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