RegressionProblem

class probnum.problems.RegressionProblem(observations, locations, solution=None)

Bases: object

Regression problem.

Fit a stochastic process to data, given a likelihood (realised by a DiscreteGaussian transition). Solved by filters and smoothers in probnum.filtsmooth.

Parameters

  • observations (numpy.ndarray) – Observations of the latent process.

  • locations (numpy.ndarray) – Grid-points on which the observations were taken.

  • solution (Optional[Callable[numpy.ndarray, Union[float, numpy.ndarray]]]) – Array containing solution to the problem at locations. Used for testing and benchmarking.

Examples

>>> obs = [11.4123, -15.5123]
>>> loc = [0.1, 0.2]
>>> rp = RegressionProblem(observations=obs, locations=loc)
>>> rp
RegressionProblem(observations=[11.4123, -15.5123], locations=[0.1, 0.2], solution=None)
>>> rp.observations
[11.4123, -15.5123]

Attributes Summary

solution

Attributes Documentation

solution: Optional[Callable[numpy.ndarray, Union[float, numpy.ndarray]]] = None