ExactGradient.h

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/*!
 * 
 *
 * \brief       -
 *
 * \author      -
 * \date        -
 *
 *
 * \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/>.
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 */
#ifndef SHARK_UNSUPERVISED_RBM_GRADIENTAPPROXIMATIONS_EXACTGRADIENT_H
#define SHARK_UNSUPERVISED_RBM_GRADIENTAPPROXIMATIONS_EXACTGRADIENT_H
 
#include <shark/ObjectiveFunctions/AbstractObjectiveFunction.h>
#include <shark/Unsupervised/RBM/Sampling/GibbsOperator.h>
#include <shark/Unsupervised/RBM/analytics.h>
 
namespace shark{
 
template<class RBMType>
class ExactGradient: public SingleObjectiveFunction{
private:
    typedef GibbsOperator<RBMType> Gibbs;
public:
    typedef RBMType RBM;
 
    ExactGradient(RBM* rbm): mpe_rbm(rbm),m_regularizer(0){
        SHARK_ASSERT(rbm != NULL);
 
        m_features |= HAS_FIRST_DERIVATIVE;
        m_features |= CAN_PROPOSE_STARTING_POINT;
    };
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "ExactGradient"; }
 
    void setData(UnlabeledData<RealVector> const& data){
        m_data = data;
    }
 
    SearchPointType proposeStartingPoint() const{
        return  mpe_rbm->parameterVector();
    }
    
    std::size_t numberOfVariables()const{
        return mpe_rbm->numberOfParameters();
    }
    
    void setRegularizer(double factor, SingleObjectiveFunction* regularizer){
        m_regularizer = regularizer;
        m_regularizationStrength = factor;
    }
    
    double eval( SearchPointType const & parameter) const {
        mpe_rbm->setParameterVector(parameter);
        
        double negLogLikelihood = negativeLogLikelihood(*mpe_rbm,m_data)/m_data.numberOfElements();
        if(m_regularizer){
            negLogLikelihood += m_regularizationStrength * m_regularizer->eval(parameter);
        }
        return negLogLikelihood;
    }
 
    double evalDerivative( SearchPointType const & parameter, FirstOrderDerivative & derivative ) const {
        mpe_rbm->setParameterVector(parameter);
        
        //the gradient approximation for the energy terms of the RBM        
        typename RBM::GradientType empiricalExpectation(mpe_rbm);
        typename RBM::GradientType modelExpectation(mpe_rbm);
 
        Gibbs gibbsSampler(mpe_rbm);
        
        //calculate the expectation of the energy gradient with respect to the data
        double negLogLikelihood = 0;
        for(RealMatrix const& batch: m_data.batches()) {
            std::size_t currentBatchSize = batch.size1();
            typename Gibbs::HiddenSampleBatch hiddenSamples(currentBatchSize,mpe_rbm->numberOfHN());
            typename Gibbs::VisibleSampleBatch visibleSamples(currentBatchSize,mpe_rbm->numberOfVN());
        
            gibbsSampler.createSample(hiddenSamples,visibleSamples,batch);
            empiricalExpectation.addVH(hiddenSamples, visibleSamples);
            negLogLikelihood -= sum(mpe_rbm->energy().logUnnormalizedProbabilityVisible(
                batch,hiddenSamples.input,blas::repeat(1,currentBatchSize)
            ));
        }
        
        //calculate the expectation of the energy gradient with respect to the model distribution
        if(mpe_rbm->numberOfVN() < mpe_rbm->numberOfHN()){
            integrateOverVisible(modelExpectation);
        }
        else{
            integrateOverHidden(modelExpectation);
        }
        
        derivative.resize(mpe_rbm->numberOfParameters());
        noalias(derivative) = modelExpectation.result() - empiricalExpectation.result();
    
        m_logPartition = modelExpectation.logWeightSum();
        negLogLikelihood/=m_data.numberOfElements();
        negLogLikelihood += m_logPartition;
        
        if(m_regularizer){
            FirstOrderDerivative regularizerDerivative;
            negLogLikelihood += m_regularizationStrength * m_regularizer->evalDerivative(parameter,regularizerDerivative);
            noalias(derivative) += m_regularizationStrength * regularizerDerivative;
        }
        
        return negLogLikelihood;
    }
 
    long double getLogPartition(){
        return m_logPartition;
    }
 
private:
    RBM* mpe_rbm;
 
