def test_dot_function_e_cat(self):
""" CONTINUOUS states and outcomes, but add a final (fourth) hidden state factor
Now, when arguments themselves are instances of Categorical """
array_path = os.path.join(os.getcwd(), "tests/data/dot_e.mat")
mat_contents = loadmat(file_name=array_path)
A = mat_contents["A"]
obs = Categorical(values=mat_contents["o"])
states = mat_contents["s"]
states_array_version = np.empty(states.shape[1], dtype=object)
for i in range(states.shape[1]):
states_array_version[i] = states[0][i][0]
states_array_version = Categorical(values=states_array_version)
result_1 = mat_contents["result1"]
result_2 = mat_contents["result2"]
result_3 = mat_contents["result3"]
A = Categorical(values=A)
result_1_py = A.dot(obs, return_numpy=True)
self.assertTrue(np.isclose(result_1, result_1_py).all())
result_2_py = A.dot(states_array_version, return_numpy=True)
result_2_py = result_2_py.astype("float64")[:, np.newaxis]
self.assertTrue(np.isclose(result_2, result_2_py).all())
result_3_py = A.dot(states_array_version,
dims_to_omit=[0],
return_numpy=True)
self.assertTrue(np.isclose(result_3, result_3_py).all())
def test_dot_function_c(self):
""" DISCRETE states and outcomes, but also a third hidden state factor """
array_path = os.path.join(os.getcwd(), "tests/data/dot_c.mat")
mat_contents = loadmat(file_name=array_path)
A = mat_contents["A"]
obs = mat_contents["o"]
states = mat_contents["s"]
states_array_version = np.empty(states.shape[1], dtype=object)
for i in range(states.shape[1]):
states_array_version[i] = states[0][i][0]
result_1 = mat_contents["result1"]
result_2 = mat_contents["result2"]
result_3 = mat_contents["result3"]
A = Categorical(values=A)
result_1_py = A.dot(obs, return_numpy=True)
self.assertTrue(np.isclose(result_1, result_1_py).all())
result_2_py = A.dot(states_array_version, return_numpy=True)
result_2_py = result_2_py.astype("float64")[:, np.newaxis]
self.assertTrue(np.isclose(result_2, result_2_py).all())
result_3_py = A.dot(states_array_version,
dims_to_omit=[0],
return_numpy=True)
self.assertTrue(np.isclose(result_3, result_3_py).all())
def test_dot_function_a_cat(self):
""" test with vectors and matrices, discrete state / outcomes
Now, when arguments themselves are instances of Categorical
"""
array_path = os.path.join(os.getcwd(), "tests/data/dot_a.mat")
mat_contents = loadmat(file_name=array_path)
A = mat_contents["A"]
obs = Categorical(values=mat_contents["o"])
states = Categorical(values=mat_contents["s"][0])
result_1 = mat_contents["result1"]
result_2 = mat_contents["result2"]
result_3 = mat_contents["result3"]
A = Categorical(values=A)
result_1_py = A.dot(obs, return_numpy=True)
self.assertTrue(np.isclose(result_1, result_1_py).all())
result_2_py = A.dot(states, return_numpy=True)
result_2_py = result_2_py.astype("float64")[:, np.newaxis]
self.assertTrue(np.isclose(result_2, result_2_py).all())
result_3_py = A.dot(states, dims_to_omit=[0], return_numpy=True)
self.assertTrue(np.isclose(result_3, result_3_py).all())
def test_update_pA_multiFactor_somemodalities(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))
learning_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 = core.update_likelihood_dirichlet(pA, A, observation, qs, lr=learning_rate, modalities=modalities_to_update,return_numpy=True)
for modality, no in enumerate(num_obs):
if modality in modalities_to_update:
validation_pA = pA[modality] + learning_rate * core.spm_cross(np.eye(no)[observation[modality]], qs.values)
else:
validation_pA = pA[modality]
self.assertTrue(np.all(pA_updated[modality]==validation_pA.values))
def test_dot_function_f(self):
""" Test for when the outcome modality is a trivially one-dimensional vector, meaning
the return of spm_dot is a scalar - this tests that the spm_dot function
successfully wraps such scalar returns into an array """
states = np.empty(2, dtype=object)
states[0] = np.array([0.75, 0.25])
states[1] = np.array([0.5, 0.5])
No = 1
A = Categorical(values=np.ones([No] + list(states.shape)))
A.normalize()
# return the result as a Categorical
result_cat = A.dot(states, return_numpy=False)
self.assertTrue(np.prod(result_cat.shape) == 1)
# return the result as a numpy array
result_np = A.dot(states, return_numpy=True)
self.assertTrue(np.prod(result_np.shape) == 1)
def test_update_pA_singleFactor_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))
learning_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 = core.update_likelihood_dirichlet(pA, A, observation, qs, lr=learning_rate, modalities="all",return_numpy=True)
validation_pA = pA + learning_rate * core.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 = core.update_likelihood_dirichlet(pA, A, observation, qs, lr=learning_rate, modalities="all",return_numpy=True)
for modality, no in enumerate(num_obs):
validation_pA = pA[modality] + learning_rate * core.spm_cross(np.eye(no)[observation[modality]], qs.values)
self.assertTrue(np.all(pA_updated[modality]==validation_pA.values))
def test_dot_function_b(self):
""" continuous states and outcomes """
array_path = os.path.join(os.getcwd(), "tests/data/dot_b.mat")
mat_contents = loadmat(file_name=array_path)
A = mat_contents["A"]
obs = mat_contents["o"]
states = mat_contents["s"]
states = np.array(states, dtype=object)
result_1 = mat_contents["result1"]
result_2 = mat_contents["result2"]
result_3 = mat_contents["result3"]
A = Categorical(values=A)
result_1_py = A.dot(obs, return_numpy=True)
self.assertTrue(np.isclose(result_1, result_1_py).all())
result_2_py = A.dot(states, return_numpy=True)
result_2_py = result_2_py.astype("float64")[:, np.newaxis]
self.assertTrue(np.isclose(result_2, result_2_py).all())
result_3_py = A.dot(states, dims_to_omit=[0], return_numpy=True)
self.assertTrue(np.isclose(result_3, result_3_py).all())
