IteratedDiscreteComponent

class probnum.filtsmooth.optim.IteratedDiscreteComponent(component, stopcrit=None)

Bases: Transition

Iterated updates.

Examples

>>> from probnum.filtsmooth.optim import FiltSmoothStoppingCriterion
>>> from probnum.filtsmooth.gaussian.approx import DiscreteEKFComponent
>>> from probnum.problems.zoo.diffeq import logistic
>>> from probnum.randprocs.markov.integrator import IntegratedWienerProcess
>>> from probnum.randprocs.markov.discrete import NonlinearGaussian
>>> from probnum.randvars import Constant
>>> import numpy as np

Set up an iterated component.

>>> iwp = IntegratedWienerProcess(
...     initarg=0., num_derivatives=2, wiener_process_dimension=1
... )
>>> H0, H1 = iwp.transition.proj2coord(coord=0), iwp.transition.proj2coord(coord=1)
>>> call = lambda t, x: H1 @ x - H0 @ x * (1 - H0 @ x)
>>> jacob = lambda t, x: H1 - (1 - 2*(H0 @ x)) @ H0
>>> nonlinear_model = NonlinearGaussian.from_callable(3, 1, call, jacob)
>>> ekf = DiscreteEKFComponent(nonlinear_model)
>>> comp = IteratedDiscreteComponent(ekf, FiltSmoothStoppingCriterion())

Generate some random variables and pseudo observations.

>>> some_array = np.array([0.1, 1., 2.])
>>> some_rv = Constant(some_array)
>>> rv, _ = iwp.transition.forward_realization(some_array , t=0., dt=0.1)
>>> rv_observed, _ =  comp.forward_rv(rv, t=0.2)
>>> rv_observed *= 0.01  # mitigate zero data

Its attributes are inherited from the component that is passed through.

>>> print(comp.input_dim)
3
>>> out, info = comp.forward_realization(some_array,some_rv,)
>>> print(out.mean)
[0.91]

But its backward values are different, because of the iteration.

>>> out_ekf, _ = ekf.backward_rv(rv_observed, rv)
>>> print(out_ekf.mean)
[  0.17081493   0.15351366 -13.73607367]
>>> out_iterated, _ = comp.backward_rv(rv_observed, rv)
>>> print(out_iterated.mean)
[  0.17076427   0.15194483 -13.76505168]

Methods Summary

backward_realization(realization_obtained, rv)	Backward-pass of a realisation of a state, according to the transition.
backward_rv(rv_obtained, rv[, rv_forwarded, ...])	Backward-pass of a state, according to the transition.
forward_realization(realization, t[, dt, ...])	Forward-pass of a realization of a state, according to the transition.
forward_rv(rv, t[, dt, compute_gain, ...])	Forward-pass of a state, according to the transition.
jointly_transform_base_measure_realization_list_backward(...)	Transform samples from a base measure into joint backward samples from a list of random variables.
jointly_transform_base_measure_realization_list_forward(...)	Transform samples from a base measure into joint backward samples from a list of random variables.
smooth_list(rv_list, locations, _diffusion_list)	Apply smoothing to a list of random variables, according to the present transition.

Methods Documentation

backward_realization(realization_obtained, rv, rv_forwarded=None, gain=None, t=None, dt=None, _diffusion=1.0, _linearise_at=None)

Backward-pass of a realisation of a state, according to the transition. In other words, return a description of

\[p(x(t) \,|\, {\mathcal{G}_t(x(t)) = \xi})\]

for an observed realization \(\xi\) of \({\mathcal{G}_t}(x(t))\). For example, this function is called in a Kalman update step.

Parameters

• realization_obtained – Observed realization \(\xi\) as an array.

• rv – “Current” distribution \(p(x(t))\) as a RandomVariable.

• rv_forwarded – “Forwarded” distribution (think: \(p(\mathcal{G}_t(x(t)) \,|\, x(t))\)) as a RandomVariable. Optional. If provided (in conjunction with gain), computation might be more efficient, because most backward passes require the solution of a forward pass. If rv_forwarded is not provided, forward_rv() might be called internally (depending on the object) which is skipped if rv_forwarded has been provided

• gain – Expected gain. Optional. If provided (in conjunction with rv_forwarded), some additional computations may be avoided (depending on the object).

