KNN.h

Go to the documentation of this file.

/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2006 Christian Gehl
 * Written (W) 1999-2009 Soeren Sonnenburg
 * Written (W) 2011 Sergey Lisitsyn
 * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
 */

#ifndef _KNN_H__
#define _KNN_H__

#include <stdio.h>
#include <shogun/lib/common.h>
#include <shogun/io/SGIO.h>
#include <shogun/features/Features.h>
#include <shogun/distance/Distance.h>
#include <shogun/machine/DistanceMachine.h>

namespace shogun
{
class CDistanceMachine;

class CKNN : public CDistanceMachine
{
    public:
        CKNN();

        CKNN(int32_t k, CDistance* d, CLabels* trainlab);
        virtual ~CKNN();

        virtual inline EClassifierType get_classifier_type() { return CT_KNN; }
        //inline EDistanceType get_distance_type() { return DT_KNN;}

        virtual CLabels* apply();

        virtual CLabels* apply(CFeatures* data);

        virtual float64_t apply(int32_t vec_idx)
        {
            SG_ERROR( "for performance reasons use apply() instead of apply(int32_t vec_idx)\n");
            return 0;
        }

        SGMatrix<int32_t> classify_for_multiple_k();

        virtual bool load(FILE* srcfile);

        virtual bool save(FILE* dstfile);

        inline void set_k(int32_t k)
        {
            ASSERT(k>0);
            m_k=k;
        }

        inline int32_t get_k()
        {
            return m_k;
        }

        inline void set_q(float64_t q)
        {
            ASSERT(q<=1.0 && q>0.0);
            m_q = q;
        }

        inline float64_t get_q() { return m_q; }

        inline virtual const char* get_name() const { return "KNN"; }

    protected:
        virtual void store_model_features();

        virtual CLabels* classify_NN();

        void init_distance(CFeatures* data);

        virtual bool train_machine(CFeatures* data=NULL);

    private:
        void init();

    protected:
        int32_t m_k;

        float64_t m_q;

        int32_t num_classes;

        int32_t min_label;

        SGVector<int32_t> train_labels;
};
}
#endif