DOC Ensures that BernoulliRBM passes numpydoc validation #20533
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sklearn/neural_network/_rbm.py
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| >>> model = BernoulliRBM(n_components=2) | ||
| >>> model.fit(X) | ||
| BernoulliRBM(n_components=2) | ||
| gibbs: Gibbs sampling step. |
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Here we should document some public functions or public classes. gibbs is a method of the estimator so it would not fit here. However, I am not sure which public class to document here. @ogrisel Do you think that it makes sense to add any manifold learning method or a PCA?
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So MLPClassifier/MLPRegressor and PCA are good candidates.
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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@glemaitre Can we specify the range of values that |
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Yes we can add information about the range. However, it should be in the description and not in the line of the data type. |
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@glemaitre I looked at the tests and I am not understanding why they are failing. |
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It seems unrelated because it is the |
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I merge with |
…n#20533) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Reference Issues/PRs
Addresses #20308
What does this implement/fix? Explain your changes.
Fixes numpydoc errors on BernouilliRBM functions
Any other comments?
#DataUmbrella LATAM sprint
Question
For
verbose, it is currently what is below. What are the possible options forverbose, the range of integer values that it can take?: