NearestNeighborClassifier.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Nearest neighbor classification
 * 
 * 
 *
 * \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_NEARESTNEIGHBORCLASSIFIER_H
#define SHARK_MODELS_NEARESTNEIGHBORCLASSIFIER_H

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


///
/// \brief Nearest Neighbor Classifier.
///
/// \par
/// 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.
///
/// This model requires the use of one of sharks nearest neighhbor Algorithms.
/// \see AbstractNearestNeighbors
template <class InputType>
class NearestNeighborClassifier : public AbstractModel<InputType, unsigned int>
{
public:
    typedef AbstractNearestNeighbors<InputType,unsigned int> NearestNeighbors;
    typedef AbstractModel<InputType, unsigned int> 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
    NearestNeighborClassifier(NearestNeighbors const* algorithm, std::size_t neighbors = 3)
    : m_algorithm(algorithm)
    , m_classes(numberOfClasses(algorithm->dataset()))
    , m_neighbors(neighbors)
    , m_distanceWeights(UNIFORM)
    { }

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


    /// return the number of neighbors
    std::size_t neighbors() const{
        return m_neighbors;
    }

    /// set the number of neighbors
    void setNeighbors(std::size_t 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) = (double)m_neighbors;
        return parameters;
    }

    /// set internal parameters of the model
    virtual void setParameterVector(RealVector const& newParameters){
        SHARK_RUNTIME_CHECK(newParameters.size() == 1, "Invalid number of parameters");
        m_neighbors = (std::size_t)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);

        output.resize(numPatterns);
        output.clear();

        for(std::size_t p = 0; p != numPatterns;++p){
            std::vector<double> histogram(m_classes, 0.0);
            for ( std::size_t k = 0; k != m_neighbors; ++k){
                if (m_distanceWeights == UNIFORM) histogram[neighbors[p*m_neighbors+k].value]++;
                else
                {
                    double d = neighbors[p*m_neighbors+k].key;
                    if (d < 1e-100) histogram[neighbors[p*m_neighbors+k].value] += 1e100;
                    else histogram[neighbors[p*m_neighbors+k].value] += 1.0 / d;
                }
            }
            output(p) = static_cast<unsigned int>(std::max_element(histogram.begin(),histogram.end()) - histogram.begin());
        }
    }

    /// 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
    std::size_t m_classes;

    /// number of neighbors to be taken into account
    std::size_t m_neighbors;

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



}
#endif