feat(core, distributions): switch to generics #96
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iraedeus
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Nov 7, 2025
rework_tests/unit/distributions/test_continuous_distribution.py
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iraedeus
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Nov 18, 2025
iraedeus
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There are many uses of np.isclose or np.assert_allclose with arbitrary atol values for all dtypes. I suggest trying atol=np.finfo(dtype).eps
| def test_init_does_not_recreate_components_with_correct_dtype(self, dtype): | ||
| """Tests that components with the correct dtype are not recreated.""" | ||
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| comp_f32 = Exponential(loc=0.0, rate=1.0, dtype=dtype) |
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Invalid variable name, here the component has arbitrary precision
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| comp_f32 = Exponential(loc=0.0, rate=1.0, dtype=dtype) | |
| comp = Exponential(loc=0.0, rate=1.0, dtype=dtype) |
| X = np.asarray(X, dtype=float64) | ||
| X = np.asarray(X, dtype=self.dtype) | ||
| component_pdfs = np.array([comp.pdf(X) for comp in self.components]) | ||
| return np.asarray(np.dot(self.weights, component_pdfs)) |
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I would rewrite this method for better numerical stability:
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| return np.asarray(np.dot(self.weights, component_pdfs)) | |
| return np.exp(self.lpdf(X)) |
Then you will need to synchronize the corresponding test like this:
| """ | ||
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| X = np.atleast_1d(X) | ||
| X = np.atleast_1d(X).astype(self.dtype) |
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| X = np.atleast_1d(X).astype(self.dtype) | |
| X = np.asarray(X, dtype=self.dtype) |
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| dtype = mixture_model.dtype | ||
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| expected_lpdf = np.log(mixture_model.pdf(X)) |
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| expected_lpdf = np.log(mixture_model.pdf(X)) | |
| expected_lpdf = np.log(w1 * c1.pdf(X) + w2 * c2.pdf(X)) |
| assert isinstance(samples, np.ndarray) | ||
| assert samples.dtype == dtype | ||
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| def test_generate_with_size_zero(self, mixture_model): |
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Forgot to check here dtype of the output
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This pull request refactors the core and distributions modules to use generic numeric types.
Implementation:
typing.Genericwith aTypeVar("DType").