def test_update(self):
# dimension = 0
dimension = 0 # len(samples) = 2**dimension
Q=2.0
list_values = [[[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],10],[[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],10],[[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],10],[[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],10],[[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],10],[[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],[10*Q],[10*Q*Q],10]]
true_power_sums = [[120.0],[240.0],[120.0],[240.0],[120.0],[240.0],[120.0],[240.0],[120.0],[240.0]]
# read parameters
parametersPath = "parameters/parameters_test_combinedMomentEstimator.json"
with open(parametersPath,'r') as parameter_file:
parameters = json.load(parameter_file)
for order in [1,5]:
parameters["momentEstimatorInpuctDict"]["order"] = order
# build momentEstimator class
test_me = me.CombinedMomentEstimator(**parameters["momentEstimatorInpuctDict"])
# update power sums
for i in range (len(list_values)):
test_me.update([[list_values[i]]])
# test update sample number
self.assertEqual(test_me._sampleCounter,60)
# test update power sums
for i in range (2*test_me.order):
self.assertEqual(test_me.powerSums[i], true_power_sums[i])
S1 = test_me.powerSums[0][0]
S2 = test_me.powerSums[1][0]
h1 = mdccm.computeCentralMomentsOrderOneDimensionZero(S1,test_me._sampleCounter)
h2 = mdccm.computeCentralMomentsOrderTwoDimensionZeroBiased(S1,S2,test_me._sampleCounter)
self.assertEqual(h1,2.0)
self.assertEqual(h2,0.0)
def test_update(self):
Q = 2.0
list_values = [
[
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
10,
],
[
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
10,
],
[
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
10,
],
[
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
10,
],
[
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
10,
],
[
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
[10 * Q],
[10 * Q * Q],
10,
],
]
true_power_sums = [
[120.0],
[240.0],
[120.0],
[240.0],
[120.0],
[240.0],
[120.0],
[240.0],
[120.0],
[240.0],
]
# read parameters
with open("parameters/parameters_test_combinedMomentEstimator.json",
"r") as parameter_file:
parameters = load(parameter_file)
for order in [1, 5]:
parameters["momentEstimatorInputDict"]["order"] = order
# build momentEstimator class
test_me = CombinedMomentEstimator(
**parameters["momentEstimatorInputDict"])
# update power sums
for value in list_values:
test_me.update([[value]])
# test update sample number
self.assertEqual(test_me._sampleCounter, 60)
# test update power sums
for i in range(2 * test_me.order):
self.assertEqual(test_me.powerSums[i], true_power_sums[i])
S1 = test_me.powerSums[0][0]
S2 = test_me.powerSums[1][0]
h1 = mdccm.computeCentralMomentsOrderOneDimensionZero(
S1, test_me._sampleCounter)
h2 = mdccm.computeCentralMomentsOrderTwoDimensionZeroBiased(
S1, S2, test_me._sampleCounter)
self.assertEqual(h1, 2.0)
self.assertEqual(h2, 0.0)
def mpi_test_mlmc_Kratos_ParMmg(self):
# read parameters
parametersList = [
"problem_settings/parameters_xmc_asynchronous_mlmc_SAR.json",
"problem_settings/parameters_xmc_asynchronous_mlmc_DAR.json",
]
with WorkFolderScope("caarc_wind_mpi/", __file__, add_to_path=True):
for parametersPath in parametersList:
with open(parametersPath, "r") as parameter_file:
parameters = json.load(parameter_file)
# SolverWrapper
parameters["solverWrapperInputDictionary"][
"qoiEstimator"] = parameters[
"monteCarloIndexInputDictionary"]["qoiEstimator"]
# SampleGenerator
samplerInputDictionary = parameters["samplerInputDictionary"]
samplerInputDictionary[
"randomGeneratorInputDictionary"] = parameters[
"randomGeneratorInputDictionary"]
samplerInputDictionary[
