PerturbedStepSolution

class probnum.diffeq.perturbed.step.PerturbedStepSolution(scales, locations, states, interpolants)[source]

Bases: probnum.diffeq.ODESolution

Probabilistic ODE solution corresponding to the PerturbedStepSolver.

Attributes Summary

frozen

locations

states

Methods Summary

__call__(t)

Evaluate the time-continuous posterior at location t

append(location, state)

rtype

None

freeze()

rtype

None

interpolate(t[, previous_index, next_index])

Evaluate the posterior at a measurement-free point.

sample(rng[, t, size])

Sample from the ODE solution.

transform_base_measure_realizations(…)

Transform a set of realizations from a base measure into realizations from the posterior.

Attributes Documentation

frozen
locations
states

Methods Documentation

__call__(t)

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[float, Real, floating, 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

append(location, state)
Return type

None

freeze()
Return type

None

interpolate(t, previous_index=None, next_index=None)[source]

Evaluate the posterior at a measurement-free point.

Returns

Dense evaluation.

Return type

randvars.RandomVariable or _randomvariablelist._RandomVariableList

sample(rng, t=None, size=())

Sample from the ODE solution.

Parameters
  • rng (Generator) – Random number generator.

  • t (Union[float, Real, floating, ndarray, None]) – Location / time at which to sample. If nothing is specified, samples at the ODE-solver grid points are computed. If it is a float, a sample of the ODE-solution at this time point is computed. Similarly, if it is a list of floats (or an array), samples at the specified grid-points are returned. This is not the same as computing i.i.d samples at the respective locations.

  • size (Union[int, Integral, integer, Iterable[Union[int, Integral, integer]], None]) – Number of samples.

Return type

ndarray

transform_base_measure_realizations(base_measure_realizations, t)

Transform a set of realizations from a base measure into realizations from the posterior.

Parameters
  • base_measure_realizations (ndarray) – Base measure realizations.

  • t (Union[float, Real, floating, ndarray]) – Locations on which the transformed realizations shall represent realizations from the posterior.

Returns

Transformed realizations.

Return type

np.ndarray