tsgDreamCorePDF.hpp

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/*
 * Copyright (c) 2017, Miroslav Stoyanov
 *
 * This file is part of
 * Toolkit for Adaptive Stochastic Modeling And Non-Intrusive ApproximatioN: TASMANIAN
 *
 * 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.
 *
 * 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse
 *    or promote products derived from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
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#ifndef __TASMANIAN_DREAM_CORE_PDF_HPP
#define __TASMANIAN_DREAM_CORE_PDF_HPP
 
#include "tsgDreamEnumerates.hpp"
 
namespace TasDREAM{
 
 
template<TypeDistribution distribution, TypeSamplingForm form = regform, typename... Params>
double getDensity(double x, Params... params){
    std::vector<typename std::tuple_element<0, std::tuple<Params...>>::type> ParameterArray = {params...};
    if (form == regform){
        if (distribution == dist_gaussian){
            return std::exp(-0.5 * (x - ParameterArray[0]) * (x - ParameterArray[0]) / ParameterArray[1]);
        }else if (distribution == dist_exponential){
            return std::exp(-ParameterArray[1] * (x - ParameterArray[0]));
        }else if (distribution == dist_beta){
            return std::pow(x - ParameterArray[0], ParameterArray[2] - 1.0) * std::pow(ParameterArray[1] - x, ParameterArray[3] - 1.0);
        }else if (distribution == dist_gamma){
            return std::pow(x - ParameterArray[0], ParameterArray[1] - 1.0) * std::exp(- ParameterArray[2] * (x - ParameterArray[0]));
        }else{ // uniform
            return 1.0;
        }
    }else{
        if (distribution == dist_gaussian){
            return -0.5 * (x - ParameterArray[0]) * (x - ParameterArray[0]) / ParameterArray[1];
        }else if (distribution == dist_exponential){
            return -ParameterArray[1] * (x - ParameterArray[0]);
        }else if (distribution == dist_beta){
            return std::log(x - ParameterArray[0]) * (ParameterArray[2] - 1.0) + std::log(ParameterArray[1] - x) * (ParameterArray[3] - 1.0);
        }else if (distribution == dist_gamma){
            return std::log(x - ParameterArray[0]) * (ParameterArray[1] - 1.0) - ParameterArray[2] * (x - ParameterArray[0]);
        }else{ // uniform
            return 0.0;
        }
    }
}
 
}
 
 
#endif