handle NaN losses and add a test, closes #145

Author: basnijholt

adaptive/learner/learner1D.py

@@ -684,7 +684,7 @@ def finite_loss(ival, loss, x_scale):
     sort intervals that have infinite loss."""
     # If the loss is infinite we return the
     # distance between the two points.
-    if math.isinf(loss):
+    if math.isinf(loss) or math.isnan(loss):
         loss = (ival[1] - ival[0]) / x_scale
         if len(ival) == 3:
             # Used when constructing quals. Last item is

adaptive/tests/test_learner1d.py

@@ -363,3 +363,15 @@ def f(x):
     learner = Learner1D(f, (-1, 1), loss_per_interval=loss)
     simple(learner, goal=lambda l: l.npoints > 100)
     assert learner.npoints > 100
+
+
+def test_NaN_loss():
+    # see https://github.com/python-adaptive/adaptive/issues/145
+    def f(x):
+        a = 0.01
+        if random.random() < 0.2:
+            return np.NaN
+        return x + a**2 / (a**2 + x**2)
+
+    learner = Learner1D(f, bounds=(-1, 1))
+    simple(learner, lambda l: l.npoints > 100)