def run(self): global DEBUG_FLAG data = self.preprocess_metafunctions() if DEBUG_FLAG: self.display_preprocessed_code(data) model = PrologString(data) if DEBUG_FLAG: print '=' * 80 print "BEFORE ESCAPING" print '=' * 80 for elem in model: print clause2str(elem) print '=' * 80 model = escape_metafunctions(model) if DEBUG_FLAG: print '=' * 80 print "AFTER ESCAPING" print '=' * 80 for elem in model: print clause2str(elem) print '=' * 80 engine = DefaultEngine(label_all=True) engine.add_builtin(METAMETAFUNCTION_FUNCTOR, 2, BooleanBuiltIn(self.builtin_metafunction)) engine.add_builtin('declare', 2, BooleanBuiltIn(self.builtin_declare)) engine.add_builtin('declare', 3, BooleanBuiltIn(self.builtin_declare)) db = engine.prepare(model) if DEBUG_FLAG: print "=" * 80 print "DATABASE" print "=" * 80 for elem in db: print elem print "=" * 80 gp = LogicFormula( keep_all=True, keep_order=True, keep_duplicates=True, avoid_name_clash=True ) gp = engine.ground_all(db, target=gp) if DEBUG_FLAG: print "=" * 80 print "GROUND PROGRAM (GROUNDER)" print "=" * 80 for elem in gp.enum_clauses(): print elem print "=" * 80 clauses = [] facts = [] for clause in unescape_metafunctions(gp.enum_clauses()): if isinstance(clause, Clause): clauses.append(clause) else: facts.append(clause) query_atoms = gp._names['query'] return self.builtin_declare.declarations, clauses + facts, query_atoms
def sample( filename, N=1, with_facts=False, oneline=False ) : pl = PrologFile(filename) engine = DefaultEngine() db = engine.prepare(pl) for i in range(0, N) : result = engine.ground_all(db, target=SampledFormula()) print ('====================') print (result.toString(db, with_facts, oneline))
def estimate( filename, N=1 ) : from collections import defaultdict pl = PrologFile(filename) engine = DefaultEngine() db = engine.prepare(pl) estimates = defaultdict(float) counts = 0.0 for i in range(0, N) : result = engine.ground_all(db, target=SampledFormula()) for k, v in result.queries() : if v == 0 : estimates[k] += 1.0 counts += 1.0 for k in estimates : estimates[k] = estimates[k] / counts return estimates
def main(filename, output): model = PrologFile(filename) engine = DefaultEngine(label_all=True) with Timer("parsing"): db = engine.prepare(model) print("\n=== Database ===") print(db) print("\n=== Queries ===") queries = engine.query(db, Term("query", None)) print("Queries:", ", ".join([str(q[0]) for q in queries])) print("\n=== Evidence ===") evidence = engine.query(db, Term("evidence", None, None)) print("Evidence:", ", ".join(["%s=%s" % ev for ev in evidence])) print("\n=== Ground Program ===") with Timer("ground"): gp = engine.ground_all(db) print(gp) print("\n=== Acyclic Ground Program ===") with Timer("acyclic"): gp = LogicDAG.createFrom(gp) print(gp) print("\n=== Conversion to CNF ===") with Timer("convert to CNF"): cnf = CNF.createFrom(gp) with open(output, "w") as f: f.write(cnf.to_dimacs(weighted=False, names=True))
class AIController(PlayerController): '''Intelligenza artificiale Contiene i metodi per la gestione delle decisioni prese dal maziere ''' ''' conoscenza del dt_problog''' conoscenza = [] ''' conoscenza del problog (calcolo inferenza) ''' conoscenza_prob = [] vincita_attuale = 0 def __init__(self, nome, soldi, prolog): '''COSTRUTTORE chiama il costruttore della superclasse PlayerController ed istanzia gli oggetti per la gestione delle query problog :type nome: string :param nome: nome del giocatore :type soldi: float :param soldi: soldi del giocatore :type prolog: PrologController :param prolog: Oggetto PrologController che estende la classe Prolog del modulo pyswip ''' super(AIController, self).__init__(nome, soldi, prolog) self.read_file(path='controller/dt_problog.pl', path1='controller/knowledge_problog.pl') self.learn() def read_file(self, path, path1): '''Legge i file .pl contenenti la conoscenza problog e crea una lista contenente tutti i predicati ''' try: with open(path) as kn_file: self.conoscenza = kn_file.read() with open(path1) as kn_file: for row in kn_file: self.conoscenza_prob.append(row.rstrip('\n')) self.conoscenza_prob = list( filter(('').__ne__, self.conoscenza_prob)) except Exception as e: print(e) def learn(self): '''Construisce gli oggetti problog per la valutazione dell'inferenza''' try: knowledge_str = '' for predicate in self.conoscenza_prob: knowledge_str += predicate + '\n' knowledge_str = PrologString(knowledge_str) self.problog = DefaultEngine() self.knowledge_database = self.problog.prepare(knowledge_str) except Exception as e: print(e) def get_used_card_evidence(self, prolog): '''construisce la lista contenente le stringhe delle evidenze per il DTProblog''' ret_list = [] for card in prolog.uscite: ret_list.append('evidence(not ' + str(card) + ').