Refactoring. Made optimization parameters command line arguments. Added optimization parameters to the settings. Exporting of optimization parameters. main.py runs a batch of optimization with different optimization parameters
This commit is contained in:
iasonmanolas
parent
3eaef7a37c
commit
46227380aa
|
|
@ -45,7 +45,7 @@ else()
|
|||
add_compile_definitions(POLYSCOPE_DEFINED)
|
||||
endif()
|
||||
set(MYSOURCES_STATIC_LINK false)
|
||||
set(USE_ENSMALLEN true)
|
||||
set(USE_ENSMALLEN false)
|
||||
if(${USE_ENSMALLEN})
|
||||
add_compile_definitions(USE_ENSMALLEN)
|
||||
endif()
|
||||
|
|
|
|||
90
main.py
90
main.py
|
|
@ -15,14 +15,16 @@ import sys
|
|||
from datetime import datetime
|
||||
from subprocess import check_output
|
||||
from shutil import copyfile
|
||||
from enum import Enum
|
||||
|
||||
numberOfOptimizedPatterns=0
|
||||
numberOfSkippedPatterns=0
|
||||
numberOfFunctionCalls=0
|
||||
numberOfFunctionCalls=10
|
||||
start_time = datetime.now()
|
||||
def listener(q):
|
||||
#print("Entered listener")
|
||||
with open(os.path.join(resultsDir,'results.csv'), 'a') as f:
|
||||
def listener(q,resultsDir):
|
||||
print("Entered")
|
||||
print("result dir in listener:",resultsDir)
|
||||
with open(os.path.join(resultsDir,'results.csv'), 'a',newline='\n') as f:
|
||||
while 1:
|
||||
m = q.get()
|
||||
if m == 'kill':
|
||||
|
|
@ -35,10 +37,11 @@ def listener(q):
|
|||
global numberOfSkippedPatterns
|
||||
numberOfSkippedPatterns=numberOfSkippedPatterns+1
|
||||
continue
|
||||
print(m)
|
||||
f.write(m)
|
||||
f.flush()
|
||||
global start_time
|
||||
#print("Before")
|
||||
# print("Before")
|
||||
if numberOfSkippedPatterns != numberOfOptimizedPatterns:
|
||||
averageTimePerPattern_min=(datetime.now()-start_time).total_seconds()/(60*(numberOfOptimizedPatterns-numberOfSkippedPatterns))
|
||||
print("Average minutes/pattern:"+str(averageTimePerPattern_min))
|
||||
|
|
@ -50,7 +53,7 @@ def listener(q):
|
|||
# completionPercentage=numberOfOptimizedPatterns/totalNumberOfPatterns
|
||||
# print("Optimized patterns:" + str(completionPercentage))
|
||||
|
||||
def optimize(fullPatternFilepath, reducedPatternFilepath,translationalObjectiveWeight):
|
||||
def optimize(fullPatternFilepath, reducedPatternFilepath,translationalObjectiveWeight,optimizationParameters,resultsDir,intermediateResultsDir):
|
||||
"""Call run(), catch exceptions."""
|
||||
dirname = os.path.abspath(os.path.dirname(__file__))
|
||||
# dirname="/home/iason/Coding/build/ReducedModelOptimization/Release"
|
||||
|
|
@ -65,7 +68,7 @@ def optimize(fullPatternFilepath, reducedPatternFilepath,translationalObjectiveW
|
|||
reducedPatternFilepath,
|
||||
str(numberOfFunctionCalls),str(translationalObjectiveWeight),
|
||||
#os.path.join(resultsDir,os.path.basename(os.path.dirname(fullPatternFilepath))))
|
||||
putResultsTo)
|
||||
putResultsTo,optimizationParameters,intermediateResultsDir)
|
||||
|
||||
patternStartTime=datetime.now()
|
||||
#print("Optimizing " + fullPatternFilepath+" at "+str(datetime.now()))
|
||||
|
|
@ -73,6 +76,7 @@ def optimize(fullPatternFilepath, reducedPatternFilepath,translationalObjectiveW
|
|||
#popen.wait()
|
||||
#output = popen.stdout.read()
|
||||
output = check_output(args).decode("utf-8")
|
||||
# print(output)
|
||||
#output,error=popen.communicate()
|
||||
duration_min=(datetime.now() - patternStartTime).total_seconds()/60
|
||||
#print("Optimized " + fullPatternFilepath+" in "+str(duration_min)+" minutes")
|
||||
|
|
@ -81,25 +85,15 @@ def optimize(fullPatternFilepath, reducedPatternFilepath,translationalObjectiveW
|
|||
except Exception as e:
|
||||
print("error: %s run(*%r, **%r)" % (e, fullPatternFilepath,reducedPatternFilepath))
