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
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@ -45,7 +45,7 @@ else()
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add_compile_definitions(POLYSCOPE_DEFINED)
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endif()
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set(MYSOURCES_STATIC_LINK false)
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set(USE_ENSMALLEN true)
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set(USE_ENSMALLEN false)
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if(${USE_ENSMALLEN})
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add_compile_definitions(USE_ENSMALLEN)
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endif()
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90
main.py
90
main.py
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@ -15,14 +15,16 @@ import sys
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from datetime import datetime
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from subprocess import check_output
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from shutil import copyfile
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from enum import Enum
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numberOfOptimizedPatterns=0
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numberOfSkippedPatterns=0
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numberOfFunctionCalls=0
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numberOfFunctionCalls=10
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start_time = datetime.now()
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def listener(q):
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#print("Entered listener")
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with open(os.path.join(resultsDir,'results.csv'), 'a') as f:
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def listener(q,resultsDir):
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print("Entered")
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print("result dir in listener:",resultsDir)
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with open(os.path.join(resultsDir,'results.csv'), 'a',newline='\n') as f:
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while 1:
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m = q.get()
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if m == 'kill':
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@ -35,10 +37,11 @@ def listener(q):
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global numberOfSkippedPatterns
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numberOfSkippedPatterns=numberOfSkippedPatterns+1
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continue
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print(m)
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f.write(m)
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f.flush()
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global start_time
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#print("Before")
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# print("Before")
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if numberOfSkippedPatterns != numberOfOptimizedPatterns:
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averageTimePerPattern_min=(datetime.now()-start_time).total_seconds()/(60*(numberOfOptimizedPatterns-numberOfSkippedPatterns))
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print("Average minutes/pattern:"+str(averageTimePerPattern_min))
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@ -50,7 +53,7 @@ def listener(q):
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# completionPercentage=numberOfOptimizedPatterns/totalNumberOfPatterns
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# print("Optimized patterns:" + str(completionPercentage))
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def optimize(fullPatternFilepath, reducedPatternFilepath,translationalObjectiveWeight):
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def optimize(fullPatternFilepath, reducedPatternFilepath,translationalObjectiveWeight,optimizationParameters,resultsDir,intermediateResultsDir):
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"""Call run(), catch exceptions."""
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dirname = os.path.abspath(os.path.dirname(__file__))
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# dirname="/home/iason/Coding/build/ReducedModelOptimization/Release"
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@ -65,7 +68,7 @@ def optimize(fullPatternFilepath, reducedPatternFilepath,translationalObjectiveW
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reducedPatternFilepath,
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str(numberOfFunctionCalls),str(translationalObjectiveWeight),
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#os.path.join(resultsDir,os.path.basename(os.path.dirname(fullPatternFilepath))))
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putResultsTo)
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putResultsTo,optimizationParameters,intermediateResultsDir)
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patternStartTime=datetime.now()
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#print("Optimizing " + fullPatternFilepath+" at "+str(datetime.now()))
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@ -73,6 +76,7 @@ def optimize(fullPatternFilepath, reducedPatternFilepath,translationalObjectiveW
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#popen.wait()
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#output = popen.stdout.read()
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output = check_output(args).decode("utf-8")
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# print(output)
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#output,error=popen.communicate()
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duration_min=(datetime.now() - patternStartTime).total_seconds()/60
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#print("Optimized " + fullPatternFilepath+" in "+str(duration_min)+" minutes")
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@ -81,25 +85,15 @@ def optimize(fullPatternFilepath, reducedPatternFilepath,translationalObjectiveW
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except Exception as e:
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print("error: %s run(*%r, **%r)" % (e, fullPatternFilepath,reducedPatternFilepath))
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# Press the green button in the gutter to run the script.
