Merge pull request #96 from smartcorelib/dependabot/cargo/rand-0.8.3

build(deps): update rand requirement from 0.7.3 to 0.8.3

Cargo.toml

@@ -23,12 +23,12 @@ ndarray = { version = "0.14", optional = true }
 nalgebra = { version = "0.23.0", optional = true }
 num-traits = "0.2.12"
 num = "0.3.0"
-rand = "0.7.3"
+rand = "0.8.3"
-rand_distr = "0.3.0"
+rand_distr = "0.4.0"
 serde = { version = "1.0.115", features = ["derive"], optional = true }
 
 [target.'cfg(target_arch = "wasm32")'.dependencies]
-rand = { version = "0.7.3", features = ["wasm-bindgen"] }
+getrandom = { version = "0.2", features = ["js"] }
 
 [dev-dependencies]
 criterion = "0.3"

src/cluster/kmeans.rs

@@ -245,7 +245,7 @@ impl<T: RealNumber + Sum> KMeans<T> {
         let mut rng = rand::thread_rng();
         let (n, m) = data.shape();
         let mut y = vec![0; n];
-        let mut centroid = data.get_row_as_vec(rng.gen_range(0, n));
+        let mut centroid = data.get_row_as_vec(rng.gen_range(0..n));
 
         let mut d = vec![T::max_value(); n];
 

src/ensemble/random_forest_classifier.rs

@@ -265,7 +265,7 @@ impl<T: RealNumber> RandomForestClassifier<T> {
 
             let size = ((n_samples as f64) / *class_weight_l) as usize;
             for _ in 0..size {
-                let xi: usize = rng.gen_range(0, n_samples);
+                let xi: usize = rng.gen_range(0..n_samples);
                 samples[index[xi]] += 1;
             }
         }

src/ensemble/random_forest_regressor.rs

@@ -218,7 +218,7 @@ impl<T: RealNumber> RandomForestRegressor<T> {
         let mut rng = rand::thread_rng();
         let mut samples = vec![0; nrows];
         for _ in 0..nrows {
-            let xi = rng.gen_range(0, nrows);
+            let xi = rng.gen_range(0..nrows);
             samples[xi] += 1;
         }
         samples