auto formatting

Author: skywardfire1

src/ensemble/random_forest_classifier.rs

@@ -688,7 +688,6 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
     ///
     /// Returns an error if the forest has not been fitted (trees are None).
     pub fn predict_proba(&self, x: &X) -> Result<Vec<Vec<f64>>, Failed> {
-
         let (n, _) = x.shape();
 
         let mut result = Vec::with_capacity(n);
@@ -896,7 +895,7 @@ mod tests {
 
         assert_eq!(forest, deserialized_forest);
     }
-    
+
     // Test for predict_proba
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),

src/metrics/distance/jaccard.rs

@@ -89,7 +89,6 @@ mod tests {
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test
     )]
-
     #[test]
     fn jaccard_distance() {
         let a = vec![1, 0, 1, 1];
@@ -133,4 +132,3 @@ mod tests {
         assert!((d1 - d2).abs() < 1e-12);
     }
 }
-

src/tree/decision_tree_classifier.rs

@@ -64,21 +64,21 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-use std::collections::LinkedList;
-use std::default::Default;
-use std::fmt::Debug;
-use std::marker::PhantomData;
-use rand::seq::SliceRandom;
-use rand::Rng;
-#[cfg(feature = "serde")]
-use serde::{Deserialize, Serialize};
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
 use crate::linalg::basic::arrays::MutArray;
 use crate::linalg::basic::arrays::{Array1, Array2, MutArrayView1};
 use crate::linalg::basic::matrix::DenseMatrix;
 use crate::numbers::basenum::Number;
 use crate::rand_custom::get_rng_impl;
+use rand::seq::SliceRandom;
+use rand::Rng;
+#[cfg(feature = "serde")]
+use serde::{Deserialize, Serialize};
+use std::collections::LinkedList;
+use std::default::Default;
+use std::fmt::Debug;
+use std::marker::PhantomData;
 
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
@@ -726,7 +726,9 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
                 }
                 let tc = true_count.iter().sum();
                 let fc = n - tc;
-                if tc < self.parameters().min_samples_leaf || fc < self.parameters().min_samples_leaf {
+                if tc < self.parameters().min_samples_leaf
+                    || fc < self.parameters().min_samples_leaf
+                {
                     prevx = Some(x_ij);
                     prevy = visitor.y[*i];
                     true_count[visitor.y[*i]] += visitor.samples[*i];
@@ -814,9 +816,17 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         debug_assert_eq!(false_sum, fc);
         debug_assert_eq!(true_sum + false_sum, original_total);
 
-        self.nodes.push(Node::new(visitor.true_child_output, tc, true_distribution));
+        self.nodes
+            .push(Node::new(visitor.true_child_output, tc, true_distribution));
+
         let false_child_idx = self.nodes().len();
-        self.nodes.push(Node::new(visitor.false_child_output, fc, false_distribution));
+
+        self.nodes.push(Node::new(
+            visitor.false_child_output,
+            fc,
+            false_distribution,
+        ));
+
         self.nodes[visitor.node].true_child = Some(true_child_idx);
         self.nodes[visitor.node].false_child = Some(false_child_idx);
         self.depth = u16::max(self.depth, visitor.level + 1);
@@ -963,7 +973,9 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
                 let total: usize = current.class_distribution.iter().sum();
                 let mut probs = vec![0.0; self.num_classes];
                 if total > 0 {
-                    for (p, count) in probs.iter_mut().zip(&current.class_distribution) { *p = *count as f64 / total as f64; }
+                    for (p, count) in probs.iter_mut().zip(&current.class_distribution) {
+                        *p = *count as f64 / total as f64;
+                    }
                 }
                 return probs;
             }
@@ -1137,8 +1149,14 @@ mod tests {
 
         // Real should have fractional probabilities for the mixed leaf
         // Leaf has 3 samples: 1 of class 0, 2 of class 1 -> probs [1/3, 2/3, 0]
-        assert!((real_probs[0] - 1.0/3.0).abs() < 1e-6, "Class 0 prob should be 1/3");
-        assert!((real_probs[1] - 2.0/3.0).abs() < 1e-6, "Class 1 prob should be 2/3");
+        assert!(
+            (real_probs[0] - 1.0 / 3.0).abs() < 1e-6,
+            "Class 0 prob should be 1/3"
+        );
+        assert!(
+            (real_probs[1] - 2.0 / 3.0).abs() < 1e-6,
+            "Class 1 prob should be 2/3"
+        );
         assert!(real_probs[2] < 1e-6, "Class 2 prob should be ~0");
     }
 
@@ -1316,4 +1334,4 @@ mod tests {
             bincode::deserialize(&bincode::serialize(&tree).unwrap()).unwrap();
         assert_eq!(tree, deserialized_tree);
     }
-}
+}