"""Abstract Base Class for posteriors over states after applying filtering/smoothing"""
from abc import ABC, abstractmethod
[docs]class FiltSmoothPosterior(ABC):
"""Posterior Distribution over States after Filtering/Smoothing"""
[docs] @abstractmethod
def __call__(self, location):
"""Evaluate the time-continuous posterior for a given location
Parameters
----------
location : float
Location, or time, at which to evaluate the posterior.
Returns
-------
rv : `RandomVariable`
"""
raise NotImplementedError
@abstractmethod
def __len__(self):
"""Length of the discrete-time solution
Corresponds to the number of filtering/smoothing steps
"""
raise NotImplementedError
@abstractmethod
def __getitem__(self, idx):
"""Return the corresponding index/slice of the discrete-time solution"""
raise NotImplementedError
[docs] def sample(self, locations=None, size=()):
"""
Draw samples from the filtering/smoothing posterior.
If nothing is specified, a single sample is drawn (supported on self.locations).
If locations are specified, the samples are drawn on those locations.
If size is specified, more than a single sample is drawn.
Parameters
----------
locations : array_like, optional
Locations on which the samples are wanted. Default is none, which implies that
self.location is used.
size : int or tuple of ints, optional
Indicates how many samples are drawn. Default is an empty tuple, in which case
a single sample is returned.
Returns
-------
numpy.ndarray
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).
"""
raise NotImplementedError("Sampling not implemented.")