refactor(distributions): standardize output types #102
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Nov 25, 2025
xImoZA
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- In some places a scalar is returned, and in others a zero-dimensional array is returned. I suggest returning a scalar everywhere (using
result[()]) - I also propose standardizing the 'generate' method, making it scipy/numpy-like. To do this, simply pass
sizeto the scipy generator. Size parameter should be of typeint | tuple[int, ...] | None
rework_pysatl_mpest/core/mixture.py
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| 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)) | ||
| return np.dot(self.weights, component_pdfs) |
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Why not just return np.exp(self.lpdf(X))?
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This PR standardizes the behavior of distributions to ensure consistent return types (scalars vs arrays) and improves the numerical stability of PDF calculations.
Changes
pdfmethods in all distributions to calculate probability density vianp.exp(lpdf(X)).pdf,lpdf,ppf,log_gradients) to strictly distinguish between inputs:dtype.generatemethod signature inContinuousDistributionand implementations to supportsizeasint,tuple, orNone(SciPy-like behavior).np.isscalarcheck).sizeshapes (scalar, 1D, ND, empty)