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if __name__ == '__main__':
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dirOfThisFile = os.path.abspath(os.path.dirname(__file__))
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copyFrom="/home/iason/Coding/build/ReducedModelOptimization/Release/ReducedModelOptimization"
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copyfile(copyFrom, os.path.join(dirOfThisFile,"ReducedModelOptimization"))
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fullPatternDirectory= "/home/iason/Coding/Projects/Approximating shapes with flat patterns/ReducedModelOptimization/TestSet/FullPatterns/selectionOfPatterns"
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numberOfFunctionCalls=100000
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optimizationBatchName='variableComparison_AllVars_ensmallen'+'_'+str(int(numberOfFunctionCalls/1000))+'k'
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resultsDir=os.path.join(dirOfThisFile,os.path.join('Results/OptimizationResults/',optimizationBatchName))
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#print(resultsDir)
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def optimizeBatch(fullPatternDirectory,optimizationParameters,resultsDir,intermediateResultsDir):
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watcher = pool.apply_async(listener, (q,resultsDir))
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# print(optimizationParameters)
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if not os.path.exists(resultsDir):
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os.makedirs(resultsDir)
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#shutil.rmtree(resultsDir)
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manager = multiprocessing.Manager()
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q = manager.Queue()
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reducedPatternFilepath= "TestSet/ReducedPatterns/single_reduced.ply"
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reducedPatternFilepath= "/home/iason/Coding/Projects/Approximating shapes with flat patterns/ReducedModelOptimization/TestSet/ReducedPatterns/single_reduced.ply"
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fullPatternFilepaths=[]
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pool=multiprocessing.Pool(11)
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watcher = pool.apply_async(listener, (q,))
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for subdir, dirs, files in os.walk(fullPatternDirectory):
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#print(subdir)
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#print(dirs)
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@ -114,7 +108,9 @@ if __name__ == '__main__':
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#print(optimizationPairs)
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jobs=[]
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translationalObjectiveWeights=[1.2] #[x/10 for x in range(2,18,2)]
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jobs.extend(list(itertools.product(fullPatternFilepaths,[reducedPatternFilepath],translationalObjectiveWeights)))
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optimizationParameters_str=list(map(str, optimizationParameters))
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print(str(optimizationParameters))
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jobs.extend(list(itertools.product(fullPatternFilepaths,[reducedPatternFilepath],translationalObjectiveWeights,[str(optimizationParameters)],[resultsDir],[intermediateResultsDir])))
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#print(optimizationPairs)
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totalNumberOfPatterns=len(jobs)
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print("Runnning:",optimizationBatchName)
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@ -125,8 +121,52 @@ if __name__ == '__main__':
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print("Start time:", start_time)
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pool.starmap(optimize,jobs)
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print("Completed")
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# f.close()
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q.put('kill')
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# f.close()
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# Press the green button in the gutter to run the script.
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if __name__ == '__main__':
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# resultsDir="uninitialized dir"
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manager = multiprocessing.Manager()
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q = manager.Queue()
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pool=multiprocessing.Pool(2)
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#get latest optimization binary
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copyFrom="/home/iason/Coding/build/ReducedModelOptimization/Release/ReducedModelOptimization"
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dirOfThisFile = os.path.abspath(os.path.dirname(__file__))
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copyfile(copyFrom, os.path.join(dirOfThisFile,"ReducedModelOptimization"))
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fullPatternDirectory= "/home/iason/Coding/Projects/Approximating shapes with flat patterns/ReducedModelOptimization/TestSet/FullPatterns/selectionOfPatterns"
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intermediateResultsDir='/home/iason/Coding/build/ReducedModelOptimization/IntermediateResults'
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E=0
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A=1
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I2=2
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I3=3
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J=4
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R=5
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Theta=6
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# optimizationParametersScenarios=[]
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optimizationParametersScenarios={
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"AllVar":{E, A, I2, I3, J, R, Theta},
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"GeoYM":{R, Theta, E},
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"noYM":{A, I2, I3, J, R, Theta},
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"YMMat_Geo":[{E, A, I2, I3, J}, {R, Theta}],
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"YM_MatGeo":[{E}, {A, I2, I3, J, R, Theta}],
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"MatGeo_YM":[{A, I2, I3, J, R, Theta}, {E}],
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"Geo_YM_Mat":[{R, Theta}, {E}, {A, I2, I3, J}],
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"YM_Geo_Mat":[{E}, {R, Theta}, {A, I2, I3, J}],
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"Geo_Mat":[{R, Theta}, {A, I2, I3, J}],
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"YMGeo_Mat":[{E, R, Theta}, {A, I2, I3, J}]
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}
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for key, optimizationParameters in optimizationParametersScenarios.items():
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optimizationBatchName='variableComparison_'+key+'_'+str(int(numberOfFunctionCalls/1000))+'k'
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resultsDir=os.path.join(dirOfThisFile,os.path.join('Results/OptimizationResults/',optimizationBatchName))
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optimizeBatch(fullPatternDirectory,optimizationParameters,resultsDir,intermediateResultsDir)
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pool.close()
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pool.join()
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181
src/main.cpp
181
src/main.cpp
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@ -10,51 +10,156 @@
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#include <iterator>
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#include <stdexcept>
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#include <string>
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#include <string_view>
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#include <vcg/complex/algorithms/update/position.h>
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#include <boost/algorithm/string.hpp>
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#ifdef POLYSCOPE_DEFINED
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#include "polyscope/curve_network.h"
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#include "polyscope/point_cloud.h"
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#include "polyscope/polyscope.h"
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#endif
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int main(int argc, char *argv[]) {
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if (argc < 3) {
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std::cerr << "Specify at least the two pattern filepaths to be "
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"optimized.Exiting.."
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<< std::endl;
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int main(int argc, char *argv[])
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{
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if (argc <= 7) {
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std::cerr << "Wrong number of input parameters. Expects at least 6 input parameters."