|
||||
|
||||
# Press the green button in the gutter to run the script.
|
||||
if __name__ == '__main__':
|
||||
dirOfThisFile = os.path.abspath(os.path.dirname(__file__))
|
||||
copyFrom="/home/iason/Coding/build/ReducedModelOptimization/Release/ReducedModelOptimization"
|
||||
copyfile(copyFrom, os.path.join(dirOfThisFile,"ReducedModelOptimization"))
|
||||
fullPatternDirectory= "/home/iason/Coding/Projects/Approximating shapes with flat patterns/ReducedModelOptimization/TestSet/FullPatterns/selectionOfPatterns"
|
||||
numberOfFunctionCalls=100000
|
||||
optimizationBatchName='variableComparison_AllVars_ensmallen'+'_'+str(int(numberOfFunctionCalls/1000))+'k'
|
||||
resultsDir=os.path.join(dirOfThisFile,os.path.join('Results/OptimizationResults/',optimizationBatchName))
|
||||
#print(resultsDir)
|
||||
def optimizeBatch(fullPatternDirectory,optimizationParameters,resultsDir,intermediateResultsDir):
|
||||
|
||||
watcher = pool.apply_async(listener, (q,resultsDir))
|
||||
# print(optimizationParameters)
|
||||
if not os.path.exists(resultsDir):
|
||||
os.makedirs(resultsDir)
|
||||
#shutil.rmtree(resultsDir)
|
||||
manager = multiprocessing.Manager()
|
||||
q = manager.Queue()
|
||||
reducedPatternFilepath= "TestSet/ReducedPatterns/single_reduced.ply"
|
||||
reducedPatternFilepath= "/home/iason/Coding/Projects/Approximating shapes with flat patterns/ReducedModelOptimization/TestSet/ReducedPatterns/single_reduced.ply"
|
||||
fullPatternFilepaths=[]
|
||||
pool=multiprocessing.Pool(11)
|
||||
watcher = pool.apply_async(listener, (q,))
|
||||
for subdir, dirs, files in os.walk(fullPatternDirectory):
|
||||
#print(subdir)
|
||||
#print(dirs)
|
||||
|
|
@ -114,7 +108,9 @@ if __name__ == '__main__':
|
|||
#print(optimizationPairs)
|
||||
jobs=[]
|
||||
translationalObjectiveWeights=[1.2] #[x/10 for x in range(2,18,2)]
|
||||
jobs.extend(list(itertools.product(fullPatternFilepaths,[reducedPatternFilepath],translationalObjectiveWeights)))
|
||||
optimizationParameters_str=list(map(str, optimizationParameters))
|
||||
print(str(optimizationParameters))
|
||||
jobs.extend(list(itertools.product(fullPatternFilepaths,[reducedPatternFilepath],translationalObjectiveWeights,[str(optimizationParameters)],[resultsDir],[intermediateResultsDir])))
|
||||
#print(optimizationPairs)
|
||||
totalNumberOfPatterns=len(jobs)
|
||||
print("Runnning:",optimizationBatchName)
|
||||
|
|
@ -125,8 +121,52 @@ if __name__ == '__main__':
|
|||
print("Start time:", start_time)
|
||||
pool.starmap(optimize,jobs)
|
||||
print("Completed")
|
||||
# f.close()
|
||||
q.put('kill')
|
||||
# f.close()
|
||||
|
||||
|
||||
# Press the green button in the gutter to run the script.
|
||||
if __name__ == '__main__':
|
||||
|
||||
# resultsDir="uninitialized dir"
|
||||
manager = multiprocessing.Manager()
|
||||
q = manager.Queue()
|
||||
pool=multiprocessing.Pool(2)
|
||||
#get latest optimization binary
|
||||
copyFrom="/home/iason/Coding/build/ReducedModelOptimization/Release/ReducedModelOptimization"
|
||||
dirOfThisFile = os.path.abspath(os.path.dirname(__file__))
|
||||
copyfile(copyFrom, os.path.join(dirOfThisFile,"ReducedModelOptimization"))
|
||||
|
||||
fullPatternDirectory= "/home/iason/Coding/Projects/Approximating shapes with flat patterns/ReducedModelOptimization/TestSet/FullPatterns/selectionOfPatterns"
|
||||
intermediateResultsDir='/home/iason/Coding/build/ReducedModelOptimization/IntermediateResults'
|
||||
|
||||
E=0
|
||||
A=1
|
||||
I2=2
|
||||
I3=3
|
||||
J=4
|
||||
R=5
|
||||
Theta=6
|
||||
|
||||
# optimizationParametersScenarios=[]
|
||||
optimizationParametersScenarios={
|
||||
"AllVar":{E, A, I2, I3, J, R, Theta},
|
||||
"GeoYM":{R, Theta, E},
|
||||
"noYM":{A, I2, I3, J, R, Theta},
|
||||
"YMMat_Geo":[{E, A, I2, I3, J}, {R, Theta}],
|
||||
"YM_MatGeo":[{E}, {A, I2, I3, J, R, Theta}],
|
||||
"MatGeo_YM":[{A, I2, I3, J, R, Theta}, {E}],
