Merge pull request #110 from morenol/nb/fix_docs

docs: fix documentation of naive bayes structs

src/linalg/evd.rs

@@ -93,7 +93,7 @@ pub trait EVDDecomposableMatrix<T: RealNumber>: BaseMatrix<T> {
             sort(&mut d, &mut e, &mut V);
         }
 
-        Ok(EVD { V, d, e })
+        Ok(EVD { d, e, V })
     }
 }
 

src/linalg/ndarray_bindings.rs

@@ -966,7 +966,7 @@ mod tests {
         let error: f64 = y
             .into_iter()
             .zip(y_hat.into_iter())
-            .map(|(&a, &b)| (a - b).abs())
+            .map(|(a, b)| (a - b).abs())
             .sum();
 
         assert!(error <= 1.0);

src/naive_bayes/bernoulli.rs

@@ -249,7 +249,8 @@ impl<T: RealNumber> BernoulliNBDistribution<T> {
     }
 }
 
-/// BernoulliNB implements the categorical naive Bayes algorithm for categorically distributed data.
+/// BernoulliNB implements the naive Bayes algorithm for data that follows the Bernoulli
+/// distribution.
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, PartialEq)]
 pub struct BernoulliNB<T: RealNumber, M: Matrix<T>> {

src/naive_bayes/categorical.rs

@@ -232,8 +232,8 @@ impl<T: RealNumber> CategoricalNBDistribution<T> {
             class_labels,
             class_priors,
             coefficients,
-            n_categories,
             n_features,
+            n_categories,
             category_count,
         })
     }

src/naive_bayes/gaussian.rs

@@ -33,7 +33,7 @@ use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
-/// Naive Bayes classifier for categorical features
+/// Naive Bayes classifier using Gaussian distribution
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, PartialEq)]
 struct GaussianNBDistribution<T: RealNumber> {
@@ -179,7 +179,8 @@ impl<T: RealNumber> GaussianNBDistribution<T> {
     }
 }
 
-/// GaussianNB implements the categorical naive Bayes algorithm for categorically distributed data.
+/// GaussianNB implements the naive Bayes algorithm for data that follows the Gaussian
+/// distribution.
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, PartialEq)]
 pub struct GaussianNB<T: RealNumber, M: Matrix<T>> {

src/naive_bayes/multinomial.rs

@@ -212,7 +212,7 @@ impl<T: RealNumber> MultinomialNBDistribution<T> {
     }
 }
 
-/// MultinomialNB implements the categorical naive Bayes algorithm for categorically distributed data.
+/// MultinomialNB implements the naive Bayes algorithm for multinomially distributed data.
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, PartialEq)]
 pub struct MultinomialNB<T: RealNumber, M: Matrix<T>> {

src/preprocessing/series_encoder.rs

@@ -134,10 +134,8 @@ where
         U: RealNumber,
         V: BaseVector<U>,
     {
-        match self.get_num(category) {
-            None => None,
-            Some(&idx) => Some(make_one_hot::<U, V>(idx, self.num_categories)),
-        }
+        self.get_num(category)
+            .map(|&idx| make_one_hot::<U, V>(idx, self.num_categories))
     }
 
     /// Invert one-hot vector, back to the category