SingleChainApproximator.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
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 */
#ifndef SHARK_UNSUPERVISED_RBM_SINGLECHAINAPPROXIMATOR_H
#define SHARK_UNSUPERVISED_RBM_SINGLECHAINAPPROXIMATOR_H
 
#include <shark/ObjectiveFunctions/AbstractObjectiveFunction.h>
#include "Impl/DataEvaluator.h"
 
namespace shark{
    
///\brief Approximates the gradient by taking samples from a single Markov chain.
///
///Taking samples only from a single chain leads to a high mixing rate but the correlation of the samples is higher than using
///several chains. This approximator should be used with a sampling scheme which also achieves a faster decorrelation of samples like
///tempering.
template<class MarkovChainType> 
class SingleChainApproximator: public SingleObjectiveFunction{
public:
    typedef typename MarkovChainType::RBM RBM;
    
    SingleChainApproximator(RBM* rbm)
    : mpe_rbm(rbm),m_chain(rbm),m_k(1)
    ,m_samples(0),m_batchSize(500)
    ,m_numBatches(0),m_regularizer(0){
        SHARK_ASSERT(rbm != NULL);
 
        m_features.reset(HAS_VALUE);
        m_features |= HAS_FIRST_DERIVATIVE;
        m_features |= CAN_PROPOSE_STARTING_POINT;
        
        m_chain.setBatchSize(1);
    };
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "SingleChainApproximator"; }
 
    void setK(unsigned int k){
        m_k = k;
    }
    void setNumberOfSamples(std::size_t samples){
        m_samples = samples;
    }
    
    /// \brief Returns the number of batches of the dataset that are used in every iteration.
    ///
    /// If it is less than all batches, the batches are chosen at random. if it is 0, all batches are used
    std::size_t numBatches()const{
        return m_numBatches;
    }
    
    /// \brief Returns a reference to the number of batches of the dataset that are used in every iteration.
    ///
    /// If it is less than all batches, the batches are chosen at random.if it is 0, all batches are used.
    std::size_t& numBatches(){
        return m_numBatches;
    }
    
    MarkovChainType& chain(){
        return m_chain;
    }
    MarkovChainType const& chain() const{
        return m_chain;
    }
    
    void setData(UnlabeledData<RealVector> const& data){
        m_data = data;
        m_chain.initializeChain(m_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 evalDerivative( SearchPointType const & parameter, FirstOrderDerivative & derivative ) const {
        mpe_rbm->setParameterVector(parameter);
        
        typename RBM::GradientType modelAverage(mpe_rbm);
        RealVector empiricalAverage = detail::evaluateData(m_data,*mpe_rbm,m_numBatches);
        
        //approximate the expectation of the energy gradient with respect to the model distribution
        //using samples from the Markov chain
        
        //calculate number of samples to draw and size of batches used in the gradient update
        std::size_t samplesToDraw = m_samples > 0 ? m_samples: m_data.numberOfElements();
        
        std::size_t batches = samplesToDraw / m_batchSize; 
        if(samplesToDraw - batches*m_batchSize != 0){
            ++batches;
        }
        
        //calculate the gradient. we do this by normal k-step sampling for exactly as many
        //samples as calculated in samplesToDraw but saving the result in an intermediate
        //batch variable gradientbatch. When this batch is full, we do an update step of the gradient.
        //this is an a bit more efficient grouping and preserves us from using batches of size1 as the argument 
        //of addVH which might be inefficient.
        for(std::size_t batch = 0; batch != batches; ++batch){
            //calculate the size of the next batch which is batchSize as long as there are enough samples left to draw
            std::size_t currentBatchSize = std::min(samplesToDraw-batch*m_batchSize, m_batchSize);
            typename MarkovChainType::SampleBatch gradientBatch(currentBatchSize, mpe_rbm->numberOfVN(),mpe_rbm->numberOfHN());
            //fill the batch with fresh samples
            for(std::size_t i = 0; i != currentBatchSize; ++i){
                m_chain.step(m_k);
                getBatchElement(gradientBatch,i) = m_chain.sample();
            }
            //do the gradient update
            modelAverage.addVH(gradientBatch.hidden, gradientBatch.visible);
        }
        
        derivative.resize(mpe_rbm->numberOfParameters());
        noalias(derivative) = modelAverage.result() - empiricalAverage;
        
        if(m_regularizer){
            FirstOrderDerivative regularizerDerivative;
            m_regularizer->evalDerivative(parameter,regularizerDerivative);
            noalias(derivative) += m_regularizationStrength*regularizerDerivative;
        }
 
        return std::numeric_limits<double>::quiet_NaN();
    }
 
private:
    RBM* mpe_rbm;
    mutable MarkovChainType m_chain; 
    UnlabeledData<RealVector> m_data;
 
    unsigned int m_k;
    unsigned int m_samples;
    std::size_t m_batchSize;
    std::size_t m_numBatches;
 
    SingleObjectiveFunction* m_regularizer;
    double m_regularizationStrength;
};  
    
}
 
#endif