LatAnalyze/examples/exFit.cpp

#include <iostream>
#include <cmath>
#include <LatAnalyze/CompiledModel.hpp>
#include <LatAnalyze/Io.hpp>
#include <LatAnalyze/MinuitMinimizer.hpp>
#include <LatAnalyze/NloptMinimizer.hpp>
#include <LatAnalyze/RandGen.hpp>
#include <LatAnalyze/XYStatData.hpp>

using namespace std;
using namespace Latan;

const Index  nPoint1 = 10, nPoint2 = 10;
const double xErr = .1, yErr   = .1;
const double exactPar[2] = {0.5,5.};
const double dx1 = 10.0/static_cast<double>(nPoint1);
const double dx2 = 5.0/static_cast<double>(nPoint2);

int main(void)
{
    // generate fake data
    XYStatData  data;
    RandGen     rg;
    double      xBuf[2];
    DoubleModel f([](const double *x, const double *p)
                  {return p[1]*exp(-x[0]*p[0])+x[1];}, 2, 2);
    
    data.addXDim(nPoint1);
    data.addXDim(nPoint2);
    data.addYDim();
    for (Index i1 = 0; i1 < nPoint1; ++i1)
    {
        xBuf[0]       = i1*dx1;
        data.x(i1, 0) = rg.gaussian(xBuf[0], xErr);
        for (Index i2 = 0; i2 < nPoint2; ++i2)
        {
            xBuf[1]                        = i2*dx2;
            data.x(i2, 1)                  = xBuf[1];
            data.y(data.dataIndex(i1, i2)) = rg.gaussian(f(xBuf, exactPar),
                                                         yErr);
        }
    }
    data.setXError(0, DVec::Constant(data.getXSize(0), xErr));
    data.assumeXExact(true, 1);
    data.setYError(0, DVec::Constant(data.getYSize(), yErr));
    
    // set minimizers
    DVec                init = DVec::Constant(2, 0.1);
    FitResult           p;
    NloptMinimizer      globalMin(NloptMinimizer::Algorithm::GN_CRS2_LM);
    MinuitMinimizer     localMin;
    vector<Minimizer *> min{&globalMin, &localMin};
    
    globalMin.setVerbosity(Minimizer::Verbosity::Normal);
    globalMin.setPrecision(0.01);
    globalMin.setMaxIteration(10000);
    globalMin.useLowLimit(0);
    globalMin.setLowLimit(0, 0.);
    globalMin.useHighLimit(0);
    globalMin.setHighLimit(0, 20.);
    globalMin.useLowLimit(1);
    globalMin.setLowLimit(1, 0.);
    globalMin.useHighLimit(1);
    globalMin.setHighLimit(1, 20.);
    localMin.setVerbosity(Minimizer::Verbosity::Normal);
    
    // fit
    f.parName().setName(0, "m");
    f.parName().setName(1, "A");
    p = data.fit(min, init, f);
    p.print();
    
    return EXIT_SUCCESS;
}