def __init__(self,
source,
examples,
max_iter=10000,
min_improv=1e-10,
verbose=0,
knowledge=None,
leakprob=None,
propagate_evidence=True,
normalize=False,
**extra):
"""
:param source: filename of file containing input model
:type source: str
:param examples: list of observed terms / value
:type examples: list[tuple(Term, bool)]
:param max_iter: maximum number of iterations to run
:type max_iter: int
:param min_improv: minimum improvement in log-likelihood for convergence detection
:type min_improv: float
:param verbose: verbosity level
:type verbose: int
:param knowledge: class to use for knowledge compilation
:type knowledge: class
:param leakprob: Add all true evidence atoms with the given probability
to avoid 'inconsistent evidence' errors. This also
allows to learn a program without constants and
retrieve the constants from the evidence file.
(default: None)
:type leakprob: float or None
:param extra: catch all for additional parameters (not used)
"""
SemiringProbability.__init__(self)
LogicProgram.__init__(self)
self.source = source
# The names of the atom for which we want to learn weights.
self.names = []
# The weights to learn.
# The initial weights are of type 'float'.
# When necessary they are replaced by a dictionary [t(arg1, arg2, ...) -> float]
# for weights of form t(SV, arg1, arg2, ...).
self._weights = []
self.examples = examples
self.leakprob = leakprob
self.leakprobatoms = None
self.propagate_evidence = propagate_evidence
self._compiled_examples = None
self.max_iter = max_iter
self.min_improv = min_improv
self.verbose = verbose
self.iteration = 0
if knowledge is None:
knowledge = get_evaluatable()
self.knowledge = knowledge
self.output_mode = False
self.extra = extra
self._enable_normalize = normalize
self._adatoms = []
def __init__(self, weights):
SemiringProbability.__init__(self)
self._weights = weights
