tsgDreamLikelyGaussian.hpp

/*
 * 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_LIKELY_GAUSS_HPP
#define __TASMANIAN_DREAM_LIKELY_GAUSS_HPP
 
#include "tsgDreamLikelihoodCore.hpp"
 
namespace TasDREAM{
 
 
class LikelihoodGaussIsotropic : public TasmanianLikelihood{
public:
    LikelihoodGaussIsotropic() : scale(0.0){}
    LikelihoodGaussIsotropic(double variance, const std::vector<double> &data_mean, size_t num_observe = 1){ setData(variance, data_mean, num_observe); }
    ~LikelihoodGaussIsotropic() = default;
 
    void setData(double variance, const std::vector<double> &data_mean, size_t num_observe = 1);
 
    void getLikelihood(TypeSamplingForm form, const std::vector<double> &model, std::vector<double> &likely) const override final;
 
    void getLikelihood(TypeSamplingForm form, double const model[], int num_samples, double likely[]) const override final;
 
    int getNumOutputs() const override{ return (int) data.size(); }
 
    void write(std::ostream &os, int outputs_begin = 0, int outputs_end = -1) const{
        if (outputs_end < 0) outputs_end = getNumOutputs();
        outputs_end = std::min(std::max(outputs_begin + 1, outputs_end), getNumOutputs());
        int num_entries = outputs_end - outputs_begin;
        TasGrid::IO::writeNumbers<TasGrid::mode_binary, TasGrid::IO::pad_none>(os, num_entries);
        TasGrid::IO::writeNumbers<TasGrid::mode_binary, TasGrid::IO::pad_none>(os, scale);
        os.write((char*) &data[outputs_begin], num_entries * sizeof(double));
    }
 
    void read(std::istream &is){
        int num_entries = TasGrid::IO::readNumber<TasGrid::IO::mode_binary_type, int>(is);
        scale = TasGrid::IO::readNumber<TasGrid::IO::mode_binary_type, double>(is);
        data = std::vector<double>((size_t) num_entries);
        TasGrid::IO::readVector<TasGrid::IO::mode_binary_type>(is, data);
    }
 
private:
    std::vector<double> data;
    double scale;
};
 
class LikelihoodGaussAnisotropic : public TasmanianLikelihood{
public:
    LikelihoodGaussAnisotropic() = default;
    LikelihoodGaussAnisotropic(std::vector<double> const &variance, std::vector<double> const &data_mean, size_t num_observe = 1){ setData(variance, data_mean, num_observe); }
    ~LikelihoodGaussAnisotropic() = default;
 
    void setData(std::vector<double> const &variance, std::vector<double> const &data_mean, size_t num_observe = 1);
 
    void getLikelihood(TypeSamplingForm form, std::vector<double> const &model, std::vector<double> &likely) const override final;
 
    void getLikelihood(TypeSamplingForm form, double const model[], int num_samples, double likely[]) const override final;
 
    int getNumOutputs() const override{ return (int) noise_variance.size(); }
 
    void write(std::ostream &os, int outputs_begin = 0, int outputs_end = -1) const{
        if (outputs_end < 0) outputs_end = getNumOutputs();
        outputs_end = std::min(std::max(outputs_begin + 1, outputs_end), getNumOutputs());
        int num_entries = outputs_end - outputs_begin;
        TasGrid::IO::writeNumbers<TasGrid::mode_binary, TasGrid::IO::pad_none>(os, num_entries);
        os.write((char*) &data_by_variance[outputs_begin], num_entries * sizeof(double));
        os.write((char*) &noise_variance[outputs_begin], num_entries * sizeof(double));
    }
 
    void read(std::istream &is){
        int num_entries = TasGrid::IO::readNumber<TasGrid::IO::mode_binary_type, int>(is);
        data_by_variance = std::vector<double>((size_t) num_entries);
        noise_variance   = std::vector<double>((size_t) num_entries);
        TasGrid::IO::readVector<TasGrid::IO::mode_binary_type>(is, data_by_variance);
        TasGrid::IO::readVector<TasGrid::IO::mode_binary_type>(is, noise_variance);
    }
 
private:
    std::vector<double> data_by_variance;
    std::vector<double> noise_variance;
};
 
 
}
 
#endif