feat: consolidates API

src/naive_bayes/bernoulli.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;
@@ -208,6 +208,14 @@ pub struct BernoulliNB<T: RealNumber, M: Matrix<T>> {
     binarize: Option<T>,
 }
 
+impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, BernoulliNBParameters<T>>
+    for BernoulliNB<T, M>
+{
+    fn fit(x: &M, y: &M::RowVector, parameters: BernoulliNBParameters<T>) -> Result<Self, Failed> {
+        BernoulliNB::fit(x, y, parameters)
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for BernoulliNB<T, M> {
     fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
         self.predict(x)

src/naive_bayes/categorical.rs

@@ -30,7 +30,7 @@
 //! let nb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
 //! let y_hat = nb.predict(&x).unwrap();
 //! ```
-use crate::base::Predictor;
+use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
 use crate::linalg::BaseVector;
 use crate::linalg::Matrix;
@@ -242,6 +242,18 @@ pub struct CategoricalNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, CategoricalNBDistribution<T>>,
 }
 
+impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, CategoricalNBParameters<T>>
+    for CategoricalNB<T, M>
+{
+    fn fit(
+        x: &M,
+        y: &M::RowVector,
+        parameters: CategoricalNBParameters<T>,
+    ) -> Result<Self, Failed> {
+        CategoricalNB::fit(x, y, parameters)
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for CategoricalNB<T, M> {
     fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
         self.predict(x)

src/naive_bayes/gaussian.rs

@@ -22,7 +22,7 @@
 //! let nb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
 //! let y_hat = nb.predict(&x).unwrap();
 //! ```
-use crate::base::Predictor;
+use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
 use crate::linalg::row_iter;
 use crate::linalg::BaseVector;
@@ -183,6 +183,14 @@ pub struct GaussianNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, GaussianNBDistribution<T>>,
 }
 
+impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, GaussianNBParameters<T>>
+    for GaussianNB<T, M>
+{
+    fn fit(x: &M, y: &M::RowVector, parameters: GaussianNBParameters<T>) -> Result<Self, Failed> {
+        GaussianNB::fit(x, y, parameters)
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for GaussianNB<T, M> {
     fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
         self.predict(x)

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)