@@ -1,6 +1,7 @@

 # -*- coding: utf-8 -*-

 from adaptive.learner import Learner1D, BalancingLearner

+from adaptive.runner import simple

 def test_balancing_learner_loss_cache():

@@ -24,9 +25,23 @@ def test_balancing_learner_loss_cache():

 def test_distribute_first_points_over_learners():

-    learners = [Learner1D(lambda x: x, bounds=(-1, 1)) for i in range(10)]

-    learner = BalancingLearner(learners)

-    points, _ = learner.ask(100)

-    i_learner, xs = zip(*points)

-    # assert that are all learners in the suggested points

-    assert len(set(i_learner)) == len(learners)

+    for strategy in ['loss', 'loss_improvements', 'npoints']:

+        learners = [Learner1D(lambda x: x, bounds=(-1, 1)) for i in range(10)]

+        learner = BalancingLearner(learners, strategy=strategy)

+        points, _ = learner.ask(100)

+        i_learner, xs = zip(*points)

+        # assert that are all learners in the suggested points

+        assert len(set(i_learner)) == len(learners)

+

+

+def test_strategies():

+    goals = {

+        'loss': lambda l: l.loss() < 0.1,

+        'loss_improvements': lambda l: l.loss() < 0.1,

+        'npoints': lambda bl: all(l.npoints > 10 for l in bl.learners)

+    }

+

+    for strategy, goal in goals.items():

+        learners = [Learner1D(lambda x: x, bounds=(-1, 1)) for i in range(10)]

+        learner = BalancingLearner(learners, strategy=strategy)

+        simple(learner, goal=goal)