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first stab at correlator fit utility classes
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331
lib/Physics/CorrelatorFitter.cpp
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331
lib/Physics/CorrelatorFitter.cpp
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/*
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* CorrelatorFitter.cpp, part of LatAnalyze 3
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*
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* Copyright (C) 2013 - 2020 Antonin Portelli
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*
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* LatAnalyze 3 is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* LatAnalyze 3 is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with LatAnalyze 3. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <LatAnalyze/Physics/CorrelatorFitter.hpp>
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#include <LatAnalyze/includes.hpp>
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using namespace std;
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using namespace Latan;
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DoubleModel CorrelatorModels::makeExpModel(const Index nState)
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{
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DoubleModel mod;
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mod.setFunction([nState](const double *x, const double *p)
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{
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double res = 0.;
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for (unsigned int i = 0; i < nState; ++i)
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{
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res += p[2*i + 1]*exp(-p[2*i]*x[0]);
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}
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return res;
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}, 1, 2*nState);
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for (unsigned int i = 0; i < nState; ++i)
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{
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mod.parName().setName(2*i, "E_" + strFrom(i));
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mod.parName().setName(2*i + 1, "Z_" + strFrom(i));
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}
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return mod;
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}
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DoubleModel CorrelatorModels::makeCoshModel(const Index nState, const Index nt)
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{
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DoubleModel mod;
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mod.setFunction([nState, nt](const double *x, const double *p)
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{
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double res = 0.;
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for (unsigned int i = 0; i < nState; ++i)
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{
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res += p[2*i + 1]*(exp(-p[2*i]*x[0]) + exp(-p[2*i]*(nt - x[0])));
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}
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return res;
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}, 1, 2*nState);
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for (unsigned int i = 0; i < nState; ++i)
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{
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mod.parName().setName(2*i, "E_" + strFrom(i));
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mod.parName().setName(2*i + 1, "Z_" + strFrom(i));
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}
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return mod;
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}
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DoubleModel CorrelatorModels::makeSinhModel(const Index nState, const Index nt)
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{
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DoubleModel mod;
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mod.setFunction([nState, nt](const double *x, const double *p)
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{
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double res = 0.;
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for (unsigned int i = 0; i < nState; ++i)
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{
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res += p[2*i + 1]*(exp(-p[2*i]*x[0]) - exp(-p[2*i]*(nt - x[0])));
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}
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return res;
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}, 1, 2*nState);
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for (unsigned int i = 0; i < nState; ++i)
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{
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mod.parName().setName(2*i, "E_" + strFrom(i));
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mod.parName().setName(2*i + 1, "Z_" + strFrom(i));
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}
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return mod;
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}
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DoubleModel CorrelatorModels::makeConstModel(void)
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{
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DoubleModel mod;
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mod.setFunction([](const double *x, const double *p __dumb)
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{
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return x[0];
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}, 1, 1);
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mod.parName().setName(0, "cst");
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return mod;
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}
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DoubleModel CorrelatorModels::makeLinearModel(void)
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{
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DoubleModel mod;
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mod.setFunction([](const double *x, const double *p)
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{
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return p[0] + p[1]*x[0];
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}, 1, 2);
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return mod;
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}
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CorrelatorModels::ModelPar CorrelatorModels::parseModel(const string s)
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{
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smatch sm;
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ModelPar par;
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if (regex_match(s, sm, regex("exp([0-9]+)")))
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{
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par.type = CorrelatorType::exp;
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par.nState = strTo<Index>(sm[0].str());
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}
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else if (regex_match(s, sm, regex("cosh([0-9]+)")))
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{
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par.type = CorrelatorType::cosh;
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par.nState = strTo<Index>(sm[0].str());
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}
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else if (regex_match(s, sm, regex("sinh([0-9]+)")))
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{
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par.type = CorrelatorType::sinh;
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par.nState = strTo<Index>(sm[0].str());
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}
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else if (s == "linear")
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{
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par.type = CorrelatorType::linear;
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par.nState = 1;
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}
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else if (s == "cst")
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{
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par.type = CorrelatorType::cst;
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par.nState = 1;
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}
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else
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{
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par.type = CorrelatorType::undefined;
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par.nState = 0;
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}
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return par;
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}
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DoubleModel CorrelatorModels::makeModel(const CorrelatorModels::ModelPar par,
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const Index nt)
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{
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switch (par.type)
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{
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case CorrelatorType::undefined:
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LATAN_ERROR(Argument, "correlator type is undefined");
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break;
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case CorrelatorType::exp:
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return makeExpModel(par.nState);
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break;
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case CorrelatorType::cosh:
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return makeCoshModel(par.nState, nt);
