Add serde to CategoricalNB (#30)

* Add serde to CategoricalNB

* Implement PartialEq for CategoricalNBDistribution

src/naive_bayes/categorical.rs

@@ -6,13 +6,41 @@ use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
 use serde::{Deserialize, Serialize};
 
 /// Naive Bayes classifier for categorical features
-#[derive(Debug)]
+#[derive(Serialize, Deserialize, Debug)]
 struct CategoricalNBDistribution<T: RealNumber> {
     class_labels: Vec<T>,
     class_priors: Vec<T>,
     coefficients: Vec<Vec<Vec<T>>>,
 }
 
+impl<T: RealNumber> PartialEq for CategoricalNBDistribution<T> {
+    fn eq(&self, other: &Self) -> bool {
+        if self.class_labels == other.class_labels && self.class_priors == other.class_priors {
+            if self.coefficients.len() != other.coefficients.len() {
+                return false;
+            }
+            for (a, b) in self.coefficients.iter().zip(other.coefficients.iter()) {
+                if a.len() != b.len() {
+                    return false;
+                }
+                for (a_i, b_i) in a.iter().zip(b.iter()) {
+                    if a_i.len() != b_i.len() {
+                        return false;
+                    }
+                    for (a_i_j, b_i_j) in a_i.iter().zip(b_i.iter()) {
+                        if (*a_i_j - *b_i_j).abs() > T::epsilon() {
+                            return false;
+                        }
+                    }
+                }
+            }
+            true
+        } else {
+            false
+        }
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for CategoricalNBDistribution<T> {
     fn prior(&self, class_index: usize) -> T {
         if class_index >= self.class_labels.len() {
@@ -181,7 +209,7 @@ impl<T: RealNumber> Default for CategoricalNBParameters<T> {
 }
 
 /// CategoricalNB implements the categorical naive Bayes algorithm for categorically distributed data.
-#[derive(Debug)]
+#[derive(Serialize, Deserialize, Debug, PartialEq)]
 pub struct CategoricalNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, CategoricalNBDistribution<T>>,
 }
@@ -269,4 +297,32 @@ mod tests {
             vec![0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 0., 1., 1., 1.]
         );
     }
+
+    #[test]
+    fn serde() {
+        let x = DenseMatrix::<f64>::from_2d_array(&[
+            &[3., 4., 0., 1.],
+            &[3., 0., 0., 1.],
+            &[4., 4., 1., 2.],
+            &[4., 2., 4., 3.],
+            &[4., 2., 4., 2.],
+            &[4., 1., 1., 0.],
+            &[1., 1., 1., 1.],
+            &[0., 4., 1., 0.],
+            &[0., 3., 2., 1.],
+            &[0., 3., 1., 1.],
+            &[3., 4., 0., 1.],
+            &[3., 4., 2., 4.],
+            &[0., 3., 1., 2.],
+            &[0., 4., 1., 2.],
+        ]);
+
+        let y = vec![0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0.];
+        let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
+
+        let deserialized_cnb: CategoricalNB<f64, DenseMatrix<f64>> =
+            serde_json::from_str(&serde_json::to_string(&cnb).unwrap()).unwrap();
+
+        assert_eq!(cnb, deserialized_cnb);
+    }
 }