def test_parameter_learning_in_linear_case(self):
is_f_linear = True
is_g_linear = True
for j, (state_dim, output_dim) in enumerate(zip([1], [1])):
# tester différentes tailles
for k, n_sample in enumerate([100]):
ssm = StateSpaceModel(is_f_linear=is_f_linear,
is_g_linear=is_g_linear,
state_dim=state_dim,
output_dim=output_dim)
ssm.draw_sample(T=n_sample)
is_extended = False
ssm.kalman_smoothing(is_extended=is_extended)
ssm.compute_f_optimal_parameters()
ssm.compute_g_optimal_parameters()
def test_linear_kalman_methods(self):
for i, (state_dim, output_dim,
input_dim) in enumerate(zip([1, 4, 3], [1, 3, 4], [0, 0, 0])):
# tester différentes tailles
for j, T in enumerate([1, 10]):
default_message = 'Parameters : T = ' + str(
T) + '. state_dim = ' + str(
state_dim) + '. output_dim = ' + str(
output_dim) + '. input_dim = ' + str(input_dim)
ssm = StateSpaceModel(is_f_linear=True,
is_g_linear=True,
state_dim=state_dim,
output_dim=output_dim,
input_dim=input_dim)
ssm.draw_sample(T=T)
ssm.kalman_smoothing(is_extended=False)
self.assertEqual(len(getattr(ssm, 'filtered_state_means')), T)
self.assertEqual(
len(getattr(ssm, 'filtered_state_covariance')), T)
self.assertEqual(len(getattr(ssm, 'smoothed_state_means')), T)
self.assertEqual(
len(getattr(ssm, 'smoothed_state_covariance')), T)
# verifier que les moyennes et covariances estimmées ont les bonnes dimensions
for i in range(0, T):
xFilter0 = ssm.filtered_state_means[i][0]
xFilter1 = ssm.filtered_state_means[i][1]
PFilter0 = ssm.filtered_state_covariance[i][0]
PFilter1 = ssm.filtered_state_covariance[i][1]
xSmooth = ssm.smoothed_state_means[i]
PSmooth = ssm.smoothed_state_covariance[T - 1 - i]
self.assertEqual(
xFilter0.size, ssm.state_dim,
'mean vector must have state_dim dimension')
self.assertEqual(
xFilter1.size, ssm.state_dim,
'mean vector must have state_dim dimension')
self.assertEqual(
PFilter0.shape, (ssm.state_dim, ssm.state_dim),
'cov matrix must have state_dim dimension')
self.assertEqual(
PFilter1.shape, (ssm.state_dim, ssm.state_dim),
'cov matrix must have state_dim dimension')
# check that matrices are definite positive
self.assertTrue(
utils.is_pos_def(PFilter0),
'filtered covariance matrix must be positive definite')
self.assertTrue(
utils.is_pos_def(PFilter1),
'filtered covariance matrix must be positive definite')
self.assertEqual(
xSmooth.size, ssm.state_dim,
'mean vector must have state_dim dimension')
self.assertEqual(
PSmooth.shape, (ssm.state_dim, ssm.state_dim),
'cov matrix must have state_dim dimension')
self.assertTrue(
utils.is_pos_def(PSmooth),
default_message + '. Smoothed covariance P_{T-' +
str(i) + '} matrix must be positive definite')