Fixed initializing ContinuousFactor to a GaussianDistribution #991
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| def test_class_init_gaussian(self): | ||
| mean = np.array([1, -3, 4]) | ||
| cov = np.array([[4, 2, -2], [2, 5, -5], [-2, -5, 8]]) | ||
| phi1 = ContinuousFactor(['x1', 'x2'], 'gaussian', mean=mean, covariance=cov) |
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@JustinTervala This distribution is a 3 variable distribution. Please add one more variable. Tests should pass then.
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@JustinTervala Please rebase your branch to latest dev. You can read about rebase here. Also, please squash the commits into a single commit. |
Codecov Report
@@ Coverage Diff @@
## dev #991 +/- ##
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+ Coverage 94.76% 94.79% +0.03%
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Files 116 116
Lines 11314 11322 +8
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+ Hits 10722 10733 +11
+ Misses 592 589 -3
Continue to review full report at Codecov.
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@JustinTervala Can you please make changes to docs. In Line 24 pdf can be of type function or a str not just function. Also, can you add an example on how to create GaussianDistribution using string.
* Fixed incorrect link to Monty Hall Example in docs * Fixed typeo in link
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@JustinTervala Thanks for the PR. Could you please rebase your branch to the latest dev ? |
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@ankurankan @JustinTervala I believe we should also add an example on how to create GaussianDistribution using string. |
Fixes
This fix allows a ContinuousFactor to be directly constructed to use a GaussianDistribution as identified in issue 990.
#990
The tests on my branch aren't passing, but the test written for this PR passes