
        inline void substractGradient(const NODE_LABELS & ,GRADIENT_VECTOR &)const;


    private:
        template<class NODE_LABELS, class GRADIENT_VECTOR, class BINARY_OPERATOR>
        inline void accumulateGradient(const NODE_LABELS & ,GRADIENT_VECTOR & ,BINARY_OPERATOR && )const;


        typedef structured_learning::instances::WeightedEdge<WEIGHT_TYPE> WeightedEdgeType;
        typedef typename GraphType:: template EdgeMap<WeightedEdgeType> WeightedEdgeCosts;

        WeightedEdgeCosts weightedEdgeCosts_;
        size_t numberOfWeights_;

    };


    template<class GRAPH, class WEIGHT_TYPE>
    LearnableLiftedMulticutObjective<GRAPH, WEIGHT_TYPE>::
    LearnableLiftedMulticutObjective(
        const GraphType & graph, 
        const size_t numberOfWeights,
        const int reserveAdditionalEdges
    )
    :   BaseType(graph,  reserveAdditionalEdges),
        weightedEdgeCosts_(this->liftedGraph()),
        numberOfWeights_(numberOfWeights){

    }


    template<class GRAPH, class WEIGHT_TYPE>
    template<class WEIGHT_INDICES_ITER, class FEATURE_ITER>
    std::pair<bool,uint64_t>  
    LearnableLiftedMulticutObjective<GRAPH, WEIGHT_TYPE>::
    addWeightedFeatures(
        const uint64_t u, const uint64_t v,
        WEIGHT_INDICES_ITER weightIndicesBegin,  
        WEIGHT_INDICES_ITER weightIndicesEnd, 
        FEATURE_ITER featuresBegin, 
        const WeightType constTerm, 
        const bool overwriteConstTerm
    ){
        
        const auto ret = this->setCost(u, v);
        const uint64_t edge = ret.first;
        const bool addedNewEdge = ret.second;
        if(addedNewEdge){
            // new WeightedEdge
            WeightedEdgeType weightedEdge;
            
            while(weightIndicesBegin != weightIndicesEnd){
                weightedEdge.addWeightedFeature(*weightIndicesBegin, *featuresBegin);
                ++weightIndicesBegin;
                ++featuresBegin;
            }
            weightedEdge.setConstTerm(constTerm,overwriteConstTerm);
            weightedEdgeCosts_.insertedEdge(edge, weightedEdge);
        }
        else{
            // existing edge
            auto & weightedEdge = weightedEdgeCosts_[edge];
            while(weightIndicesBegin != weightIndicesEnd){
                weightedEdge.addWeightedFeature(*weightIndicesBegin, *featuresBegin);
                ++weightIndicesBegin;
                ++featuresBegin;
            }
            weightedEdge.setConstTerm(constTerm,overwriteConstTerm);

        }
        return addedNewEdge;
    }

    template<class GRAPH, class WEIGHT_TYPE>
    template<class WEIGHT_VECTOR>
    void 
    LearnableLiftedMulticutObjective<GRAPH, WEIGHT_TYPE>::
    changeWeights(
        const WEIGHT_VECTOR & weightVector
    ){
        this->liftedGraph().forEachEdge([&](const uint64_t edge){
            this->weights_[edge] = weightedEdgeCosts_[edge].value(weightVector);
        });
    }



        
    template<class GRAPH, class WEIGHT_TYPE>
    template<class NODE_LABELS, class GRADIENT_VECTOR>
    void 
    LearnableLiftedMulticutObjective<GRAPH, WEIGHT_TYPE>::
    getGradient(const NODE_LABELS  & nodeLabels, GRADIENT_VECTOR & gradient) const {<commit_msg>hotfix<commit_after>#pragma once
#ifndef NIFTY_GRAPH_OPTIMIZATION_LIFTED_MULTICUT_LEARNABLE_LIFTED_MULTICUT_OBJECTIVE_HXX
#define NIFTY_GRAPH_OPTIMIZATION_LIFTED_MULTICUT_LEARNABLE_LIFTED_MULTICUT_OBJECTIVE_HXX


#include "nifty/graph/optimization/lifted_multicut/lifted_multicut_objective.hxx"
#include "nifty/structured_learning/weight_vector.hxx"
#include "nifty/structured_learning/instances/weighted_edge.hxx"

namespace nifty{
namespace graph{
namespace lifted_multicut{





    /**
     * @brief      Class for learnable lifted multicut objective.
     * 
     *
     * @tparam     GRAPH        The graph class
     * @tparam     WEIGHT_TYPE  float or double
     */
    template<class GRAPH, class WEIGHT_TYPE>   
    class LearnableLiftedMulticutObjective :  
        public LiftedMulticutObjective<GRAPH, WEIGHT_TYPE>
    {   
    public:
        typedef LiftedMulticutObjective<GRAPH, WEIGHT_TYPE> BaseType;
        typedef typename BaseType::GraphType       GraphType;
        typedef typename BaseType::LiftedGraphType LiftedGraphType;
        typedef typename BaseType::WeightType      WeightType;