    SingleObjectiveFunction* m_regularizer;
    double m_regularizationStrength;
    
    //batchwise loops over all hidden units to calculate the gradient as well as partition
    template<class GradientApproximator>//mostly dummy right now
    void integrateOverVisible(GradientApproximator & modelExpectation) const{
        
        Gibbs sampler(mpe_rbm);
        
        typedef typename RBM::VisibleType::StateSpace VisibleStateSpace;
        std::size_t values = VisibleStateSpace::numberOfStates(mpe_rbm->numberOfVN());
        std::size_t batchSize = std::min(values, std::size_t(256));
        
        for (std::size_t x = 0; x < values; x+=batchSize) {
            //create batch of states
            std::size_t currentBatchSize=std::min(batchSize,values-x);
            typename Batch<RealVector>::type stateBatch(currentBatchSize,mpe_rbm->numberOfVN());
            for(std::size_t elem = 0; elem != currentBatchSize;++elem){
                //generation of the x+elem-th state vector
                VisibleStateSpace::state(row(stateBatch,elem),x+elem);
            }
            
            //create sample from state batch
            typename Gibbs::HiddenSampleBatch hiddenBatch(currentBatchSize,mpe_rbm->numberOfHN());
            typename Gibbs::VisibleSampleBatch visibleBatch(currentBatchSize,mpe_rbm->numberOfVN());
            sampler.createSample(hiddenBatch,visibleBatch,stateBatch);
            
            //calculate probabilities and update 
            RealVector logP = mpe_rbm->energy().logUnnormalizedProbabilityVisible(
                stateBatch,hiddenBatch.input,blas::repeat(1,currentBatchSize)
            );
            modelExpectation.addVH(hiddenBatch, visibleBatch, logP);
        }
    }
    
    //batchwise loops over all hidden units to calculate the gradient as well as partition
    template<class GradientApproximator>//mostly dummy right now
    void integrateOverHidden(GradientApproximator & modelExpectation) const{
        
        Gibbs sampler(mpe_rbm);
        
        typedef typename RBM::HiddenType::StateSpace HiddenStateSpace;
        std::size_t values = HiddenStateSpace::numberOfStates(mpe_rbm->numberOfHN());
        std::size_t batchSize = std::min(values, std::size_t(256) );
        
        for (std::size_t x = 0; x < values; x+=batchSize) {
            //create batch of states
            std::size_t currentBatchSize=std::min(batchSize,values-x);
            typename Batch<RealVector>::type stateBatch(currentBatchSize,mpe_rbm->numberOfHN());
            for(std::size_t elem = 0; elem != currentBatchSize;++elem){
                //generation of the x+elem-th state vector
                HiddenStateSpace::state(row(stateBatch,elem),x+elem);
            }
            
            //create sample from state batch
            typename Gibbs::HiddenSampleBatch hiddenBatch(currentBatchSize,mpe_rbm->numberOfHN());
            typename Gibbs::VisibleSampleBatch visibleBatch(currentBatchSize,mpe_rbm->numberOfVN());
            hiddenBatch.state=stateBatch;
            sampler.precomputeVisible(hiddenBatch,visibleBatch, blas::repeat(1,currentBatchSize));
            
            //calculate probabilities and update 
            RealVector logP = mpe_rbm->energy().logUnnormalizedProbabilityHidden(
                stateBatch,visibleBatch.input,blas::repeat(1,currentBatchSize)
            );
            modelExpectation.addHV(hiddenBatch, visibleBatch, logP);
        }
    }
 
    UnlabeledData<RealVector> m_data;
 
    mutable double m_logPartition; //the partition function of the model distribution
};  
    
}
 
#endif