feat: + cross_validate, trait Predictor, refactoring

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