Random.h

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/*
 * Copyright (C) 2012 by
 *   MetraLabs GmbH (MLAB), GERMANY
 * and
 *   Neuroinformatics and Cognitive Robotics Labs (NICR) at TU Ilmenau, GERMANY
 * All rights reserved.
 *
 * Contact: info@mira-project.org
 *
 * Commercial Usage:
 *   Licensees holding valid commercial licenses may use this file in
 *   accordance with the commercial license agreement provided with the
 *   software or, alternatively, in accordance with the terms contained in
 *   a written agreement between you and MLAB or NICR.
 *
 * GNU General Public License Usage:
 *   Alternatively, this file may be used under the terms of the GNU
 *   General Public License version 3.0 as published by the Free Software
 *   Foundation and appearing in the file LICENSE.GPL3 included in the
 *   packaging of this file. Please review the following information to
 *   ensure the GNU General Public License version 3.0 requirements will be
 *   met: http://www.gnu.org/copyleft/gpl.html.
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 */

#ifndef _MIRA_RANDOM_H_
#define _MIRA_RANDOM_H_

#include <stdlib.h>

#include <utils/Singleton.h>
#include <utils/Time.h>

#define MIRA_RANDOM mira::RandomGeneratorSingleton::instance()

namespace mira
{


class RandomGeneratorSingleton : public LazySingleton<RandomGeneratorSingleton>
{
private:
    friend struct mira::singleton::CreateUsingNew<RandomGeneratorSingleton>;
    RandomGeneratorSingleton() {
        seed();
    }

public:
    void seed(uint64 seed = 0)
    {
#if defined(MIRA_LINUX)
        if (seed == 0)
            srand48(Time::now().toUnixTimestamp());
        else
            srand48(seed);
#elif defined(MIRA_WINDOWS)
        if (seed == 0)
            srand((unsigned int)(Time::now().toUnixTimestamp()));
        else
            srand((unsigned int)seed);
#endif
    }

    double random()
    {
#if defined(MIRA_LINUX)
        return drand48();
#elif defined(MIRA_WINDOWS)
        return 1.0*rand()/RAND_MAX;
#endif
    }

    template<typename T>
    T uniform(T min, T max)
    {
        return min + (T)(random()*(max-min));
    }

    double normal(double sigma = 1.0)
    {
        assert(sigma >= 0.0);
        if (sigma == 0.0)
            return 0.0;
        double x, y, r2;
        do {
            // choose x,y in uniform square [-1,-1) to [+1,+1)
            x = uniform(-1.0, 1.0);
            y = uniform(-1.0, 1.0);

            // see if it is in the unit circle
            r2 = x*x + y*y;
        } while ((r2 > 1.0) || (r2 == 0));

        // Marsaglia polar method
        return sigma * y * sqrt(-2.0*log(r2)/r2);
    }

    double gaussian(double sigma = 1.0) {
        return normal(sigma);
    }

};


}

#endif