bring back sample_dim in log_joint #44
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esennesh
reviewed
Nov 5, 2019
| @@ -80,7 +80,7 @@ def elbo(q, p, sample_dim=None, batch_dim=None, alpha=0.1, beta=(1.0, 1.0, 1.0, | |||
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| def kl(q, p, sample_dim=None, batch_dim=None, log_weights=None, beta=(1.0, 1.0, 1.0, 1.0, 1.0), | |||
| size_average=True, reduce=True, bias=None): | |||
| size_average=True, reduce=True, bias=None): | |||
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This appears to just be a whitespace edit. I'm fine with it going in if JW is.
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only review-worthy change is the sample_dim thing, rest is pep8 etc.- don't waste your time on those...
esennesh
reviewed
Nov 5, 2019
probtorch/stochastic.py
Outdated
| batch_dim(int): The dimension that enumerates batch items. | ||
| """ | ||
| if (sample_dim is not None): | ||
| if sample_dims is None: | ||
| sample_dims = sample_dim |
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Shouldn't this contain a single-element tuple as the default when assigning sample_dim to sample_dims?
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because backwards compatibility is a thing. But seriously, having only
sample_dimsfor one method andsample_dimcreates confusion in namespace. I agree thatsample_dimsis semantically correct where it's used and we should permit multiple sample dims (and usesample_dims) everywhere. In the meantime, let's allow forsample_dim(with a deprecation warning).