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ReducedModelOptimization
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Exporting number of function calls in the csv

This commit is contained in:
iasonmanolas 2021-11-10 11:28:22 +02:00
parent 1c3cc8e014
commit 717f631095
2 changed files with 21 additions and 18 deletions
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2
src/main.cpp
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@ -135,7 +135,7 @@ int main(int argc, char *argv[]) {
settings_optimization.writeHeaderTo(csv_results);
csv_results << endrow;
csv_results << std::to_string(fullPattern.EN()) + "#" + pairName;
optimizationResults.writeResultsTo(settings_optimization, csv_results);
optimizationResults.writeResultsTo(csv_results);
settings_optimization.writeSettingsTo(csv_results);
csv_results << endrow;
} else {

37
src/reducedmodeloptimizer.cpp
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@ -23,6 +23,7 @@ struct GlobalOptimizationVariables {
std::vector<double> objectiveValueHistoryY;
std::vector<double> objectiveValueHistoryX;
std::vector<double> plotColors;
size_t iterationOfMinima{0};
Eigen::VectorXd initialParameters;
std::vector<int> simulationScenarioIndices;
double minY{DBL_MAX};
@ -253,34 +254,35 @@ double ReducedModelOptimizer::objective(const dlib::matrix<double, 0, 1> &x)
}
}
// std::cout << error << std::endl;
// global.objectiveValueHistory.push_back(totalError);
// global.plotColors.push_back(10);
++global.numberOfFunctionCalls;
if (totalError < global.minY) {
global.minY = totalError;
std::cout << "New best:" << totalError << std::endl;
// global.minX.assign(x.begin(), x.begin() + n);
std::cout.precision(17);
for (int i = 0; i < x.size(); i++) {
std::cout << x(i) << " ";
}
std::cout << std::endl;
global.objectiveValueHistoryY.push_back(std::log(totalError));
global.objectiveValueHistoryX.push_back(global.numberOfFunctionCalls);
global.plotColors.push_back(0.1);
global.iterationOfMinima = global.numberOfFunctionCalls;
// std::cout << "New best:" << totalError << std::endl;
// // global.minX.assign(x.begin(), x.begin() + n);
// std::cout.precision(17);
// for (int i = 0; i < x.size(); i++) {
// std::cout << x(i) << " ";
// }
// std::cout << std::endl;
// global.objectiveValueHistoryY.push_back(std::log(totalError));
// global.objectiveValueHistoryX.push_back(global.numberOfFunctionCalls);
// global.plotColors.push_back(0.1);
// auto xPlot = matplot::linspace(0,
// global.objectiveValueHistoryY.size(),
// global.objectiveValueHistoryY.size());
global.gPlotHandle = matplot::scatter(global.objectiveValueHistoryX,
global.objectiveValueHistoryY,
4,
global.plotColors);
// global.gPlotHandle = matplot::scatter(global.objectiveValueHistoryX,
// global.objectiveValueHistoryY,
// 4,
// global.plotColors);
// matplot::show();
// SimulationResultsReporter::createPlot("Number of Steps",
// "Objective value",
// global.objectiveValueHistoryY);
}
// global.objectiveValueHistory.push_back(totalError);
// global.plotColors.push_back(10);
#ifdef POLYSCOPE_DEFINED
++global.numberOfFunctionCalls;
if (global.optimizationSettings.numberOfFunctionCalls >= 100
&& global.numberOfFunctionCalls % (global.optimizationSettings.numberOfFunctionCalls / 100)
== 0) {
@ -959,6 +961,7 @@ void ReducedModelOptimizer::getResults(const dlib::function_evaluation &optimiza
// global.reducedPatternSimulationJobs[simulationScenarioIndex]->pMesh->registerForDrawing();
// global.reducedPatternSimulationJobs[simulationScenarioIndex]->pMesh->setLabel(temp);
}
results.iterationOfMinima = global.iterationOfMinima;
// results.draw();
}