def test_uncert_not_scalar(self):
"""
We test whether the uncertainty (third element of derivative_data)
is only accepted as a scalar.
"""
# Set Model Parameters
odeparam = 1.
y0, y0_unc = 1.0, 0
t0, tmax = 0.0, 1.25
# Set Method Parameters
q = 1
h = 0.1
# Set up and solve ODE
ibm = statespace.IBM(q=q, dim=1)
solver = linsolve.LinearisedODESolver(ibm)
ivp = linode.LinearODE(t0, tmax, odeparam, y0, y0_unc)
tsteps, means, __, rhs_parts, should_not_work = solver.solve(ivp, stepsize=h)
self.mean = odesolver.get_trajectory(means, 0, 0)
# Set up BM and IBM covariance matrices
evalpt = np.array(tsteps[[-1]])
with self.assertRaises(TypeError):
derdat = (tsteps, rhs_parts, should_not_work)
linearisation.compute_linearisation(ssm=ibm, initial_value=y0,
derivative_data=derdat,
prdct_tsteps=evalpt)
def setUp(self):
"""
Set up linear ODE (i.e. one-dim, one parameter) and one evalpt.
"""
# Set Model Parameters
odeparam = 1.
y0, y0_unc = 1.0, 0
t0, tmax = 0.0, 1.25
# Set Method Parameters
q = 1
h = 0.1
# Set up and solve ODE
ibm = statespace.IBM(q=q, dim=1)
solver = linsolve.LinearisedODESolver(ibm)
ivp = linode.LinearODE(t0, tmax, odeparam, y0, y0_unc)
tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)
self.mean = odesolver.get_trajectory(means, 0, 0)
# Set up BM and IBM covariance matrices
evalpt = np.array([tsteps[-1]])
derdat = (tsteps, rhs_parts, 0.)
const, jacob = linearisation.compute_linearisation(
ssm=ibm, initial_value=y0,
derivative_data=derdat, prdct_tsteps=evalpt)
# Compute GP Estimation of filter mean at t=tmax
self.postmean = const + np.dot(jacob[:, 0], odeparam)
def setUp(self):
"""
Set up Lotka-Volterra ODE (i.e. two-dim, four parameter) and
multiple (two) evalpts.
"""
# Set Model Parameters
odeparam = np.array([0, 1, 1, 2])
y0, y0_unc = np.ones(2), 0 * np.ones(2)
t0, tmax = 0.0, 1.25
# Set Method Parameters
q = 1
h = 0.1
# Set up and solve ODE
ibm = statespace.IBM(q=q, dim=len(y0))
solver = linsolve.LinearisedODESolver(ibm)
ivp = linode.LotkaVolterra(t0, tmax, odeparam, y0, y0_unc)
tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)
self.mean = odesolver.get_trajectory_multidim(means, [0, 1], 0)
# Set up BM and IBM covariance matrices
evalpt = np.array(tsteps[[-1, -10]])
derdat = (tsteps, rhs_parts, 0.)
const, jacob = linearisation.compute_linearisation(
ssm=ibm, initial_value=y0,
derivative_data=derdat, prdct_tsteps=evalpt)
# Compute GP Estimation of filter mean at t=tmax
postmean = const + np.dot(jacob, odeparam)
self.postmean = postmean.reshape((2, 2))