tsgDreamCoreRandom.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
 *
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#ifndef __TASMANIAN_DREAM_CORE_RANDOM_HPP
#define __TASMANIAN_DREAM_CORE_RANDOM_HPP
 
namespace TasDREAM{
 
 
inline double tsgCoreUniform01(){ return ((double) rand()) / ((double) RAND_MAX); }
 
 
inline void applyUniformUpdate(std::vector<double> &x, double magnitude, std::function<double(void)> get_random01 = tsgCoreUniform01){
    if (magnitude == 0.0) return;
    for(auto &v : x) v += magnitude * (2.0 * get_random01() -1.0);
}
 
 
inline void applyGaussianUpdate(std::vector<double> &x, double magnitude, std::function<double(void)> get_random01 = tsgCoreUniform01){
    if (magnitude == 0.0) return;
    bool tictoc = false;
    double g = 0.0;
    for(auto &v : x){
        tictoc = !tictoc;
        if (tictoc){
            double r = magnitude * std::sqrt(-2.0 * std::log(get_random01())), t = 2.0 * DreamMaths::pi * get_random01(); // radius and angle
            v += r * std::cos(t);
            g = r * std::sin(t);
        }else{
            v += g;
        }
    }
}
 
 
inline void genUniformSamples(const std::vector<double> &lower, const std::vector<double> &upper, int num_samples, std::vector<double> &x, std::function<double(void)> get_random01 = tsgCoreUniform01){
    if (lower.size() != upper.size()) throw std::runtime_error("ERROR: genUniformSamples() requires lower and upper vectors with matching size.");
    if (x.size() != lower.size() * num_samples) x.resize(lower.size() * num_samples);
    for(auto &v : x) v = get_random01();
 
    std::vector<double> length(lower.size());
    std::transform(lower.begin(), lower.end(), upper.begin(), length.begin(), [&](double l, double u)->double{ return (u - l); });
 
    auto ix = x.begin();
    while(ix != x.end()){
        auto ilow = lower.begin();
        for(auto l : length){
            *ix *= l;
            *ix++ += *ilow++;
        }
    }
}
 
inline std::vector<double> genUniformSamples(const std::vector<double> &lower, const std::vector<double> &upper,
                                             int num_samples, std::function<double(void)> get_random01 = tsgCoreUniform01){
    std::vector<double> x;
    genUniformSamples(lower, upper, num_samples, x, get_random01);
    return x;
}
 
 
inline void genGaussianSamples(const std::vector<double> &means, const std::vector<double> &deviations,
                               int num_samples, std::vector<double> &x, std::function<double(void)> get_random01 = tsgCoreUniform01){
    if (means.size() != deviations.size()) throw std::runtime_error("ERROR: genGaussianSamples() means and deviations vectors must have the same size.");
    if (x.size() != means.size() * num_samples) x.resize(means.size() * num_samples);
 
    std::fill_n(x.data(), x.size(), 0.0);
    applyGaussianUpdate(x, 1.0, get_random01);
 
    auto ix = x.begin();
    while(ix != x.end()){
        auto im = means.begin();
        for(auto s : deviations){
            *ix *= s;
            *ix++ += *im++;
        }
    }
}
 
inline std::vector<double> genGaussianSamples(const std::vector<double> &means, const std::vector<double> &deviations,
                                              int num_samples, std::function<double(void)> get_random01 = tsgCoreUniform01){
    std::vector<double> x;
    genGaussianSamples(means, deviations, num_samples, x, get_random01);
    return x;
}
 
}
 
#endif