feat: BernoulliNB

[morenol]

Implement BernoulliNB as stated in #14

References:

1. https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
2. https://github.com/scikit-learn/scikit-learn/blob/5fb02bb7bfed1a5edbe12ce942bb77cacde8180b/sklearn/naive_bayes.py#L915

[morenol]

I have added this in all the other implementations, but maybe this should be -infinity or NaN, right? @VolodymyrOrlov

[VolodymyrOrlov]

I think we should panic here instead, since else branch is unreachable, right?

[morenol]

I will remove just this if and else branches since as you say this is unreachable (at least in the public api)

[VolodymyrOrlov]

Code looks good and numbers match Scikit's! Great clean code. I love it!

There are some minor changes I'd love to make but after your are done feel free to merge it in development

[VolodymyrOrlov]

Can you please move this method to https://github.com/smartcorelib/smartcore/blob/development/src/linalg/stats.rs and doc string describing how it works? It would be awesome to have a short example in doc string as well

[morenol]

I added that in linals/stats.rs in a new Trait, let me know if that is what you were thinking about

[VolodymyrOrlov]

I think we should panic here instead, since else branch is unreachable, right?

[VolodymyrOrlov]

Can you also add this test please? It doesn't change anything but it's good to have a direct comparison with Scikit.

#[test]
    fn bernoulli_nb_scikit_parity() {
        let x = DenseMatrix::<f64>::from_2d_array(&[
            &[2., 4., 0., 0., 2., 1., 2., 4., 2., 0.],
            &[3., 4., 0., 2., 1., 0., 1., 4., 0., 3.],
            &[1., 4., 2., 4., 1., 0., 1., 2., 3., 2.],
            &[0., 3., 3., 4., 1., 0., 3., 1., 1., 1.],
            &[0., 2., 1., 4., 3., 4., 1., 2., 3., 1.],
            &[3., 2., 4., 1., 3., 0., 2., 4., 0., 2.],
            &[3., 1., 3., 0., 2., 0., 4., 4., 3., 4.],
            &[2., 2., 2., 0., 1., 1., 2., 1., 0., 1.],
            &[3., 3., 2., 2., 0., 2., 3., 2., 2., 3.],
            &[4., 3., 4., 4., 4., 2., 2., 0., 1., 4.],
            &[3., 4., 2., 2., 1., 4., 4., 4., 1., 3.],
            &[3., 0., 1., 4., 4., 0., 0., 3., 2., 4.],
            &[2., 0., 3., 3., 1., 2., 0., 2., 4., 1.],
            &[2., 4., 0., 4., 2., 4., 1., 3., 1., 4.],
            &[0., 2., 2., 3., 4., 0., 4., 4., 4., 4.]]);
        let y = vec![2., 2., 0., 0., 0., 2., 1., 1., 0., 1., 0., 0., 2., 0., 2.];
        let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();        

        let y_hat = bnb.predict(&x).unwrap();

        assert!(bnb.inner.distribution.class_priors.approximate_eq(&vec!(0.46, 0.2, 0.33), 1e-2));
        assert!(bnb.inner.distribution.feature_prob[1].approximate_eq(&vec!(0.8, 0.8, 0.8, 0.4, 0.8, 0.6, 0.8, 0.6, 0.6, 0.8), 1e-1));
        assert!(y_hat.approximate_eq(&vec!(2.0, 2.0, 0.0, 0.0, 0.0, 2.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), 1e-5));
    }

[Mec-iS]

I would suggest to add a documentation here to describe the general objectives of the module and sources

[VolodymyrOrlov]

I can help with documentation. I plan to cover most of the new functionality by a short summary, similar to this one right before releasing v0.2.0. What would be helpful to me are links to resources that you used to learn about these methods either in description of the PR, issue or in the code somewhere. While I can (and will) use links to official papers describing all NB methods, in most of the cases there are other resources in the web that do a better job of explaining an algorithm and I like to share such resources with our users.