ax.grid(True)
figures.append(plt)
for p in figures:
p.show()
visualize(my_testword, model)
from my_model_selectors import SelectorConstant
training = asl.build_training(
features_ground
) # Experiment here with different feature sets defined in part 1
word = 'VEGETABLE' # Experiment here with different words
model = SelectorConstant(training.get_all_sequences(),
training.get_all_Xlengths(),
word,
n_constant=3).select()
print("Number of states trained in model for {} is {}".format(
word, model.n_components))
from sklearn.model_selection import KFold
training = asl.build_training(
features_ground) # Experiment here with different feature sets
word = 'VEGETABLE' # Experiment here with different words
word_sequences = training.get_word_sequences(word)
split_method = KFold()
for cv_train_idx, cv_test_idx in split_method.split(word_sequences):
print("Train fold indices:{} Test fold indices:{}".format(
cv_train_idx, cv_test_idx)) # view indices of the folds
def test_select_constant_interface(self):
model = SelectorConstant(self.sequences, self.xlengths, 'BUY').select()
self.assertGreaterEqual(model.n_components, 2)
model = SelectorConstant(self.sequences, self.xlengths, 'BOOK').select()
self.assertGreaterEqual(model.n_components, 2)
