def discrete_exhaustive_test():
model = random_model(2)
obs_distns = [d.Categorical(K=3,alpha_0=1.),d.Categorical(K=3,alpha_0=1.)]
stateseq = np.random.randint(2,size=10)
data = np.array([obs_distns[a].rvs() for a in stateseq])
target_val = compute_likelihood_enumeration(obs_distns=obs_distns,data=data,**model)
likelihood_check(target_val=target_val,data=data,obs_distns=obs_distns,**model)
def like_hand_test_1():
likelihood_check(
obs_distns=[d.Categorical(weights=row) for row in np.eye(2)],
trans_matrix=np.eye(2),
init_distn=np.array([1., 0.]),
data=np.zeros(10, dtype=int),
target_val=0.)
def like_hand_test_5():
likelihood_check(
obs_distns=[d.Categorical(weights=row) for row in np.eye(2)],
trans_matrix=np.array([[0.9, 0.1], [0.2, 0.8]]),
init_distn=np.array([1., 0.]),
data=np.tile((0, 1), 5),
target_val=5 * np.log(0.1) + 4 * np.log(0.2))
def like_hand_test_4():
likelihood_check(
obs_distns=[d.Categorical(weights=row) for row in np.eye(2)],
trans_matrix=np.array([[0., 1.], [1., 0.]]),
init_distn=np.array([1., 0.]),
data=np.tile([0, 1], 5).astype(int),
target_val=0.)
def likelihood_hand_tests():
yield make_nose_tuple(
_likelihood_helper,
obs_distns=[d.Categorical(weights=row) for row in np.eye(2)],
trans_matrix=np.array([[0.9, 0.1], [0.1, 0.9]]),
init_distn=np.array([1., 0.]),
data=np.zeros(10, dtype=int),
target_val=9 * np.log(0.9))
yield make_nose_tuple(
_likelihood_helper,
obs_distns=[d.Categorical(weights=row) for row in np.eye(2)],
trans_matrix=np.array([[0.9, 0.1], [0.2, 0.8]]),
init_distn=np.array([1., 0.]),
data=np.tile((0, 1), 5),
target_val=5 * np.log(0.1) + 4 * np.log(0.2))
def discrete_geweke_test(fig):
Nstates = 2
Nemissions = 2
alpha = 3.
init_state_concentration=3.
T = 10
num_iter = 10000
num_checks = 10
obs_distns = [distributions.Categorical(K=Nemissions,alpha_0=1.)
for _ in range(Nstates)]
hmm = models.HMM(
alpha=alpha,init_state_concentration=init_state_concentration,
obs_distns=obs_distns)
# generate state sequences and parameters from the prior
prior_stateseqs = []
prior_weights = []
for itr in xrange(num_iter):
hmm.resample_model() # sample parameters from the prior
_, stateseq = hmm.generate(T,keep=False)
prior_stateseqs.append(stateseq)
prior_weights.append(hmm.obs_distns[0].weights)
prior_stateseqs = np.array(prior_stateseqs)
prior_weights = np.array(prior_weights)
# generate state sequences and parameters using Gibbs
hmm.generate(T,keep=True)
s = hmm.states_list[0]
gibbs_stateseqs = []
gibbs_weights = []
for itr in xrange(num_iter):
s.generate_obs() # resamples data given state sequence, obs params
hmm.resample_model() # resamples everything else as usual
gibbs_stateseqs.append(s.stateseq)
gibbs_weights.append(hmm.obs_distns[0].weights)
gibbs_stateseqs = np.array(gibbs_stateseqs)
gibbs_weights = np.array(gibbs_weights)
# test that they look similar by checking probability of co-assignment
time_indices = np.arange(T)
for itr in xrange(num_checks):
i,j = np.random.choice(time_indices,replace=False,size=2)
prior_prob_of_coassignment = (prior_stateseqs[:,i] == prior_stateseqs[:,j]).std()
gibbs_prob_of_coassignment = (gibbs_stateseqs[:,i] == gibbs_stateseqs[:,j]).std()
assert np.isclose(
prior_prob_of_coassignment,gibbs_prob_of_coassignment,
rtol=0.025,atol=0.025,
)
# test that they look similar by checking parameters
testing.populations_eq_quantile_plot(prior_weights,gibbs_weights,fig=fig)
figpath = os.path.join(figure_dir_path,'discrete_geweke_test_weights.pdf')
plt.savefig(figpath)
def likelihood_exhaustive_tests():
### discrete data
for i in range(2):
model = random_model(2)
obs_distns = [
d.Categorical(K=3, alpha_0=1.),
d.Categorical(K=3, alpha_0=1.)
]
stateseq = np.random.randint(2, size=10)
data = np.array([obs_distns[a].rvs() for a in stateseq])
target_val = compute_likelihood_enumeration(obs_distns=obs_distns,
data=data,
**model)
yield make_nose_tuple(_likelihood_helper,
target_val=target_val,
data=data,
obs_distns=obs_distns,
**model)
# Gaussian data
for i in range(2):
model = random_model(3)
obs_distns = [
d.Gaussian(mu=np.random.randn(2), sigma=np.eye(2)),
d.Gaussian(mu=np.random.randn(2), sigma=np.eye(2)),
d.Gaussian(mu=np.random.randn(2), sigma=np.eye(2))
]
stateseq = np.random.randint(3, size=10)
data = np.vstack([obs_distns[a].rvs() for a in stateseq])
target_val = compute_likelihood_enumeration(obs_distns=obs_distns,
data=data,
**model)
yield make_nose_tuple(_likelihood_helper,
target_val=target_val,
data=data,
obs_distns=obs_distns,
**model)