|
||||
"Geo_YM_Mat":[{R, Theta}, {E}, {A, I2, I3, J}],
|
||||
"YM_Geo_Mat":[{E}, {R, Theta}, {A, I2, I3, J}],
|
||||
"Geo_Mat":[{R, Theta}, {A, I2, I3, J}],
|
||||
"YMGeo_Mat":[{E, R, Theta}, {A, I2, I3, J}]
|
||||
}
|
||||
|
||||
|
||||
for key, optimizationParameters in optimizationParametersScenarios.items():
|
||||
optimizationBatchName='variableComparison_'+key+'_'+str(int(numberOfFunctionCalls/1000))+'k'
|
||||
resultsDir=os.path.join(dirOfThisFile,os.path.join('Results/OptimizationResults/',optimizationBatchName))
|
||||
optimizeBatch(fullPatternDirectory,optimizationParameters,resultsDir,intermediateResultsDir)
|
||||
|
||||
pool.close()
|
||||
pool.join()
|
||||
|
||||
|
|
|
|||
181
src/main.cpp
181
src/main.cpp
|
|
@ -10,51 +10,156 @@
|
|||
#include <iterator>
|
||||
#include <stdexcept>
|
||||
#include <string>
|
||||
#include <string_view>
|
||||
#include <vcg/complex/algorithms/update/position.h>
|
||||
#include <boost/algorithm/string.hpp>
|
||||
|
||||
#ifdef POLYSCOPE_DEFINED
|
||||
#include "polyscope/curve_network.h"
|
||||
#include "polyscope/point_cloud.h"
|
||||
#include "polyscope/polyscope.h"
|
||||
#endif
|
||||
int main(int argc, char *argv[]) {
|
||||
if (argc < 3) {
|
||||
std::cerr << "Specify at least the two pattern filepaths to be "
|
||||
"optimized.Exiting.."
|
||||
<< std::endl;
|
||||
int main(int argc, char *argv[])
|
||||
{
|
||||
if (argc <= 7) {
|
||||
std::cerr << "Wrong number of input parameters. Expects at least 6 input parameters."
|
||||
"Usage:\n"
|
||||
"1)full pattern file path\n"
|
||||
"2)reduced pattern file path\n"
|
||||
"3)Number of optimizaion function calls\n"
|
||||
"4)Translational error weight\n"
|
||||
"5)Optimization results directory path\n"
|
||||
"6)[optional]Optimization parameters\n"
|
||||
"7)[optional]Intermediate results directory path\n"
|
||||
"Exiting.."
|
||||
<< std::endl;
|
||||
return 1;
|
||||
}
|
||||
|
||||
// Populate the pattern pair to be optimized
|
||||
const int interfaceNodeIndex = 3;
|
||||
////Full pattern
|
||||
const std::string filepath_fullPattern = argv[1];
|
||||
PatternGeometry fullPattern(filepath_fullPattern);
|
||||
// fullPattern.prependToLabel(std::to_string(fullPattern.EN()) + "#");
|
||||
fullPattern.scale(0.03, interfaceNodeIndex);
|
||||
////Reduced pattern
|
||||
const std::string filepath_reducedPattern = argv[2];
|
||||
PatternGeometry reducedPattern(filepath_reducedPattern);
|
||||
reducedPattern.scale(0.03, interfaceNodeIndex);
|
||||
|
||||
// Set the optization settings
|
||||
ReducedPatternOptimization::xRange beamE{"E", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange beamA{"A", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange beamI2{"I2", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange beamI3{"I3", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange beamJ{"J", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange innerHexagonSize{"R", 0.05, 0.95};
|
||||
ReducedPatternOptimization::xRange innerHexagonAngle{"Theta", -30.0, 30.0};
|
||||
ReducedPatternOptimization::Settings settings_optimization;
|
||||
settings_optimization.parameterRanges
|
||||
= {beamE, beamA, beamI2, beamI3, beamJ, innerHexagonSize, innerHexagonAngle};
|
||||
settings_optimization.numberOfFunctionCalls = std::atoi(argv[3]);
|
||||
|
||||
const bool input_optimizationParametersDefined = argc >= 8;
|
||||
if (input_optimizationParametersDefined) {
|
||||
const std::string optimizationParametersTag = argv[7];
|
||||
std::vector <std::string> split=Utilities::split(optimizationParametersTag,",");
|
||||
//parse parameter tag
|
||||
std::vector<std::vector<ReducedPatternOptimization::OptimizationParameterIndex>>
|
||||
optimizationParameters;
|
||||
std::vector<ReducedPatternOptimization::OptimizationParameterIndex> parameterGroup;
|
||||
for (std::string &s : split) {
|
||||
// std::cout<<s<<std::endl;
|
