def train_all_words(training: WordsData, model_selector):
sequences = training.get_all_sequences()
Xlengths = training.get_all_Xlengths()
model_dict = {}
for word in training.words:
model = model_selector(sequences, Xlengths, word,
n_constant=3).select()
model_dict[word] = model
return model_dict
def train_all_words(training: WordsData, model_selector):
""" train all words given a training set and selector
:param training: WordsData object (training set)
:param model_selector: class (subclassed from ModelSelector)
:return: dict of models keyed by word
"""
sequences = training.get_all_sequences()
Xlengths = training.get_all_Xlengths()
model_dict = {}
for word in training.words:
model = model_selector(sequences, Xlengths, word, n_constant=3).select
model_dict[word] = model
return model_dict
def train_all_words(training: WordsData, model_selector):
""" train all words given a training set and selector
:param training: WordsData object (training set)
:param model_selector: class (subclassed from ModelSelector)
:return: dict of models keyed by word
"""
sequences = training.get_all_sequences()
Xlengths = training.get_all_Xlengths()
model_dict = {}
for word in training.words:
model = model_selector(sequences, Xlengths, word,
n_constant=3).select()
model_dict[word] = model
return model_dict
def train_all_words(training_set: WordsData,
model_selector,
word_list=None,
verbose=False,
features=[]):
"""
Train all words given a training set and selector
:param training: WordsData object (training set)
:param model_selector: class (subclassed from ModelSelector)
:return: dict of models keyed by word
"""
sequences = training_set.get_all_sequences()
Xlengths = training_set.get_all_Xlengths()
model_dict = {}
if word_list == None: word_list = training_set.words
for word in word_list:
try:
start = timeit.default_timer()
model = model_selector(sequences,
Xlengths,
word,
verbose=verbose,
features=features).select()
model_dict[word] = model
end = timeit.default_timer() - start
if model is not None:
if verbose:
print(
"Training complete for {} with {} states with time {} seconds"
.format(word, model.n_components, end))
else:
if verbose: print("Training failed for {}".format(word))
except Exception as e:
if verbose:
print("Training failed for {}, error: {}".format(word, e))
model_dict[word] = None
return model_dict