def test_EKFState_with_NcvContinuous():
d = 6
ncv = NcvContinuous(dimension=d, sa2=2.0)
x = torch.rand(d)
P = torch.eye(d)
t = 0.0
dt = 2.0
ekf_state = EKFState(
dynamic_model=ncv, mean=x, cov=P, time=t)
assert ekf_state.dynamic_model.__class__ == NcvContinuous
assert ekf_state.dimension == d
assert ekf_state.dimension_pv == d
assert_equal(x, ekf_state.mean, prec=1e-5)
assert_equal(P, ekf_state.cov, prec=1e-5)
assert_equal(x, ekf_state.mean_pv, prec=1e-5)
assert_equal(P, ekf_state.cov_pv, prec=1e-5)
assert_equal(t, ekf_state.time, prec=1e-5)
ekf_state1 = EKFState(ncv, 2*x, 2*P, t)
ekf_state2 = ekf_state1.predict(dt)
assert ekf_state2.dynamic_model.__class__ == NcvContinuous
measurement = PositionMeasurement(
mean=torch.rand(d),
cov=torch.eye(d),
time=t + dt)
log_likelihood = ekf_state2.log_likelihood_of_update(measurement)
assert (log_likelihood < 0.).all()
ekf_state3, (dz, S) = ekf_state2.update(measurement)
assert dz.shape == (measurement.dimension,)
assert S.shape == (measurement.dimension, measurement.dimension)
assert_not_equal(ekf_state3.mean, ekf_state2.mean, prec=1e-5)
def filter_states(self, value):
"""
Returns the ekf states given measurements
:param value: measurement means of shape `(time_steps, event_shape)`
:type value: torch.Tensor
"""
states = []
state = EKFState(self.dynamic_model, self.x0, self.P0, time=0.)
assert value.shape[-1] == self.event_shape[-1]
for i, measurement_mean in enumerate(value):
if i:
state = state.predict(self.dt)
measurement = PositionMeasurement(measurement_mean, self.measurement_cov,
time=state.time)
state, (dz, S) = state.update(measurement)
states.append(state)
return states
def log_prob(self, value):
"""
Returns the joint log probability of the innovations of a tensor of measurements
:param value: measurement means of shape `(time_steps, event_shape)`
:type value: torch.Tensor
"""
state = EKFState(self.dynamic_model, self.x0, self.P0, time=0.)
result = 0.
assert value.shape == self.event_shape
zero = torch.zeros(self.event_shape[-1], dtype=value.dtype, device=value.device)
for i, measurement_mean in enumerate(value):
if i:
state = state.predict(self.dt)
measurement = PositionMeasurement(measurement_mean, self.measurement_cov,
time=state.time)
state, (dz, S) = state.update(measurement)
result = result + dist.MultivariateNormal(dz, S).log_prob(zero)
return result