Update Cargo.toml (#299)

* Update Cargo.toml

* chore: fix clippy

* chore: bump actions

* chore: fix clippy

* chore: update target name

---------

Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>

.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: ${{ matrix.platform.target }}
           profile: minimal
           default: true
       - name: Install test runner for wasm
         if: matrix.platform.target == 'wasm32-unknown-unknown'
         run: curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh
-      - name: Install test runner for wasi
-        if: matrix.platform.target == 'wasm32-wasi'
-        run: curl https://wasmtime.dev/install.sh -sSf | bash
       - name: Stable Build with all features
         uses: actions-rs/cargo@v1
         with:
@@ -65,12 +61,6 @@ jobs:
       - name: Tests in WASM
         if: matrix.platform.target == 'wasm32-unknown-unknown'
         run: wasm-pack test --node -- --all-features
-      - name: Tests in WASI
-        if: matrix.platform.target == 'wasm32-wasi'
-        run: |
-          export WASMTIME_HOME="$HOME/.wasmtime"
-          export PATH="$WASMTIME_HOME/bin:$PATH"
-          cargo install cargo-wasi && cargo wasi test
   
   check_features:
     runs-on: "${{ matrix.platform.os }}-latest"
@@ -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"

README.md

@@ -18,4 +18,4 @@
 -----
 [![CI](https://github.com/smartcorelib/smartcore/actions/workflows/ci.yml/badge.svg)](https://github.com/smartcorelib/smartcore/actions/workflows/ci.yml)
 
-To start getting familiar with the new smartcore v0.3 API, there is now available a [**Jupyter Notebook environment repository**](https://github.com/smartcorelib/smartcore-jupyter). Please see instructions there, contributions welcome see [CONTRIBUTING](.github/CONTRIBUTING.md).
+To start getting familiar with the new smartcore v0.4 API, there is now available a [**Jupyter Notebook environment repository**](https://github.com/smartcorelib/smartcore-jupyter). Please see instructions there, contributions welcome see [CONTRIBUTING](.github/CONTRIBUTING.md).

