def test_ekf0_initialdistribution(self):
filtsmooth_object = ivp2filter.ivp2ekf0(self.ivp, self.prior,
self.evlvar)
expected_initval = np.array([
self.ivp.initrv.mean,
self.ivp(self.ivp.t0, self.ivp.initrv.mean),
self.ivp.jacobian(self.ivp.t0, self.ivp.initrv.mean) @ self.ivp(
self.ivp.t0, self.ivp.initrv.mean),
])
self.assertAllClose(filtsmooth_object.initialrandomvariable.mean,
expected_initval.T.flatten())
def test_ekf0_measmod(self):
filtsmooth_object = ivp2filter.ivp2ekf0(self.ivp, self.prior,
self.evlvar)
random_time, random_eval = np.random.rand(), np.random.rand(
self.prior.ndim)
e0, e1 = self.prior.proj2coord(0), self.prior.proj2coord(1)
expected_measmodel_output = e1 @ random_eval - self.ivp.rhs(
random_time, e0 @ random_eval)
measmodel_output = filtsmooth_object.measurementmodel.dynamics(
random_time, random_eval)
self.assertAllClose(expected_measmodel_output, measmodel_output)
def test_ivp2ekf0_output(self):
filtsmooth_object = ivp2filter.ivp2ekf0(self.ivp, self.prior, self.evlvar)
self.assertEqual(issubclass(type(filtsmooth_object), ExtendedKalman), True)