        LearnableLiftedMulticutObjective(
            const GraphType & graph, 
            const size_t numberOfWeights,
            const int reserveAdditionalEdges = -1
        );


        template<class WEIGHT_INDICES_ITER, class FEATURE_ITER>
        std::pair<bool,uint64_t> addWeightedFeatures(const uint64_t u, const uint64_t v, 
                                 WEIGHT_INDICES_ITER, WEIGHT_INDICES_ITER, 
                                 FEATURE_ITER, const WeightType constTerm=0.0, 
                                 const bool overwriteConstTerm = false);

        template<class WEIGHT_VECTOR>
        inline void changeWeights(const WEIGHT_VECTOR & weightVector);
        
        template<class NODE_LABELS, class GRADIENT_VECTOR>
        inline void getGradient(const NODE_LABELS & ,GRADIENT_VECTOR &)const;

        template<class NODE_LABELS, class GRADIENT_VECTOR>
        inline void addGradient(const NODE_LABELS & ,GRADIENT_VECTOR &)const;

        template<class NODE_LABELS, class GRADIENT_VECTOR>
        inline void substractGradient(const NODE_LABELS & ,GRADIENT_VECTOR &)const;


    private:
        template<class NODE_LABELS, class GRADIENT_VECTOR, class BINARY_OPERATOR>
        inline void accumulateGradient(const NODE_LABELS & ,GRADIENT_VECTOR & ,BINARY_OPERATOR && )const;


        typedef structured_learning::instances::WeightedEdge<WEIGHT_TYPE> WeightedEdgeType;
        typedef typename GraphType:: template EdgeMap<WeightedEdgeType> WeightedEdgeCosts;

        WeightedEdgeCosts weightedEdgeCosts_;
        size_t numberOfWeights_;

    };


    template<class GRAPH, class WEIGHT_TYPE>
    LearnableLiftedMulticutObjective<GRAPH, WEIGHT_TYPE>::
    LearnableLiftedMulticutObjective(
        const GraphType & graph, 
        const size_t numberOfWeights,
        const int reserveAdditionalEdges
    )
    :   BaseType(graph,  reserveAdditionalEdges),
        weightedEdgeCosts_(this->liftedGraph()),
        numberOfWeights_(numberOfWeights){

    }


    template<class GRAPH, class WEIGHT_TYPE>
    template<class WEIGHT_INDICES_ITER, class FEATURE_ITER>
    std::pair<bool,uint64_t>  
    LearnableLiftedMulticutObjective<GRAPH, WEIGHT_TYPE>::
    addWeightedFeatures(
        const uint64_t u, const uint64_t v,
        WEIGHT_INDICES_ITER weightIndicesBegin,  
        WEIGHT_INDICES_ITER weightIndicesEnd, 
        FEATURE_ITER featuresBegin, 
        const WeightType constTerm, 
        const bool overwriteConstTerm
    ){
        
        const auto ret = this->setCost(u, v);
        const uint64_t edge = ret.first;
        const bool addedNewEdge = ret.second;
        if(addedNewEdge){
            // new WeightedEdge
            WeightedEdgeType weightedEdge;
            
            while(weightIndicesBegin != weightIndicesEnd){
                weightedEdge.addWeightedFeature(*weightIndicesBegin, *featuresBegin);
                ++weightIndicesBegin;
                ++featuresBegin;
            }
            weightedEdge.setConstTerm(constTerm,overwriteConstTerm);
            weightedEdgeCosts_.insertedEdge(edge, weightedEdge);
        }
        else{
            // existing edge
            auto & weightedEdge = weightedEdgeCosts_[edge];
            while(weightIndicesBegin != weightIndicesEnd){
                weightedEdge.addWeightedFeature(*weightIndicesBegin, *featuresBegin);
                ++weightIndicesBegin;
                ++featuresBegin;
            }
            weightedEdge.setConstTerm(constTerm,overwriteConstTerm);

        }
        return ret;
    }

    template<class GRAPH, class WEIGHT_TYPE>
    template<class WEIGHT_VECTOR>
    void 
    LearnableLiftedMulticutObjective<GRAPH, WEIGHT_TYPE>::
    changeWeights(
        const WEIGHT_VECTOR & weightVector
    ){
        this->liftedGraph().forEachEdge([&](const uint64_t edge){
            this->weights_[edge] = weightedEdgeCosts_[edge].value(weightVector);
        });
    }



        
    template<class GRAPH, class WEIGHT_TYPE>
    template<class NODE_LABELS, class GRADIENT_VECTOR>
    void 
    LearnableLiftedMulticutObjective<GRAPH, WEIGHT_TYPE>::
    getGradient(const NODE_LABELS  & nodeLabels, GRADIENT_VECTOR & gradient) const {
{'fraction_non_alphanumeric': 0.06641580432737536, 'fraction_numerical': 0.003574788334901223, 'mean_word_length': 2.830990990990991, 'pattern_counts': {'":': 0, '<': 51, '<?xml version=': 0, '>': 59, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 6, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6545498', 'n_tokens_mistral': 3270, 'n_tokens_neox': 3125, 'n_words': 568}