HyperGeometric.h

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/*!
 * 
 *
 * \brief       Hypergeometric distribution.
 * 
 * 
 *
 * \author      O. Krause
 * \date        2010-01-01
 *
 *
 * \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_RNG_HYPERGEOMETRIC_H
#define SHARK_RNG_HYPERGEOMETRIC_H




#include <cmath>
#include <boost/random/uniform_01.hpp>

#ifndef BOOST_RANDOM_NO_STREAM_OPERATORS
#include <iostream>
#endif

namespace shark{
/// \brief Hypergeometric distribution.
    template<class IntType=int,class RealType = double>
    class HyperGeometric_distribution
    {
    public:
        typedef RealType input_type;
        typedef IntType result_type;

        explicit HyperGeometric_distribution(RealType mean,RealType variance)
            :mean_(mean),variance_(variance)
        {
            if (mean == 0)
            {
                p = 0.;
            }
            else
            {
                double z = variance / (mean * mean);
                p = (1 - std::sqrt((z - 1) / (z + 1))) / 2;

                if (p < 0.) p = 0.;
            }
        }

        RealType mean() const
        {
            return mean_;
        }
        RealType variance()const
        {
            return variance_;
        }

        void reset() { }

        template<class Engine>
        result_type operator()(Engine& eng)
        {
            double uni1 =  boost::uniform_01<RealType>(eng);
            double uni2 =  boost::uniform_01<RealType>(eng);
            return -mean_ * ::log( uni1) / (2 *( uni2 > p? 1 - p : p));
        }

#ifndef BOOST_RANDOM_NO_STREAM_OPERATORS
        template<class CharT, class Traits>
        friend std::basic_ostream<CharT,Traits>&
            operator<<(std::basic_ostream<CharT,Traits>& os, const HyperGeometric_distribution& d)
        {
            os << d.mean_;
            os << d.variance_;
            return os;
        }

        template<class CharT, class Traits>
        friend std::basic_istream<CharT,Traits>&
            operator>>(std::basic_istream<CharT,Traits>& is, HyperGeometric_distribution& d)
        {
            is >> d.mean_;
            is >> d.variance_;
            return is;
        }
#endif
    private:
        RealType mean_;
        RealType variance_;
        RealType p;
    };

    /**
    * \brief Random variable with a hypergeometric distribution.
    */
    template<typename RngType = shark::DefaultRngType>
    class HyperGeometric:public boost::variate_generator<RngType*,HyperGeometric_distribution<> > {
    private:
        typedef boost::variate_generator<RngType*,HyperGeometric_distribution<> > Base;
    public:

        HyperGeometric( RngType & rng, double k=1, double theta=1 )
            :Base(&rng,HyperGeometric_distribution<>(k,theta))
        {}

        using Base::operator();

        double operator()(double k,double theta)
        {
            HyperGeometric_distribution<> dist(k,theta);
            return dist(Base::engine());
        }

        double mean()const
        {
            return Base::distribution().mean();
        }
        double variance()const
        {
            return Base::distribution().variance();
        }
        void mean(double newMean)
        {
            Base::distribution()=HyperGeometric_distribution<>(newMean,variance());
        }
        void variance(double newVariance)
        {
            Base::distribution()=HyperGeometric_distribution<>(mean(),newVariance);
        }

        double p(double x)const
        {
            return 0.0;///!!!
        }

    };
}
#endif