# CPD we would using the transpose of it.
emission_prob = np.array([[0.045, 0.15, 0.2, 0.6, 0.005],
[0.2, 0.2, 0.2, 0.3, 0.1],
[0.3, 0.1, 0.1, 0.05, 0.45],
[0.1, 0.1, 0.2, 0.05, 0.55]])
# Setting the emission probability
model_multinomial.emissionprob_ = emission_prob
# model.sample returns both observations as well as hidden states
# the first return argument being the observation and the second
# being the hidden states
# Z, X = model_multinomial.sample(5)
#
# print('X',X)
#
# print('Z',Z)
states = [0] #,1,2,3]
emisions = []
for state in states:
em = []
for i in range(100):
em.append(model_multinomial._generate_sample_from_state(state)[0])
emisions.append(em) # print(state)
df = pd.DataFrame(emisions).T
df.plot(kind='hist')
plt.xticks([0, 1, 2, 3, 4])
plt.show()