||||
// s=Utilities::trimLeftAndRightSpaces(s);
|
||||
if(boost::algorithm::contains(s,"{")){
|
||||
// std::cout<<"{ detected"<<std::endl;
|
||||
parameterGroup.clear();
|
||||
}
|
||||
if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::E)))){
|
||||
parameterGroup.push_back(ReducedPatternOptimization::E);
|
||||
}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::A)))){
|
||||
parameterGroup.push_back(ReducedPatternOptimization::A);
|
||||
}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::I2)))){
|
||||
parameterGroup.push_back(ReducedPatternOptimization::I2);
|
||||
}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::I3)))){
|
||||
parameterGroup.push_back(ReducedPatternOptimization::I3);
|
||||
}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::J)))){
|
||||
parameterGroup.push_back(ReducedPatternOptimization::J);
|
||||
}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::R)))){
|
||||
parameterGroup.push_back(ReducedPatternOptimization::R);
|
||||
}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::Theta)))){
|
||||
parameterGroup.push_back(ReducedPatternOptimization::Theta);
|
||||
}
|
||||
else{
|
||||
std::cerr << "Wrong optimization parameter input: " << optimizationParametersTag
|
||||
<< std::endl;
|
||||
|
||||
}
|
||||
if(boost::algorithm::contains(s,"}")){
|
||||
optimizationParameters.push_back(parameterGroup);
|
||||
}
|
||||
|
||||
}
|
||||
settings_optimization.optimizationVariables=optimizationParameters;
|
||||
#ifdef POLYSCOPE_DEFINED
|
||||
for(const std::vector<ReducedPatternOptimization::OptimizationParameterIndex>& v:settings_optimization.optimizationVariables){
|
||||
for(const ReducedPatternOptimization::OptimizationParameterIndex& i:v ){
|
||||
std::cout<<static_cast<int>(i)<<" ";
|
||||
}
|
||||
std::cout<<std::endl;
|
||||
}
|
||||
#endif
|
||||
|
||||
} else {
|
||||
enum OptimizationParameterComparisonScenarioIndex {
|
||||
AllVar,
|
||||
GeoYM,
|
||||
noYM,
|
||||
YMMat_Geo,
|
||||
YM_MatGeo,
|
||||
MatGeo_YM,
|
||||
Geo_YM_Mat,
|
||||
YM_Geo_Mat,
|
||||
Geo_Mat,
|
||||
YMGeo_Mat,
|
||||
NumberOfScenarios
|
||||
};
|
||||
const std::vector<
|
||||
std::vector<std::vector<ReducedPatternOptimization::OptimizationParameterIndex>>>
|
||||
optimizationParameters = [&]() {
|
||||
std::vector<
|
||||
std::vector<std::vector<ReducedPatternOptimization::OptimizationParameterIndex>>>
|
||||
optimizationParameters(NumberOfScenarios);
|
||||
using namespace ReducedPatternOptimization;
|
||||
optimizationParameters[AllVar] = {{E, A, I2, I3, J, R, Theta}};
|
||||
optimizationParameters[GeoYM] = {{R, Theta, E}};
|
||||
optimizationParameters[noYM] = {{A, I2, I3, J, R, Theta}};
|
||||
optimizationParameters[YMMat_Geo] = {{E, A, I2, I3, J}, {R, Theta}};
|
||||
optimizationParameters[YM_MatGeo] = {{E}, {A, I2, I3, J, R, Theta}};
|
||||
optimizationParameters[MatGeo_YM] = {{A, I2, I3, J, R, Theta}, {E}};
|
||||
optimizationParameters[Geo_YM_Mat] = {{R, Theta}, {E}, {A, I2, I3, J}};
|
||||
optimizationParameters[YM_Geo_Mat] = {{E}, {R, Theta}, {A, I2, I3, J}};
|
||||
optimizationParameters[Geo_Mat] = {{R, Theta}, {A, I2, I3, J}};
|
||||
optimizationParameters[YMGeo_Mat] = {{E, R, Theta}, {A, I2, I3, J}};
|
||||
return optimizationParameters;
|
||||
}();
|
||||
|
||||
constexpr OptimizationParameterComparisonScenarioIndex scenario = YMGeo_Mat;
|
||||
settings_optimization.optimizationVariables = optimizationParameters[scenario];
|
||||
}
|
||||
if(settings_optimization.optimizationVariables.empty()){
|
||||
std::cerr<<"No optimization variables. Exiting.."<<std::endl;
|
||||
std::terminate();
|
||||
}
|
||||
}
|
||||
|
||||
// Populate the pattern pair to be optimized
|
||||
const int interfaceNodeIndex=3;
|
||||
////Full pattern
|
||||
const std::string filepath_fullPattern = argv[1];
|
||||
PatternGeometry fullPattern(filepath_fullPattern);
|
||||
