def test_baum_welch_mp(self):
"""
Does the same as L{test_baum_welch}, but uses multiprocessing.
"""
options = BaumWelchTrainer.process_option_dict({'trainprocs': -1})
trainer = BaumWelchTrainer(self.model, options)
# Train the model with Baum Welch
trainer.train(self.TRAINING_DATA)
def test_baum_welch_mp(self):
"""
Does the same as L{test_baum_welch}, but uses multiprocessing.
"""
options = BaumWelchTrainer.process_option_dict({'trainprocs':-1})
trainer = BaumWelchTrainer(self.model, options)
# Train the model with Baum Welch
trainer.train(self.TRAINING_DATA)
def test_baum_welch(self):
"""
Runs the Baum Welch trainer using the training data.
"""
options = BaumWelchTrainer.process_option_dict({})
trainer = BaumWelchTrainer(self.model, options)
# Train the model with Baum Welch
trainer.train(self.TRAINING_DATA)
model = trainer.model
# Try decoding using the trained model to check it still works
model.viterbi_decode(self.TEST_DATA)
def test_baum_welch(self):
"""
Runs the Baum Welch trainer using the training data.
"""
options = BaumWelchTrainer.process_option_dict({})
trainer = BaumWelchTrainer(self.model, options)
# Train the model with Baum Welch
trainer.train(self.TRAINING_DATA)
model = trainer.model
# Try decoding using the trained model to check it still works
model.viterbi_decode(self.TEST_DATA)