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Add ability to fix/unfix parameters #186
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@@ Coverage Diff @@
## develop #186 +/- ##
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- Coverage 88.15% 88.15% -0.01%
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Files 80 80
Lines 4077 4085 +8
Branches 692 693 +1
===========================================
+ Hits 3594 3601 +7
Misses 310 310
- Partials 173 174 +1
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|
meissnereric
approved these changes
Jun 28, 2019
| m.y = mf.components.distributions.Normal.define_variable(mean=m.x, variance=mx.nd.array([1], dtype=dtype), shape=(m.N,)) | ||
| self.m = m | ||
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| def test_variable_fixing(self): |
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Can you add onto this test something like this that runs inference without fixing first, to verify that the value does change.
alg = MAP(model=self.m, observed=observed)
infr = GradBasedInference(inference_algorithm=alg, grad_loop=BatchInferenceLoop())
infr.initialize(y=mx.nd.array(np.random.rand(N)))
infr.run(y=mx.nd.array(np.random.rand(N), dtype=dtype), max_iter=10)
assert infr.params[self.m.x.factor.mean] != mx.nd.ones(1)
# Create m2?
alg = MAP(model=self.m2, observed=observed)
infr = GradBasedInference(inference_algorithm=alg, grad_loop=BatchInferenceLoop())
infr.initialize(y=mx.nd.array(np.random.rand(N)))
infr.params.fix_variable(self.m.x.factor.mean, mx.nd.ones(1))
infr.run(y=mx.nd.array(np.random.rand(N), dtype=dtype), max_iter=10)
assert infr.params[self.m.x.factor.mean] == mx.nd.ones(1)
``
| infr.initialize(y=mx.nd.array(np.random.rand(N))) | ||
| infr.params.fix_variable(self.m.x.factor.mean, mx.nd.ones(1)) | ||
| infr.run(y=mx.nd.array(y, dtype=dtype), max_iter=10) | ||
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|
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Add an assert infr.params[self.m.x.factor.mean] == mx.nd.ones(1) here?
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Issue #, if available: #94
Description of changes: Added convenience functions to fix/unfix parameters on the
InferenceParametersclass. I didn't think it was necessary to put them in theInferenceclass tooBy submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.