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This is a proof of concept for Hypothesis tests. I selected a test that was easy to convert and replaced the existing test with hard-coded values with a few tests that use Hypothesis. Since it was pretty easy to do, I also added tests for complex inputs. One thing I discovered is that cscalar doesn't work for any of the numpy functions. Hypothesis inevitably finds a value that can't be represented precisely and the test fails. All the functions work with zscalar. see: aesara-devs#197
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This looks great, thanks! Will review shortly. |
Remove unused imports and fix one formatting issue.
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Sounds good. Let me know what you think. It looks like I missed a few Black issues, so I fixed them (I hope) with this commit: fa832d4 |
| "cons", | ||
| "typing_extensions", | ||
| "setuptools >=48.0.0", | ||
| "hypothesis", |
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You might need to add this to the test set up script (i.e. here).
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I added it. I'm not sure how to test it, as I'm a little fuzzy on what this script is used for, but I guess we'll find out when something happens on GitHub. :^)
Add Hypothesis installation to test.yaml in the GitHub workflow.
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This is a proof of concept for Hypothesis tests. I selected a test that was easy to convert and replaced the existing test with hard-coded values with a few tests that use Hypothesis.
Since it was pretty easy to do, I also added tests for complex inputs. One thing I discovered is that cscalar doesn't work for any of the numpy functions. Hypothesis inevitably finds a value that can't be represented precisely and the test fails. All the functions work with zscalar.
see: #197
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