RegressionProblem¶
-
class
probnum.problems.
RegressionProblem
(observations, locations, likelihood=None, 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 inprobnum.filtsmooth
.- Parameters
observations (numpy.ndarray) – Observations of the latent process.
locations (numpy.ndarray) – Grid-points on which the observations were taken.
likelihood (Optional[probnum.statespace.DiscreteGaussian]) – Likelihood of the observations; that is, relation between the latent process and the observed values. Encodes for example noise.
solution (Optional[Callable[numpy.ndarray, Union[float, numpy.ndarray]]]) – Closed form, analytic solution to the problem. 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], likelihood=None, solution=None) >>> rp.observations [11.4123, -15.5123]
Attributes Summary
Attributes Documentation
-
likelihood
: Optional[probnum.statespace.DiscreteGaussian] = None¶
-
solution
: Optional[Callable[numpy.ndarray, Union[float, numpy.ndarray]]] = None¶