role_constraints = utils.get_role_constraints(roles, instances)
formulas = get_formulas(args.formula_file)
domains = get_domains(roles, instances)
# generates the markov logic network
mln = MLN(logic="FirstOrderLogic", grammar="PRACGrammar")
for domain, values in domains.items(): # domains
for value in values:
if len(value) > 1:
const = ''.join(value)
elif len(value) > 0:
const = value[0]
else:
const = "None"
mln.update_domain({domain: [const]})
for role in roles.keys(): # predicates
mln.predicate(Predicate(
role, [role + "_d!"])) # hard-functional constraints only
for formula in formulas: # formulas
formula_str = "0.0 "
for idx in range(len(formula)):
role = formula[idx]
if idx + 1 < len(formula):
formula_str += role + "(+?" + role[:2] + ")" + " ^ "
else:
formula_str += role + "(+?" + role[:2] + ")"
mln << formula_str
mln.write()
mln.tofile(args.output_mln)
print("The initial MLN has been written to '" + args.output_mln + "'.")
print("Verify the MLN using the print out above.")
print("NOTE: Weights of '" + args.output_mln + "' have NOT been learned.")
"LocInRoom": [["locations", "rooms"], ["IsLocation(?x)",
"IsRoom(?y)"]],
"ObjOnLoc": [["objects", "locations"],
["IsObject(?x)", "IsLocation(?y)"]],
"ObjInLoc": [["objects", "locations"],
["IsObject(?x)", "IsLocation(?y)"]],
"ObjCanBe": [["objects", "actions"], ["IsObject(?x)", "IsAction(?y)"]],
"ObjUsedTo": [["objects", "actions"], ["IsObject(?x)",
"IsAction(?y)"]],
"ObjhasState": [["objects", "states"], ["IsObject(?x)",
"IsState(?y)"]],
"OperatesOn": [["objects", "objects"],
["IsObject(?x)", "IsObject(?y)"]]
}
for rel in r2i.keys():
mln.predicate(Predicate(rel, rel2mln[rel][0]))
mln.predicate(Predicate("IsRoom", ["rooms"]))
mln.predicate(Predicate("IsLocation", ["locations"]))
mln.predicate(Predicate("IsObject", ["objects"]))
mln.predicate(Predicate("IsAction", ["actions"]))
mln.predicate(Predicate("IsState", ["states"]))
# declares the markov logic formulas in the markov logic network
for pred in mln.iterpreds():
if "Is" not in pred.name:
mln << "0.0 " + rel2mln[pred.name][1][0] + " ^ " + rel2mln[
pred.name][1][1] + " ^ " + pred.name + "(?x, ?y)"
# loads the 'evidence' to learn markov logic network weights
db = Database(mln)
for ent in e2i.keys():
ent_type = ent.split("-")[-1]
if ent_type == "r":