\n') return ret_list def get_utility(self, prolog): '''constuisce la lista delle utility per il DTProblog''' vs_score, utility = prolog.get_utility() ret_list = [] for i in range(len(vs_score)): ret_list.append( f'utility(vinco({self.punteggio},{vs_score[i]}), {utility[i]}).\n' ) ret_list.append( f'utility(perdo({self.punteggio},{vs_score[i]}), {-1*utility[i]}).\n' ) ret_list.append( f'utility(sballo({self.punteggio}), {-1*prolog.get_winnable_bet()}).\n' ) vincita, _ = prolog.get_gain() prob_di_migliorare = self.query('prob_di_migliorare', self.punteggio, evidence=self.get_used_card(prolog)) util = 0 for i in range(len(vincita)): util += prob_di_migliorare * vincita[i] ret_list.append(f'utility(miglioro({self.punteggio}), {util}).\n') ''' prob_di_migliorare = self.query('prob_di_migliorare',self.punteggio,evidence = self.get_used_card(prolog)) for i in range(len(vincita)): ret_list.append(f'utility(miglioro({self.punteggio}), {prob_di_migliorare*vincita[i]}).\n') ''' return ret_list def decidi(self, prolog): '''prende la decisione''' temp = self.conoscenza evidence = self.get_used_card_evidence(prolog) for ev in evidence: temp += ev utilities = self.get_utility(prolog) for ut in utilities: temp += ut program = PrologString(temp) decisions, _, _ = dtproblog(program) for _, value in decisions.items(): if value == 1: return True print('STO\n') return False def eval_query(self, term, *args, **kwargs): try: if args: t = '\'' + term + '\'' termine = f'Term({t},' for termine_arg in args: if isinstance(termine_arg, str): termine += 'Term(\'' + termine_arg + '\')' + ',' elif isinstance(termine_arg, int) or isinstance( termine_arg, float): termine += 'Constant(' + str(termine_arg) + ')' + ',' termine = termine[:-1] + ')' query_term = eval(termine) else: query_term = Term(term) evidenze = [] if kwargs: for i in kwargs['evidence']: # Term(Constant(1),Term('spade')) i = i.replace('card(', '').replace(' ', '').replace(')', '').split(',') term_str = f'Term(\'card\',Constant({i[0]}), Term(\'{i[1]}\'))' tupla = (eval(term_str), False) evidenze.append(tupla) lf = self.problog.ground_all(self.knowledge_database, queries=[query_term], evidence=evidenze) return get_evaluatable().create_from(lf).evaluate() except Exception as e: print(e) print("QUESTA SOPRA è L'ECCEZIONE") return None def query(self, term, *args, **kwargs): # print(ai.query('prob_di_sballare', 7, evidence=['card(2,bastoni)'])) query_result = self.eval_query(term, *args, **kwargs) if len(query_result) > 1: res = [] for prob_res in query_result.values(): res.append(prob_res) else: for prob_res in query_result.values(): res = prob_res return res def get_used_card(self, prolog): '''ritorna una lista di stringhe contenenti le carte uscite''' uscite = [] for card in prolog.uscite: uscite.append(str(card)) return uscite
def main(filename, with_dot, knowledge): dotprefix = None if with_dot: dotprefix = os.path.splitext(filename)[0] + "_" model = PrologFile(filename) engine = DefaultEngine(label_all=True) with Timer("parsing"): db = engine.prepare(model) print("\n=== Database ===") print(db) print("\n=== Queries ===") queries = engine.query(db, Term("query", None)) print("Queries:", ", ".join([str(q[0]) for q in queries])) print("\n=== Evidence ===") evidence = engine.query(db, Term("evidence", None, None)) print("Evidence:", ", ".join(["%s=%s" % ev for ev in evidence])) print("\n=== Ground Program ===") with Timer("ground"): gp = engine.ground_all(db) print(gp) if dotprefix != None: with open(dotprefix + "gp.dot", "w") as f: print(gp.toDot(), file=f) print("\n=== Acyclic Ground Program ===") with Timer("acyclic"): gp = gp.makeAcyclic() print(gp) if dotprefix != None: with open(dotprefix + "agp.dot", "w") as f: print(gp.toDot(), file=f) if knowledge == "sdd": print("\n=== SDD compilation ===") with Timer("compile"): nnf = SDD.createFrom(gp) if dotprefix != None: nnf.saveSDDToDot(dotprefix + "sdd.dot") else: print("\n=== Conversion to CNF ===") with Timer("convert to CNF"): cnf = CNF.createFrom(gp) print("\n=== Compile to d-DNNF ===") with Timer("compile"): nnf = DDNNF.createFrom(cnf) if dotprefix != None: with open(dotprefix + "nnf.dot", "w") as f: print(nnf.toDot(), file=f) print("\n=== Evaluation result ===") with Timer("evaluate"): result = nnf.evaluate() for it in result.items(): print("%s : %s" % (it))
