Merge branch 'master' into master

Author: relf

.github/workflows/checking.yml

@@ -10,7 +10,7 @@ jobs:
       fail-fast: false
       matrix:
         toolchain:
-          - 1.70.0
+          - 1.71.1
           - stable
           - nightly
         os:

.github/workflows/codequality.yml

@@ -10,7 +10,7 @@ jobs:
     strategy:
       matrix:
         toolchain:
-          - 1.70.0
+          - 1.71.1
           - stable
 
     steps:
@@ -46,7 +46,7 @@ jobs:
         run: echo "::set-output name=version::$(cargo --version | cut -d ' ' -f 2)"
         shell: bash
 
-      - uses: actions/cache@v2
+      - uses: actions/cache@v4
         id: tarpaulin-cache
         with:
           path: |
@@ -61,6 +61,8 @@ jobs:
         run: |
           cargo tarpaulin --verbose --timeout 120 --out Xml --all --release
       - name: Upload to codecov.io
-        uses: codecov/codecov-action@v1
+        uses: codecov/codecov-action@v4
         with:
+          token: ${{ secrets.CODECOV_TOKEN }}
           fail_ci_if_error: true
+          verbose: true

.github/workflows/testing.yml

@@ -10,7 +10,7 @@ jobs:
       fail-fast: false
       matrix:
         toolchain:
-          - 1.70.0
+          - 1.71.1
           - stable
         os:
           - ubuntu-latest
@@ -35,7 +35,7 @@ jobs:
       fail-fast: false
       matrix:
         toolchain:
-          - 1.70.0
+          - 1.71.1
           - stable
         os:
           - ubuntu-latest

algorithms/linfa-bayes/README.md

@@ -10,8 +10,8 @@
 
 `linfa-bayes` currently provides an implementation of the following methods: 
 
-- Gaussian Naive Bayes ([`GaussianNb`](crate::GaussianNb))
-- Multinomial Naive Nayes ([`MultinomialNb`](crate::MultinomialNb))
+- Gaussian Naive Bayes ([`GaussianNb`])
+- Multinomial Naive Nayes ([`MultinomialNb`]))
 
 ## Examples
 

algorithms/linfa-bayes/src/base_nb.rs

@@ -79,7 +79,8 @@ pub fn filter<F: Float, L: Label + Ord>(
     let index = y
         .into_iter()
         .enumerate()
-        .filter_map(|(i, y)| (*ycondition == *y).then(|| i))
+        .filter(|(_, y)| (*ycondition == **y))
+        .map(|(i, _)| i)
         .collect::<Vec<_>>();
 
     // We subset x to only records corresponding to the class represented in `ycondition`

algorithms/linfa-bayes/src/gaussian_nb.rs

@@ -133,7 +133,7 @@ where
     }
 }
 
-impl<'a, F, L> GaussianNbValidParams<F, L>
+impl<F, L> GaussianNbValidParams<F, L>
 where
     F: Float,
 {
@@ -259,7 +259,7 @@ impl<F: Float, L: Label> GaussianNb<F, L> {
     }
 }
 
-impl<'a, F, L> NaiveBayes<'a, F, L> for GaussianNb<F, L>
+impl<F, L> NaiveBayes<'_, F, L> for GaussianNb<F, L>
 where
     F: Float,
     L: Label + Ord,

algorithms/linfa-bayes/src/multinomial_nb.rs

@@ -229,7 +229,7 @@ impl<F: Float, L: Label> MultinomialNb<F, L> {
     }
 }
 
-impl<'a, F, L> NaiveBayes<'a, F, L> for MultinomialNb<F, L>
+impl<F, L> NaiveBayes<'_, F, L> for MultinomialNb<F, L>
 where
     F: Float,
     L: Label + Ord,

algorithms/linfa-clustering/Cargo.toml

@@ -24,6 +24,7 @@ categories = ["algorithms", "mathematics", "science"]
 
 [features]
 default = []
+blas = []
 serde = ["serde_crate", "ndarray/serde", "linfa-nn/serde"]
 
 [dependencies.serde_crate]

algorithms/linfa-clustering/src/dbscan/algorithm.rs

@@ -38,10 +38,8 @@ use linfa::{traits::Transformer, DatasetBase};
 /// The algorithm iterates over each point in the dataset and for every point
 /// not yet assigned to a cluster:
 /// - Find all points within the neighborhood of size `tolerance`
-/// - If the number of points in the neighborhood is below a minimum size label
-/// as noise
-/// - Otherwise label the point with the cluster ID and repeat with each of the
-/// neighbours
+/// - If the number of points in the neighborhood is below a minimum size label as noise
+/// - Otherwise label the point with the cluster ID and repeat with each of the neighbours
 ///
 /// ## Tutorial
 ///

algorithms/linfa-clustering/src/gaussian_mixture/algorithm.rs

@@ -211,6 +211,7 @@ impl<F: Float> GaussianMixtureModel<F> {
         self.means()
     }
 
+    #[allow(clippy::type_complexity)]
     fn estimate_gaussian_parameters<D: Data<Elem = F>>(
         observations: &ArrayBase<D, Ix2>,
         resp: &Array2<F>,
@@ -505,17 +506,15 @@ mod tests {
     }
 
     pub struct MultivariateNormal {
-        pub mean: Array1<f64>,
-        pub covariance: Array2<f64>,
-        /// Lower triangular matrix (Cholesky decomposition of the coviariance matrix)
+        mean: Array1<f64>,
+        /// Lower triangular matrix (Cholesky decomposition of the covariance matrix)
         lower: Array2<f64>,
     }
     impl MultivariateNormal {
         pub fn new(mean: &ArrayView1<f64>, covariance: &ArrayView2<f64>) -> LAResult<Self> {
             let lower = covariance.cholesky()?;
             Ok(MultivariateNormal {
                 mean: mean.to_owned(),
-                covariance: covariance.to_owned(),
                 lower,
             })
         }