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https://github.com/aportelli/LatAnalyze.git
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63 lines
1.9 KiB
C++
63 lines
1.9 KiB
C++
#include <iostream>
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#include <cmath>
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#include <LatAnalyze/CompiledModel.hpp>
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#include <LatAnalyze/Io.hpp>
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#include <LatAnalyze/MinuitMinimizer.hpp>
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#include <LatAnalyze/RandGen.hpp>
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#include <LatAnalyze/XYStatData.hpp>
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using namespace std;
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using namespace Latan;
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const Index nPoint1 = 5, nPoint2 = 5;
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const double xErr = .1, yErr = .1;
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const double exactPar[2] = {0.5,5.};
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const double dx1 = 10.0/static_cast<double>(nPoint1);
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const double dx2 = 5.0/static_cast<double>(nPoint2);
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int main(void)
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{
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// generate fake data
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XYStatData data;
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RandGen rg;
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double xBuf[2];
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DoubleModel f([](const double *x, const double *p)
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{return p[1]*exp(-x[0]*p[0])+x[1];}, 2, 2);
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data.addXDim("x", nPoint1);
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data.addXDim("off", nPoint2);
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data.addYDim("y");
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for (Index i1 = 0; i1 < nPoint1; ++i1)
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{
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xBuf[0] = i1*dx1;
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data.x(i1, 0) = rg.gaussian(xBuf[0], xErr);
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for (Index i2 = 0; i2 < nPoint2; ++i2)
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{
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xBuf[1] = i2*dx2;
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data.x(i2, 1) = xBuf[1];
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data.y(data.dataIndex(i1, i2)) = rg.gaussian(f(xBuf, exactPar),
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yErr);
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printf("% 8e % 8e % 8e % 8e % 8e\n", data.x(i1, 0), xErr,
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data.x(i2, 1), data.y(i1), yErr);
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}
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}
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cout << endl;
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data.setXError(0, DVec::Constant(data.getXSize(0), xErr));
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data.assumeXExact(true, 1);
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data.setYError(0, DVec::Constant(data.getYSize(), yErr));
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cout << data << endl;
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// fit
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DVec init = DVec::Constant(2, 0.1);
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FitResult p;
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MinuitMinimizer minimizer;
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minimizer.setVerbosity(Minimizer::Verbosity::Normal);
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p = data.fit(minimizer, init, f);
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cout << "a= " << p(0) << " b= " << p(1) << endl;
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cout << "chi^2/ndof= " << p.getChi2PerDof() << endl;
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cout << "p-value= " << p.getPValue() <<endl;
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return EXIT_SUCCESS;
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}
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