def classify(hmm):
unclassified_data = gen.right_turn() + gen.left_turn()
for time_t in xrange(1, len(unclassified_data)):
obs_interval = vq.dataset_to_alphabet(unclassified_data[:time_t][-observation_size:] )
obs_sequence = EmissionSequence( emit_domain, obs_interval )
state = hmm.viterbi( obs_sequence )
# print state
print state_to_string( state[0][-1]) , unclassified_data[time_t] , state[1]
def train_hmm(hmm, emit_domain):
for x, label in gen.gen_data_set(size_generated_training_set):
training_set = vq.dataset_to_alphabet( x )
ghmm_training_set = EmissionSequence( emit_domain, training_set)
ghmm_training_set.setSeqLabel(label)
hmm.baumWelch( ghmm_training_set,loglikelihoodCutoff=0.000001, nrSteps = 10)
for x in xrange(size_sampled_training_set):
ghmm_training_set = hmm.sample(100,500)
hmm.baumWelch( ghmm_training_set )
return hmm
def generate_emit_p():
straight = [ vq.dataset_to_alphabet(gen.straight()) for x in xrange(50) ]
left_turns = [ vq.dataset_to_alphabet(gen.left_turn()) for x in xrange(100) ]
right_turns = [ vq.dataset_to_alphabet(gen.right_turn()) for x in xrange(100) ]
data_sets = [ straight, left_turns, right_turns ]
gen_data_set = { 0: {}, 1: {}, 2: {} }
for idx, dataset in enumerate(data_sets):
for sample in dataset:
for alpha in sample:
if alpha not in gen_data_set[idx]:
gen_data_set[idx][alpha] = 1
else:
gen_data_set[idx][alpha] += 1
alpha_size = len(vq.gen_alphabet() )
emit_p = [ [0.01 for x in xrange(alpha_size) ] for x in xrange(3) ]
for idx, stats in gen_data_set.iteritems():
for k,v in stats.iteritems():
emit_p[idx][k] = v
return normalize_emit_probabilites( emit_p )
