def test_kalman_filter_update():
kf = KalmanFilter(
data.transition_matrix,
data.observation_matrix,
data.transition_covariance,
data.observation_covariance,
data.transition_offsets,
data.observation_offset,
data.initial_state_mean,
data.initial_state_covariance)
# use Kalman Filter
(x_filt, V_filt) = kf.filter(X=data.observations)
# use online Kalman Filter
n_timesteps = data.observations.shape[0]
n_dim_state = data.transition_matrix.shape[0]
x_filt2 = np.zeros((n_timesteps, n_dim_state))
V_filt2 = np.zeros((n_timesteps, n_dim_state, n_dim_state))
for t in range(n_timesteps - 1):
if t == 0:
x_filt2[0] = data.initial_state_mean
V_filt2[0] = data.initial_state_covariance
(x_filt2[t + 1], V_filt2[t + 1]) = kf.filter_update(
x_filt2[t], V_filt2[t], data.observations[t + 1],
transition_offset=data.transition_offsets[t]
)
assert_array_almost_equal(x_filt, x_filt2)
assert_array_almost_equal(V_filt, V_filt2)
def test_kalman_pickle():
kf = KalmanFilter(
data.transition_matrix,
data.observation_matrix,
data.transition_covariance,
data.observation_covariance,
data.transition_offsets,
data.observation_offset,
data.initial_state_mean,
data.initial_state_covariance,
em_vars='all')
# train and get log likelihood
X = data.observations[0:10]
kf = kf.em(X, n_iter=5)
loglikelihood = kf.loglikelihood(X)
# pickle Kalman Filter
store = StringIO()
pickle.dump(kf, store)
clf = pickle.load(StringIO(store.getvalue()))
# check that parameters came out already
np.testing.assert_almost_equal(loglikelihood, kf.loglikelihood(X))
# store it as BytesIO as well
store = BytesIO()
pickle.dump(kf, store)
kf = pickle.load(BytesIO(store.getvalue()))
def test_kalman_sampling():
kf = KalmanFilter(
data.transition_matrix,
data.observation_matrix,
data.transition_covariance,
data.observation_covariance,
data.transition_offsets,
data.observation_offset,
data.initial_state_mean,
data.initial_state_covariance)
(x, z) = kf.sample(100)
assert_true(x.shape == (100, data.transition_matrix.shape[0]))
assert_true(z.shape == (100, data.observation_matrix.shape[0]))
def test_kalman_fit():
# check against MATLAB dataset
kf = KalmanFilter(
data.transition_matrix,
data.observation_matrix,
data.initial_transition_covariance,
data.initial_observation_covariance,
data.transition_offsets,
data.observation_offset,
data.initial_state_mean,
data.initial_state_covariance,
em_vars=['transition_covariance', 'observation_covariance'])
loglikelihoods = np.zeros(5)
for i in range(len(loglikelihoods)):
loglikelihoods[i] = kf.loglikelihood(data.observations)
kf.em(X=data.observations, n_iter=1)
assert_true(np.allclose(loglikelihoods, data.loglikelihoods[:5]))
# check that EM for all parameters is working
kf.em_vars = 'all'
n_timesteps = 30
for i in range(len(loglikelihoods)):
kf.em(X=data.observations[0:n_timesteps], n_iter=1)
loglikelihoods[i] = kf.loglikelihood(data.observations[0:n_timesteps])
for i in range(len(loglikelihoods) - 1):
assert_true(loglikelihoods[i] < loglikelihoods[i + 1])
def check_dims(n_dim_state, n_dim_obs, kwargs):
kf = KalmanFilter(**kwargs)
(transition_matrices, transition_offsets, transition_covariance,
observation_matrices, observation_offsets, observation_covariance,
initial_state_mean, initial_state_covariance) = (
kf._initialize_parameters()
)
assert_true(transition_matrices.shape == (n_dim_state, n_dim_state))
assert_true(transition_offsets.shape == (n_dim_state,))
assert_true(transition_covariance.shape == (n_dim_state, n_dim_state))
assert_true(observation_matrices.shape == (n_dim_obs, n_dim_state))
assert_true(observation_offsets.shape == (n_dim_obs,))
assert_true(observation_covariance.shape == (n_dim_obs, n_dim_obs))
assert_true(initial_state_mean.shape == (n_dim_state,))
assert_true(
initial_state_covariance.shape == (n_dim_state, n_dim_state)
)
def test_kalman_predict():
kf = KalmanFilter(
data.transition_matrix,
data.observation_matrix,
data.transition_covariance,
data.observation_covariance,
data.transition_offsets,
data.observation_offset,
data.initial_state_mean,
data.initial_state_covariance)
x_smooth = kf.smooth(X=data.observations)[0]
assert_array_almost_equal(
x_smooth[:501],
data.smoothed_state_means[:501],
decimal=7
)
def test_kalman_filter():
kf = KalmanFilter(
data.transition_matrix,
data.observation_matrix,
data.transition_covariance,
data.observation_covariance,
data.transition_offsets,
data.observation_offset,
data.initial_state_mean,
data.initial_state_covariance)
(x_filt, V_filt) = kf.filter(X=data.observations)
assert_array_almost_equal(
x_filt[:500],
data.filtered_state_means[:500],
decimal=7
)
assert_array_almost_equal(
V_filt[:500],
data.filtered_state_covariances[:500],
decimal=7
)
