NearestNeighborRegression.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Nearest neighbor regression
 * 
 * 
 *
 * \author      T. Glasmachers, O.Krause
 * \date        2012
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <http://shark-ml.org/>
 * 
 * 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_MODELS_NEARESTNEIGHBORREGRESSION_H
#define SHARK_MODELS_NEARESTNEIGHBORREGRESSION_H


#include <shark/Models/AbstractModel.h>
#include <shark/Algorithms/NearestNeighbors/AbstractNearestNeighbors.h>
namespace shark {


/// \brief Nearest neighbor regression model.
///
/// The NearestNeighborClassifier predicts a class label
/// according to a local majority decision among its k
/// nearest neighbors. It is not specified how ties are
/// broken.
///
/// \tparam InputType Type of input data
/// \tparam tree_type Type of binary tree for nearest neighbor search. See KDTree and LCTree for Euclidean distance, and KHCTree for kernel distance.
template <class InputType>
class NearestNeighborRegression : public AbstractModel<InputType, RealVector>
{
public:

    typedef AbstractNearestNeighbors<InputType,RealVector> NearestNeighbors;
    typedef AbstractModel<InputType, RealVector> base_type;
    typedef typename base_type::BatchInputType BatchInputType;
    typedef typename base_type::BatchOutputType BatchOutputType;

    /// \brief Type of distance-based weights.
    enum DistanceWeights
    {
        UNIFORM,                ///< uniform (= no) distance-based weights
        ONE_OVER_DISTANCE,      ///< weight each neighbor's label with 1/distance
    };

    ///\brief Constructor
    ///
    /// \param algorithm the used algorithm for nearst neighbor search
    /// \param neighbors: number of neighbors
    NearestNeighborRegression(NearestNeighbors const* algorithm,  unsigned int neighbors = 3)
    : m_algorithm(algorithm)
    , m_neighbors(neighbors)
    , m_distanceWeights(UNIFORM)
    {}

    /// \brief From INameable: return the class name.
    std::string name() const
    { return "NearestNeighborRegression"; }


    /// return the number of neighbors
    unsigned int neighbors() const{
        return m_neighbors;
    }

    /// set the number of neighbors
    void setNeighbors(unsigned int neighbors){
        m_neighbors=neighbors;
    }

    /// query the way distances enter as weights
    DistanceWeights getDistanceWeightType() const
    { return m_distanceWeights; }

    /// set the way distances enter as weights
    void setDistanceWeightType(DistanceWeights dw)
    { m_distanceWeights = dw; }

    /// get internal parameters of the model
    virtual RealVector parameterVector() const{
        RealVector parameters(1);
        parameters(0) = m_neighbors;
        return parameters;
    }

    /// set internal parameters of the model
    virtual void setParameterVector(RealVector const& newParameters){
        SHARK_RUNTIME_CHECK(newParameters.size() == 1,
            "[SoftNearestNeighborClassifier::setParameterVector] invalid number of parameters");
        //~ SHARK_RUNTIME_CHECK((unsigned int)newParameters(0) == newParameters(0) && newParameters(0) >= 1.0,
            //~ "[SoftNearestNeighborClassifier::setParameterVector] invalid number of neighbors");
        m_neighbors = (unsigned int)newParameters(0);
    }

    /// return the size of the parameter vector
    virtual std::size_t numberOfParameters() const{
        return 1;
    }
    
    boost::shared_ptr<State> createState()const{
        return boost::shared_ptr<State>(new EmptyState());
    }

    using base_type::eval;

    /// soft k-nearest-neighbor prediction
    void eval(BatchInputType const& patterns, BatchOutputType& output, State& state)const{
        std::size_t numPatterns = batchSize(patterns);
        std::vector<typename NearestNeighbors::DistancePair> neighbors = m_algorithm->getNeighbors(patterns,m_neighbors);
        
        std::size_t dimension = neighbors[0].value.size();
        output.resize(numPatterns,dimension);
        output.clear();

        for(std::size_t p = 0; p != numPatterns;++p)
        {
            double wsum = 0.0;
            for ( std::size_t k = 0; k != m_neighbors; ++k)
            {
                double w;
                if (m_distanceWeights == UNIFORM) w = 1.0;
                else
                {
                    double d = neighbors[p*m_neighbors+k].key;
                    if (d < 1e-100) w = 1e100;
                    else w = 1.0 / d;
                }
                noalias(row(output,p)) += w * neighbors[k+p*m_neighbors].value;
                wsum += w;
            }
            row(output,p) *= (1.0 / wsum);
        }
    }

    /// from ISerializable, reads a model from an archive
    void read(InArchive& archive){
        archive & m_neighbors;
        archive & m_classes;
    }

    /// from ISerializable, writes a model to an archive
    void write(OutArchive& archive) const{
        archive & m_neighbors;
        archive & m_classes;
    }

protected:
    NearestNeighbors const* m_algorithm;

    /// number of classes
    unsigned int m_classes;

    /// number of neighbors to be taken into account
    unsigned int m_neighbors;

    /// type of distance-based weights computation
    DistanceWeights m_distanceWeights;
};


}
#endif