def _expandQueries(self, queries):
''' expands the list of queries where necessary, e.g. queries that are just predicate names are expanded to the corresponding list of atoms '''
equeries = []
for query in queries:
#print "got query '%s' of type '%s'" % (str(query), str(type(query)))
if type(query) == str:
prevLen = len(equeries)
if "(" in query: # a fully or partially grounded formula
f = FOL.parseFormula(query)
for gndFormula in f.iterGroundings(self.mln):
equeries.append(gndFormula[0])
else: # just a predicate name
try:
for gndPred in self.mln._getPredGroundings(query):
equeries.append(FOL.parseFormula(gndPred).ground(self.mln, {}))
except:
raise #Exception("Could not expand query '%s'" % query)
if len(equeries) - prevLen == 0:
raise Exception("String query '%s' could not be expanded." % query)
elif isinstance(query, FOL.Formula):
equeries.append(query)
else:
raise Exception("Received query of unsupported type '%s'" % str(type(query)))
return equeries
def _infer(self, verbose=True, details=False, fittingMethod=InferenceMethods.Exact, fittingThreshold=1e-3, fittingSteps=100, fittingParams=None, maxThreshold=None, greedy=False, **args):
# add formulas to the model whose weights we can then fit
if verbose: print "extending model with %d formulas whose weights will be fit..." % len(self.mrf.softEvidence)
for req in self.mrf.softEvidence:
formula = FOL.parseFormula(req["expr"])
idxFormula = self.mrf._addFormula(formula, 0.0)
gndFormula = formula.ground(self.mrf, {})
self.mrf._addGroundFormula(gndFormula, idxFormula)
req["gndExpr"] = req["expr"]
req["gndFormula"] = gndFormula
req["idxFormula"] = idxFormula
# do fitting
if fittingParams is None: fittingParams = {}
fittingParams.update(args)
results, self.data = self.mrf._fitProbabilityConstraints(self.mrf.softEvidence, fittingMethod=fittingMethod, fittingThreshold=fittingThreshold, fittingSteps=fittingSteps, given=self.given, queries=self.queries, verbose=details, fittingParams=fittingParams, maxThreshold=maxThreshold, greedy=greedy)
return results
