def test_copy(self):
values = np.random.rand(3, 2)
c = Categorical(values=values)
c_copy = c.copy()
self.assertTrue(np.array_equal(c_copy.values, c.values))
c_copy.values = c_copy.values * 2
self.assertFalse(np.array_equal(c_copy.values, c.values))
# get an observation, given the state
first_obs = gp_likelihood[:, first_state]
# turn observation into an index
first_obs = np.where(first_obs)[0][0]
print("Initial Location {}".format(first_state))
print("Initial Observation {}".format(first_obs))
# infer initial state, given first observation
qs = maths.softmax(A[first_obs, :].log() + D.log(), return_numpy=False)
# loop over time
for t in range(T):
qs_past = qs.copy()
s_t = env.set_state(s[t + 1])
# evoke observation, given the state
obs = gp_likelihood[:, s_t]
# turn observation into an index
obs = np.where(obs)[0][0]
# get transition likelihood
B = b.mean()
# infer new hidden state
qs = maths.softmax(A[obs, :].log() + B.dot(qs_past).log(),
return_numpy=False)
