@@ -147,6 +147,44 @@ class BalancingLearner(BaseLearner):

     @classmethod

     def from_combos(cls, f, learner_type, learner_kwargs, combos):

+        """Create a `BalancingLearner` with learners of all combinations of

+        named variables’ values.

+

+        Parameters

+        ----------

+        f : callable

+            Function to learn, must take arguments provided in in `combos`.

+        learner_type : BaseLearner

+            The learner that should wrap the function. For example `Learner1D`.

+        learner_kwargs : dict

+            Keyword argument for the `learner_type`. For example `dict(bounds=[0, 1])`.

+        combos : dict (mapping individual fn arguments -> sequence of values)

+            For all combinations of each argument a learner will be instantiated.

+

+        Returns

+        -------

+        learner : `BalancingLearner`

+            A `BalancingLearner` with learners of all combinations of `combos`

+

+        Example

+        -------

+        >>> def f(x, n, alpha, beta):

+        ...     return scipy.special.eval_jacobi(n, alpha, beta, x)

+

+        >>> combos = {

+        ...     'n': [1, 2, 4, 8, 16],

+        ...     'alpha': np.linspace(0, 2, 3),

+        ...     'beta': np.linspace(0, 1, 5),

+        ... }

+

+        >>> learner = BalancingLearner.from_combos(

+        ...     f, Learner1D, dict(bounds=(0, 1)), combos)

+

+        Notes

+        -----

+        The order of the child learners inside `learner.learners` is the same

+        as `adaptive.utils.named_product(**combos)`.

+        """

         learners = []

         for combo in named_product(**combos):

             learner = learner_type(partial(f, **combo), **learner_kwargs)