def train(self):
"""Train HMMs"""
initial_hmm_files = self.get_initial_hmm_files()
if len(initial_hmm_files) == 0:
raise ModelException, "No initial HMM files found."
if not os.path.exists(self.TRAIN_HMM_ROOT):
os.makedirs(self.TRAIN_HMM_ROOT)
trainer = self.Trainer()
viterbi_trainer = ViterbiTrainer(self.ViterbiCalculator(),
non_diagonal=self.NON_DIAGONAL)
for file in initial_hmm_files:
char_code = int(os.path.basename(file).split(".")[0])
hmm = MultivariateHmm.from_file(file)
sset_file = os.path.join(self.TRAIN_FEATURES_ROOT,
str(char_code) + ".sset")
sset = self.get_sequence_set(sset_file)
output_file = os.path.join(self.TRAIN_HMM_ROOT,
"%d.xml" % char_code)
if self.TRAINING in (self.TRAINING_VITERBI, self.TRAINING_BOTH):
self.print_verbose("Viterbi training: " + output_file)
viterbi_trainer.train(hmm, sset)
if self.TRAINING in (self.TRAINING_BAUM_WELCH, self.TRAINING_BOTH):
self.print_verbose("Baum-Welch training: " + output_file)
trainer.train(hmm, sset)
hmm.write(output_file)
def train(self):
"""Train HMMs"""
initial_hmm_files = self.get_initial_hmm_files()
if len(initial_hmm_files) == 0:
raise ModelException, "No initial HMM files found."
if not os.path.exists(self.TRAIN_HMM_ROOT):
os.makedirs(self.TRAIN_HMM_ROOT)
trainer = self.Trainer()
viterbi_trainer = ViterbiTrainer(self.ViterbiCalculator(),
non_diagonal=self.NON_DIAGONAL)
for file in initial_hmm_files:
char_code = int(os.path.basename(file).split(".")[0])
hmm = MultivariateHmm.from_file(file)
sset_file = os.path.join(self.TRAIN_FEATURES_ROOT,
str(char_code) + ".sset")
sset = self.get_sequence_set(sset_file)
output_file = os.path.join(self.TRAIN_HMM_ROOT,
"%d.xml" % char_code)
if self.TRAINING in (self.TRAINING_VITERBI, self.TRAINING_BOTH):
self.print_verbose("Viterbi training: " + output_file)
viterbi_trainer.train(hmm, sset)
if self.TRAINING in (self.TRAINING_BAUM_WELCH, self.TRAINING_BOTH):
self.print_verbose("Baum-Welch training: " + output_file)
trainer.train(hmm, sset)
hmm.write(output_file)
def get_hmms_from_files(self, files):
hmms = []
for file in files:
char_code = int(os.path.basename(file).split(".")[0])
hmm = MultivariateHmm.from_file(file)
hmm.char_code = char_code
hmms.append(hmm)
return hmms
def get_hmms_from_files(self, files):
hmms = []
for file in files:
char_code = int(os.path.basename(file).split(".")[0])
hmm = MultivariateHmm.from_file(file)
hmm.char_code = char_code
hmms.append(hmm)
return hmms
def get_initial_hmm(self, sset):
n_states = self.get_n_strokes(sset.char_code) * \
self.N_STATES_PER_STROKE
pi = self.get_initial_state_probabilities(n_states)
A = self.get_state_transition_matrix(n_states)
B = self.get_emission_matrix(n_states, sset)
hmm = MultivariateHmm(A, B, pi)
return hmm
def get_initial_hmm(self, sset, avg_n_obs_per_char):
obs, chars = self.get_n_observations(sset)
n_obs = float(obs) / chars
n_states = round(n_obs / avg_n_obs_per_char * self.AVERAGE_N_STATES)
n_states = int(n_states)
self.print_verbose("%s (%d): %d" % \
(self.get_utf8_from_char_code(sset.char_code),
sset.char_code,
n_states))
pi = self.get_initial_state_probabilities(n_states)
A = self.get_state_transition_matrix(n_states)
B = self.get_emission_matrix(n_states, sset)
return MultivariateHmm(A, B, pi)