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"Usage:\n"
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"1)full pattern file path\n"
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"2)reduced pattern file path\n"
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"3)Number of optimizaion function calls\n"
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"4)Translational error weight\n"
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"5)Optimization results directory path\n"
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"6)[optional]Optimization parameters\n"
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"7)[optional]Intermediate results directory path\n"
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"Exiting.."
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<< std::endl;
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return 1;
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}
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// Populate the pattern pair to be optimized
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const int interfaceNodeIndex = 3;
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////Full pattern
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const std::string filepath_fullPattern = argv[1];
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PatternGeometry fullPattern(filepath_fullPattern);
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// fullPattern.prependToLabel(std::to_string(fullPattern.EN()) + "#");
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fullPattern.scale(0.03, interfaceNodeIndex);
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////Reduced pattern
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const std::string filepath_reducedPattern = argv[2];
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PatternGeometry reducedPattern(filepath_reducedPattern);
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reducedPattern.scale(0.03, interfaceNodeIndex);
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// Set the optization settings
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ReducedPatternOptimization::xRange beamE{"E", 0.001, 1000};
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ReducedPatternOptimization::xRange beamA{"A", 0.001, 1000};
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ReducedPatternOptimization::xRange beamI2{"I2", 0.001, 1000};
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ReducedPatternOptimization::xRange beamI3{"I3", 0.001, 1000};
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ReducedPatternOptimization::xRange beamJ{"J", 0.001, 1000};
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ReducedPatternOptimization::xRange innerHexagonSize{"R", 0.05, 0.95};
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ReducedPatternOptimization::xRange innerHexagonAngle{"Theta", -30.0, 30.0};
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ReducedPatternOptimization::Settings settings_optimization;
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settings_optimization.parameterRanges
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= {beamE, beamA, beamI2, beamI3, beamJ, innerHexagonSize, innerHexagonAngle};
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settings_optimization.numberOfFunctionCalls = std::atoi(argv[3]);
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const bool input_optimizationParametersDefined = argc >= 8;
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if (input_optimizationParametersDefined) {
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const std::string optimizationParametersTag = argv[7];
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std::vector <std::string> split=Utilities::split(optimizationParametersTag,",");
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//parse parameter tag
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std::vector<std::vector<ReducedPatternOptimization::OptimizationParameterIndex>>
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optimizationParameters;
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std::vector<ReducedPatternOptimization::OptimizationParameterIndex> parameterGroup;
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for (std::string &s : split) {
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// std::cout<<s<<std::endl;
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// s=Utilities::trimLeftAndRightSpaces(s);
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if(boost::algorithm::contains(s,"{")){
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// std::cout<<"{ detected"<<std::endl;
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parameterGroup.clear();
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}
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if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::E)))){
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parameterGroup.push_back(ReducedPatternOptimization::E);
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}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::A)))){
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parameterGroup.push_back(ReducedPatternOptimization::A);
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}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::I2)))){
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parameterGroup.push_back(ReducedPatternOptimization::I2);
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}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::I3)))){
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parameterGroup.push_back(ReducedPatternOptimization::I3);
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}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::J)))){
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parameterGroup.push_back(ReducedPatternOptimization::J);
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}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::R)))){
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parameterGroup.push_back(ReducedPatternOptimization::R);
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}else if(boost::algorithm::contains(s,std::to_string(static_cast<int>(ReducedPatternOptimization::Theta)))){
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parameterGroup.push_back(ReducedPatternOptimization::Theta);
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}
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else{
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std::cerr << "Wrong optimization parameter input: " << optimizationParametersTag
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<< std::endl;
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}
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if(boost::algorithm::contains(s,"}")){
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optimizationParameters.push_back(parameterGroup);
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}
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}
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settings_optimization.optimizationVariables=optimizationParameters;
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#ifdef POLYSCOPE_DEFINED
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for(const std::vector<ReducedPatternOptimization::OptimizationParameterIndex>& v:settings_optimization.optimizationVariables){
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for(const ReducedPatternOptimization::OptimizationParameterIndex& i:v ){
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std::cout<<static_cast<int>(i)<<" ";
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}
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std::cout<<std::endl;
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}
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#endif
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} else {
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enum OptimizationParameterComparisonScenarioIndex {
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AllVar,
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GeoYM,
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noYM,
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YMMat_Geo,
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YM_MatGeo,
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MatGeo_YM,
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Geo_YM_Mat,