class Engine(object): """ Adapter class to Problog grounding and query engine. :param program: a valid MDP-ProbLog program :type program: str """ def __init__(self, program): self._engine = DefaultEngine() self._db = self._engine.prepare(PrologString(program)) self._gp = None self._knowledge = None def declarations(self, declaration_type): """ Return a list of all terms of type `declaration_type`. :param declaration_type: declaration type. :type declaration_type: str :rtype: list of problog.logic.Term """ return [ t[0] for t in self._engine.query(self._db, Term(declaration_type, None)) ] def assignments(self, assignment_type): """ Return a dictionary of assignments of type `assignment_type`. :param assignment_type: assignment type. :type assignment_type: str :rtype: dict of (problog.logic.Term, problog.logic.Constant) items. """ return dict( self._engine.query(self._db, Term(assignment_type, None, None))) def get_instructions_table(self): """ Return the table of instructions separated by instruction type as described in problog.engine.ClauseDB. :rtype: dict of (str, list of (node,namedtuple)) """ instructions = {} for node, instruction in enumerate(self._db._ClauseDB__nodes): instruction_type = str(instruction) instruction_type = instruction_type[:instruction_type.find('(')] if instruction_type not in instructions: instructions[instruction_type] = [] assert (self._db.get_node(node) == instruction) # sanity check instructions[instruction_type].append((node, instruction)) return instructions def add_fact(self, term, probability=None): """ Add a new `term` with a given `probability` to the program database. Return the corresponding node number. :param term: a predicate :type term: problog.logic.Term :param probability: a number in [0,1] :type probability: float :rtype: int """ return self._db.add_fact(term.with_probability(Constant(probability))) def get_fact(self, node): """ Return the fact in the table of instructions corresponding to `node`. :param node: identifier of fact in table of instructions :type node: int :rtype: problog.engine.fact """ fact = self._db.get_node(node) if not str(fact).startswith('fact'): raise IndexError('Node `%d` is not a fact.' % node) return fact def add_rule(self, head, body): """ Add a new rule defined by a `head` and `body` arguments to the program database. Return the corresponding node number. :param head: a predicate :type head: problog.logic.Term :param body: a list of literals :type body: list of problog.logic.Term or problog.logic.Not :rtype: int """ b = body[0] for term in body[1:]: b = b & term rule = head << b return self._db.add_clause(rule) def get_rule(self, node): """ Return the rule in the table of instructions corresponding to `node`. :param node: identifier of rule in table of instructions :type node: int :rtype: problog.engine.clause """ rule = self._db.get_node(node) if not str(rule).startswith('clause'): raise IndexError('Node `%d` is not a rule.' % node) return rule def add_assignment(self, term, value): """ Add a new utility assignment of `value` to `term` in the program database. Return the corresponding node number. :param term: a predicate :type term: problog.logic.Term :param value: a numeric value :type value: float :rtype: int """ args = (term.with_probability(None), Constant(1.0 * value)) utility = Term('utility', *args) return self._db.add_fact(utility) def get_assignment(self, node): """ Return the assignment in the table of instructions corresponding to `node`. :param node: identifier of assignment in table of instructions :type node: int :rtype: pair of (problog.logic.Term, problog.logic.Constant) """ fact = self._db.get_node(node) if not (str(fact).startswith('fact') and fact.functor == 'utility'): raise IndexError('Node `%d` is not an assignment.' % node) return (fact.args[0], fact.args[1]) def add_annotated_disjunction(self, facts, probabilities): """ Add a new annotated disjunction to the program database from a list of `facts` and its `probabilities`. Return a list of choice nodes. :param facts: list of probabilistic facts :type facts: list of problog.logic.Term :param probabilities: list of valid individual probabilities such that the total probability is less than or equal to 1.0 :type probabilities: list of float in [0.0, 1.0] :rtype: list of int """ disjunction = [ f.with_probability(Constant(p)) for f, p in