def __init__(self):
"""
Default Constructor that will initialize member variables with reasonable
defaults or empty lists/dictionaries where applicable.
@ In, None
@ Out, None
"""
AdaptiveSampler.__init__(self)
self._initialValues = {
} # dict stores the user provided initial values, i.e. {var: val}
self._updateValues = {
} # dict stores input variables values for the current MCMC iteration, i.e. {var:val}
self._proposal = {
} # dict stores the proposal distributions for input variables, i.e. {var:dist}
self._priorFuns = {
} # dict stores the prior functions for input variables, i.e. {var:fun}
self._burnIn = 0 # integers indicate how many samples will be discarded
self._likelihood = None # stores the output from the likelihood
self._logLikelihood = False # True if the user provided likelihood is in log format
self._availProposal = {
'normal': Distributions.Normal(0.0, 1.0),
'uniform': Distributions.Uniform(-1.0, 1.0)
} # available proposal distributions
self._acceptDist = Distributions.Uniform(
0.0, 1.0) # uniform distribution for accept/rejection purpose
self.toBeCalibrated = {} # parameters that will be calibrated
# assembler objects
self.addAssemblerObject('proposal',
InputData.Quantity.zero_to_infinity)
self.addAssemblerObject('probabilityFunction',
InputData.Quantity.zero_to_infinity)
def makeDistribution(self):
"""
Used to make standardized distribution for this poly type.
@ In, None
@ Out, None
"""
uniform = Distributions.Uniform(-1.0, 1.0)
uniform.initializeDistribution()
return uniform
def makeDistribution(self):
"""
Used to make standardized distribution for this poly type.
@ In, None
@ Out, None
"""
uniformElement = ET.Element("Uniform")
element = ET.Element("lowerBound", {})
element.text = "-1"
uniformElement.append(element)
element = ET.Element("upperBound", {})
element.text = "1"
uniformElement.append(element)
uniform = Distributions.Uniform()
uniform._readMoreXML(uniformElement)
uniform.initializeDistribution()
return uniform
def __init__(self):
"""
Default Constructor that will initialize member variables with reasonable
defaults or empty lists/dictionaries where applicable.
@ In, None
@ Out, None
"""
AdaptiveSampler.__init__(self)
self.onlySampleAfterCollecting = True
self._initialValues = {} # dict stores the user provided initial values, i.e. {var: val}
self._updateValues = {} # dict stores input variables values for the current MCMC iteration, i.e. {var:val}
self._proposal = {} # dict stores the proposal distributions for input variables, i.e. {var:dist}
self._proposalDist = {} # dist stores the input variables for each proposal distribution, i.e. {distName:[(var,dim)]}
self._priorFuns = {} # dict stores the prior functions for input variables, i.e. {var:fun}
self._burnIn = 0 # integers indicate how many samples will be discarded
self._likelihood = None # stores the output from the likelihood
self._logLikelihood = False # True if the user provided likelihood is in log format
self._availProposal = {'normal': Distributions.Normal,
'multivariateNormal': Distributions.MultivariateNormal} # available proposal distributions
self._acceptDist = Distributions.Uniform(0.0, 1.0) # uniform distribution for accept/rejection purpose
self.toBeCalibrated = {} # parameters that will be calibrated
self._correlated = False # True if input variables are correlated else False
self.netLogPosterior = 0.0 # log-posterior vs iteration
self._localReady = True # True if the submitted job finished
self._currentRlz = None # dict stores the current realizations, i.e. {var: val}
self._acceptRate = 1. # The accept rate for MCMC
self._acceptCount = 1 # The total number of accepted samples
self._tune = True # Tune the scaling parameter if True
self._tuneInterval = 100 # the number of sample steps for each tuning of scaling parameter
self._scaling = 1.0 # The initial scaling parameter
self._countsUntilTune = self._tuneInterval # The remain number of sample steps until the next tuning
self._acceptInTune = 0 # The accepted number of samples for given tune interval
self._accepted = False # The indication of current samples, True if accepted otherwise False
self._stdProposalDefault = 0.2 # the initial scaling of the std of proposal distribution (only apply to default)
# assembler objects
self.addAssemblerObject('proposal', InputData.Quantity.zero_to_infinity)
self.addAssemblerObject('probabilityFunction', InputData.Quantity.zero_to_infinity)
print(Distributions.knownTypes())
#Test error
try:
Distributions.returnInstance("unknown", 'dud')
except:
print("error worked")
#Test Uniform
uniformElement = ET.Element("Uniform")
uniformElement.append(createElement("lowerBound", text="1.0"))
uniformElement.append(createElement("upperBound", text="3.0"))
#ET.dump(uniformElement)
uniform = Distributions.Uniform()
uniform._readMoreXML(uniformElement)
uniform.initializeDistribution()
uniform.setMessageHandler(mh)
#check pickled version as well
pk.dump(uniform, open('testDistrDump.pk', 'wb'))
puniform = pk.load(open('testDistrDump.pk', 'rb'))
checkCrowDist("uniform", uniform, {
'xMin': 1.0,
'type': 'UniformDistribution',
'xMax': 3.0
})
checkCrowDist("puniform", puniform, {
'xMin': 1.0,