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YM_Geo_Mat,
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Geo_Mat,
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YMGeo_Mat,
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NumberOfScenarios
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};
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const std::vector<
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std::vector<std::vector<ReducedPatternOptimization::OptimizationParameterIndex>>>
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optimizationParameters = [&]() {
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std::vector<
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std::vector<std::vector<ReducedPatternOptimization::OptimizationParameterIndex>>>
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optimizationParameters(NumberOfScenarios);
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using namespace ReducedPatternOptimization;
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optimizationParameters[AllVar] = {{E, A, I2, I3, J, R, Theta}};
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optimizationParameters[GeoYM] = {{R, Theta, E}};
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optimizationParameters[noYM] = {{A, I2, I3, J, R, Theta}};
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optimizationParameters[YMMat_Geo] = {{E, A, I2, I3, J}, {R, Theta}};
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optimizationParameters[YM_MatGeo] = {{E}, {A, I2, I3, J, R, Theta}};
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optimizationParameters[MatGeo_YM] = {{A, I2, I3, J, R, Theta}, {E}};
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optimizationParameters[Geo_YM_Mat] = {{R, Theta}, {E}, {A, I2, I3, J}};
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optimizationParameters[YM_Geo_Mat] = {{E}, {R, Theta}, {A, I2, I3, J}};
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optimizationParameters[Geo_Mat] = {{R, Theta}, {A, I2, I3, J}};
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optimizationParameters[YMGeo_Mat] = {{E, R, Theta}, {A, I2, I3, J}};
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return optimizationParameters;
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}();
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constexpr OptimizationParameterComparisonScenarioIndex scenario = YMGeo_Mat;
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settings_optimization.optimizationVariables = optimizationParameters[scenario];
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}
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if(settings_optimization.optimizationVariables.empty()){
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std::cerr<<"No optimization variables. Exiting.."<<std::endl;
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std::terminate();
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}
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}
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// Populate the pattern pair to be optimized
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const int interfaceNodeIndex=3;
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////Full pattern
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const std::string filepath_fullPattern = argv[1];
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PatternGeometry fullPattern(filepath_fullPattern);
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// fullPattern.prependToLabel(std::to_string(fullPattern.EN()) + "#");
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fullPattern.scale(0.03, interfaceNodeIndex);
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////Reduced pattern
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const std::string filepath_reducedPattern = argv[2];
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PatternGeometry reducedPattern(filepath_reducedPattern);
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reducedPattern.scale(0.03, interfaceNodeIndex);
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const bool input_intermediateResultsDirectoryDefined = argc >= 7;
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if (input_intermediateResultsDirectoryDefined) {
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settings_optimization.intermediateResultsDirectoryPath = std::filesystem::path(argv[6]);
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}
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// Set the optization settings
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ReducedPatternOptimization::xRange beamE{"E", 0.001, 1000};
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ReducedPatternOptimization::xRange beamA{"A", 0.001, 1000};
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ReducedPatternOptimization::xRange beamI{"I", 0.001, 1000};
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ReducedPatternOptimization::xRange beamI2{"I2", 0.001, 1000};
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ReducedPatternOptimization::xRange beamI3{"I3", 0.001, 1000};
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ReducedPatternOptimization::xRange beamJ{"J", 0.001, 1000};
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ReducedPatternOptimization::xRange innerHexagonSize{"R", 0.05, 0.95};
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ReducedPatternOptimization::xRange innerHexagonAngle{"Theta", -30.0, 30.0};
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ReducedPatternOptimization::Settings settings_optimization;
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settings_optimization.parameterRanges
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= {beamE, beamA, beamI2, beamI3, beamJ, innerHexagonSize, innerHexagonAngle};
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const bool input_numberOfFunctionCallsDefined = argc >= 4;
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settings_optimization.numberOfFunctionCalls = input_numberOfFunctionCallsDefined
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? std::atoi(argv[3])
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: 100;
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settings_optimization.normalizationStrategy
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= ReducedPatternOptimization::Settings::NormalizationStrategy::Epsilon;
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settings_optimization.normalizationStrategy
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= ReducedPatternOptimization::Settings::NormalizationStrategy::Epsilon;
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#ifdef POLYSCOPE_DEFINED
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settings_optimization.translationNormalizationParameter = 0;
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settings_optimization.rotationNormalizationParameter = 0;
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@ -80,11 +185,7 @@ int main(int argc, char *argv[]) {
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+ to_string_with_precision(
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settings_optimization.objectiveWeights.translational)
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+ ")" + "_" + xConcatNames;
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const bool input_resultDirectoryDefined = argc >= 6;
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const std::string optimizationResultsDirectory = input_resultDirectoryDefined
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? argv[5]
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: std::filesystem::current_path().append(
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"OptimizationResults").string();
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const std::string optimizationResultsDirectory = argv[5];
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std::string resultsOutputDir;
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bool optimizationResultFolderExists = false;
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const std::filesystem::path crashedJobsDirPath(std::filesystem::path(optimizationResultsDirectory)
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@ -30,10 +30,10 @@ struct GlobalOptimizationVariables
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std::vector<double> objectiveValueHistory;
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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