fix: minor refactoring

src/ensemble/random_forest.rs

@@ -3,7 +3,7 @@ extern crate rand;
 use rand::Rng;
 use std::default::Default;
 use crate::linalg::Matrix;
-use crate::tree::decision_tree::{DecisionTree, DecisionTreeParameters, SplitCriterion, which_max};
+use crate::tree::decision_tree_classifier::{DecisionTreeClassifier, DecisionTreeClassifierParameters, SplitCriterion, which_max};
 
 #[derive(Debug, Clone)]
 pub struct RandomForestParameters {  
@@ -17,7 +17,7 @@ pub struct RandomForestParameters {
 #[derive(Debug)]
 pub struct RandomForest {    
     parameters: RandomForestParameters,
-    trees: Vec<DecisionTree>,
+    trees: Vec<DecisionTreeClassifier>,
     classes: Vec<f64>
 }
 
@@ -51,16 +51,16 @@ impl RandomForest {
 
         let classes = y_m.unique();        
         let k = classes.len(); 
-        let mut trees: Vec<DecisionTree> = Vec::new();
+        let mut trees: Vec<DecisionTreeClassifier> = Vec::new();
 
         for _ in 0..parameters.n_trees {
             let samples = RandomForest::sample_with_replacement(&yi, k);
-            let params = DecisionTreeParameters{
+            let params = DecisionTreeClassifierParameters{
                 criterion: parameters.criterion.clone(),
                 max_depth: parameters.max_depth,
                 min_samples_leaf: parameters.min_samples_leaf                
             };
-            let tree = DecisionTree::fit_weak_learner(x, y, samples, mtry, params);
+            let tree = DecisionTreeClassifier::fit_weak_learner(x, y, samples, mtry, params);
             trees.push(tree);
         }