import data import model import korali k = korali.Engine() e = korali.Experiment() # Defining Concurrent Jobs popSize = 512 if (len(sys.argv) > 1): popSize = int(sys.argv[1]) # Setting up the reference likelihood for the Bayesian Problem e["Problem"]["Type"] = "Bayesian/Reference" e["Problem"]["Likelihood Model"] = "Normal" e["Problem"]["Reference Data"] = data.getReferenceData().tolist() e["Problem"]["Computational Model"] = lambda koraliData: model.evaluate( koraliData, data.getReferencePoints(), korali.getMPIComm() ) e["Distributions"][0]["Name"] = "Uniform 0" e["Distributions"][0]["Type"] = "Univariate/Uniform" e["Distributions"][0]["Minimum"] = 0.2 e["Distributions"][0]["Maximum"] = 0.6 e["Distributions"][1]["Name"] = "Uniform 1" e["Distributions"][1]["Type"] = "Univariate/Uniform" e["Distributions"][1]["Minimum"] = 10.0 e["Distributions"][1]["Maximum"] = 40.0 e["Distributions"][2]["Name"] = "Uniform 2" e["Distributions"][2]["Type"] = "Univariate/Uniform" e["Distributions"][2]["Minimum"] = 1e-5 e["Distributions"][2]["Maximum"] = 2.0
e["Variables"][0]["Prior Distribution"] = "Uniform 0"
e["Variables"][0]["Initial Value"] = 16000
e["Variables"][0]["Initial Standard Deviation"] = 7200
e["Variables"][1]["Name"] = "[Sigma]"
e["Variables"][1]["Prior Distribution"] = "Uniform 1"
e["Variables"][1]["Initial Value"] = 0.5
e["Variables"][1]["Initial Standard Deviation"] = 0.66
# General Settings
e["Console Output"]["Verbosity"] = "Detailed"
e["File Output"]["Path"] = resFolder
e["Store Sample Information"] = True
# Loading previous results, if they exist.
found = e.loadState(resFolder + '/latest')
# Setting Model after loading previous results to prevent bad function pointer
e["Problem"]["Computational Model"] = lambda sample: relaxModel(
sample, refPoints, expName, expTend, ini_mesh_fname, korali.getMPIComm())
# Configuring Linked (Distributed) Conduit
k = korali.Engine()
k["Conduit"]["Type"] = "Distributed"
k["Conduit"]["Workers Per Team"] = 2
k["Profiling"]["Detail"] = "Full"
k["Profiling"]["Path"] = profFile
k["Profiling"]["Frequency"] = 60
k.run(e)
e["Variables"][1]["Prior Distribution"] = "Uniform 1"
e["Variables"][1]["Initial Value"] = 0.5
e["Variables"][1]["Initial Standard Deviation"] = 0.66
# General Settings
e["Console Output"]["Verbosity"] = "Detailed"
e["File Output"]["Path"] = resFolder
e["Store Sample Information"] = True
# Loading previous results, if they exist.
e.loadState(resFolder + '/latest')
eList.append(e)
eList[0]["Problem"]["Computational Model"] = lambda sample: relaxModel(
sample, getReferencePoints('hochmuth01'), 'hochmuth01', 0.4,
'./data/off_files/stretch_H1979_d01.off', korali.getMPIComm())
eList[1]["Problem"]["Computational Model"] = lambda sample: relaxModel(
sample, getReferencePoints('hochmuth02'), 'hochmuth02', 0.4,
'./data/off_files/stretch_H1979_d02.off', korali.getMPIComm())
eList[2]["Problem"]["Computational Model"] = lambda sample: relaxModel(
sample, getReferencePoints('hochmuth03'), 'hochmuth03', 0.4,
'./data/off_files/stretch_H1979_d03.off', korali.getMPIComm())
eList[3]["Problem"]["Computational Model"] = lambda sample: relaxModel(
sample, getReferencePoints('hochmuth04'), 'hochmuth04', 0.4,
'./data/off_files/stretch_H1979_d04.off', korali.getMPIComm())
eList[4]["Problem"]["Computational Model"] = lambda sample: relaxModel(
sample, getReferencePoints('henon'), 'henon', 0.4,
'./data/off_files/stretch_Hen1999_d01.off', korali.getMPIComm())
# Configuring Linked (Distributed) Conduit
k = korali.Engine()
import model
import korali
k = korali.Engine()
e = korali.Experiment()
# Defining Concurrent Jobs
popSize = 512
if (len(sys.argv) > 1): popSize = int(sys.argv[1])
# Setting up the reference likelihood for the Bayesian Problem
e["Problem"]["Type"] = "Bayesian/Reference"
e["Problem"]["Likelihood Model"] = "Normal"
e["Problem"]["Reference Data"] = data.getReferenceData().tolist()
e["Problem"]["Computational Model"] = lambda koraliData: model.evaluate(
koraliData, data.getReferencePoints(), korali.getMPIComm())
e["Distributions"][0]["Name"] = "Uniform 0"
e["Distributions"][0]["Type"] = "Univariate/Uniform"
e["Distributions"][0]["Minimum"] = 0.2
e["Distributions"][0]["Maximum"] = 0.6
e["Distributions"][1]["Name"] = "Uniform 1"
e["Distributions"][1]["Type"] = "Univariate/Uniform"
e["Distributions"][1]["Minimum"] = 10.0
e["Distributions"][1]["Maximum"] = 40.0
e["Distributions"][2]["Name"] = "Uniform 2"
e["Distributions"][2]["Type"] = "Univariate/Uniform"
e["Distributions"][2]["Minimum"] = 1e-5
e["Distributions"][2]["Maximum"] = 2.0
