def train_with_bic():
print("training with bic")
from my_model_selectors import SelectorBIC
training = asl.build_training(
features_ground
) # Experiment here with different feature sets defined in part 1
sequences = training.get_all_sequences()
Xlengths = training.get_all_Xlengths()
for word in words_to_train:
start = timeit.default_timer()
model = SelectorBIC(sequences,
Xlengths,
word,
min_n_components=2,
max_n_components=15,
random_state=14).select()
end = timeit.default_timer() - start
if model is not None:
print(
"Training complete for {} with {} states with time {} seconds".
format(word, model.n_components, end))
else:
print("Training failed for {}".format(word))
def run_bic(asl, features_ground, words_to_train, min_c, max_c, rand_s):
# Copied from asl_recognizer.ipynb for IDE debugging using breakpoints.
# Execute the implementation of SelectorBIC in module my_model_selectors.py
from my_model_selectors import SelectorBIC
training = asl.build_training(features_ground)
print("BIC Available Training words - words: ", training.words)
print("BIC Quantity of Training words - num_items: ", training.num_items)
print("BIC Chosen Training words: ", words_to_train)
print("BIC Chosen Features: ", features_ground)
sequences = training.get_all_sequences()
Xlengths = training.get_all_Xlengths()
for word in words_to_train:
start = timeit.default_timer()
model = SelectorBIC(sequences,
Xlengths,
word,
min_n_components=min_c,
max_n_components=max_c,
random_state=rand_s).select()
end = timeit.default_timer() - start
if model is not None:
print(
"Training complete for {} with {} states with time {} seconds".
format(word, model.n_components, end))
else:
print("Training failed for {}".format(word))
def test_select_bic_interface(self):
model = SelectorBIC(self.sequences,
self.xlengths,
'VEGETABLE',
min_n_components=2,
max_n_components=15).select()
self.assertGreaterEqual(model.n_components, 2)
def test_selectorBIC():
for word in words_to_train:
start = timeit.default_timer()
model = SelectorBIC(sequences,
Xlengths,
word,
min_n_components=2,
max_n_components=15,
random_state=14).select()
end = timeit.default_timer() - start
if model is not None:
print(
"Training complete for {} with {} states with time {} seconds".
format(word, model.n_components, end))
else:
print("Training failed for {}".format(word))
else:
print("Training failed for {}".format(word))
# TODO: Implement SelectorBIC in module my_model_selectors.py
from my_model_selectors import SelectorBIC
training = asl.build_training(
features_ground
) # Experiment here with different feature sets defined in part 1
sequences = training.get_all_sequences()
Xlengths = training.get_all_Xlengths()
for word in words_to_train:
start = timeit.default_timer()
model = SelectorBIC(sequences,
Xlengths,
word,
min_n_components=2,
max_n_components=15,
random_state=14).select()
end = timeit.default_timer() - start
if model is not None:
print("Training complete for {} with {} states with time {} seconds".
format(word, model.n_components, end))
else:
print("Training failed for {}".format(word))
# TODO: Implement SelectorDIC in module my_model_selectors.py
from my_model_selectors import SelectorDIC
training = asl.build_training(
features_ground
) # Experiment here with different feature sets defined in part 1
def vest_select_bic_interface(self):
model = SelectorBIC(self.sequences, self.xlengths, 'FRANK').select()
self.assertGreaterEqual(model.n_components, 2)
model = SelectorBIC(self.sequences, self.xlengths,
'VEGETABLE').select()
self.assertGreaterEqual(model.n_components, 2)