• t – Current time point.

• dt – Increment \(\Delta t\). Ignored for discrete-time transitions.

• _diffusion – Special diffusion of the driving stochastic process, which is used internally.

• _linearise_at – Specific point of linearisation for approximate forward passes (think: extended Kalman filtering). Used internally for iterated filtering and smoothing.

Returns

• RandomVariable – New state, after applying the backward-pass.

• Dict – Information about the backward-pass.

backward_rv(rv_obtained, rv, rv_forwarded=None, gain=None, t=None, dt=None, _diffusion=1.0, _linearise_at=None)

Backward-pass of a state, according to the transition. In other words, return a description of

\[p(x(t) \,|\, z_{\mathcal{G}_t}) = \int p(x(t) \,|\, z_{\mathcal{G}_t}, \mathcal{G}_t(x(t))) p(\mathcal{G}_t(x(t)) \,|\, z_{\mathcal{G}_t})) d \mathcal{G}_t(x(t)),\]

for observations \(z_{\mathcal{G}_t}\) of \({\mathcal{G}_t}(x(t))\). For example, this function is called in a Rauch-Tung-Striebel smoothing step, which computes a Gaussian distribution

\[p(x(t) \,|\, z_{\leq t+\Delta t}) = \int p(x(t) \,|\, z_{\leq t+\Delta t}, x(t+\Delta t)) p(x(t+\Delta t) \,|\, z_{\leq t+\Delta t})) d x(t+\Delta t),\]

from filtering distribution \(p(x(t) \,|\, z_{\leq t})\) and smoothing distribution \(p(x(t+\Delta t) \,|\, z_{\leq t+\Delta t})\), where \(z_{\leq t + \Delta t}\) contains both \(z_{\leq t}\) and \(z_{t + \Delta t}\).

Parameters

• rv_obtained – “Incoming” distribution (think: \(p(x(t+\Delta t) \,|\, z_{\leq t+\Delta t})\)) as a RandomVariable.

• rv – “Current” distribution (think: \(p(x(t) \,|\, z_{\leq t})\)) as a RandomVariable.

• rv_forwarded – “Forwarded” distribution (think: \(p(x(t+\Delta t) \,|\, z_{\leq t})\)) as a RandomVariable. Optional. If provided (in conjunction with gain), computation might be more efficient, because most backward passes require the solution of a forward pass. If rv_forwarded is not provided, forward_rv() might be called internally (depending on the object) which is skipped if rv_forwarded has been provided

• gain – Expected gain from “observing states at time \(t+\Delta t\) from time \(t\)). Optional. If provided (in conjunction with rv_forwarded), some additional computations may be avoided (depending on the object).

• t – Current time point.

• dt – Increment \(\Delta t\). Ignored for discrete-time transitions.

• _diffusion – Special diffusion of the driving stochastic process, which is used internally.

• _linearise_at – Specific point of linearisation for approximate forward passes (think: extended Kalman filtering). Used internally for iterated filtering and smoothing.

Returns

• RandomVariable – New state, after applying the backward-pass.

• Dict – Information about the backward-pass.

forward_realization(realization, t, dt=None, compute_gain=False, _diffusion=1.0, _linearise_at=None)

Forward-pass of a realization of a state, according to the transition. In other words, return a description of

\[p(\mathcal{G}_t[x(t)] \,|\, x(t)=\xi),\]

for some realization \(\xi\).

Parameters

• realization – Realization \(\xi\) of the random variable \(x(t)\) that describes the current state.

• t – Current time point.

• dt – Increment \(\Delta t\). Ignored for discrete-time transitions.

• compute_gain – Flag that indicates whether the expected gain of the forward transition shall be computed. This is important if the forward-pass is computed as part of a forward-backward pass, as it is for instance the case in a Kalman update.

• _diffusion – Special diffusion of the driving stochastic process, which is used internally.