zip(facts, probabilities) ] self._db += AnnotatedDisjunction(heads=disjunction, body=Constant('true')) choices = [] for node, term in enumerate(self._db._ClauseDB__nodes): if str(term).startswith('choice'): choices.append((term, node)) nodes = [] for term in disjunction: term = term.with_probability(None) for choice, node in choices: if term in choice.functor.args: nodes.append(node) return nodes def get_annotated_disjunction(self, nodes): """ Return the list of choice nodes in the table of instructions corresponding to `nodes`. :param nodes: list of node identifiers :type nodes: list of int :rtype: list of problog.engine.choice """ choices = [self._db.get_node(node) for node in nodes] for choice in choices: if not str(choice).startswith('choice'): raise IndexError('Node `%d` is not a choice node.' % choice) return choices def relevant_ground(self, queries): """ Create ground program with respect to `queries`. :param queries: list of predicates :type queries: list of problog.logic.Term """ self._gp = self._engine.ground_all(self._db, queries=queries) def compile(self, terms=[]): """ Create compiled knowledge database from ground program. Return mapping of `terms` to nodes in the compiled knowledge database. :param terms: list of predicates :type terms: list of problog.logic.Term :rtype: dict of (problog.logic.Term, int) """ self._knowledge = get_evaluatable(None).create_from(self._gp) term2node = {} for term in terms: term2node[term] = self._knowledge.get_node_by_name(term) return term2node def evaluate(self, queries, evidence): """ Compute probabilities of `queries` given `evidence`. :param queries: mapping of predicates to nodes :type queries: dict of (problog.logic.Term, int) :param evidence: mapping of predicate and evidence weight :type evidence: dictionary of (problog.logic.Term, {0, 1}) :rtype: list of (problog.logic.Term, [0.0, 1.0]) """ evaluator = self._knowledge.get_evaluator(semiring=None, evidence=None, weights=evidence) return [(query, evaluator.evaluate(queries[query])) for query in sorted(queries, key=str)]
def sample_object(pl, N=1): engine = DefaultEngine() db = engine.prepare(pl) result = [engine.ground_all(db, target=SampledFormula()) for i in range(N)] return result, db
class GDLIIIProblogRep(object): def __init__(self, program, fformat): #GlobalEngine self._engine = DefaultEngine() gdl_parser = GDLIIIParser() self._model = gdl_parser.output_model(program, fformat) self._baseModelFile = self._model.as_problog() self._playerList = [] self._randomIdentifier = Constant(0) #Hardcoded to give the random player a specific constant id as we apply some special rules to the random player worlds = self._initialiseKB() self._cur_node = GDLNode(worlds, GameData(self._playerList, self._randomIdentifier)) self._moveList = dict([(i,None) for i in self._playerList]) self.terminal = False def getMoveList(self): return self._moveList def undo(self, increment=1): for _ in range(increment): self._cur_node = self._cur_node.parent self._moveList = dict([(i,None) for i in self._playerList]) if self.terminal: self.terminal = False def getLegalMovesForPlayer(self, player): return self._cur_node.legal_moves[player] def _resetKnowledgeBase(self): self._kb = self._engine.prepare(self._baseModelFile) def getPlayersPossibleWorlds(self, player): return self._cur_node.worlds[player] #Assumption, term contains a single argument def extractSingleArg(self,nArg, term): return term.args[nArg] #Recommended to never call this function with step > 1 as it will likely take a long time, exponential time complexity for values of step > 0. def query(self, player, query, step=0): if step == 0: return self._cur_node.raw_query(player, Term('thinks', player, query)) else: world_set = set([self._cur_node]) for _ in range(step): #Create set of all possible move sequences from perspective of player action_set = set() for world in world_set: action_set = action_set.union(set([i for i in world.get_legal_moves()[player].keys()])) legal_moves_seqs = [{k:(None if k != player else a) for k in self._playerList} for a in action_set] #Generate possible (but not always valid) successor worlds new_set = set() for world in world_set: for actions in legal_moves_seqs: new_set.add(world.generate_speculative_worlds(player, actions)) world_set = new_set query_dict = {} size = len(world_set) for w in world_set: for (item,val) in w.raw_query(player, Term('thinks', player, query)).items(): if item in query_dict.keys(): query_dict[item] += val/size else: query_dict[item] = val/size return query_dict #Private def _initialiseKB(self): self._kb = self._engine.prepare(self._baseModelFile) initialState = \ set(map(lambda a: Term('ptrue', a[0].args[0]), self._engine.ground_all(self._kb, queries=[Term('init',Var('_'))]).get_names())) players = \ set(map(lambda a: a[0], self._engine.ground_all(self._kb, queries=[Term('role',Var('_'))]).get_names())) #Not needed, but dont care to remove right now self._step = 0 playerWorldState = {} for playerNum in map(lambda a: a.args[0], players): knowledge = map(lambda a: Term('thinks', playerNum, a.args[0]), initialState) playerPreds = initialState.union(set(knowledge)) self._playerList.append(playerNum) #Each player starts with a single initial world if playerNum == self._randomIdentifier: #Random Specific world has no thinks predicates playerWorldState[playerNum] = [RandomWorld(self._engine, self._baseModelFile, self._step, 1, initialState, playerNum)] else: playerWorldState[playerNum] = [World(self._engine, self._baseModelFile, self._step, 1, playerPreds, playerNum)] return playerWorldState def applyActionsToModelAndUpdate(self): if (None in self._moveList.values()): raise Exception("Error: Must have submitted moves for all players before proceeding") self._cur_node = self._cur_node.get_next_node(self._moveList) #Assume, there is at least one player if len([(k,v) for (k,v) in self._cur_node.raw_query(\ self._playerList[0], Term('terminal')).items() if v > 0]) > 0: self.terminal = True self._step += 1 self._moveList = dict([(i,None) for i in self._playerList]) def submitAction(self, action, player): if ( action not in self.getLegalMovesForPlayer(player)): raise Exception("{} is not a legal action".format(action)) self._moveList[player] = action
db = engine.prepare(pl) print(db) query_term = sibling(tom, sally) res = engine.query(db, query_term) print ('%s? %s' % (query_term, bool(res))) query_term = sibling(sally, erica) res = engine.query(db, query_term) print(res) print ('%s? %s' % (query_term, bool(res))) # NOTE: variables can be replaced by None of a negative number # the difference is that each None is a different variable, # while each variable with the same negative number is the same variable query_term = sibling(None, None) res = engine.query(db, query_term) for args in res: print(query_term(*args)) print('siblings of sally:') query_term = Term('sibling', Term('sally'), None) res = engine.query(db, query_term) for args in res: print(query_term(*args)) print(engine.ground_all(db, queries=[query_term]))
class Engine(object): """ Adapter class to Problog grounding and query engine. :param program: a valid MDP-ProbLog program :type program: str """ def __init__(self, program): self._engine = DefaultEngine() self._db = self._engine.prepare(PrologString(program)) self._gp = None self._knowledge = None def declarations(self, declaration_type): """ Return a list of all terms of type `declaration_type`. :param declaration_type: declaration type. :type declaration_type: str :rtype: list of problog.logic.Term """ return [t[0] for t in self._engine.query(self._db, Term(declaration_type, None))] def assignments(self, assignment_type): """ Return a dictionary of assignments of type `assignment_type`. :param assignment_type: assignment type. :type assignment_type: str :rtype: dict of (problog.logic.Term, problog.logic.Constant) items. """ return dict(self._engine.query(self._db, Term(assignment_type, None, None))) def get_instructions_table(self): """ Return the table of instructions separated by instruction type as described in problog.engine.ClauseDB. :rtype: dict of (str, list of (node,namedtuple)) """ instructions = {} for node, instruction in enumerate(self._db._ClauseDB__nodes): instruction_type = str(instruction) instruction_type = instruction_type[:instruction_type.find('(')] if instruction_type not in instructions: instructions[instruction_type] = [] assert(self._db.get_node(node) == instruction) # sanity check instructions[instruction_type].append((node, instruction)) return instructions def add_fact(self, term, probability=None): """ Add a new `term` with a given `probability` to the program database. Return the corresponding node number. :param term: a predicate :type term: problog.logic.Term :param probability: a number in [0,1] :type probability: float :rtype: int """ return self._db.add_fact(term.with_probability(Constant(probability))) def get_fact(self, node): """ Return the fact in the table of