// fullPattern.prependToLabel(std::to_string(fullPattern.EN()) + "#");
|
||||
fullPattern.scale(0.03, interfaceNodeIndex);
|
||||
////Reduced pattern
|
||||
const std::string filepath_reducedPattern = argv[2];
|
||||
PatternGeometry reducedPattern(filepath_reducedPattern);
|
||||
reducedPattern.scale(0.03, interfaceNodeIndex);
|
||||
const bool input_intermediateResultsDirectoryDefined = argc >= 7;
|
||||
if (input_intermediateResultsDirectoryDefined) {
|
||||
settings_optimization.intermediateResultsDirectoryPath = std::filesystem::path(argv[6]);
|
||||
}
|
||||
|
||||
// Set the optization settings
|
||||
ReducedPatternOptimization::xRange beamE{"E", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange beamA{"A", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange beamI{"I", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange beamI2{"I2", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange beamI3{"I3", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange beamJ{"J", 0.001, 1000};
|
||||
ReducedPatternOptimization::xRange innerHexagonSize{"R", 0.05, 0.95};
|
||||
ReducedPatternOptimization::xRange innerHexagonAngle{"Theta", -30.0, 30.0};
|
||||
ReducedPatternOptimization::Settings settings_optimization;
|
||||
settings_optimization.parameterRanges
|
||||
= {beamE, beamA, beamI2, beamI3, beamJ, innerHexagonSize, innerHexagonAngle};
|
||||
const bool input_numberOfFunctionCallsDefined = argc >= 4;
|
||||
settings_optimization.numberOfFunctionCalls = input_numberOfFunctionCallsDefined
|
||||
? std::atoi(argv[3])
|
||||
: 100;
|
||||
settings_optimization.normalizationStrategy
|
||||
= ReducedPatternOptimization::Settings::NormalizationStrategy::Epsilon;
|
||||
settings_optimization.normalizationStrategy
|
||||
= ReducedPatternOptimization::Settings::NormalizationStrategy::Epsilon;
|
||||
#ifdef POLYSCOPE_DEFINED
|
||||
settings_optimization.translationNormalizationParameter = 0;
|
||||
settings_optimization.rotationNormalizationParameter = 0;
|
||||
|
|
@ -80,11 +185,7 @@ int main(int argc, char *argv[]) {
|
|||
+ to_string_with_precision(
|
||||
settings_optimization.objectiveWeights.translational)
|
||||
+ ")" + "_" + xConcatNames;
|
||||
const bool input_resultDirectoryDefined = argc >= 6;
|
||||
const std::string optimizationResultsDirectory = input_resultDirectoryDefined
|
||||
? argv[5]
|
||||
: std::filesystem::current_path().append(
|
||||
"OptimizationResults").string();
|
||||
const std::string optimizationResultsDirectory = argv[5];
|
||||
std::string resultsOutputDir;
|
||||
bool optimizationResultFolderExists = false;
|
||||
const std::filesystem::path crashedJobsDirPath(std::filesystem::path(optimizationResultsDirectory)
|
||||
|
|
|
|||
|
|
@ -30,10 +30,10 @@ struct GlobalOptimizationVariables
|
|||
std::vector<double> objectiveValueHistory;
|
||||
std::vector<double> plotColors;
|
||||
std::array<double,
|
||||
ReducedModelOptimizer::OptimizationParameterIndex::NumberOfOptimizationParameters>
|
||||
ReducedPatternOptimization::OptimizationParameterIndex::NumberOfOptimizationParameters>
|
||||
parametersInitialValue;
|
||||
std::array<double,
|
||||
ReducedModelOptimizer::OptimizationParameterIndex::NumberOfOptimizationParameters>
|
||||
ReducedPatternOptimization::OptimizationParameterIndex::NumberOfOptimizationParameters>
|
||||
optimizationInitialValue;
|
||||
std::vector<int> simulationScenarioIndices;
|
||||
double minY{DBL_MAX};
|
||||
|
|
@ -949,7 +949,7 @@ ReducedModelOptimizer::getFullPatternMaxSimulationForces(
|
|||
const std::filesystem::path &intermediateResultsDirectoryPath)
|
||||
{
|
||||
std::array<double, NumberOfBaseSimulationScenarios> fullPatternSimulationScenarioMaxMagnitudes;
|
||||
#ifdef POLYSCOPE_DEFINED
|
||||
//#ifdef POLYSCOPE_DEFINED
|
||||
const std::filesystem::path forceMagnitudesDirectoryPath(
|
||||
std::filesystem::path(intermediateResultsDirectoryPath).append("ForceMagnitudes"));
