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LatAnalyze/examples/exFitSample.cpp

79 lines
2.4 KiB
C++

#include <LatAnalyze/Functional/CompiledModel.hpp>
#include <LatAnalyze/Numerical/GslMinimizer.hpp>
#include <LatAnalyze/Core/Plot.hpp>
#include <LatAnalyze/Statistics/XYSampleData.hpp>
using namespace std;
using namespace Latan;
const Index nPoint1 = 10, nPoint2 = 10;
const Index nSample = 1000;
const double xErr = .1, yErr = .3;
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);
double xBuf[2];
random_device rd;
mt19937 gen(rd());
normal_distribution<> dis;
DoubleModel f([](const double *x, const double *p)
{return p[1]*exp(-x[0]*p[0])+x[1];}, 2, 2);
cout << "-- generating fake data..." << endl;
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] = xErr*dis(gen) + xBuf[0];
for (Index i2 = 0; i2 < nPoint2; ++i2)
{
xBuf[1] = i2*dx2;
data.x(i2, 1)[s] = xBuf[1];
data.y(data.dataIndex(i1, i2), 0)[s] = yErr*dis(gen)
+ f(xBuf, exactPar);
}
}
}
data.assumeXExact(true, 1);
// set minimizers
DVec init = DVec::Constant(2, 0.1);
SampleFitResult p;
GslMinimizer min(GslMinimizer::Algorithm::bfgs2);
// fit
cout << "-- fit..." << endl;
f.parName().setName(0, "m");
f.parName().setName(1, "A");
p = data.fit(min, init, f);
p.print();
// plot
Plot plot;
DVec ref(2);
XYStatData res;
cout << "-- generating plots..." << endl;
ref(1) = 0.;
res = data.getPartialResiduals(p, ref, 0).getData();
plot << PlotRange(Axis::x, 0., 10.);
plot << Color("rgb 'blue'");
plot << PlotPredBand(p.getModel(_).bind(0, ref), 0., 10.);
plot << Color("rgb 'blue'");
plot << PlotFunction(p.getModel().bind(0, ref), 0., 10.);
plot << Color("rgb 'red'");
plot << PlotData(res);
plot.display();
return EXIT_SUCCESS;
}