Complete grid search params (#166)

* grid search draft

* hyperparam search for linear estimators

* grid search for ensembles

* support grid search for more algos

* grid search for unsupervised algos

* minor cleanup

src/naive_bayes/categorical.rs

@@ -261,6 +261,60 @@ impl<T: RealNumber> Default for CategoricalNBParameters<T> {
     }
 }
 
+/// CategoricalNB grid search parameters
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
+pub struct CategoricalNBSearchParameters<T: RealNumber> {
+    /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
+    pub alpha: Vec<T>,
+}
+
+/// CategoricalNB grid search iterator
+pub struct CategoricalNBSearchParametersIterator<T: RealNumber> {
+    categorical_nb_search_parameters: CategoricalNBSearchParameters<T>,
+    current_alpha: usize,
+}
+
+impl<T: RealNumber> IntoIterator for CategoricalNBSearchParameters<T> {
+    type Item = CategoricalNBParameters<T>;
+    type IntoIter = CategoricalNBSearchParametersIterator<T>;
+
+    fn into_iter(self) -> Self::IntoIter {
+        CategoricalNBSearchParametersIterator {
+            categorical_nb_search_parameters: self,
+            current_alpha: 0,
+        }
+    }
+}
+
+impl<T: RealNumber> Iterator for CategoricalNBSearchParametersIterator<T> {
+    type Item = CategoricalNBParameters<T>;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        if self.current_alpha == self.categorical_nb_search_parameters.alpha.len() {
+            return None;
+        }
+
+        let next = CategoricalNBParameters {
+            alpha: self.categorical_nb_search_parameters.alpha[self.current_alpha],
+        };
+
+        self.current_alpha += 1;
+
+        Some(next)
+    }
+}
+
+impl<T: RealNumber> Default for CategoricalNBSearchParameters<T> {
+    fn default() -> Self {
+        let default_params = CategoricalNBParameters::default();
+
+        CategoricalNBSearchParameters {
+            alpha: vec![default_params.alpha],
+        }
+    }
+}
+
 /// CategoricalNB implements the categorical naive Bayes algorithm for categorically distributed data.
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, PartialEq)]
@@ -351,6 +405,20 @@ mod tests {
     use super::*;
     use crate::linalg::naive::dense_matrix::DenseMatrix;
 
+    #[test]
+    fn search_parameters() {
+        let parameters = CategoricalNBSearchParameters {
+            alpha: vec![1., 2.],
+            ..Default::default()
+        };
+        let mut iter = parameters.into_iter();
+        let next = iter.next().unwrap();
+        assert_eq!(next.alpha, 1.);
+        let next = iter.next().unwrap();
+        assert_eq!(next.alpha, 2.);
+        assert!(iter.next().is_none());
+    }
+
     #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
     #[test]
     fn run_categorical_naive_bayes() {