Comments (1)
I think this this due to floating precision
Almost certainly. Quite a few of the tests are super finicky regarding numerical precision, which is one of the reason we often seed the RNG when we need "random" numbers --- if we'd take actual (psuedo)random numbers the test would "fail".
This actually requires a more fundamental decision on what we want with the tests, and how accurate they should be, since it will also depend on e.g. your scipy installation.
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from symfit.