divide by zero warnings in LearnerND

[basnijholt]

(original issue on GitLab)

opened by Joseph Weston ([gitlab:@jbweston](https://gitlab.kwant-project.org/jbweston))) at 2018-12-12T10:57:01.525Z

jbw[gitlab:@broadway](https://gitlab.kwant-project.org/broadway) adaptive-evaluation ((HEAD detached at v0.7.0)) $ python
Python 3.6.5 | packaged by conda-forge | (default, Apr  6 2018, 13:39:56) 
[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import adaptive
>>> import numpy as np
>>> adaptive.__version__
'0.7.0'
>>> np.__version__
'1.15.2'
>>> def f(xy):
...     return 1
... 
>>> learner = adaptive.LearnerND(f, ((-1, 1), (-1, 1)))
>>> adaptive.runner.simple(learner, lambda l: l.npoints >= 100)
/home/jbw/work/code/2017/adaptive-evaluation/adaptive/learner/learnerND.py:524: RuntimeWarning: divide by zero encountered in long_scalars
  scale_multiplier = 1 / self._scale
/home/jbw/work/code/2017/adaptive-evaluation/adaptive/learner/learnerND.py:543: RuntimeWarning: invalid value encountered in long_scalars
  scale_factor = np.max(np.nan_to_num(self._scale / self._old_scale))

I guess we should explicitly set the places where _scale is zero to have an infinite multiplier?