Wnb

- Add new attributes `feature_names_in_` and `class_count_` to `GeneralNB` and `GaussianWNB`
- Use Scikit-learn's `BaseEstimator` to check and calculate `feature_names_in_` and `n_features_in_`
- Add class attribute definitions to `GeneralNB` and `GaussianWNB`
- Minor improvements in internal methods of `GeneralNB` and `GaussianWNB`
- Fix bug in calculating `n_iter_` for `GaussianWNB`
- Rename the `mu_` attribute of `GaussianWNB` to `theta_` to match Scikit-learn API
- Add new attribute `var_` (in addition to existing `std_` attribute) to match Scikit-learn API
- Add more data validators to `GeneralNB` and `GaussianWNB`
- Scikit-learn's compatibility test is enabled and fully passed for `GeneralNB`

- Fix bug in the setup.py script
- Add LICENSE file (for OSD license)
- Add MANIFEST.in file to include package data

- Extend Python version compatibility to 3.11
- Minor code improvements
- Remove unnecessary traces of Multinomial distribution
- Fix issues in not installing the _tests_ package
- Add `black` to dev requirements
- Reformat the codes using black
- Minor change in docstrings

- New feature: `GeneralNB` takes a smoothing parameter `alpha` for estimating distribution parameters (currently, for Bernoulli and Categorical distributions)
- Reformat the codes using black
- Minor change in docstrings
- Minor change in error messages

- Remove `MultinomialDist` from the supported distributions
- Use a constant smoothing parameter (1e-10) in `BernoulliDist` and `CategoricalDist` when estimating their PMFs
- Add support for nun-numerical features for some distributions (e.g., categorical distribution)
- Add new tests methods to compare `GeneralNB` with sklearn's `BernoulliNB` and `CategoricalNB`
- Minor bug fixing and improvements
- Minor update in docstrings

- Add more unit tests for `GaussianWNB`
- Move `learning_hist` parameter from `fit` method to `__init__` method
- Now, `cost_hist_` attribute will store `None` if `learning_hist=False`