def run_dtproblog(node_size):
model_text = generate_program(node_size)
print(model_text)
program = PrologString(model_text)
decisions, score, statistics = dtproblog(program)
print("++++++++ Program for node size = "+ str(node_size)+"++++++++")
for name, value in decisions.items():
print ('%s: %s' % (name, value))
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 run_dtproblog(node_size, model_path, output_file):
text = ''
model_text = get_program(model_path, node_size)
print(model_text)
program = PrologString(model_text)
current_milli_time = lambda: int(round(time.time() * 1000))
decisions, score, statistics = dtproblog(program)
after_milli_time = lambda: int(round(time.time() * 1000))
processing_time = after_milli_time - current_milli_time
text+="++++++++ Program for node size = "+ str(node_size)+"++++++++"+'\n'
text+= '+ statistics: '+str(statistics)+'\n'
text+= '+ score: '+str(score)+'\n'
text+= '+ processing time: '+str(processing_time)+'\n'
print("++++++++ Program for node size = "+ str(node_size)+"++++++++")
for name, value in decisions.items():
print ('%s: %s' % (name, value))
text+= str(name)+'\t'+str(value)+'\n'
save_file(output_file, text)
def generate_offer(self) -> Offer:
if not self.offer_queue:
program = PrologString(self._compile_dtproblog_model())
query_output, score, _ = dtproblog(program)
# score of offers are now below acceptability threshold
# so we should terminate
if score < self.acceptability_threshold:
self.active = False
raise StopIteration()
cleaned_query_output = self._clean_query_output(query_output)
extended_offers = self._extend_partial_offer(cleaned_query_output)
for offer in extended_offers:
if offer not in self.generated_offers:
self.generated_offers[offer.get_sparse_repr()] = score
self.offer_queue.append(offer)
if not self.offer_queue:
raise RuntimeError()
return self.offer_queue.pop()
from problog.tasks.dtproblog import dtproblog
from problog.program import PrologString
from problog.program import PrologFile
program = PrologFile("models/poicarp.pl")
decisions, score, statistics = dtproblog(program)
for name, value in decisions.items():
print ('%s: %s' % (name, value))
print score
print statistics