KernelDensity.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2014 Parijat Mazumdar
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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 * either expressed or implied, of the Shogun Development Team.
 */

#ifndef _KERNELDENSITY_H__
#define _KERNELDENSITY_H__

#include <shogun/lib/config.h>
#include <shogun/distributions/Distribution.h>
#include <shogun/kernel/Kernel.h>
#include <shogun/multiclass/tree/NbodyTree.h>

namespace shogun
{

enum EEvaluationMode
{
    EM_KDTREE_SINGLE,
    EM_BALLTREE_SINGLE,
    EM_KDTREE_DUAL,
    EM_BALLTREE_DUAL
};

class CKernelDensity : public CDistribution
{
public :
    CKernelDensity(float64_t bandwidth=1.0, EKernelType kernel_type=K_GAUSSIAN, EDistanceType dist=D_EUCLIDEAN, EEvaluationMode eval=EM_BALLTREE_SINGLE, int32_t leaf_size=1, float64_t atol=0, float64_t rtol=0);

    ~CKernelDensity();

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

    virtual bool train(CFeatures* data=NULL);

    SGVector<float64_t> get_log_density(CDenseFeatures<float64_t>* test, int32_t leaf_size=1);

    virtual int32_t get_num_model_parameters();

    virtual float64_t get_log_model_parameter(int32_t num_param);

    virtual float64_t get_log_derivative(int32_t num_param, int32_t num_example);

    virtual float64_t get_log_likelihood_example(int32_t num_example);

    inline static float64_t log_norm(EKernelType kernel, float64_t width, int32_t dim)
    {
        switch(kernel)
        {
            case K_GAUSSIAN:
            {
                return -0.5*dim* CMath::log(2*CMath::PI)-dim*CMath::log(width);
                break;
            }
            default:
                SG_SPRINT("kernel type not recognized\n");
        }

        return 0.0;
    }

    inline static float64_t log_kernel(EKernelType kernel, float64_t dist, float64_t width)
    {
        switch(kernel)
        {
            case K_GAUSSIAN:
            {
                return -0.5*dist*dist/(width*width);
                break;
            }
            default:
                SG_SPRINT("kernel type not recognized\n");
        }

        return 0.0;
    }

private:
    void init();

private :
    float64_t m_bandwidth;

    int32_t m_leaf_size;

    float64_t m_atol;

    float64_t m_rtol;

    EEvaluationMode m_eval;

    EKernelType m_kernel_type;

    EDistanceType m_dist;

    CNbodyTree* tree;
};
} /* shogun */

#endif /* _KERNELDENSITY_H__ */