/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.

This library 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 2.1 of the License, or (at your option) any later version.

This library 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 this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA

Copyright (C) 2010 Thales group
*/
/*
Authors:
    Johann Dréo <johann.dreo@thalesgroup.com>
    Caner Candan <caner.candan@thalesgroup.com>
*/

#ifndef _edoSamplerUniform_h
#define _edoSamplerUniform_h

#include <utils/eoRNG.h>

#include "edoSampler.h"
#include "edoUniform.h"

/**
 * edoSamplerUniform
 * This class uses the Uniform distribution parameters (bounds) to return
 * a random position used for population sampling.
 *
 * Returns a random number in [min,max[ for each variable defined by the given
 * distribution.
 *
 * Note: if the distribution given at call defines a min==max for one of the
 * variable, the result will be the same number.
 */
template < typename EOT, class D = edoUniform<EOT> >
class edoSamplerUniform : public edoSampler< D >
{
public:
    typedef D Distrib;

    edoSamplerUniform( edoRepairer<EOT> & repairer ) : edoSampler< D >( repairer) {}

    EOT sample( edoUniform< EOT >& distrib )
    {
        unsigned int size = distrib.size();
        assert(size > 0);

        // Point we want to sample to get higher a set of points
        // (coordinates in n dimension)
        // x = {x1, x2, ..., xn}
        EOT solution;

        // Sampling all dimensions
        for (unsigned int i = 0; i < size; ++i)
        {
            double min = distrib.min()[i];
            double max = distrib.max()[i];
            double random = rng.uniform(min, max);

            assert( ( min == random && random == max ) || ( min <= random && random < max) ); // random in [ min, max [

            solution.push_back(random);
        }

        return solution;
    }
};

#endif // !_edoSamplerUniform_h