instructions corresponding to `node`. :param node: identifier of fact in table of instructions :type node: int :rtype: problog.engine.fact """ fact = self._db.get_node(node) if not str(fact).startswith('fact'): raise IndexError('Node `%d` is not a fact.' % node) return fact def add_rule(self, head, body): """ Add a new rule defined by a `head` and `body` arguments to the program database. Return the corresponding node number. :param head: a predicate :type head: problog.logic.Term :param body: a list of literals :type body: list of problog.logic.Term or problog.logic.Not :rtype: int """ b = body[0] for term in body[1:]: b = b & term rule = head << b return self._db.add_clause(rule) def get_rule(self, node): """ Return the rule in the table of instructions corresponding to `node`. :param node: identifier of rule in table of instructions :type node: int :rtype: problog.engine.clause """ rule = self._db.get_node(node) if not str(rule).startswith('clause'): raise IndexError('Node `%d` is not a rule.' % node) return rule def add_assignment(self, term, value): """ Add a new utility assignment of `value` to `term` in the program database. Return the corresponding node number. :param term: a predicate :type term: problog.logic.Term :param value: a numeric value :type value: float :rtype: int """ args = (term.with_probability(None), Constant(1.0 * value)) utility = Term('utility', *args) return self._db.add_fact(utility) def get_assignment(self, node): """ Return the assignment in the table of instructions corresponding to `node`. :param node: identifier of assignment in table of instructions :type node: int :rtype: pair of (problog.logic.Term, problog.logic.Constant) """ fact = self._db.get_node(node) if not (str(fact).startswith('fact') and fact.functor == 'utility'): raise IndexError('Node `%d` is not an assignment.' % node) return (fact.args[0], fact.args[1]) def add_annotated_disjunction(self, facts, probabilities): """ Add a new annotated disjunction to the program database from a list of `facts` and its `probabilities`. Return a list of choice nodes. :param facts: list of probabilistic facts :type facts: list of problog.logic.Term :param probabilities: list of valid individual probabilities such that the total probability is less than or equal to 1.0 :type probabilities: list of float in [0.0, 1.0] :rtype: list of int """ disjunction = [ f.with_probability(Constant(p)) for f, p in zip(facts, probabilities) ] self._db += AnnotatedDisjunction(heads=disjunction, body=Constant('true')) choices = [] for node, term in enumerate(self._db._ClauseDB__nodes): if str(term).startswith('choice'): choices.append((term, node)) nodes = [] for term in disjunction: term = term.with_probability(None) for choice, node in choices: if term in choice.functor.args: nodes.append(node) return nodes def get_annotated_disjunction(self, nodes): """ Return the list of choice nodes in the table of instructions corresponding to `nodes`. :param nodes: list of node identifiers :type nodes: list of int :rtype: list of problog.engine.choice """ choices = [ self._db.get_node(node) for node in nodes ] for choice in choices: if not str(choice).startswith('choice'): raise IndexError('Node `%d` is not a choice node.' % choice) return choices def relevant_ground(self, queries): """ Create ground program with respect to `queries`. :param queries: list of predicates :type queries: list of problog.logic.Term """ self._gp = self._engine.ground_all(self._db, queries=queries) def compile(self, terms=[]): """ Create compiled knowledge database from ground program. Return mapping of `terms` to nodes in the compiled knowledge database. :param terms: list of predicates :type terms: list of problog.logic.Term :rtype: dict of (problog.logic.Term, int) """ self._knowledge = get_evaluatable(None).create_from(self._gp) term2node = {} for term in terms: term2node[term] = self._knowledge.get_node_by_name(term) return term2node def evaluate(self, queries, evidence): """ Compute probabilities of `queries` given `evidence`. :param queries: mapping of predicates to nodes :type queries: dict of (problog.logic.Term, int) :param evidence: mapping of predicate and evidence weight :type evidence: dictionary of (problog.logic.Term, {0, 1}) :rtype: list of (problog.logic.Term, [0.0, 1.0]) """ evaluator = self._knowledge.get_evaluator(semiring=None, evidence=None, weights=evidence) return [ (query, evaluator.evaluate(queries[query])) for query in sorted(queries, key=str) ]