Implement Display for NaiveBayes

src/naive_bayes/bernoulli.rs

@@ -364,6 +364,20 @@ pub struct BernoulliNB<
     binarize: Option<TX>,
 }
 
+impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>>
+    fmt::Display for BernoulliNB<TX, TY, X, Y>
+{
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        writeln!(
+            f,
+            "BernoulliNB:\ninner: {:?}\nbinarize: {:?}",
+            self.inner.as_ref().unwrap(),
+            self.binarize.as_ref().unwrap()
+        )?;
+        Ok(())
+    }
+}
+
 impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>>
     SupervisedEstimator<X, Y, BernoulliNBParameters<TX>> for BernoulliNB<TX, TY, X, Y>
 {
@@ -594,6 +608,9 @@ mod tests {
             ]
         );
 
+        // test Display
+        println!("{}", &bnb);
+
         let distribution = bnb.inner.clone().unwrap().distribution;
 
         assert_eq!(

src/naive_bayes/categorical.rs

@@ -139,6 +139,17 @@ impl<T: Number + Unsigned> NBDistribution<T, T> for CategoricalNBDistribution<T>
     }
 }
 
+impl<T: Number + Unsigned, X: Array2<T>, Y: Array1<T>> fmt::Display for CategoricalNB<T, X, Y> {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        writeln!(
+            f,
+            "CategoricalNB:\ninner: {:?}",
+            self.inner.as_ref().unwrap()
+        )?;
+        Ok(())
+    }
+}
+
 impl<T: Number + Unsigned> CategoricalNBDistribution<T> {
     /// Fits the distribution to a NxM matrix where N is number of samples and M is number of features.
     /// * `x` - training data.
@@ -539,6 +550,8 @@ mod tests {
         let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
         let y_hat = cnb.predict(&x).unwrap();
         assert_eq!(y_hat, vec![0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1]);
+
+        println!("{}", &cnb);
     }
 
     #[cfg_attr(

src/naive_bayes/gaussian.rs

@@ -271,6 +271,19 @@ pub struct GaussianNB<
     inner: Option<BaseNaiveBayes<TX, TY, X, Y, GaussianNBDistribution<TY>>>,
 }
 
+impl<
+        TX: Number + RealNumber + RealNumber,
+        TY: Number + Ord + Unsigned,
+        X: Array2<TX>,
+        Y: Array1<TY>,
+    > fmt::Display for GaussianNB<TX, TY, X, Y>
+{
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        writeln!(f, "GaussianNB:\ninner: {:?}", self.inner.as_ref().unwrap())?;
+        Ok(())
+    }
+}
+
 impl<
         TX: Number + RealNumber + RealNumber,
         TY: Number + Ord + Unsigned,
@@ -433,6 +446,9 @@ mod tests {
         let gnb = GaussianNB::fit(&x, &y, parameters).unwrap();
 
         assert_eq!(gnb.class_priors(), &priors);
+
+        // test display for GNB
+        println!("{}", &gnb);
     }
 
     #[cfg_attr(

src/naive_bayes/multinomial.rs

@@ -309,6 +309,19 @@ pub struct MultinomialNB<
     inner: Option<BaseNaiveBayes<TX, TY, X, Y, MultinomialNBDistribution<TY>>>,
 }
 
+impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>> fmt::Display
+    for MultinomialNB<TX, TY, X, Y>
+{
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        writeln!(
+            f,
+            "MultinomialNB:\ninner: {:?}",
+            self.inner.as_ref().unwrap()
+        )?;
+        Ok(())
+    }
+}
+
 impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>>
     SupervisedEstimator<X, Y, MultinomialNBParameters> for MultinomialNB<TX, TY, X, Y>
 {
@@ -500,6 +513,9 @@ mod tests {
             ]
         );
 
+        // test display
+        println!("{}", &nb);
+
         let y_hat = nb.predict(&x).unwrap();
 
         let distribution = nb.inner.clone().unwrap().distribution;