def setUp(self):
config.StringParameter('test.param0', 'a param', default='foo')
config.StringParameter('test.param1', 'a param', config.oneof('foo', 'bar'))
config.IntParameter('test.param2', 'a param', default=20)
config.IntParameter('test.param3', 'a param', config.atmost(100))
config.IntParameter('test.param4', 'a param', config.atleast(100))
config.IntParameter('test.param5', 'a param', config.between(10,100))
config.IntParameter('test.param6', 'a param', lambda x: x == 50)
config.FloatParameter('test.param7', 'a param', config.between(1.3, 2.7))
#
# Parameters
#
_pmissingdatahandler = config.StringParameter(
'evaluator.missingdata_evaluator',
"""
Evaluator to use for handling missing data. Choices include:
* gibbs: Gibb's sampling
* maxentropy_gibbs: Gibbs's sampling over all completions of the
missing values that result in maximum entropy discretization for the
variables.
* exact: exact enumeration of all possible missing values (only
useable when there are few missing values)
""",
config.oneof('gibbs', 'exact', 'maxentropy_gibbs'),
default='gibbs'
)
_missingdata_evaluators = {
'gibbs': MissingDataNetworkEvaluator,
'exact': MissingDataExactNetworkEvaluator,
'maxentropy_gibbs': MissingDataMaximumEntropyNetworkEvaluator
}
def fromconfig(data_=None, network_=None, prior_=None):
"""Create an evaluator based on configuration parameters.
This function will return the correct evaluator based on the relevant
configuration parameters.