src/algorithm/neighbour/eppstein.rs

@@ -1,219 +0,0 @@
-//! This module provides FastPair, a data-structure for efficiently tracking the dynamic
-//! closest pairs in a set of points, with an example usage in hierarchical clustering.[2][3][5]
-//!
-//! ## Purpose
-//!
-//! FastPair allows quick retrieval of the nearest neighbor for each data point by maintaining
-//! a "conga line" of closest pairs. Each point retains a link to its known nearest neighbor,
-//! and updates in the data structure propagate accordingly. This can be leveraged in
-//! agglomerative clustering steps, where merging or insertion of new points must be reflected
-//! in nearest-neighbor relationships.
-//!
-//! ## Example
-//!
-//! ```
-//! use smartcore::metrics::distance::PairwiseDistance;
-//! use smartcore::linalg::basic::matrix::DenseMatrix;
-//! use smartcore::algorithm::neighbour::fastpair::FastPair;
-//!
-//! let x = DenseMatrix::from_2d_array(&[
-//!     &[5.1, 3.5, 1.4, 0.2],
-//!     &[4.9, 3.0, 1.4, 0.2],
-//!     &[4.7, 3.2, 1.3, 0.2],
-//!     &[4.6, 3.1, 1.5, 0.2],
-//!     &[5.0, 3.6, 1.4, 0.2],
-//!     &[5.4, 3.9, 1.7, 0.4],
-//! ]).unwrap();
-//!
-//! let fastpair = FastPair::new(&x).unwrap();
-//! let closest = fastpair.closest_pair();
-//! println!("Closest pair: {:?}", closest);
-//! ```
-use std::collections::HashMap;
-
-use num::Bounded;
-
-use crate::error::{Failed, FailedError};
-use crate::linalg::basic::arrays::{Array, Array1, Array2};
-use crate::metrics::distance::euclidian::Euclidian;
-use crate::metrics::distance::PairwiseDistance;
-use crate::numbers::floatnum::FloatNumber;
-use crate::numbers::realnum::RealNumber;
-
-/// Eppstein dynamic closet-pair structure
-/// 'M' can be a matrix-like trait that provides row access
-#[derive(Debug)]
-pub struct EppsteinDCP<'a, T: RealNumber + FloatNumber, M: Array2<T>> {
-    samples: &'a M,
-    // "buckets" store, for each row, a small structure recording potential neighbors
-    neighbors: HashMap<usize, PairwiseDistance<T>>,
-}
-
-impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> EppsteinDCP<'a, T, M> {
-    /// Creates a new EppsteinDCP instance with the given data
-    pub fn new(m: &'a M) -> Result<Self, Failed> {
-        if m.shape().0 < 3 {
-            return Err(Failed::because(
-                FailedError::FindFailed,
-                "min number of rows should be 3",
-            ));
-        }
-
-        let mut this = Self {
-            samples: m,
-            neighbors: HashMap::with_capacity(m.shape().0),
-        };
-        this.initialize();
-        Ok(this)
-    }
-
-    /// Build an initial "conga line" or chain of potential neighbors
-    /// akin to Eppstein’s technique[2].
-    fn initialize(&mut self) {
-        let n = self.samples.shape().0;
-        if n < 2 {
-            return;
-        }
-        // Assign each row i some large distance by default
-        for i in 0..n {
-            self.neighbors.insert(
-                i,
-                PairwiseDistance {
-                    node: i,
-                    neighbour: None,
-                    distance: Some(<T as Bounded>::max_value()),
-                },
-            );
-        }
-        // Example: link each i to the next, forming a chain
-        // (depending on the actual Eppstein approach, can refine)
-        for i in 0..(n - 1) {
-            let dist = self.compute_dist(i, i + 1);
-            self.neighbors.entry(i).and_modify(|pd| {
-                pd.neighbour = Some(i + 1);
-                pd.distance = Some(dist);
-            });
-        }
-        // Potential refinement steps omitted for brevity
-    }
-
-    /// Insert a point into the structure.
-    pub fn insert(&mut self, row_idx: usize) {
-        // Expand data, find neighbor to link with
-        // For example, link row_idx to nearest among existing
-        let mut best_neighbor = None;
-        let mut best_d = <T as Bounded>::max_value();
-        for (i, _) in &self.neighbors {
-            let d = self.compute_dist(*i, row_idx);
-            if d < best_d {
-                best_d = d;
-                best_neighbor = Some(*i);
-            }
-        }
-        self.neighbors.insert(
-            row_idx,
-            PairwiseDistance {
-                node: row_idx,
-                neighbour: best_neighbor,
-                distance: Some(best_d),
-            },
-        );
-        // For the best_neighbor, you might want to see if row_idx becomes closer
-        if let Some(kn) = best_neighbor {
-            let dist = self.compute_dist(row_idx, kn);
-            let entry = self.neighbors.get_mut(&kn).unwrap();
-            if dist < entry.distance.unwrap() {
-                entry.neighbour = Some(row_idx);
-                entry.distance = Some(dist);
-            }
-        }
-    }
-
-    /// For hierarchical clustering, discover minimal pairs, then merge
-    pub fn closest_pair(&self) -> Option<PairwiseDistance<T>> {
-        let mut min_pair: Option<PairwiseDistance<T>> = None;
-        for (_, pd) in &self.neighbors {
-            if let Some(d) = pd.distance {
-                if min_pair.is_none() || d < min_pair.as_ref().unwrap().distance.unwrap() {
-                    min_pair = Some(pd.clone());
-                }
-            }
-        }
-        min_pair
-    }
-
-    fn compute_dist(&self, i: usize, j: usize) -> T {
-        // Example: Euclidean
-        let row_i = self.samples.get_row(i);
-        let row_j = self.samples.get_row(j);
-        row_i
-            .iterator(0)
-            .zip(row_j.iterator(0))
-            .map(|(a, b)| (*a - *b) * (*a - *b))
-            .sum()
-    }
-}
-
-/// Simple usage
-#[cfg(test)]
-mod tests_eppstein {
-    use super::*;
-    use crate::linalg::basic::matrix::DenseMatrix;
-
-    #[test]
-    fn test_eppstein() {
-        let matrix =
-            DenseMatrix::from_2d_array(&[&vec![1.0, 2.0], &vec![2.0, 2.0], &vec![5.0, 3.0]])
-                .unwrap();
-        let mut dcp = EppsteinDCP::new(&matrix).unwrap();
-        dcp.insert(2);
-        let cp = dcp.closest_pair();
-        assert!(cp.is_some());
-    }
-
-    #[test]
-    fn compare_fastpair_eppstein() {
-        use crate::algorithm::neighbour::fastpair::FastPair;
-        // Assuming EppsteinDCP is implemented in a similar module
-        use crate::algorithm::neighbour::eppstein::EppsteinDCP;
-
-        // Create a static example matrix
-        let x = DenseMatrix::from_2d_array(&[
-            &[5.1, 3.5, 1.4, 0.2],
-            &[4.9, 3.0, 1.4, 0.2],
-            &[4.7, 3.2, 1.3, 0.2],
-            &[4.6, 3.1, 1.5, 0.2],
-            &[5.0, 3.6, 1.4, 0.2],
-            &[5.4, 3.9, 1.7, 0.4],
-            &[4.6, 3.4, 1.4, 0.3],
-            &[5.0, 3.4, 1.5, 0.2],
-            &[4.4, 2.9, 1.4, 0.2],
-            &[4.9, 3.1, 1.5, 0.1],
-        ])
-        .unwrap();
-
-        // Build FastPair
-        let fastpair = FastPair::new(&x).unwrap();
-        let pair_fastpair = fastpair.closest_pair();
-
-        // Build EppsteinDCP
-        let eppstein = EppsteinDCP::new(&x).unwrap();
-        let pair_eppstein = eppstein.closest_pair();
-
-        // Compare the results
-        assert_eq!(pair_fastpair.node, pair_eppstein.as_ref().unwrap().node);
-        assert_eq!(
-            pair_fastpair.neighbour.unwrap(),
-            pair_eppstein.as_ref().unwrap().neighbour.unwrap()
-        );
-
-        // Use a small epsilon for floating-point comparison
-        let epsilon = 1e-9;
-        let diff: f64 =
-            pair_fastpair.distance.unwrap() - pair_eppstein.as_ref().unwrap().distance.unwrap();
-        assert!(diff.abs() < epsilon);
-
-        println!("FastPair result: {:?}", pair_fastpair);
-        println!("EppsteinDCP result: {:?}", pair_eppstein);
-    }
-}

src/algorithm/neighbour/mod.rs

@@ -41,9 +41,7 @@ use serde::{Deserialize, Serialize};
 pub(crate) mod bbd_tree;
 /// tree data structure for fast nearest neighbor search
 pub mod cover_tree;
-/// eppstein pairwise closest neighbour algorithm
-pub mod eppstein;
-/// fastpair pairwise closest neighbour algorithm
+/// fastpair closest neighbour algorithm
 pub mod fastpair;
 /// very simple algorithm that sequentially checks each element of the list until a match is found or the whole list has been searched.
 pub mod linear_search;

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());