def add_character_info(character_name: str, room_name: str, is_alive: bool, has_marks_on_neck: bool):
if is_alive:
s = [character_name + ' est vivant']
print(s[0])
a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/personne_vivant.fcfg')))
else:
s = [character_name + ' est morte']
print(s[0])
a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/personne_morte.fcfg')))
if has_marks_on_neck:
s = [character_name + ' a des marques au cou']
print(s[0])
a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/personne_marque.fcfg')))
s = [character_name + ' est dans le ' + room_name]
print(s[0])
a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/personne_piece.fcfg')))
def sem_parser(sents, syntax, verbose=False, is_cs=False):
"""
It parses sentences with an FDFG grammar and returns a dictionary of
sentences to their semantic representations.
Parameters:
sents: a list of sentences to be parsed.
fcfg_string: a string listing all fcfg rules with SEM for the
FeatureGrammar.
verbose: boolean value. default value is `False`.
if verbose is True it prints results.
is_cs: boolean value. Inicating if it is using Cooper Storage. Default value is `False`.
Returns:
dict: dictionary of sentences translated to a list of their
semantic representaions.
"""
sents_reps = {sent: [] for sent in sents}
for sent, results in zip(sents, nltk.interpret_sents(sents, syntax)):
if verbose:
display(Markdown(f"----\n{sent}: {len(results)} result(s)"))
for j, (synrep, semrep) in enumerate(results):
if is_cs:
cs_semrep = cs.CooperStore(semrep)
cs_semrep.s_retrieve(trace=False)
for reading in cs_semrep.readings:
sents_reps[sent].append(reading)
if verbose:
display_latex(
reading) # prints the SEM feature of a tree
if verbose:
display_tree(synrep) # show the parse tree
else:
sents_reps[sent].append(semrep)
if verbose:
display_latex(semrep) # prints the SEM feature of a tree
display_tree(synrep) # show the parse tree
return sents_reps
#example4('Jean tua Marie avec une corde dans la cuisine')
def example5(s1):
ep = load_parser('635/quant01.fcfg')
for tree in ep.parse(s1.split()):
tree.draw()
#example5('tous les cochons sont gros')
sents1 = ['La victime a été tuée à 22h']
sents2 = ['Jean tua Marie avec une corde']
sents3 = ['une corde tua Marie avec une corde dans la cuisine']
sents4 = ['tous les cochons sont gros']
def printResults(results):
for result in results:
for (synrep, semrep) in result:
print(semrep)
printResults(nltk.interpret_sents(sents1, 'heure_crime.fcfg'))
#printResults(nltk.interpret_sents(sents2, '635/sem02app.fcfg'))
#printResults(nltk.interpret_sents(sents3, '635/sem03app.fcfg'))
#printResults(nltk.interpret_sents(sents3, '635/sem04.fcfg'))
#printResults(nltk.interpret_sents(sents4, '635/quant01.fcfg'))
def to_fol(self, fact, grammar):
results = nltk.interpret_sents([fact,], grammar)
sent = self.results_as_string(results)
return sent
print(expr.simplify()) # β-约简
# simple-sem.fcfg包含了一个用于分析和翻译简单例子的小型规则集合
from nltk import load_parser
parser = load_parser('grammars/book_grammars/simple-sem.fcfg', trace=0)
parser = load_parser('grammars/book_grammars/simple-sem.fcfg', trace=2)
sentence = 'Angus gives a bone to every dog'
tokens = sentence.split()
for tree in parser.parse(tokens):
print(tree.label()['SEM'])
# interpret_sents() 用于批量地解释输入的语句列表。输出的是语法表达（synrep）和语义表达（semrep）
sents = ['Irene walks', 'Cyril bites an ankle']
grammar_file = 'grammars/book_grammars/simple-sem.fcfg'
for results in nltk.interpret_sents(sents, grammar_file):
for (synrep, semrep) in results:
print("语法表达：")
print(synrep)
print("语义表达：")
print(semrep)
# evaluate_sents() 用于批量地评估输入的语句列表，输出的是语法表达（synrep）、语义表达（semrep）和真值(value)
v = """
bertie=>b
olive=>o
cyril=>c
boy=>{b}
girl=>{o}
dog=>{c}
walk=>{o,c}
def to_fol(self, fact, grammar):
sent = self.results_as_string(nltk.interpret_sents(fact, grammar))
# print(sent)
return sent
def who_was_with_who(self, sentence):
result = str(nltk.interpret_sents(sentence, self.was_with_who_grammar)[0][0][1])
return self.parse_result_was_with(result)
def where_in_room_in_hour(self, sentence):
result = str(nltk.interpret_sents(sentence, self._location_hour_grammar_file)[0][0][1])
return self.parse_result_room_hour(result)
def what_is_the_crime_hour(self, sentence):
return 'H' + str(nltk.interpret_sents(sentence, self._crime_hour_grammar_file)[0][0][1])
def where_is_person(self, sentence):
result = str(nltk.interpret_sents(sentence, self._location_person_grammar)[0][0][1])
return self.parse_result_person_location(result)
def where_am_i(self, sentence):
return str(nltk.interpret_sents(sentence, self._location_grammar_file)[0][0][1])
def who_is_the_victime(self, sentence):
return str(nltk.interpret_sents(sentence, self._victim_grammar_file)[0][0][1])
def add_weapon_found_fact(weapon_name: str, room_name: str):
s = ['Le ' + weapon_name + ' est dans le ' + room_name]
print(s[0])
a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/arme_piece.fcfg')))
def add_hour_of_death(victim_name: str, hour_of_death: int):
s = [victim_name + ' est morte à ' + str(hour_of_death) + 'h']
print(s[0])
a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/personne_morte_heure.fcfg')))
a.add_any_clause('HeureCrimePlusOne({})'.format(hour_of_death + 1))
def add_room_after_crime(character_name: str, une_heure_apres: int, room_after_crime: str):
s = [character_name + ' était dans le ' + room_after_crime + ' à ' + str(une_heure_apres) + 'h']
print(s[0])
a.add_any_clause(getStringResults(nltk.interpret_sents(s, '635/personne_piece_heure.fcfg')))
from src import inference
import nltk
a = inference.InferenceCrime()
def getStringResults(results):
res = ''
for result in results:
for (synrep, semrep) in result:
res += str(semrep)
return res
sents1 = ['Scarlet est morte']
a.add_any_clause(getStringResults(nltk.interpret_sents(sents1, '635/personne_morte.fcfg')))
sents2 = ['Mustard est vivant']
a.add_any_clause(getStringResults(nltk.interpret_sents(sents2, '635/personne_vivant.fcfg')))
sents3 = ['Peacock est vivant']
a.add_any_clause(getStringResults(nltk.interpret_sents(sents3, '635/personne_vivant.fcfg')))
sents4 = ['Plum est vivant']
a.add_any_clause(getStringResults(nltk.interpret_sents(sents4, '635/personne_vivant.fcfg')))
sents5 = ['White est vivant']
a.add_any_clause(getStringResults(nltk.interpret_sents(sents5, '635/personne_vivant.fcfg')))
##### step 1 : Bureau