|
||||
std::filesystem::path patternMaxForceMagnitudesFilePath(
|
||||
|
|
@ -965,11 +965,11 @@ ReducedModelOptimizer::getFullPatternMaxSimulationForces(
|
|||
= static_cast<std::array<double, NumberOfBaseSimulationScenarios>>(json.at("maxMagn"));
|
||||
return fullPatternSimulationScenarioMaxMagnitudes;
|
||||
}
|
||||
#endif
|
||||
//#endif
|
||||
fullPatternSimulationScenarioMaxMagnitudes = computeFullPatternMaxSimulationForces(
|
||||
desiredBaseSimulationScenarioIndices);
|
||||
|
||||
#ifdef POLYSCOPE_DEFINED
|
||||
//#ifdef POLYSCOPE_DEFINED
|
||||
nlohmann::json json;
|
||||
json["maxMagn"] = fullPatternSimulationScenarioMaxMagnitudes;
|
||||
|
||||
|
|
@ -977,7 +977,7 @@ ReducedModelOptimizer::getFullPatternMaxSimulationForces(
|
|||
std::ofstream jsonFile(patternMaxForceMagnitudesFilePath.string());
|
||||
jsonFile << json;
|
||||
|
||||
#endif
|
||||
//#endif
|
||||
assert(fullPatternSimulationScenarioMaxMagnitudes.size()
|
||||
== desiredBaseSimulationScenarioIndices.size());
|
||||
|
||||
|
|
@ -995,94 +995,65 @@ void ReducedModelOptimizer::runOptimization(const Settings &settings,
|
|||
#if POLYSCOPE_DEFINED
|
||||
// global.plotColors.reserve(settings.numberOfFunctionCalls);
|
||||
#endif
|
||||
#ifdef USE_ENSMALLEN
|
||||
#else
|
||||
#endif
|
||||
enum OptimizationParameterComparisonScenarioIndex {
|
||||
AllVar, //ok
|
||||
GeoYM, //ok
|
||||
noYM, //ok
|
||||
YMMat_Geo, //ok
|
||||
YM_MatGeo, //executing
|
||||
MatGeo_YM, //ok
|
||||
Geo_YM_Mat, //ok
|
||||
YM_Geo_Mat, //ok
|
||||
Geo_Mat, //ok
|
||||
YMGeo_Mat,
|
||||
NumberOfScenarios
|
||||
};
|
||||
const std::vector<std::vector<std::vector<OptimizationParameterIndex>>> scenarioParameters =
|
||||
[&]() {
|
||||
std::vector<std::vector<std::vector<OptimizationParameterIndex>>> scenarioParameters(
|
||||
NumberOfScenarios);
|
||||
scenarioParameters[AllVar] = {{E, A, I2, I3, J, R, Theta}};
|
||||
scenarioParameters[GeoYM] = {{R, Theta, E}};
|
||||
scenarioParameters[noYM] = {{A, I2, I3, J, R, Theta}};
|
||||
scenarioParameters[YMMat_Geo] = {{E, A, I2, I3, J}, {R, Theta}};
|
||||
scenarioParameters[YM_MatGeo] = {{E}, {A, I2, I3, J, R, Theta}};
|
||||
scenarioParameters[MatGeo_YM] = {{A, I2, I3, J, R, Theta}, {E}};
|
||||
scenarioParameters[Geo_YM_Mat] = {{R, Theta}, {E}, {A, I2, I3, J}};
|
||||
scenarioParameters[YM_Geo_Mat] = {{E},{R, Theta}, {A, I2, I3, J}};
|
||||
scenarioParameters[Geo_Mat] = {{R, Theta}, {A, I2, I3, J}};
|
||||
scenarioParameters[YMGeo_Mat] = {{E, R, Theta}, {A, I2, I3, J}};
|
||||
return scenarioParameters;
|
||||
|
||||
}();
|
||||
assert(!settings.optimizationVariables.empty());
|
||||
const std::vector<std::vector<OptimizationParameterIndex>> &optimizationParametersGroups
|
||||
= settings.optimizationVariables;
|
||||
|
||||
constexpr OptimizationParameterComparisonScenarioIndex scenario = YM_MatGeo;
|
||||
const std::vector<std::vector<OptimizationParameterIndex>> scenarioParameterGroups
|
||||
= scenarioParameters[scenario];
|
||||
#ifndef USE_ENSMALLEN
|
||||
const int totalNumberOfOptimizationParameters
|
||||
= std::accumulate(scenarioParameterGroups.begin(),
|
||||
scenarioParameterGroups.end(),
|
||||
= std::accumulate(optimizationParametersGroups.begin(),
|
||||
optimizationParametersGroups.end(),
|
||||
0,
|
||||
[](const int &sum,
|
||||
const std::vector<OptimizationParameterIndex> ¶meterGroup) {
|
||||
return sum + parameterGroup.size();
|
||||
});
|
||||
#endif
|
||||
|
||||
FunctionEvaluation optimization_optimalResult;
|
||||
optimization_optimalResult.x.resize(NumberOfOptimizationParameters,0);
|
||||
for (int optimizationParameterIndex = E;
|
||||
optimizationParameterIndex != NumberOfOptimizationParameters;
|
||||
optimizationParameterIndex++) {
|
||||
optimization_optimalResult.x[optimizationParameterIndex]=global.parametersInitialValue[optimizationParameterIndex];
|
||||
}
|
||||
