TimeSeriesPosterior¶
-
class
probnum.filtsmooth.
TimeSeriesPosterior
(locations, states)[source]¶ Bases:
abc.ABC
Posterior Distribution over States after time-series algorithms such as filtering/smoothing or solving ODEs.
- Parameters
Methods Summary
__call__
(t)Evaluate the time-continuous posterior at location t
interpolate
(t[, previous_location, …])Evaluate the posterior at a measurement-free point.
sample
([t, size, random_state])Draw samples from the filtering/smoothing posterior.
Transform a set of realizations from a base measure into realizations from the posterior.
Methods Documentation
-
__call__
(t)[source]¶ Evaluate the time-continuous posterior at location t
Algorithm: 1. Find closest t_prev and t_next, with t_prev < t < t_next 2. Predict from t_prev to t 3. (if self._with_smoothing=True) Predict from t to t_next 4. (if self._with_smoothing=True) Smooth from t_next to t 5. Return random variable for time t
- Parameters
t (
Union
[Real
,ndarray
]) – Location, or time, at which to evaluate the posterior.- Returns
Estimate of the states at time
t
.- Return type
randvars.RandomVariable or _randomvariablelist._RandomVariableList
-
abstract
interpolate
(t, previous_location=None, previous_state=None, next_location=None, next_state=None)[source]¶ Evaluate the posterior at a measurement-free point.
- Parameters
t (
Real
) – Location to evaluate at.- Returns
Dense evaluation.
- Return type
randvars.RandomVariable or _randomvariablelist._RandomVariableList
-
abstract
sample
(t=None, size=(), random_state=None)[source]¶ Draw samples from the filtering/smoothing posterior.
If nothing is specified, a single sample is drawn (supported on self.locations). If locations are specified, a single sample is drawn on those locations. If size is specified, more than a single sample is drawn.
Internally, samples from a base measure are drawn and transformed via self.transform_base_measure_realizations.
- Parameters
t (
Union
[Real
,ndarray
,None
]) – Locations on which the samples are wanted. Default is none, which implies that self.location is used.size (
Union
[Integral
,Iterable
[Integral
],None
]) – Indicates how many samples are drawn. Default is an empty tuple, in which case a single sample is returned.random_state (
Union
[None
,int
,RandomState
,Generator
]) – Random state (seed, generator) to be used for sampling base measure realizations.
- Returns
Drawn samples. If size has shape (A1, …, Z1), locations have shape (L,), and the state space model has shape (A2, …, Z2), the output has shape (A1, …, Z1, L, A2, …, Z2). For example: size=4, len(locations)=4, dim=3 gives shape (4, 4, 3).
- Return type
np.ndarray