ReducedModelOptimization/src/main.cpp

#include "beamformfinder.hpp"
#include "csvfile.hpp"
#include "edgemesh.hpp"
#include "flatpattern.hpp"
#include "polyscope/curve_network.h"
#include "polyscope/point_cloud.h"
#include "polyscope/polyscope.h"
#include "reducedmodeloptimizer.hpp"
#include "simulationhistoryplotter.hpp"
#include "trianglepattterntopology.hpp"
#include <chrono>
#include <filesystem>
#include <iostream>
#include <stdexcept>
#include <string>
#include <vcg/complex/algorithms/update/position.h>

int main(int argc, char *argv[]) {

  // Create reduced models
  //  FormFinder::runUnitTests();
  const std::vector<size_t> numberOfNodesPerSlot{1, 0, 0, 2, 1, 2, 1};
  std::vector<vcg::Point2i> singleBarReducedModelEdges{vcg::Point2i(0, 3)};
  FlatPattern singleBarReducedModel(numberOfNodesPerSlot,
                                    singleBarReducedModelEdges);
  singleBarReducedModel.setLabel("Single bar reduced model");
  singleBarReducedModel.scale(0.03);

  std::vector<vcg::Point2i> CWreducedModelEdges{vcg::Point2i(1, 5),
                                                vcg::Point2i(3, 1)};
  FlatPattern CWReducedModel(numberOfNodesPerSlot, CWreducedModelEdges);
  CWReducedModel.setLabel("CW reduced model");
  CWReducedModel.scale(0.03);

  std::vector<vcg::Point2i> CCWreducedModelEdges{vcg::Point2i(1, 5),
                                                 vcg::Point2i(3, 5)};
  FlatPattern CCWReducedModel(numberOfNodesPerSlot, CCWreducedModelEdges);
  CCWReducedModel.setLabel("CCW reduced model");
  CCWReducedModel.scale(0.03);

  std::vector<FlatPattern *> reducedModels{&singleBarReducedModel,
                                           &CWReducedModel, &CCWReducedModel};

  ReducedModelOptimizer optimizer(numberOfNodesPerSlot);
  for (double dimRationMax = 0.75; dimRationMax < 2; dimRationMax += 0.05) {
    ReducedModelOptimizer::xRange beamWidth{"B", 0.5, 1.5};
    ReducedModelOptimizer::xRange beamDimensionsRatio{"bOverh", 0.7,
                                                      dimRationMax};
    ReducedModelOptimizer::xRange beamE{"E", 0.07, 1.5};
    std::string xRangesString = beamWidth.toString() + " " +
                                beamDimensionsRatio.toString() + " " +
                                beamE.toString();
    std::cout << xRangesString << std::endl;
    ReducedModelOptimizer::Settings settings;
    settings.xRanges = {beamWidth, beamDimensionsRatio, beamE};

    std::filesystem::path thisOptimizationDirectory(
        std::filesystem::path("../OptimizationResults").append(xRangesString));
    std::filesystem::create_directory(thisOptimizationDirectory);
    csvfile thisOptimizationStatistics(
        std::filesystem::path(thisOptimizationDirectory)
            .append("statistics.csv")
            .string(),
        true);

    double totalError = 0;
    int totalNumberOfSimulationCrashes = 0;
    std::vector<double> errors;
    std::string fullPatternsTestSetDirectory = "../TestSet";
    //      "/home/iason/Documents/PhD/Research/Approximating shapes with flat "
    //      "patterns/Pattern_enumerator/Results/1v_0v_2e_1e_1c_6fan/3/Valid";
    for (const auto &entry :
         filesystem::directory_iterator(fullPatternsTestSetDirectory)) {
      const auto filepath =
          //        std::filesystem::path(fullPatternsTestSetDirectory).append("305.ply");
          entry.path();
      const auto filepathString = filepath.string();
      // Use only the base triangle version
      const std::string tiledSuffix = "_tiled.ply";
      if (filepathString.compare(filepathString.size() - tiledSuffix.size(),
                                 tiledSuffix.size(), tiledSuffix) == 0) {
        continue;
      }
      //    std::cout << "Full pattern:" << filepathString << std::endl;
      FlatPattern pattern(filepathString);
      pattern.setLabel(filepath.stem().string());
      pattern.scale(0.03);
      //    for (int reducedPatternIndex = 0;
      //         reducedPatternIndex < reducedModels.size();
      //         reducedPatternIndex++) {
      //          FlatPattern *pReducedModel =
      // reducedModels[reducedPatternIndex];
      std::unordered_set<size_t> optimizationExcludedEi;
      //    if (pReducedModel !=
      //        reducedModels[0]) { // assumes that the singleBar reduced model
      //                            //              is
      //      // the
      //      // first in the reducedModels vector
      //      optimizationExcludedEi.insert(0);
      //    }
      //    FlatPattern cp;
      //    cp.copy(*reducedModels[0]);
      optimizer.initializePatterns(pattern, *reducedModels[0],
                                   optimizationExcludedEi);
      //    optimizer.optimize({ReducedModelOptimizer::Axial});
      ReducedModelOptimizer::Results optimizationResults =
          optimizer.optimize(settings);
      errors.push_back(optimizationResults.objectiveValue);
      SimulationResultsReporter::createPlot(
          "", "Objective value", errors,
          std::filesystem::path(thisOptimizationDirectory)
              .append("ObjectiveValues.png")
              .string());
      thisOptimizationStatistics << filepath.stem().string()
                                 << optimizationResults.objectiveValue;
      if (optimizationResults.numberOfSimulationCrashes == 0) {
        thisOptimizationStatistics << "No crashes";
      } else {
        thisOptimizationStatistics
            << optimizationResults.numberOfSimulationCrashes;
      }
      thisOptimizationStatistics << endrow;

      totalError += optimizationResults.objectiveValue;
      totalNumberOfSimulationCrashes +=
          optimizationResults.numberOfSimulationCrashes;
      //    }
    }

    csvfile statistics(std::filesystem::path("../OptimizationResults")
                           .append("statistics.csv")
                           .string(),
                       false);
    for (const auto &range : settings.xRanges) {
      statistics << range.min << range.max;
    }
    statistics << settings.maxSimulations;
    if (totalNumberOfSimulationCrashes == 0) {
      statistics << "No crashes";
    } else {
      statistics << totalNumberOfSimulationCrashes;
    }

    statistics << totalError << endrow;
  }
  return 0;
}