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andrew-pr
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c73b609ac5
Author | SHA1 | Date | |
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c73b609ac5 | |||
05138baa08 | |||
a0bdbfd9dd |
2
.github/workflows/build-macos.yml
vendored
2
.github/workflows/build-macos.yml
vendored
@ -1,6 +1,6 @@
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name: Build macOS
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name: Build macOS
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on: [push]
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on: [push, workflow_dispatch]
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jobs:
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jobs:
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build:
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build:
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@ -7,7 +7,7 @@
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using namespace std;
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using namespace std;
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using namespace Latan;
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using namespace Latan;
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constexpr Index size = 8;
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constexpr Index n = 8;
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constexpr Index nDraw = 20000;
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constexpr Index nDraw = 20000;
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constexpr Index nSample = 2000;
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constexpr Index nSample = 2000;
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const string stateFileName = "exRand.seed";
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const string stateFileName = "exRand.seed";
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@ -40,14 +40,14 @@ int main(void)
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p << PlotFunction(compile("return exp(-x_0^2/2)/sqrt(2*pi);", 1), -5., 5.);
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p << PlotFunction(compile("return exp(-x_0^2/2)/sqrt(2*pi);", 1), -5., 5.);
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p.display();
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p.display();
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DMat var(size, size);
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DMat var(n, n);
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DVec mean(size);
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DVec mean(n);
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DMatSample sample(nSample, size, 1);
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DMatSample sample(nSample, n, 1);
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cout << "-- generating " << nSample << " Gaussian random vectors..." << endl;
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cout << "-- generating " << nSample << " Gaussian random vectors..." << endl;
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var = DMat::Random(size, size);
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var = DMat::Random(n, n);
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var *= var.adjoint();
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var *= var.adjoint();
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mean = DVec::Random(size);
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mean = DVec::Random(n);
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RandomNormal mgauss(mean, var, rd());
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RandomNormal mgauss(mean, var, rd());
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sample[central] = mgauss();
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sample[central] = mgauss();
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FOR_STAT_ARRAY(sample, s)
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FOR_STAT_ARRAY(sample, s)
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@ -24,7 +24,7 @@ int main(int argc, char *argv[])
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{
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{
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// parse arguments /////////////////////////////////////////////////////////
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// parse arguments /////////////////////////////////////////////////////////
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OptParser opt;
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OptParser opt;
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bool parsed, doLaplace, doPlot, doHeatmap, doCorr, fold, doScan;
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bool parsed, doLaplace, doPlot, doHeatmap, doCorr, fold, doScan, noGuess;
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string corrFileName, model, outFileName, outFmt, savePlot;
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string corrFileName, model, outFileName, outFmt, savePlot;
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Index ti, tf, shift, nPar, thinning;
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Index ti, tf, shift, nPar, thinning;
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double svdTol;
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double svdTol;
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@ -59,6 +59,8 @@ int main(int argc, char *argv[])
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"show the fit plot");
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"show the fit plot");
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opt.addOption("h", "heatmap" , OptParser::OptType::trigger, true,
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opt.addOption("h", "heatmap" , OptParser::OptType::trigger, true,
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"show the fit correlation heatmap");
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"show the fit correlation heatmap");
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opt.addOption("", "no-guess" , OptParser::OptType::trigger, true,
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"do not try to guess fit parameters");
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opt.addOption("", "save-plot", OptParser::OptType::value, true,
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opt.addOption("", "save-plot", OptParser::OptType::value, true,
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"saves the source and .pdf", "");
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"saves the source and .pdf", "");
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opt.addOption("", "scan", OptParser::OptType::trigger, true,
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opt.addOption("", "scan", OptParser::OptType::trigger, true,
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@ -87,6 +89,7 @@ int main(int argc, char *argv[])
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fold = opt.gotOption("fold");
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fold = opt.gotOption("fold");
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doPlot = opt.gotOption("p");
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doPlot = opt.gotOption("p");
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doHeatmap = opt.gotOption("h");
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doHeatmap = opt.gotOption("h");
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noGuess = opt.gotOption("no-guess");
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savePlot = opt.optionValue("save-plot");
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savePlot = opt.optionValue("save-plot");
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doScan = opt.gotOption("scan");
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doScan = opt.gotOption("scan");
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switch (opt.optionValue<unsigned int>("v"))
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switch (opt.optionValue<unsigned int>("v"))
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@ -167,13 +170,14 @@ int main(int argc, char *argv[])
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fitter.setThinning(thinning);
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fitter.setThinning(thinning);
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// set initial values ******************************************************
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// set initial values ******************************************************
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if (modelPar.type != CorrelatorType::undefined)
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if ((modelPar.type != CorrelatorType::undefined) and !noGuess)
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{
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{
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init = CorrelatorModels::parameterGuess(corr, modelPar);
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init = CorrelatorModels::parameterGuess(corr, modelPar);
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}
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}
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else
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else
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{
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{
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init.fill(0.1);
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init.fill(1.);
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init(0) = 0.2;
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}
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}
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// set limits for minimisers ***********************************************
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// set limits for minimisers ***********************************************
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