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Changing variable broadcasting for factors #133
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added 8 commits
November 16, 2018 22:01
…and changed all the corresponding test cases.
Codecov Report
@@ Coverage Diff @@
## develop #133 +/- ##
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- Coverage 85.25% 85.11% -0.14%
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Files 77 77
Lines 4163 3817 -346
Branches 731 653 -78
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- Hits 3549 3249 -300
+ Misses 411 375 -36
+ Partials 203 193 -10
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meissnereric
approved these changes
Nov 20, 2018
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| def broadcast_samples_dict(F, array_dict, num_samples=None): | ||
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meissnereric
suggested changes
Nov 20, 2018
| @@ -155,7 +89,19 @@ def log_cdf(self, F=None, **kwargs): | |||
| """ | |||
| raise NotImplementedError | |||
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| def draw_samples(self, rv_shape, num_samples=1, F=None, **kwargs): | |||
| def draw_samples(self, F, variables, num_samples=1, targets=None, | |||
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Docstring? As above with the variable replacement comment?
| replicant.broadcastable = self.broadcastable | ||
| return replicant | ||
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| def eval(self, F, variables, always_return_tuple=False): |
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Docstring? Explaining what this function does with samples in particular thanks.
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| def broadcast_to(data, shape): | ||
| """The broadcast_to operator""" |
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Can you expand this operator's docstring a bit?
Maybe even include a small example/typical usage like in the docs:
m.x.set_prior(Gaussian(mean=broadcast_to(array([0]), m.x.shape),
variance=broadcast_to(array([1]), m.x.shape)))
Co-Authored-By: zhenwendai <zhenwendai@users.noreply.github.com>
meissnereric
approved these changes
Nov 23, 2018
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Description of changes:
Implemented the according to the Design Proposal 1.
The changes include:
broadcast_tooperator to allow users to explicitly broadcast variables.By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.