for (const std::vector<OptimizationParameterIndex> ¶meterGroup : scenarioParameterGroups) {
|
||||
FunctionEvaluation parameterGroup_optimalResult;
|
||||
//Set update function. TODO: Make this function immutable by defining it once and using the global variable to set parameterGroup
|
||||
function_updateReducedPattern = [&](const std::vector<double> &x,
|
||||
std::shared_ptr<SimulationMesh> &pMesh) {
|
||||
optimization_optimalResult.x[optimizationParameterIndex]
|
||||
= global.parametersInitialValue[optimizationParameterIndex];
|
||||
}
|
||||
for (const std::vector<OptimizationParameterIndex> ¶meterGroup :
|
||||
optimizationParametersGroups) {
|
||||
FunctionEvaluation parameterGroup_optimalResult;
|
||||
//Set update function. TODO: Make this function immutable by defining it once and using the global variable to set parameterGroup
|
||||
function_updateReducedPattern = [&](const std::vector<double> &x,
|
||||
std::shared_ptr<SimulationMesh> &pMesh) {
|
||||
for (int xIndex = 0; xIndex < parameterGroup.size(); xIndex++) {
|
||||
const OptimizationParameterIndex parameterIndex = parameterGroup[xIndex];
|
||||
// const double parameterInitialValue=optimizationSettings.parameterRanges[parameterIndex].initialValue;
|
||||
const double parameterNewValue = [&]() {
|
||||
if (parameterIndex == R || parameterIndex == Theta) {
|
||||
return x[xIndex] /*+ parameterInitialValue*/;
|
||||
}
|
||||
//and the material parameters exponentially(?).TODO: Check what happens if I make all linear
|
||||
const double parameterInitialValue
|
||||
= global.parametersInitialValue[parameterIndex];
|
||||
return x[xIndex] * parameterInitialValue;
|
||||
}();
|
||||
// std::cout << "Optimization parameter:" << parameterIndex << std::endl;
|
||||
// std::cout << "New value:" << parameterNewValue << std::endl;
|
||||
global.functions_updateReducedPatternParameter[parameterIndex](parameterNewValue,
|
||||
pMesh);
|
||||
}
|
||||
pMesh->reset(); //NOTE: I could put this code into each updateParameter function for avoiding unessecary calculations
|
||||
};
|
||||
|
||||
std::vector<double> xMin;
|
||||
std::vector<double> xMax;
|
||||
xMin.resize(parameterGroup.size());
|
||||
xMax.resize(parameterGroup.size());
|
||||
for (int xIndex = 0; xIndex < parameterGroup.size(); xIndex++) {
|
||||
const OptimizationParameterIndex parameterIndex = parameterGroup[xIndex];
|
||||
// const double parameterInitialValue=optimizationSettings.parameterRanges[parameterIndex].initialValue;
|
||||
const double parameterNewValue = [&]() {
|
||||
if (parameterIndex == R || parameterIndex == Theta) {
|
||||
return x[xIndex] /*+ parameterInitialValue*/;
|
||||
}
|
||||
//and the material parameters exponentially(?).TODO: Check what happens if I make all linear
|
||||
const double parameterInitialValue
|
||||
= global.parametersInitialValue[parameterIndex];
|
||||
return x[xIndex] * parameterInitialValue;
|
||||
}();
|
||||
// std::cout << "Optimization parameter:" << parameterIndex << std::endl;
|
||||
// std::cout << "New value:" << parameterNewValue << std::endl;
|
||||
global.functions_updateReducedPatternParameter[parameterIndex](parameterNewValue,
|
||||
pMesh);
|
||||
|
||||
xMin[xIndex] = settings.parameterRanges[parameterIndex].min;
|
||||
xMax[xIndex] = settings.parameterRanges[parameterIndex].max;
|
||||
}
|
||||
pMesh->reset(); //NOTE: I could put this code into each updateParameter function for avoiding unessecary calculations
|
||||
};
|
||||
|
||||
std::vector<double> xMin;
|
||||
std::vector<double> xMax;
|
||||
xMin.resize(parameterGroup.size());
|
||||
xMax.resize(parameterGroup.size());
|
||||
for (int xIndex = 0; xIndex < parameterGroup.size(); xIndex++) {
|
||||
const OptimizationParameterIndex parameterIndex = parameterGroup[xIndex];
|
||||
|
||||
xMin[xIndex] = settings.parameterRanges[parameterIndex].min;
|
||||
