for j in range(segments)
] # segmentsXsegments identity matrix
# Optional definitions for pbsGridWalker that depend on the number of segments
pointsPerJob = 10
queue = 'workq'
expectedWallClockTime = '30:00:00'
# Optional definitions for pbsGridWalker that are constant
maxJobs = 25
involvedGitRepositories = mmr.involvedGitRepositories
# Required pbsGridWalker definitions
parametricGrid = gr.Grid1d(
'sensorAttachmentType', attachments.keys()) * gr.Grid1d(
'initialPopulationType', initialPopulationTypes) * gr.Grid1dFromFile(
'randomSeed', mmr.randSeedFile, size=numTrials)
def prepareEnvironment(experiment):
if not exists(mmr.arrowbotsExecutable):
raise RuntimeError('Arrowbots executable not found at ' +
mmr.arrowbotsExecutable)
if not exists(mmr.evsExecutable):
raise RuntimeError('EVS executable not found at ' + mmr.evsExecutable)
def runComputationAtPoint(worker, params):
print('Running evs-arrowbots pair with the following parameters: ' +
str(params))
parsedParams = tal.classifyDict(params, classifiers.serverClientClassifier)
serverParams = tal.sumOfDicts(parsedParams['server'], evsAdditionalParams)
'populationSize': 50,
'genStopAfter': 4000,
'initialPopulationType': 'sparse', 'secondObjectiveProbability': 1.0, 'newIndividualsPerGeneration': 1,
'backup': 'yes', 'backupPeriod': 100, 'trackAncestry': 'no',
'logBestIndividual': 'yes', 'logPopulation': 'no',
'printGeneration': 'yes', 'printPopulation': 'no', 'printParetoFront': 'yes',
'logParetoFront': 'yes', 'logParetoFrontKeepAllGenerations': 'yes', 'logParetoFrontPeriod': 10,
'randomSeed': 42}
### Required pbsGridWalker definitions
computationName = 'temporal_pool'
nonRSGrid = gr.Grid1d(
'initialPopulationType', ['random', 'sparse']) * gr.Grid1d(
'newIndividualsPerGeneration', [0, 5]) * gr.Grid1d(
'evolver', ['temporalUnrolling', 'temporalUnrollingVer0'])
parametricGrid = nonRSGrid * numTrials + gr.Grid1dFromFile(
'randomSeed', cr.randSeedFile, size=len(nonRSGrid) * numTrials)
for par in parametricGrid.paramNames():
evsDefaults.pop(par)
def prepareEnvironment(experiment):
ce.prepareEnvironment(experiment)
def runComputationAtPoint(worker, params):
return ce.runComputationAtPoint(worker,
params,
evsDefaults,
parallelClients=cores)
arrowbotsAdditionalParams = {'segments': segments,
'simulationTime': 10., 'timeStep': 0.1, 'integrateError': 'no', 'writeTrajectories': 'no'}
arrowbotInitialConditions = [[0]*segments]*segments # segmentsXsegments null matrix
arrowbotTargetOrientations = [ [1 if i==j else 0 for i in range(segments)] for j in range(segments) ] # segmentsXsegments identity matrix
# Optional definitions for pbsGridWalker that depend on the number of segments
pointsPerJob = 25
maxJobs = 8
queue = 'shortq'
expectedWallClockTime = '01:00:00'
# Optional definitions for pbsGridWalker that are constant
involvedGitRepositories = mmr.involvedGitRepositories
# Required pbsGridWalker definitions
parametricGrid = gr.Grid1d('sensorAttachmentType', attachments.keys())*gr.Grid1d('initialPopulationType', initialPopulationTypes)*gr.Grid1dFromFile('randomSeed', mmr.randSeedFile, size=numTrials)
def prepareEnvironment(experiment):
if not exists(mmr.arrowbotsExecutable):
raise RuntimeError('Arrowbots executable not found at ' + mmr.arrowbotsExecutable)
if not exists(mmr.evsExecutable):
raise RuntimeError('EVS executable not found at ' + mmr.evsExecutable)
def runComputationAtPoint(worker, params):
print('Running evs-arrowbots pair with the following parameters: ' + str(params))
parsedParams = tal.classifyDict(params, classifiers.serverClientClassifier)
serverParams = tal.sumOfDicts(parsedParams['server'], evsAdditionalParams)
clientParams = tal.sumOfDicts(parsedParams['client'], arrowbotsAdditionalParams)
tiniw.write(serverParams, classifiers.evsClassifier, 'evs.ini')
tiniw.write(clientParams, classifiers.arrowbotsClassifier, 'arrowbot.ini')
tfio.writeColumns(arrowbotInitialConditions, 'initialConditions.dat')
