def test_select_cv_interface(self):
print("TESTING FOR JOHN")
model = SelectorCV(self.sequences, self.xlengths, 'JOHN').select()
self.assertGreaterEqual(model.n_components, 2)
print("TESTING FOR CHICKEN")
model = SelectorCV(self.sequences, self.xlengths, 'CHICKEN').select()
self.assertGreaterEqual(model.n_components, 2)
def test_select_cv_interface(self):
model = SelectorCV(self.sequences, self.xlengths, 'JOHN').select()
self.assertGreaterEqual(model.n_components, 2)
model = SelectorCV(self.sequences, self.xlengths, 'CHICKEN').select()
self.assertGreaterEqual(model.n_components, 2)
model = SelectorCV(self.sequences, self.xlengths, 'WRITE').select()
self.assertGreaterEqual(model.n_components, 2)
model = SelectorCV(self.sequences, self.xlengths, 'HOMEWORK').select()
self.assertGreaterEqual(model.n_components, 2)
def run_cv(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 SelectorCV in module my_model_selectors.py
from my_model_selectors import SelectorCV
training = asl.build_training(features_ground)
print("CV Available Training words - words: ", training.words)
print("CV Quantity of Training words - num_items: ", training.num_items)
print("CV Chosen Training words: ", words_to_train)
print("CV 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 = SelectorCV(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_selectorCV():
for word in words_to_train:
start = timeit.default_timer()
model = SelectorCV(sequences,
Xlengths,
word,
verbose=False,
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 train_with_cv():
from my_model_selectors import SelectorCV
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 = SelectorCV(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 select_models(asl):
import warnings
from hmmlearn.hmm import GaussianHMM
def train_a_word(word, num_hidden_states, features):
warnings.filterwarnings("ignore", category=DeprecationWarning)
training = asl.build_training(features)
X, lengths = training.get_word_Xlengths(word)
model = GaussianHMM(n_components=num_hidden_states,
n_iter=1000).fit(X, lengths)
logL = model.score(X, lengths)
return model, logL
def show_model_stats(word, model):
print("Number of states trained in model for {} is {}".format(
word, model.n_components))
variance = np.array(
[np.diag(model.covars_[i]) for i in range(model.n_components)])
for i in range(model.n_components): # for each hidden state
print("hidden state #{}".format(i))
print("mean = ", model.means_[i])
print("variance = ", variance[i])
print()
features_ground = ['grnd-rx', 'grnd-ry', 'grnd-lx', 'grnd-ly']
features_polar = ['polar-rr', 'polar-rtheta', 'polar-lr', 'polar-ltheta']
features_norm = ['norm-rx', 'norm-ry', 'norm-lx', 'norm-ly']
features_delta = ['delta-rx', 'delta-ry', 'delta-lx', 'delta-ly']
features_custom = [
'grnd-rx', 'grnd-ry', 'grnd-lx', 'grnd-ly', 'delta-rx', 'delta-ry',
'delta-lx', 'delta-ly'
]
#my_testword = 'CHOCOLATE'
#model, logL = train_a_word(my_testword, 3, features_norm) # Experiment here with different parameters
#print("Number of states trained in model for {} is {}".format(my_testword, model.n_components))
#print("logL = {}".format(logL))
#show_model_stats(my_testword, model)
words_to_train = ['FISH', 'BOOK', 'VEGETABLE', 'FUTURE', 'JOHN']
import timeit
from my_model_selectors import SelectorBIC
from my_model_selectors import SelectorDIC
from my_model_selectors import SelectorCV
training = asl.build_training(
features_norm
) # 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 = SelectorCV(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))
words_to_train = ['FISH', 'BOOK', 'VEGETABLE', 'FUTURE', 'JOHN']
import timeit
# TODO: Implement SelectorCV in my_model_selector.py
from my_model_selectors import SelectorCV
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 = SelectorCV(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 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
def test_select_cv_interface(self):
model = SelectorCV(self.sequences, self.xlengths, 'JOHN').select()
self.assertGreaterEqual(model.n_components, 2)
model = SelectorCV(self.sequences, self.xlengths, 'MARY').select()
self.assertGreaterEqual(model.n_components, 2)
