update #1
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* feat: Add GaussianNB
* Add serde to CategoricalNB * Implement PartialEq for CategoricalNBDistribution
* Add failing test * Change implementation of to_row_vector for nalgebra
feat: add MultinomialNB
* feat: BernoulliNB * Move preprocessing to a trait in linalg/stats.rs
* Add benches for GNB * use [black_box](https://github.com/bheisler/criterion.rs/blob/master/book/src/faq.md#when-should-i-use-criterionblack_box)
* Fix needless for loop range * Do not ignore clippy::ptr_arg
fix: Update ndarray version
Elastic Net
Singular Value Decomposition (SVD)
feat: NB documentation
Derive clone for NB Parameters structs,
feat: makes smartcore::error:FailedError non-exhaustive
…egorical variables. Since we only support RealNumbers for now, the idea is to treat round numbers as ordinal (or nominal if user chooses to ignore order) categories.
(changed the order of coping, first do the categorical, than copy ther rest)
… to SeriesEncoders
One hot encoder
* Fix new clippy warnings * Allow clippy::suspicious-operation-groupings
Adding `make_moons` data generator
* feat: Add getters for GaussianNB * Add classes getter to BernoulliNB Add classes getter to CategoricalNB Add classes getter to MultinomialNB * Add feature_log_prob getter to MultinomialNB * Add class_count to NB structs * Add n_features getter for NB * Add feature_count to MultinomialNB and BernoulliNB * Add n_categories to CategoricalNB * Implement feature_log_prob and category_count getter for CategoricalNB * Implement feature_log_prob for BernoulliNB
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