fix: post-review changes

Author: Volodymyr Orlov

src/naive_bayes/bernoulli.rs

@@ -88,14 +88,6 @@ pub struct BernoulliNBParameters<T: RealNumber> {
 }
 
 impl<T: RealNumber> BernoulliNBParameters<T> {
-    /// Create BernoulliNBParameters with specific paramaters.
-    pub fn new(alpha: T, priors: Option<Vec<T>>, binarize: Option<T>) -> Self {
-        Self {
-            alpha,
-            priors,
-            binarize,
-        }
-    }
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub fn with_alpha(mut self, alpha: T) -> Self {
         self.alpha = alpha;

src/naive_bayes/categorical.rs

@@ -223,17 +223,6 @@ pub struct CategoricalNBParameters<T: RealNumber> {
 }
 
 impl<T: RealNumber> CategoricalNBParameters<T> {
-    /// Create CategoricalNBParameters with specific paramaters.
-    pub fn new(alpha: T) -> Result<Self, Failed> {
-        if alpha > T::zero() {
-            Ok(Self { alpha })
-        } else {
-            Err(Failed::fit(&format!(
-                "alpha should be >= 0, alpha=[{}]",
-                alpha
-            )))
-        }
-    }
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub fn with_alpha(mut self, alpha: T) -> Self {
         self.alpha = alpha;

src/naive_bayes/gaussian.rs

@@ -82,10 +82,6 @@ pub struct GaussianNBParameters<T: RealNumber> {
 }
 
 impl<T: RealNumber> GaussianNBParameters<T> {
-    /// Create GaussianNBParameters with specific paramaters.
-    pub fn new(priors: Option<Vec<T>>) -> Self {
-        Self { priors }
-    }
     /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
     pub fn with_priors(mut self, priors: Vec<T>) -> Self {
         self.priors = Some(priors);
@@ -266,7 +262,7 @@ mod tests {
         let y = vec![1., 1., 1., 2., 2., 2.];
 
         let priors = vec![0.3, 0.7];
-        let parameters = GaussianNBParameters::new(Some(priors.clone()));
+        let parameters = GaussianNBParameters::default().with_priors(priors.clone());
         let gnb = GaussianNB::fit(&x, &y, parameters).unwrap();
 
         assert_eq!(gnb.inner.distribution.class_priors, priors);

src/naive_bayes/multinomial.rs

@@ -82,10 +82,6 @@ pub struct MultinomialNBParameters<T: RealNumber> {
 }
 
 impl<T: RealNumber> MultinomialNBParameters<T> {
-    /// Create MultinomialNBParameters with specific paramaters.
-    pub fn new(alpha: T, priors: Option<Vec<T>>) -> Self {
-        Self { alpha, priors }
-    }
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub fn with_alpha(mut self, alpha: T) -> Self {
         self.alpha = alpha;