Run: cargo clippy --fix -Z unstable-options and cargo fmt

src/metrics/auc.rs

@@ -42,9 +42,9 @@ impl AUC {
 
         for i in 0..n {
             if y_true.get(i) == T::zero() {
-                neg = neg + T::one();
+                neg += T::one();
             } else if y_true.get(i) == T::one() {
-                pos = pos + T::one();
+                pos += T::one();
             } else {
                 panic!(
                     "AUC is only for binary classification. Invalid label: {}",
@@ -79,7 +79,7 @@ impl AUC {
         let mut auc = T::zero();
         for i in 0..n {
             if y_true.get(label_idx[i]) == T::one() {
-                auc = auc + rank[i];
+                auc += rank[i];
             }
         }
 

src/metrics/cluster_helpers.rs

@@ -37,7 +37,7 @@ pub fn entropy<T: RealNumber>(data: &Vec<T>) -> Option<T> {
     for &c in bincounts.values() {
         if c > 0 {
             let pi = T::from_usize(c).unwrap();
-            entropy = entropy - (pi / sum) * (pi.ln() - sum.ln());
+            entropy -= (pi / sum) * (pi.ln() - sum.ln());
         }
     }
 
@@ -89,9 +89,8 @@ pub fn mutual_info_score<T: RealNumber>(contingency: &Vec<Vec<usize>>) -> T {
     let mut result = T::zero();
 
     for i in 0..log_outer.len() {
-        result = result
-            + ((contingency_nm[i] * (log_contingency_nm[i] - contingency_sum_ln))
-                + contingency_nm[i] * log_outer[i])
+        result += (contingency_nm[i] * (log_contingency_nm[i] - contingency_sum_ln))
+            + contingency_nm[i] * log_outer[i]
     }
 
     result.max(T::zero())

src/metrics/mean_absolute_error.rs

@@ -43,7 +43,7 @@ impl MeanAbsoluteError {
         let n = y_true.len();
         let mut ras = T::zero();
         for i in 0..n {
-            ras = ras + (y_true.get(i) - y_pred.get(i)).abs();
+            ras += (y_true.get(i) - y_pred.get(i)).abs();
         }
 
         ras / T::from_usize(n).unwrap()

src/metrics/mean_squared_error.rs

@@ -43,7 +43,7 @@ impl MeanSquareError {
         let n = y_true.len();
         let mut rss = T::zero();
         for i in 0..n {
-            rss = rss + (y_true.get(i) - y_pred.get(i)).square();
+            rss += (y_true.get(i) - y_pred.get(i)).square();
         }
 
         rss / T::from_usize(n).unwrap()

src/metrics/mod.rs

@@ -101,7 +101,7 @@ impl ClassificationMetrics {
 
     /// F1 score, also known as balanced F-score or F-measure, see [F1](f1/index.html).
     pub fn f1<T: RealNumber>(beta: T) -> f1::F1<T> {
-        f1::F1 { beta: beta }
+        f1::F1 { beta }
     }
 
     /// Area Under the Receiver Operating Characteristic Curve (ROC AUC), see [AUC](auc/index.html).

src/metrics/r2.rs

@@ -45,10 +45,10 @@ impl R2 {
         let mut mean = T::zero();
 
         for i in 0..n {
-            mean = mean + y_true.get(i);
+            mean += y_true.get(i);
         }
 
-        mean = mean / T::from_usize(n).unwrap();
+        mean /= T::from_usize(n).unwrap();
 
         let mut ss_tot = T::zero();
         let mut ss_res = T::zero();
@@ -56,8 +56,8 @@ impl R2 {
         for i in 0..n {
             let y_i = y_true.get(i);
             let f_i = y_pred.get(i);
-            ss_tot = ss_tot + (y_i - mean).square();
-            ss_res = ss_res + (y_i - f_i).square();
+            ss_tot += (y_i - mean).square();
+            ss_res += (y_i - f_i).square();
         }
 
         T::one() - (ss_res / ss_tot)