GaussianDistribution.h

Go to the documentation of this file.

/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2014 Wu Lin
 * Written (W) 2013 Heiko Strathmann
 * 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
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 * The views and conclusions contained in the software and documentation are those
 * of the authors and should not be interpreted as representing official policies,
 * either expressed or implied, of the Shogun Development Team.
 *
 */

#ifndef GAUSSIANDISTRIBUTION_H
#define GAUSSIANDISTRIBUTION_H

#include <shogun/lib/config.h>


#include <shogun/distributions/classical/ProbabilityDistribution.h>
#include <shogun/lib/SGVector.h>
#include <shogun/mathematics/Math.h>

namespace shogun
{


class CGaussianDistribution: public CProbabilityDistribution
{
public:
    CGaussianDistribution();

    CGaussianDistribution(SGVector<float64_t> mean, SGMatrix<float64_t> cov,
            bool cov_is_factor=false);

    virtual ~CGaussianDistribution();

    virtual SGMatrix<float64_t> sample(int32_t num_samples,
            SGMatrix<float64_t> pre_samples=SGMatrix<float64_t>()) const;

    virtual SGVector<float64_t> log_pdf_multiple(SGMatrix<float64_t> samples) const;

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


    static float64_t univariate_log_pdf(float64_t sample, float64_t mu = 0.0, float64_t sigma2 = 1.0)
    {
        REQUIRE(sigma2 > 0, "Variance should be positive\n");
        return -0.5 * (CMath::pow(sample - mu, 2) / sigma2
            + CMath::log(2.0 * CMath::PI) + CMath::log(sigma2));
    }
private:

    void init();

protected:
    SGVector<float64_t> m_mean;

    SGMatrix<float64_t> m_L;
};

}

#endif // GAUSSIANDISTRIBUTION_H