Update Cargo.toml

.github/workflows/ci.yml

@@ -19,14 +19,13 @@ jobs:
             { os: "ubuntu", target: "i686-unknown-linux-gnu" },
             { os: "ubuntu", target: "wasm32-unknown-unknown" },
             { os: "macos", target: "aarch64-apple-darwin" },
-            { os: "ubuntu", target: "wasm32-wasi" },
           ]
     env:
       TZ: "/usr/share/zoneinfo/your/location"
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Cache .cargo and target
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: |
             ~/.cargo
@@ -36,16 +35,13 @@ jobs:
       - name: Install Rust toolchain
         uses: actions-rs/toolchain@v1
         with:
-          toolchain: 1.81 # 1.82 seems to break wasm32 tests https://github.com/rustwasm/wasm-bindgen/issues/4274
+          toolchain: stable
           target: ${{ matrix.platform.target }}
           profile: minimal
           default: true
       - name: Install test runner for wasm
         if: matrix.platform.target == 'wasm32-unknown-unknown'
         run: curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh
-      - name: Install test runner for wasi
-        if: matrix.platform.target == 'wasm32-wasi'
-        run: curl https://wasmtime.dev/install.sh -sSf | bash
       - name: Stable Build with all features
         uses: actions-rs/cargo@v1
         with:
@@ -65,13 +61,7 @@ jobs:
       - name: Tests in WASM
         if: matrix.platform.target == 'wasm32-unknown-unknown'
         run: wasm-pack test --node -- --all-features
-      - name: Tests in WASI
-        if: matrix.platform.target == 'wasm32-wasi'
-        run: |
-          export WASMTIME_HOME="$HOME/.wasmtime"
-          export PATH="$WASMTIME_HOME/bin:$PATH"
-          cargo install cargo-wasi && cargo wasi test
-
+
   check_features:
     runs-on: "${{ matrix.platform.os }}-latest"
     strategy:
@@ -81,9 +71,9 @@ jobs:
     env:
       TZ: "/usr/share/zoneinfo/your/location"
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Cache .cargo and target
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: |
             ~/.cargo

.github/workflows/coverage.yml

@@ -12,9 +12,9 @@ jobs:
     env:
       TZ: "/usr/share/zoneinfo/your/location"
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
       - name: Cache .cargo
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: |
             ~/.cargo

.github/workflows/lint.yml

@@ -14,7 +14,7 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - name: Cache .cargo and target
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: |
             ~/.cargo

Cargo.toml

@@ -2,7 +2,7 @@
 name = "smartcore"
 description = "Machine Learning in Rust."
 homepage = "https://smartcorelib.org"
-version = "0.4.0"
+version = "0.4.1"
 authors = ["smartcore Developers"]
 edition = "2021"
 license = "Apache-2.0"

src/linalg/basic/matrix.rs

@@ -663,6 +663,7 @@ mod tests {
     #[test]
     fn test_instantiate_err_view3() {
         let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
+        #[allow(clippy::reversed_empty_ranges)]
         let v = DenseMatrixView::new(&x, 0..3, 4..3);
         assert!(v.is_err());
     }

src/naive_bayes/bernoulli.rs

@@ -257,8 +257,7 @@ impl<TY: Number + Ord + Unsigned> BernoulliNBDistribution<TY> {
     /// Fits the distribution to a NxM matrix where N is number of samples and M is number of features.
     /// * `x` - training data.
     /// * `y` - vector with target values (classes) of length N.
-    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
-    ///     priors are adjusted according to the data.
+    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined, priors are adjusted according to the data.
     /// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter.
     /// * `binarize` - Threshold for binarizing.
     fn fit<TX: Number + PartialOrd, X: Array2<TX>, Y: Array1<TY>>(

src/naive_bayes/gaussian.rs

@@ -174,8 +174,7 @@ impl<TY: Number + Ord + Unsigned> GaussianNBDistribution<TY> {
     /// Fits the distribution to a NxM matrix where N is number of samples and M is number of features.
     /// * `x` - training data.
     /// * `y` - vector with target values (classes) of length N.
-    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
-    ///     priors are adjusted according to the data.
+    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined, priors are adjusted according to the data.
     pub fn fit<TX: Number + RealNumber, X: Array2<TX>, Y: Array1<TY>>(
         x: &X,
         y: &Y,

src/naive_bayes/multinomial.rs

@@ -207,8 +207,7 @@ impl<TY: Number + Ord + Unsigned> MultinomialNBDistribution<TY> {
     /// Fits the distribution to a NxM matrix where N is number of samples and M is number of features.
     /// * `x` - training data.
     /// * `y` - vector with target values (classes) of length N.
-    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
-    ///     priors are adjusted according to the data.
+    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined, priors are adjusted according to the data.
     /// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter.
     pub fn fit<TX: Number + Unsigned, X: Array2<TX>, Y: Array1<TY>>(
         x: &X,

src/preprocessing/categorical.rs

@@ -24,7 +24,7 @@
 //! //    &[1.5, 1.0, 0.0, 1.5, 0.0, 0.0, 1.0, 0.0]
 //! //    &[1.5, 0.0, 1.0, 1.5, 0.0, 0.0, 0.0, 1.0]
 //! ```
-use std::iter;
+use std::iter::repeat_n;
 
 use crate::error::Failed;
 use crate::linalg::basic::arrays::Array2;
@@ -75,11 +75,7 @@ fn find_new_idxs(num_params: usize, cat_sizes: &[usize], cat_idxs: &[usize]) ->
     let offset = (0..1).chain(offset_);
 
     let new_param_idxs: Vec<usize> = (0..num_params)
-        .zip(
-            repeats
-                .zip(offset)
-                .flat_map(|(r, o)| iter::repeat(o).take(r)),
-        )
+        .zip(repeats.zip(offset).flat_map(|(r, o)| repeat_n(o, r)))
         .map(|(idx, ofst)| idx + ofst)
         .collect();
     new_param_idxs
@@ -124,7 +120,7 @@ impl OneHotEncoder {
                 let (nrows, _) = data.shape();
 
                 // col buffer to avoid allocations
-                let mut col_buf: Vec<T> = iter::repeat(T::zero()).take(nrows).collect();
+                let mut col_buf: Vec<T> = repeat_n(T::zero(), nrows).collect();
 
                 let mut res: Vec<CategoryMapper<CategoricalFloat>> = Vec::with_capacity(idxs.len());