def setUp(self):
"""
Set up linearised ODESolver and Res2Bod as a non-linearised ODE.
"""
prior = stsp.IBM(q=1, dim=4)
self.solver = linsolver.LinearisedODESolver(prior, filtertype="kalman")
self.ode = standard_ode.Res2Bod(t0=0.0, tmax=1.2)
def check_custom_filter_not_implemented(self):
"""
Custom filter should be an option, yet raise an error.
"""
with self.assertRaises(NotImplementedError):
linsolver.LinearisedODESolver(self.working_2d_prior,
filtertype="custom")
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 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 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))
def check_nonexistant_filterkey(self):
"""
Entering any filterkey other than 'kalman', 'particle'
or 'custom' raises AssertionError.
"""
with self.assertRaises(NameError):
linsolver.LinearisedODESolver(self.working_2d_prior,
filtertype="rubbish")
def setUp(self):
"""
Set up linear ode and solve with stepsize h = 0.1.
"""
prior = stsp.IBM(q=1, dim=1)
self.solver = linsolver.LinearisedODESolver(prior, filtertype="kalman")
self.ode = linode.LinearODE(t0=0.0, tmax=1.2, params=2.0, initval=4.1)
self.h = 0.1
output = self.solver.solve(self.ode, self.h)
self.tsteps, self.means, self.stdevs, __, __ = output
def setUp(self):
"""
Set up linear ODE and linearised ODESolver.
"""
prior = stsp.IBM(q=1, dim=1)
solver = linsolver.LinearisedODESolver(prior, filtertype="kalman")
ode = linode.LinearODE(t0=0.0, tmax=1.2, params=2.0, initval=4.1)
h = 0.1
output = solver.solve(ode, h)
__, __, __, self.rhs_parts, self.uncert = output
def test_inconsistent_prior_and_ssm(self):
"""
Prior uses dim=2, so a scalar ODE should raise an AssertionError.
"""
solver = linsolver.LinearisedODESolver(self.working_2d_prior,
self.working_filtertype)
wrong_dimensional_ode = linode.LinearODE(t0=0.,
tmax=1.,
params=1.123,
initval=1.)
with self.assertRaises(AssertionError):
solver.solve(wrong_dimensional_ode, stepsize=0.1)
def setUp(self):
"""
Set up LotkaVolterra ODE and LinearisedODESolver.
"""
prior = stsp.IBM(q=1, dim=2)
solver = linsolver.LinearisedODESolver(prior, filtertype="kalman")
params = [0.1, 0.2, 0.3, 0.4]
ode = linode.LotkaVolterra(t0=0.0,
tmax=1.2,
params=params,
initval=np.ones(2))
h = 0.1
output = solver.solve(ode, h)
__, __, __, self.rhs_parts, self.uncert = output
np.random.seed(1)
# Set Model Parameters
initial_value = np.array([20, 20])
initial_time, end_time = 0.0, 5.0
ivpvar = 1e-2
thetatrue = np.array([1.0, 0.1, 0.1, 1.0])
ivp = linode.LotkaVolterra(initial_time,
end_time,
params=thetatrue,
initval=initial_value)
# Set Method Parameters
h_for_data = (end_time - initial_time) / 10000
h = (end_time - initial_time) / 200
solver = linsolver.LinearisedODESolver(statespace.IBM(q=1, dim=2))
ipdata = create_data(solver, ivp, thetatrue, h_for_data, ivpvar)
iplklhd = ip.InvProblemLklhd(ipdata, ivp, solver, h, with_jacob=True)
# Sample from posteriors
niter = 50
init_theta = np.array([0.8, 0.2, 0.05, 1.1])
samples_ham, probs_ham = hamiltonian(niter,
iplklhd,
init_theta,
stepsize=0.2,
nsteps=6)
samples_lang, probs_lang = langevin(niter,
iplklhd,
init_theta,
stepsize=1.2)
np.random.seed(1)
# Set Model Parameters
initial_value = 2.0
initial_time = 0.1
end_time = 1.1
ivpnoise = 0.01
thetatrue = 0.25
# Set Method Parameters
q = 1
h = 0.2
nsamps = 25
init_theta = 0.99 * np.ones(1)
ibm = statespace.IBM(q=q, dim=1)
solver = linsolver.LinearisedODESolver(ibm)
pwidth = 0.004
# Create Data and Jacobian
ivp = linode.LinearODE(initial_time,
end_time,
params=thetatrue,
initval=initial_value,
initval_unc=0.0)
evalpt, data = create_data(solver, ivp, thetatrue, 1e-04, ivpnoise)
tsteps, __, __, __, __ = solver.solve(ivp, stepsize=h)
evalpt = np.array(tsteps[[-1]])
kernel_prefactor = linearisation.compute_kernel_prefactor(
ibm, 0.0, tsteps, evalpt)
# Sample states from Markov chain
