ensemble_averaged_method.h

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/* -------------------------------------------------------------------------*
*                               SPHinXsys                                   *
* --------------------------------------------------------------------------*
* SPHinXsys (pronunciation: s'finksis) is an acronym from Smoothed Particle *
* Hydrodynamics for industrial compleX systems. It provides C++ APIs for    *
* physical accurate simulation and aims to model coupled industrial dynamic *
* systems including fluid, solid, multi-body dynamics and beyond with SPH   *
* (smoothed particle hydrodynamics), a meshless computational method using  *
* particle discretization.                                                  *
*                                                                           *
* SPHinXsys is partially funded by German Research Foundation               *
* (Deutsche Forschungsgemeinschaft) DFG HU1527/6-1, HU1527/10-1             *
* and HU1527/12-1.                                                          *
*                                                                           *
* Portions copyright (c) 2017-2020 Technical University of Munich and       *
* the authors' affiliations.                                                *
*                                                                           *
* Licensed under the Apache License, Version 2.0 (the "License"); you may   *
* not use this file except in compliance with the License. You may obtain a *
* copy of the License at http://www.apache.org/licenses/LICENSE-2.0.        *
*                                                                           *
* --------------------------------------------------------------------------*/
#pragma once
#include "regression_test_base.hpp"

namespace SPH
{
    template<class ObserveMethodType>
    class RegressionTestEnsembleAveraged : public RegressionTestTimeAveraged<ObserveMethodType>
    {
        /* identify the variable type from the parent class. */
        using VariableType = decltype(ObserveMethodType::type_indicator_);

    protected:
        DoubleVec<VariableType> meanvalue_, meanvalue_new_;  /* the container of (new) meanvalue. [different from time-averaged]*/
        DoubleVec<VariableType> variance_, variance_new_;    /* the container of (new) variance. [different from time-averaged]*/
        
        void calculateNewVariance(TripleVec<Real> &result, DoubleVec<Real> &meanvalue_new, DoubleVec<Real> &variance, DoubleVec<Real> &variance_new);
        void calculateNewVariance(TripleVec<Vecd> &result, DoubleVec<Vecd> &meanvalue_new, DoubleVec<Vecd> &variance, DoubleVec<Vecd> &variance_new);
        void calculateNewVariance(TripleVec<Matd> &result, DoubleVec<Matd> &meanvalue_new, DoubleVec<Matd> &variance, DoubleVec<Matd> &variance_new);

        int compareParameter(string par_name, DoubleVec<Real> &parameter, DoubleVec<Real> &parameter_new, Real &threshold);
        int compareParameter(string par_name, DoubleVec<Vecd> &parameter, DoubleVec<Vecd> &parameter_new, Vecd &threshold);
        int compareParameter(string par_name, DoubleVec<Matd> &parameter, DoubleVec<Matd> &parameter_new, Matd &threshold);
        
        int testNewResult(int diff, DoubleVec<Real> &current_result, DoubleVec<Real> &meanvalue, DoubleVec<Real> &variance);
        int testNewResult(int diff, DoubleVec<Vecd> &current_result, DoubleVec<Vecd> &meanvalue, DoubleVec<Vecd> &variance);
        int testNewResult(int diff, DoubleVec<Matd> &current_result, DoubleVec<Matd> &meanvalue, DoubleVec<Matd> &variance);

    public:
        template<typename... ConstructorArgs>
        explicit RegressionTestEnsembleAveraged(ConstructorArgs &&...args) :
            RegressionTestTimeAveraged<ObserveMethodType>(std::forward<ConstructorArgs>(args)...)
        {
            this->mean_variance_filefullpath_ = this->input_folder_path_ + "/" + this->body_name_ 
                + "_" + this->quantity_name_ + "_ensemble_averaged_mean_variance.xml";
        };
        virtual ~RegressionTestEnsembleAveraged() {};

        void settingupAndCorrection(); 
        void readMeanVarianceFromXml(); 
        void updateMeanVariance(); 
        void writeMeanVarianceToXml(); 
        bool compareMeanVariance();  
        void resultTest(); 
        /* the interface for generating the priori converged result with M&V. */
        void generateDataBase(VariableType threshold_mean, VariableType threshold_variance, string filter = "false")
        {
            this->writeXmlToXmlFile();
            this->readXmlFromXmlFile();
            this->initializeThreshold(threshold_mean, threshold_variance);
            if (this->converged == "false")
            {
                settingupAndCorrection();
                this->readResultFromXml();
                if (filter == "true")
                    this->filterExtremeValues();
                readMeanVarianceFromXml();
                updateMeanVariance();
                this->writeResultToXml();
                writeMeanVarianceToXml();
                compareMeanVariance();
            };
            /*else
                std::cout << "The results have been converged." << endl;*/
        };

        void newResultTest(string filter = "false")
        {
            this->writeXmlToXmlFile();
            this->readXmlFromXmlFile();
            settingupAndCorrection();
            if (filter == "true")
                this->filterExtremeValues();
            readMeanVarianceFromXml();
            resultTest();
        };
    };
};