emission_probability[1][0] += 1
if train_data['return'][i] < 0.0 and analysis_data['v'][i] == 1:
emission_probability[1][1] += 1
if train_data['return'][i] < 0.0 and analysis_data['v'][i] == 2:
emission_probability[1][2] += 1
if train_data['return'][i] < 0.0 and analysis_data['v'][i] == 3:
emission_probability[1][3] += 1
emission_probability[0] /= sum(1 for e in train_data['return'] if e >= 0.0)
emission_probability[1] /= sum(1 for e in train_data['return'] if e < 0.0)
#print(emission_probability)
hmm = MultinomialHMM(n_components=n_states)
hmm.startprob = start_probability
hmm.transmat = transition_probability
hmm.emissionprob = emission_probability
bob_says = np.array([[0, 2, 1, 1, 2, 0]]).T
hmm = hmm.fit(bob_says)
logprob, alice_hears = hmm.decode(bob_says, algorithm="viterbi")
print("Bob says:", ", ".join(map(lambda x: observations[x], bob_says)))
print("Alice hears:", ", ".join(map(lambda x: states[x], alice_hears)))
'''
law_data['hmm_states'] = hmm.predict(rets)
panel = Figure_Util.Figure()
panel.draw(law_data, title='close', subplots=['hmm_states'], figsize=(20, 10))
'''
