Learner1D doesn't correctly set the interpolated loss when a point is added about adaptive HOT 2 CLOSED

originally posted by Bas Nijholt (@basnijholt) at 2018-09-14T14:34:11.008Z on GitLab

Another bug in the loss interpolation:

l = adaptive.Learner1D(lambda x: x, (0, 4))

l.tell(0, 0)
l.tell(1, 0)
l.tell(2, 0)
l.ask(1)
l.tell(3.5, 0)
assert set(l.losses_combined.keys()) == {(0, 1), (1, 2), (2, 3.5), (3.5, 4.0)}

@jorn I am pretty sure that this has to do with your update_interpolated_loss_in_interval and update_losses code. I am trying to figure out what happens but it's not clear to me at all 😜

from adaptive.

originally posted by Jorn Hoofwijk (@Jorn) at 2018-09-15T15:44:02.683Z on GitLab

yes, you are correct, working on it

from adaptive.