mirror of
https://github.com/aportelli/LatAnalyze.git
synced 2024-09-20 05:25:37 +01:00
63 lines
1.8 KiB
C++
63 lines
1.8 KiB
C++
#include <iostream>
|
|
#include <cmath>
|
|
#include <LatAnalyze/CompiledModel.hpp>
|
|
#include <LatAnalyze/MinuitMinimizer.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("x", nPoint1);
|
|
data.addXDim("off", nPoint2);
|
|
data.addYDim("y");
|
|
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);
|
|
cout << data << endl;
|
|
|
|
// fit
|
|
DVec init = DVec::Constant(2, 0.1);
|
|
DMat err;
|
|
SampleFitResult p;
|
|
MinuitMinimizer minimizer;
|
|
|
|
p = data.fit(minimizer, init, f);
|
|
err = p.variance().cwiseSqrt();
|
|
cout << "a= " << p[central](0) << " +/- " << err(0) << endl;
|
|
cout << "b= " << p[central](1) << " +/- " << err(1) << endl;
|
|
cout << "chi^2/ndof= " << p.getChi2PerDof() << endl;
|
|
cout << "p-value= " << p.getPValue() << endl;
|
|
|
|
return EXIT_SUCCESS;
|
|
}
|