feat: consolidates API

src/naive_bayes/multinomial.rs

@@ -33,7 +33,7 @@
 //! ## References:
 //!
 //! * ["Introduction to Information Retrieval", Manning C. D., Raghavan P., Schutze H., 2009, Chapter 13 ](https://nlp.stanford.edu/IR-book/information-retrieval-book.html)
-use crate::base::Predictor;
+use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
 use crate::linalg::row_iter;
 use crate::linalg::BaseVector;
@@ -194,6 +194,18 @@ pub struct MultinomialNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, MultinomialNBDistribution<T>>,
 }
 
+impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, MultinomialNBParameters<T>>
+    for MultinomialNB<T, M>
+{
+    fn fit(
+        x: &M,
+        y: &M::RowVector,
+        parameters: MultinomialNBParameters<T>,
+    ) -> Result<Self, Failed> {
+        MultinomialNB::fit(x, y, parameters)
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for MultinomialNB<T, M> {
     fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
         self.predict(x)