feat: + cross_validate, trait Predictor, refactoring

src/naive_bayes/bernoulli.rs

@@ -33,6 +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::error::Failed;
 use crate::linalg::row_iter;
 use crate::linalg::BaseVector;
@@ -200,6 +201,12 @@ pub struct BernoulliNB<T: RealNumber, M: Matrix<T>> {
     binarize: Option<T>,
 }
 
+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)
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> BernoulliNB<T, M> {
     /// Fits BernoulliNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of

src/naive_bayes/categorical.rs

@@ -30,6 +30,7 @@
 //! let nb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
 //! let y_hat = nb.predict(&x).unwrap();
 //! ```
+use crate::base::Predictor;
 use crate::error::Failed;
 use crate::linalg::BaseVector;
 use crate::linalg::Matrix;
@@ -246,6 +247,12 @@ pub struct CategoricalNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, CategoricalNBDistribution<T>>,
 }
 
+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)
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> CategoricalNB<T, M> {
     /// Fits CategoricalNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of

src/naive_bayes/gaussian.rs

@@ -22,6 +22,7 @@
 //! let nb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
 //! let y_hat = nb.predict(&x).unwrap();
 //! ```
+use crate::base::Predictor;
 use crate::error::Failed;
 use crate::linalg::row_iter;
 use crate::linalg::BaseVector;
@@ -181,6 +182,12 @@ pub struct GaussianNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, GaussianNBDistribution<T>>,
 }
 
+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)
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> GaussianNB<T, M> {
     /// Fits GaussianNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of

src/naive_bayes/multinomial.rs

@@ -33,6 +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::error::Failed;
 use crate::linalg::row_iter;
 use crate::linalg::BaseVector;
@@ -187,6 +188,12 @@ pub struct MultinomialNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, MultinomialNBDistribution<T>>,
 }
 
+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)
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> MultinomialNB<T, M> {
     /// Fits MultinomialNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of