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break;
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case CorrelatorType::sinh:
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return makeSinhModel(par.nState, nt);
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break;
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case CorrelatorType::linear:
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return makeLinearModel();
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break;
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case CorrelatorType::cst:
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return makeConstModel();
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break;
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}
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}
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DVec CorrelatorModels::parameterGuess(const DMatSample &corr,
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const ModelPar par)
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{
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DVec init;
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Index nt = corr.size();
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switch (par.type)
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{
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case CorrelatorType::undefined:
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LATAN_ERROR(Argument, "correlator type is undefined");
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break;
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case CorrelatorType::exp:
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case CorrelatorType::cosh:
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case CorrelatorType::sinh:
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init.resize(2*par.nState);
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init(0) = log(corr[central](nt/4)/corr[central](nt/4 + 1));
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init(1) = corr[central](nt/4)/(exp(-init(0)*nt/4));
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for (Index p = 2; p < init.size(); p += 2)
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{
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init(p) = 2*init(p - 2);
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init(p + 1) = init(p - 1)/2.;
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}
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break;
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case CorrelatorType::linear:
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init.resize(2);
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init(0) = corr[central](nt/4) - corr[central](nt/4 + 1, 0);
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init(1) = corr[central](nt/4, 0) + nt/4*init(0);
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break;
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case CorrelatorType::cst:
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init.resize(1);
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init(0) = corr[central](nt/4);
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break;
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default:
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break;
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}
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return init;
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}
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CorrelatorFitter::CorrelatorFitter(const DMatSample &corr)
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{
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setCorrelator(corr);
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}
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CorrelatorFitter::CorrelatorFitter(const std::vector<DMatSample> &corr)
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{
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setCorrelators(corr);
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}
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void CorrelatorFitter::setCorrelator(const DMatSample &corr)
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{
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std::vector<DMatSample> vec;
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vec.push_back(corr);
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setCorrelators(vec);
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}
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void CorrelatorFitter::setCorrelators(const std::vector<DMatSample> &corr)
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{
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Index nSample = corr[0].size();
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DMatSample tVec(nSample);
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std::vector<const DMatSample *> ptVec;
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nt_ = corr[0][central].rows();
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tVec.fill(DVec::LinSpaced(nt_, 0, nt_ - 1));
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for (auto &c: corr)
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{
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ptVec.push_back(&c);
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}
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data_.reset(new XYSampleData(corr[0].size()));
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data_->addXDim(nt_, "t/a", true);
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for (unsigned int i = 0; i < corr.size(); ++i)
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{
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data_->addYDim("C_" + strFrom(i) + "(t)");
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}
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data_->setUnidimData(tVec, ptVec);
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model_.resize(corr.size());
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range_.resize(corr.size(), make_pair(0, nt_ - 1));
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thinning_.resize(corr.size(), 1);
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}
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void CorrelatorFitter::setModel(const DoubleModel &model, const Index i)
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{
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model_[i] = model;
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}
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const DoubleModel & CorrelatorFitter::getModel(const Index i) const
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{
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return model_.at(i);
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}
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void CorrelatorFitter::setFitRange(const Index tMin, const Index tMax,
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const Index i)
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{
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range_[i] = make_pair(tMin, tMax);
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refreshRanges();
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}
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void CorrelatorFitter::setCorrelation(const bool isCorrelated, const Index i,
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const Index j)
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{
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data_->assumeYYCorrelated(isCorrelated, i, j);
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}
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DMat CorrelatorFitter::getVarianceMatrix(void)
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{
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return data_->getFitVarMat();
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}
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void CorrelatorFitter::setThinning(const Index thinning, const Index i)
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{
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thinning_[i] = thinning;
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refreshRanges();
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}
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SampleFitResult CorrelatorFitter::fit(Minimizer &minimizer, const DVec &init)
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{
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vector<Minimizer *> vecPt = {&minimizer};
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return fit(vecPt, init);
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}
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SampleFitResult CorrelatorFitter::fit(vector<Minimizer *> &minimizer,
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const DVec &init)
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{
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vector<const DoubleModel *> vecPt(model_.size());
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for (unsigned int i = 0; i < model_.size(); ++i)
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{
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vecPt[i] = &(model_[i]);
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}
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return data_->fit(minimizer, init, vecPt);
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}
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void CorrelatorFitter::refreshRanges(void)
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{
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for (unsigned int i = 0; i < range_.size(); ++i)
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for (Index t = 0; t < nt_; ++t)
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{
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data_->fitPoint((t >= range_[i].first) and (t <= range_[i].second)
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and ((t - range_[i].first) % thinning_[i] == 0), t);
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}
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}
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