Release 0.3 (#235)

src/tree/decision_tree_classifier.rs

@@ -163,7 +163,6 @@ impl Default for SplitCriterion {
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 struct Node {
-    index: usize,
     output: usize,
     split_feature: usize,
     split_value: Option<f64>,
@@ -406,9 +405,8 @@ impl Default for DecisionTreeClassifierSearchParameters {
 }
 
 impl Node {
-    fn new(index: usize, output: usize) -> Self {
+    fn new(output: usize) -> Self {
         Node {
-            index,
             output,
             split_feature: 0,
             split_value: Option::None,
@@ -582,7 +580,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
             count[yi[i]] += samples[i];
         }
 
-        let root = Node::new(0, which_max(&count));
+        let root = Node::new(which_max(&count));
         change_nodes.push(root);
         let mut order: Vec<Vec<usize>> = Vec::new();
 
@@ -831,11 +829,9 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
 
         let true_child_idx = self.nodes().len();
 
-        self.nodes
-            .push(Node::new(true_child_idx, visitor.true_child_output));
+        self.nodes.push(Node::new(visitor.true_child_output));
         let false_child_idx = self.nodes().len();
-        self.nodes
-            .push(Node::new(false_child_idx, visitor.false_child_output));
+        self.nodes.push(Node::new(visitor.false_child_output));
         self.nodes[visitor.node].true_child = Some(true_child_idx);
         self.nodes[visitor.node].false_child = Some(false_child_idx);
 
@@ -923,6 +919,7 @@ mod tests {
         wasm_bindgen_test::wasm_bindgen_test
     )]
     #[test]
+    #[cfg(feature = "datasets")]
     fn fit_predict_iris() {
         let x: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],