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Fix failing test for AKKF proposal multivariate sampling to compare mean of particle state #1057

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Jul 4, 2024
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Fix failing test for AKKF proposal multivariate sampling to compare mean of particle state #1057

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6 changes: 3 additions & 3 deletions stonesoup/predictor/kernel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,9 +12,9 @@


class AdaptiveKernelKalmanPredictor(KalmanPredictor):
r"""An implementation of the adaptive kernel Kalman filter (AKKF) predictor. Here, the AKKF draws
inspiration from the concepts of kernel mean embeddings (KME) and Kalman Filter to address
tracking problems in nonlinear systems.
r"""An implementation of the adaptive kernel Kalman filter (AKKF) predictor. Here, the AKKF
draws inspiration from the concepts of kernel mean embeddings (KME) and Kalman Filter to
address tracking problems in nonlinear systems.

In the state space, at time :math:`k`, the prior state
particles are generated by passing the proposal particles at time :math:`k-1`, i.e.,
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1 change: 0 additions & 1 deletion stonesoup/predictor/tests/test_kernel.py
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Expand Up @@ -13,7 +13,6 @@
from ...types.update import KernelParticleStateUpdate

number_particles = 4
np.random.seed(50)
timestamp = datetime.datetime.now()
samples = multivariate_normal.rvs([0, 0, 0, 0],
np.diag([0.01, 0.005, 0.1, 0.5])**2,
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1 change: 0 additions & 1 deletion stonesoup/types/tests/test_state.py
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Expand Up @@ -845,7 +845,6 @@ def test_asd_weighted_gaussian_state():
def test_kernel_particle_state():
number_particles = 5
weights = np.array([1 / number_particles] * number_particles)
np.random.seed(50)

samples = multivariate_normal.rvs([0, 0, 0, 0],
np.diag([0.01, 0.005, 0.1, 0.5]) ** 2,
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6 changes: 4 additions & 2 deletions stonesoup/updater/tests/test_kernel.py
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Expand Up @@ -19,7 +19,6 @@
new_timestamp = timestamp + time_diff

number_particles = 5
np.random.seed(50)
samples = multivariate_normal.rvs([0, 0, 0, 0],
np.diag([0.01, 0.005, 0.1, 0.5])**2,
size=number_particles)
Expand Down Expand Up @@ -107,7 +106,10 @@ def test_kernel_updater(kernel, measurement_model, c, ialpha):
assert measurement.timestamp == gt_state.timestamp
assert update.hypothesis.measurement.timestamp == gt_state.timestamp
assert np.allclose(update.state_vector, prediction.state_vector)
assert np.allclose(update.proposal, StateVectors(new_state_vector.T), atol=1e0)
assert np.allclose(
np.mean(update.proposal, axis=1),
np.mean(StateVectors(new_state_vector.T), axis=1),
atol=2)
assert np.allclose(update.weight, updated_weights.ravel())
assert np.allclose(update.kernel_covar, updated_covariance)

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