def test_update_pA_multi_factor_some_modalities(self):
"""
Test for updating prior Dirichlet parameters over sensory likelihood (pA)
in the case that SOME observation modalities are updated and the generative model
has multiple hidden state factors
"""
n_states = [2, 6]
qs = Categorical(values=construct_init_qs(n_states))
l_rate = 1.0
# multiple observation modalities
num_obs = [3, 4, 5]
modalities_to_update = [0, 2]
A = Categorical(values=construct_generic_A(num_obs, n_states))
pA = Dirichlet(values=construct_pA(num_obs, n_states))
observation = A.dot(qs, return_numpy=False).sample()
pA_updated = learning.update_likelihood_dirichlet(
pA,
A,
observation,
qs,
lr=l_rate,
modalities=modalities_to_update,
return_numpy=True)
for modality, no in enumerate(num_obs):
if modality in modalities_to_update:
update = maths.spm_cross(
np.eye(no)[observation[modality]], qs.values)
validation_pA = pA[modality] + l_rate * update
else:
validation_pA = pA[modality]
self.assertTrue(
np.all(pA_updated[modality] == validation_pA.values))
def test_update_pA_single_factor_all(self):
"""
Test for updating prior Dirichlet parameters over sensory likelihood (pA)
in the case that all observation modalities are updated and the generative model
has a single hidden state factor
"""
n_states = [3]
qs = Categorical(values=construct_init_qs(n_states))
l_rate = 1.0
# single observation modality
num_obs = [4]
A = Categorical(values=construct_generic_A(num_obs, n_states))
pA = Dirichlet(values=construct_pA(num_obs, n_states))
observation = A.dot(qs, return_numpy=False).sample()
pA_updated = learning.update_likelihood_dirichlet(pA,
A,
observation,
qs,
lr=l_rate,
modalities="all",
return_numpy=True)
validation_pA = pA + l_rate * maths.spm_cross(
np.eye(*num_obs)[observation], qs.values)
self.assertTrue(np.all(pA_updated == validation_pA.values))
# multiple observation modalities
num_obs = [3, 4]
A = Categorical(values=construct_generic_A(num_obs, n_states))
pA = Dirichlet(values=construct_pA(num_obs, n_states))
observation = A.dot(qs, return_numpy=False).sample()
pA_updated = learning.update_likelihood_dirichlet(pA,
A,
observation,
qs,
lr=l_rate,
modalities="all",
return_numpy=True)
for modality, no in enumerate(num_obs):
update = maths.spm_cross(
np.eye(no)[observation[modality]], qs.values)
validation_pA = pA[modality] + l_rate * update
self.assertTrue(
np.all(pA_updated[modality] == validation_pA.values))
def update_A(self, obs):
pA_updated = learning.update_likelihood_dirichlet(
self.pA,
self.A,
obs,
self.qs,
self.lr_pA,
self.modalities_to_learn,
return_numpy=False)
self.pA = pA_updated
self.A = pA_updated.mean()
return pA_updated