Add support for power transforms #210
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Just a note that #184 originally introduced a rewrite for
Another arguments in favor of propagating domain information; we shouldn't have to make assumptions here. |
| return -2 * at.log(value) | ||
| from aeppl.logprob import xlogy0 | ||
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| power = self.transform_args_fn(*inputs) |
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Don't you need to use inv_power = at.reciprocal(power) instead of power in the expression of the jacobian?
| @@ -422,8 +422,20 @@ def transform(measurable_input, *other_inputs): | |||
| def measurable_reciprocal(fgraph, node): | |||
| """Rewrite a `reciprocal` node to a `MeasurableVariable`.""" | |||
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| def transform(measurable_input, *other_inputs): | |||
| return ReciprocalTransform(), (measurable_input,) | |||
| new_node = at.power(node.inputs[0], at.as_tensor(-1)).owner | |||
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What's the advantage of using pow to represent reciprocal here (besides a few less lines of code)? I feel such equivalences should be handled at the Aesara-level, either by aliasing or a canonicalization pass.
This PR adds support for general power transforms and replaces #184. It uses a positive support in cases of ambiguity.
In its current form, it appears to work as a replacement for$Z = X^2$ , where $X \sim \operatorname{N}\left(0, 1\right)$ , test example fails.
at.reciprocal, but the