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 __init__(self):
"""
Constructor.
@ In, None
@ Out, None
"""
AdaptiveSampler.__init__(self)
## Instance Variable Initialization
# public
# _protected
self._seed = None # random seed to apply
self._minMax = 'min' # maximization or minimization?
self._activeTraj = [] # tracks live trajectories
self._cancelledTraj = {} # tracks cancelled trajectories, and reasons
self._convergedTraj = {} # tracks converged trajectories, and values obtained
self._numRepeatSamples = 1 # number of times to repeat sampling (e.g. denoising)
self._objectiveVar = None # objective variable for optimization
self._initialValues = None # initial variable values (trajectory starting locations), list of dicts
self._variableBounds = None # dictionary of upper/lower bounds for each variable (may be inf?)
self._trajCounter = 0 # tracks numbers to assign to trajectories
self._initSampler = None # sampler to use for picking initial seeds
self._constraintFunctions = [] # list of constraint functions
self._impConstraintFunctions = [] # list of implicit constraint functions
# __private
# additional methods
self.addAssemblerObject('Constraint', '-1') # Explicit (input-based) constraints
self.addAssemblerObject('ImplicitConstraint', '-1') # Implicit constraints
self.addAssemblerObject('Sampler', '-1') # This Sampler can be used to initialize the optimization initial points (e.g. partially replace the <initial> blocks for some variables)
# register adaptive sample identification criteria
self.registerIdentifier('traj') # the trajectory of interest
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)