merge_regression_problems

probnum.filtsmooth.utils.merge_regression_problems(regression_problem1, regression_problem2)[source]

Make a new regression problem out of two other regression problems.

Parameters
Raises

ValueError – If the locations in both regression problems are not disjoint. Multiple observations at a single grid point are not supported currently.

Returns

problem – Time series regression problem.

Return type

problems.TimeSeriesRegressionProblem

Note

To merge more than two problems, combine this function with functools.reduce.

Examples

Create two car-tracking problems with similar parameters and disjoint locations.

>>> import probnum.problems.zoo.filtsmooth as filtsmooth_zoo
>>> import numpy as np
>>> rng = np.random.default_rng(seed=1)
>>> prob1, _ = filtsmooth_zoo.car_tracking(
...     rng=rng, measurement_variance=2.0, timespan=(0.0, 10.0), step=0.5
... )
>>> print(prob1.locations)
[0.  0.5 1.  1.5 2.  2.5 3.  3.5 4.  4.5 5.  5.5 6.  6.5 7.  7.5 8.  8.5
 9.  9.5]

>>> prob2, _ = filtsmooth_zoo.car_tracking(
...     rng=rng, measurement_variance=2.0, timespan=(0.25, 10.25), step=0.5
... )
>>> print(prob2.locations)
[0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25 6.75
 7.25 7.75 8.25 8.75 9.25 9.75]

Merge them with merge_regression_problems

>>> new_prob = merge_regression_problems(prob1, prob2)
>>> print(new_prob.locations)
[0.   0.25 0.5  0.75 1.   1.25 1.5  1.75 2.   2.25 2.5  2.75 3.   3.25
 3.5  3.75 4.   4.25 4.5  4.75 5.   5.25 5.5  5.75 6.   6.25 6.5  6.75
 7.   7.25 7.5  7.75 8.   8.25 8.5  8.75 9.   9.25 9.5  9.75]

If you have more than two problems that you want to merge, do this with functools.reduce.

>>> import functools
>>> prob3, _ = filtsmooth_zoo.car_tracking(
...     rng=rng, measurement_variance=2.0, timespan=(0.35, 10.35), step=0.5
... )
>>> new_prob = functools.reduce(
...     merge_regression_problems,
...     (prob1, prob2, prob3),
... )
>>> print(new_prob.locations)
[0.   0.25 0.35 0.5  0.75 0.85 1.   1.25 1.35 1.5  1.75 1.85 2.   2.25
 2.35 2.5  2.75 2.85 3.   3.25 3.35 3.5  3.75 3.85 4.   4.25 4.35 4.5
 4.75 4.85 5.   5.25 5.35 5.5  5.75 5.85 6.   6.25 6.35 6.5  6.75 6.85
 7.   7.25 7.35 7.5  7.75 7.85 8.   8.25 8.35 8.5  8.75 8.85 9.   9.25
 9.35 9.5  9.75 9.85]