xMax[xIndex] = settings.parameterRanges[parameterIndex].max;
|
||||
}
|
||||
#ifdef USE_ENSMALLEN
|
||||
arma::mat x(parameterGroup.size(), 1);
|
||||
for (int xIndex = 0; xIndex < parameterGroup.size(); xIndex++) {
|
||||
|
|
@ -1229,10 +1200,9 @@ void ReducedModelOptimizer::runOptimization(const Settings &settings,
|
|||
optimization_optimalResult.y=parameterGroup_optimalResult.y;
|
||||
for (int xIndex = 0; xIndex < parameterGroup.size(); xIndex++) {
|
||||
const OptimizationParameterIndex parameterIndex = parameterGroup[xIndex];
|
||||
optimization_optimalResult.x[parameterIndex]=parameterGroup_optimalResult.x[xIndex];
|
||||
}
|
||||
|
||||
}
|
||||
optimization_optimalResult.x[parameterIndex] = parameterGroup_optimalResult.x[xIndex];
|
||||
}
|
||||
}
|
||||
getResults(optimization_optimalResult, settings, results);
|
||||
}
|
||||
|
||||
|
|
@ -1806,6 +1776,7 @@ void ReducedModelOptimizer::optimize(
|
|||
ReducedPatternOptimization::Results &results,
|
||||
const std::vector<BaseSimulationScenario> &desiredBaseSimulationScenarioIndices)
|
||||
{
|
||||
assert(!optimizationSettings.optimizationVariables.empty());
|
||||
for (int baseSimulationScenarioIndex : desiredBaseSimulationScenarioIndices) {
|
||||
//Increase the size of the vector holding the simulation scenario indices
|
||||
global.simulationScenarioIndices.resize(
|
||||
|
|
@ -1831,14 +1802,9 @@ void ReducedModelOptimizer::optimize(
|
|||
global.optimizationSettings = optimizationSettings;
|
||||
global.pFullPatternSimulationMesh = m_pFullPatternSimulationMesh;
|
||||
|
||||
#ifdef POLYSCOPE_DEFINED
|
||||
const std::filesystem::path intermediateResultsDirectoryPath(
|
||||
std::filesystem::current_path().parent_path().append("IntermediateResults"));
|
||||
#else
|
||||
const std::filesystem::path intermediateResultsDirectoryPath(
|
||||
std::filesystem::current_path().append("IntermediateResults"));
|
||||
optimizationSettings.intermediateResultsDirectoryPath);
|
||||
|
||||
#endif
|
||||
std::array<double, NumberOfBaseSimulationScenarios> fullPatternSimulationScenarioMaxMagnitudes
|
||||
= getFullPatternMaxSimulationForces(desiredBaseSimulationScenarioIndices,
|
||||
intermediateResultsDirectoryPath);
|
||||
|
|
|
|||
|
|
@ -52,20 +52,20 @@ public:
|
|||
double y = std::numeric_limits<double>::quiet_NaN();
|
||||
};
|
||||
|
||||
struct ParameterLabels
|
||||
{
|
||||
inline const static std::string E = {"E"};
|
||||
inline const static std::string A = {"A"};
|
||||
inline const static std::string I2 = {"I2"};
|
||||
inline const static std::string I3 = {"I3"};
|
||||
inline const static std::string J = {"J"};
|
||||
inline const static std::string theta = {"Theta"};
|
||||
inline const static std::string R = {"R"};
|
||||
};
|
||||
inline constexpr static ParameterLabels parameterLabels();
|
||||
|
||||
enum OptimizationParameterIndex { E, A, I2, I3, J, R, Theta, NumberOfOptimizationParameters };
|
||||
// struct ParameterLabels
|
||||
// {
|
||||
// inline const static std::string E = {"E"};
|
||||
// inline const static std::string A = {"A"};
|
||||
// inline const static std::string I2 ={"I2"};
|
||||
// inline const static std::string I3 ={"I3"};
|
||||
// inline const static std::string J = {"J"};
|
||||
// inline const static std::string th= {"Theta"};
|
||||
// inline const static std::string R = {"R"};
|
||||
// };
|
||||
// inline constexpr static ParameterLabels parameterLabels();
|
||||
|
||||
inline static std::array<std::string, ReducedPatternOptimization::NumberOfOptimizationParameters>
|
||||
parameterLabels = {"R", "A", "I2", "I3", "J", "Theta", "R"};
|
||||
constexpr static std::array<int, ReducedPatternOptimization::NumberOfBaseSimulationScenarios>
|
||||
simulationScenariosResolution = {11, 11, 20, 20, 20};
|
||||
constexpr static std::array<int, ReducedPatternOptimization::NumberOfBaseSimulationScenarios>
|
||||
|
|
|
|||
Loading…
Reference in New Issue