def model_subset_of_dfa_query(self, dfa: DFA, start_time, timeout):
"""
Tests whether the model language is a subset of the dfa language by testing random words.
If not subset returns an example
"""
number_of_rounds = int(
(1 / self.epsilon) * (np.log(1 / self.delta) + np.log(2) *
(self._num_equivalence_asked + 1)))
self._num_equivalence_asked = self._num_equivalence_asked + 1
if isinstance(self.model, RNNLanguageClasifier):
batch_size = 200
for i in range(int(number_of_rounds / batch_size) + 1):
if time.time() - start_time > timeout:
return None
batch = [
random_word(self.model.alphabet) for _ in range(batch_size)
]
for x, y, w in zip(
self.model.is_words_in_batch(batch) > 0.5,
[dfa.is_word_in(w) for w in batch], batch):
if x and (not y):
return w
return None
else:
for i in range(number_of_rounds):
word = random_word(self.model.alphabet)
if self.model.is_word_in(word) != dfa.is_word_in(word):
return word
return None
def equivalence_query(self, dfa: DFA, start_time, timeout):
"""
Tests whether the dfa is equivalent to the model by testing random words.
If not equivalent returns an example
"""
number_of_rounds = int(
(1 / self.epsilon) * (np.log(1 / self.delta) + np.log(2) *
(self._num_equivalence_asked + 1)))
self._num_equivalence_asked = self._num_equivalence_asked + 1
if self.is_counter_example_in_batches:
batch_size = 200
for i in range(int(number_of_rounds / batch_size) + 1):
if time.time() - start_time > timeout:
return None
batch = [
random_word(self.model.alphabet) for _ in range(batch_size)
]
for x, y, w in zip(self.model.is_words_in_batch(batch),
[dfa.is_word_in(w) for w in batch], batch):
if x != y:
return w
return None
else:
for i in range(number_of_rounds):
word = random_word(self.model.alphabet)
if self.model.is_word_in(word) != dfa.is_word_in(word):
return word
return None