Enforce Array consistency across distributions#93
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Uniform had no __init__ at all, so `low`/`high` were stored as whatever was passed in (e.g. bare Python floats) instead of being cast to jax arrays like Normal, Beta, Gamma, etc. already do. Bernoulli/Categorical/ OneHotCategorical actively rejected non-jax-array probs/logits via an isinstance check instead of casting them, and the four MultivariateNormal classes accessed .shape/.ndim on loc/scale/covariance before casting, crashing with AttributeError for plain floats or lists. Cast inputs to arrays up front in all of these, matching the existing convention, so construction behaves consistently across the library regardless of whether callers pass raw Python numbers/lists or arrays.
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lockwo
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Jul 6, 2026
| self, | ||
| logits: Optional[Array] = None, | ||
| probs: Optional[Array] = None, | ||
| logits: Optional[Union[float, Array]] = None, |
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can we do | over Union. it has >3.10 anyway so this should be fine
| - `low`: Lower bound of the distribution. | ||
| - `high`: Upper bound of the distribution. | ||
| """ | ||
| self.low = jnp.asarray(low) |
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for these ones (where we don't have like an if none for the other), we can make this nice with a low: Float[Array, "..."] = eqx.field(converter=jnp.asarray) (or something very close to that)
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Ah, yeah I thought about this but wasn't sure if you want to use Equinox fields. Will update
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Some distributions cast inputs to arrays during initialization (e.g.
Normal) while others do not (e.g.Uniform). This PR ensures that all distribution fields are arrays across all distributions.