Gaussian.h

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/*
 * 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) 2011 Alesis Novik
 * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
 */

#ifndef _GAUSSIAN_H__
#define _GAUSSIAN_H__

#include <shogun/lib/config.h>

#ifdef HAVE_LAPACK

#include <shogun/distributions/Distribution.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/lib/common.h>
#include <shogun/mathematics/lapack.h>
#include <shogun/mathematics/Math.h>

namespace shogun
{
class CDotFeatures;

enum ECovType
{
    FULL,
    DIAG,
    SPHERICAL
};

class CGaussian : public CDistribution
{
    public:
        CGaussian();
        CGaussian(SGVector<float64_t> mean, SGMatrix<float64_t> cov, ECovType cov_type=FULL);
        virtual ~CGaussian();

        void init();

        virtual bool train(CFeatures* data=NULL);

        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);

        virtual inline float64_t compute_PDF(SGVector<float64_t> point)
        {
            return CMath::exp(compute_log_PDF(point));
        }

        virtual float64_t compute_log_PDF(SGVector<float64_t> point);

        virtual inline SGVector<float64_t> get_mean()
        {
            return m_mean;
        }

        virtual inline void set_mean(SGVector<float64_t> mean)
        {
            m_mean.destroy_vector();
            if (mean.vlen==1)
                m_cov_type=SPHERICAL;

            m_mean=mean;
        }

        virtual SGMatrix<float64_t> get_cov();

        virtual inline void set_cov(SGMatrix<float64_t> cov)
        {
            ASSERT(cov.num_rows==cov.num_cols);
            ASSERT(cov.num_rows==m_mean.vlen);
            decompose_cov(cov);
            init();
            if (cov.do_free)
                cov.free_matrix();
        }

        inline ECovType get_cov_type()
        {
            return m_cov_type;
        }

        inline void set_cov_type(ECovType cov_type)
        {
            m_cov_type = cov_type;
        }

        inline SGVector<float64_t> get_d()
        {
            return m_d;
        }

        inline void set_d(SGVector<float64_t> d)
        {
            m_d.destroy_vector();
            m_d = d;
            init();
        }

        inline SGMatrix<float64_t> get_u()
        {
            return m_u;
        }

        inline void set_u(SGMatrix<float64_t> u)
        {
            m_u.destroy_matrix();
            m_u = u;
        }

        SGVector<float64_t> sample();

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

    private:
        void register_params();

        void decompose_cov(SGMatrix<float64_t> cov);

    protected:
        float64_t m_constant;
        SGVector<float64_t> m_d;
        SGMatrix<float64_t> m_u;
        SGVector<float64_t> m_mean;
        ECovType m_cov_type;
};
}
#endif //HAVE_LAPACK
#endif //_GAUSSIAN_H__