tsgDreamState.hpp

/*
 * 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.
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 * 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.
 *
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#ifndef __TASMANIAN_DREAM_STATE_HPP
#define __TASMANIAN_DREAM_STATE_HPP
 
#include "tsgDreamEnumerates.hpp"
 
 
namespace TasDREAM{
 
 
class TasmanianDREAM{
public:
    TasmanianDREAM();
    TasmanianDREAM(int cnum_chains, int cnum_dimensions);
    TasmanianDREAM(int cnum_chains, const TasGrid::TasmanianSparseGrid &grid);
    ~TasmanianDREAM();
 
    static const char* getVersion(){ return TASMANIAN_VERSION_STRING; }
    static const char* getLicense(){ return TASMANIAN_LICENSE; }
    static int getVersionMajor(){ return TASMANIAN_VERSION_MAJOR; }
    static int getVersionMinor(){ return TASMANIAN_VERSION_MINOR; }
 
    int getNumDimensions() const{ return (int) num_dimensions; }
    int getNumChains() const{ return (int) num_chains; }
    size_t getNumHistory() const{ return pdf_history.size(); }
 
    bool isStateReady() const{ return init_state; }
    bool isPDFReady() const{ return init_values; }
 
 
    void setState(const std::vector<double> &new_state);
 
 
    void setState(std::function<void(double *)> update_state);
 
 
    void setPDFvalues(const std::vector<double> &new_values);
 
 
    void setPDFvalues(std::function<void(const std::vector<double> &state, std::vector<double> &values)> probability_distribution);
 
    void clearPDFvalues();
 
 
    void getIJKdelta(size_t i, size_t j, size_t k, double w, std::vector<double> &x) const;
 
 
    void getChainState(size_t i, double *x) const{ std::copy_n(state.begin() + i * num_dimensions, num_dimensions, x); }
 
    const std::vector<double>& getChainState() const{ return state; }
 
 
    double getPDFvalue(size_t i) const{ return pdf_values[i]; }
 
 
    void expandHistory(int num_snapshots);
 
 
    void saveStateHistory(size_t num_accepted);
 
    const std::vector<double>& getHistory() const{ return history; }
 
    const std::vector<double>& getHistoryPDF() const{ return pdf_history; }
 
    void getHistoryMeanVariance(std::vector<double> &mean, std::vector<double> &var) const;
 
    void getApproximateMode(std::vector<double> &mode) const;
 
    std::vector<double> getApproximateMode() const{
        std::vector<double> mode;
        getApproximateMode(mode);
        return mode;
    }
 
    void clearHistory();
 
    double getAcceptanceRate() const{ return ((pdf_history.empty()) ? 0 : ((double) accepted) / ((double) pdf_history.size())); }
 
    // file I/O
private:
    size_t num_chains, num_dimensions;
    bool init_state, init_values;
 
    size_t accepted;
 
    std::vector<double> state, history;
    std::vector<double> pdf_values, pdf_history;
};
 
}
 
#endif