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

using namespace std;
using namespace Latan;

const Index  nPoint1 = 10, nPoint2 = 10;
const Index  nSample = 1000;
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
    XYSampleData data(nSample);
    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 s = central; s < nSample; ++s)
    {
        for (Index i1 = 0; i1 < nPoint1; ++i1)
        {
            xBuf[0]          = i1*dx1;
            data.x(i1, 0)[s] = rg.gaussian(xBuf[0], xErr);
            for (Index i2 = 0; i2 < nPoint2; ++i2)
            {
                xBuf[1]                           = i2*dx2;
                data.x(i2, 1)[s]                  = xBuf[1];
                data.y(data.dataIndex(i1, i2))[s] =
                    rg.gaussian(f(xBuf, exactPar), yErr);
            }
        }
    }
    data.assumeXExact(true, 1);
    
    // set minimizers
    DVec                init = DVec::Constant(2, 0.1);
    SampleFitResult     p;
    NloptMinimizer      globalMin(NloptMinimizer::Algorithm::GN_CRS2_LM);
    MinuitMinimizer     localMin;
    vector<Minimizer *> min{&globalMin, &localMin};
    
    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.);
    
    // fit
    f.parName().setName(0, "m");
    f.parName().setName(1, "A");
    p = data.fit(min, init, f);
    p.print();
    
    return EXIT_SUCCESS;
}