• _linearise_at – Specific point of linearisation for approximate forward passes (think: extended Kalman filtering). Used internally for iterated filtering and smoothing.

Returns

• RandomVariable – New state, after applying the forward-pass.

• Dict – Information about the forward pass. Can for instance contain a gain key, if compute_gain was set to True (and if the transition supports this functionality).

forward_rv(rv, t, dt=None, compute_gain=False, _diffusion=1.0, _linearise_at=None)

Forward-pass of a state, according to the transition. In other words, return a description of

\[p(\mathcal{G}_t[x(t)] \,|\, x(t)),\]

or, if we take a message passing perspective,

\[p(\mathcal{G}_t[x(t)] \,|\, x(t), z_{\leq t}),\]

for past observations \(z_{\leq t}\). (This perspective will be more interesting in light of backward_rv()).

Parameters

• rv – Random variable that describes the current state.

• t – Current time point.

• dt – Increment \(\Delta t\). Ignored for discrete-time transitions.

• compute_gain – Flag that indicates whether the expected gain of the forward transition shall be computed. This is important if the forward-pass is computed as part of a forward-backward pass, as it is for instance the case in a Kalman update.

• _diffusion – Special diffusion of the driving stochastic process, which is used internally.

• _linearise_at – Specific point of linearisation for approximate forward passes (think: extended Kalman filtering). Used internally for iterated filtering and smoothing.

Returns

• RandomVariable – New state, after applying the forward-pass.

• Dict – Information about the forward pass. Can for instance contain a gain key, if compute_gain was set to True (and if the transition supports this functionality).

jointly_transform_base_measure_realization_list_backward(base_measure_realizations, t, rv_list, _diffusion_list, _previous_posterior=None)

Transform samples from a base measure into joint backward samples from a list of random variables.

Parameters

• base_measure_realizations (ndarray) – Base measure realizations (usually samples from a standard Normal distribution). These are transformed into joint realizations of the random variable list.

• rv_list (_RandomVariableList) – List of random variables to be jointly sampled from.

• t (Union[float, Real, floating]) – Locations of the random variables in the list. Assumed to be sorted.

• _diffusion_list (ndarray) – List of diffusions that correspond to the intervals in the locations. If locations=(t0, …, tN), then _diffusion_list=(d1, …, dN), i.e. it contains one element less.

• _previous_posterior – Previous posterior. Used for iterative posterior linearisation.

Returns

Jointly transformed realizations.

Return type

np.ndarray

jointly_transform_base_measure_realization_list_forward(base_measure_realizations, t, initrv, _diffusion_list, _previous_posterior=None)

Transform samples from a base measure into joint backward samples from a list of random variables.

Parameters

• base_measure_realizations (ndarray) – Base measure realizations (usually samples from a standard Normal distribution). These are transformed into joint realizations of the random variable list.

• initrv (RandomVariable) – Initial random variable.

• t (Union[float, Real, floating]) – Locations of the random variables in the list. Assumed to be sorted.

• _diffusion_list (ndarray) – List of diffusions that correspond to the intervals in the locations. If locations=(t0, …, tN), then _diffusion_list=(d1, …, dN), i.e. it contains one element less.

• _previous_posterior – Previous posterior. Used for iterative posterior linearisation.

Returns

Jointly transformed realizations.

Return type

np.ndarray

smooth_list(rv_list, locations, _diffusion_list, _previous_posterior=None)

Apply smoothing to a list of random variables, according to the present transition.

Parameters

• rv_list (randvars._RandomVariableList) – List of random variables to be smoothed.

• locations – Locations \(t\) of the random variables in the time-domain. Used for continuous-time transitions.

• _diffusion_list – List of diffusions that correspond to the intervals in the locations. If locations=(t0, …, tN), then _diffusion_list=(d1, …, dN), i.e. it contains one element less.

• _previous_posterior – Specify a previous posterior to improve linearisation in approximate backward passes. Used in iterated smoothing based on posterior linearisation.

Returns

List of smoothed random variables.

Return type

randvars._RandomVariableList