"solverWrapperInputDictionary"] = parameters[
"solverWrapperInputDictionary"]
# MonteCarloIndex Constructor
monteCarloIndexInputDictionary = parameters[
"monteCarloIndexInputDictionary"]
monteCarloIndexInputDictionary[
"samplerInputDictionary"] = samplerInputDictionary
# MonoCriterion
criteriaArray = []
criteriaInputs = []
for monoCriterion in parameters["monoCriteriaInputDictionary"]:
criteriaArray.append(
xmc.monoCriterion.MonoCriterion(
parameters["monoCriteriaInputDictionary"]
[monoCriterion]["criteria"],
parameters["monoCriteriaInputDictionary"]
[monoCriterion]["tolerance"],
))
criteriaInputs.append([
parameters["monoCriteriaInputDictionary"]
[monoCriterion]["input"]
])
# MultiCriterion
multiCriterionInputDictionary = parameters[
"multiCriterionInputDictionary"]
multiCriterionInputDictionary["criteria"] = criteriaArray
multiCriterionInputDictionary[
"inputsForCriterion"] = criteriaInputs
criterion = xmc.multiCriterion.MultiCriterion(
**multiCriterionInputDictionary)
# ErrorEstimator
MSEErrorEstimator = xmc.errorEstimator.ErrorEstimator(
**parameters["errorEstimatorInputDictionary"])
# HierarchyOptimiser
hierarchyCostOptimiser = xmc.hierarchyOptimiser.HierarchyOptimiser(
**parameters["hierarchyOptimiserInputDictionary"])
# MonteCarloSampler
monteCarloSamplerInputDictionary = parameters[
"monteCarloSamplerInputDictionary"]
monteCarloSamplerInputDictionary[
"indexConstructorDictionary"] = monteCarloIndexInputDictionary
monteCarloSamplerInputDictionary["errorEstimators"] = [
MSEErrorEstimator
]
# EstimationAssembler
monteCarloSamplerInputDictionary["assemblers"] = []
for key, dicArgs in parameters[
"estimationAssemblerInputDictionary"].items():
monteCarloSamplerInputDictionary["assemblers"].append(
xmc.estimationAssembler.EstimationAssembler(**dicArgs))
mcSampler = xmc.monteCarloSampler.MonteCarloSampler(
**monteCarloSamplerInputDictionary)
# XMCAlgorithm
XMCAlgorithmInputDictionary = parameters[
"XMCAlgorithmInputDictionary"]
XMCAlgorithmInputDictionary["monteCarloSampler"] = mcSampler
XMCAlgorithmInputDictionary[
"hierarchyOptimiser"] = hierarchyCostOptimiser
XMCAlgorithmInputDictionary["stoppingCriterion"] = criterion
algo = xmc.XMCAlgorithm(**XMCAlgorithmInputDictionary)
if parameters["solverWrapperInputDictionary"][
"asynchronous"] is True:
algo.runAsynchronousXMC()
else:
algo.runXMC()
# test
# such results are not accurate, since we run the problem for few decimals
# and coarse meshes instead of hundreds of seconds and finer meshes
estimations = get_value_from_remote(algo.estimation())
self.assertGreater(sum(estimations), 0)
for level in algo.hierarchy():
self.assertEqual(level[1], 5)
# check moment estimator - level 0
sample_counter = (algo.monteCarloSampler.indices[0].
qoiEstimator[0]._sampleCounter)
S1 = get_value_from_remote(algo.monteCarloSampler.indices[0].
qoiEstimator[0].powerSums[0][0])
h1 = get_value_from_remote(
ccm.computeCentralMomentsOrderOneDimensionZero(
S1, sample_counter))
self.assertGreater(-h1, 0)
self.assertEqual(sample_counter, 5)
# check multi moment estimator - level 1
sample_counter = (algo.monteCarloSampler.indices[1].
qoiEstimator[1]._sampleCounter)
self.assertEqual(sample_counter, 5)
# check combined moment estimator - level 2
sample_counter = (algo.monteCarloSampler.indices[2].
qoiEstimator[2]._sampleCounter)
S1 = get_value_from_remote(algo.monteCarloSampler.indices[2].
qoiEstimator[2].powerSums[0][0])
h1 = get_value_from_remote(
ccm.computeCentralMomentsOrderOneDimensionZero(
S1, sample_counter))
self.assertEqual(sample_counter, 5)
# check multi combined moment estimator - level 2
sample_counter = (algo.monteCarloSampler.indices[2].
qoiEstimator[3]._sampleCounter)
self.assertEqual(sample_counter, 5)