Merge branch 'development' into fix-245

Author: Mec-iS

.github/workflows/coverage.yml

@@ -41,4 +41,4 @@ jobs:
       - name: Upload to codecov.io
         uses: codecov/codecov-action@v2
         with:
-          fail_ci_if_error:     true
+          fail_ci_if_error: false

Cargo.toml

@@ -2,7 +2,7 @@
 name = "smartcore"
 description = "Machine Learning in Rust."
 homepage = "https://smartcorelib.org"
-version = "0.3.0"
+version = "0.3.1"
 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 v0.5 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 v0.3 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).

src/algorithm/neighbour/fastpair.rs

@@ -260,8 +260,8 @@ mod tests_fastpair {
         let distances = fastpair.distances;
         let neighbours = fastpair.neighbours;
 
-        assert!(distances.len() != 0);
-        assert!(neighbours.len() != 0);
+        assert!(!distances.is_empty());
+        assert!(!neighbours.is_empty());
 
         assert_eq!(10, neighbours.len());
         assert_eq!(10, distances.len());
@@ -276,17 +276,13 @@ mod tests_fastpair {
         // We expect an error when we run `FastPair` on this dataset,
         // becuase `FastPair` currently only works on a minimum of 3
         // points.
-        let _fastpair = FastPair::new(&dataset);
+        let fastpair = FastPair::new(&dataset);
+        assert!(fastpair.is_err());
 
-        match _fastpair {
-            Err(e) => {
-                let expected_error =
-                    Failed::because(FailedError::FindFailed, "min number of rows should be 3");
-                assert_eq!(e, expected_error)
-            }
-            _ => {
-                assert!(false);
-            }
+        if let Err(e) = fastpair {
+            let expected_error =
+                Failed::because(FailedError::FindFailed, "min number of rows should be 3");
+            assert_eq!(e, expected_error)
         }
     }
 
@@ -582,7 +578,7 @@ mod tests_fastpair {
         };
         for p in dissimilarities.iter() {
             if p.distance.unwrap() < min_dissimilarity.distance.unwrap() {
-                min_dissimilarity = p.clone()
+                min_dissimilarity = *p
             }
         }
 

src/algorithm/neighbour/mod.rs

@@ -49,20 +49,15 @@ pub mod linear_search;
 /// Both, KNN classifier and regressor benefits from underlying search algorithms that helps to speed up queries.
 /// `KNNAlgorithmName` maintains a list of supported search algorithms, see [KNN algorithms](../algorithm/neighbour/index.html)
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
-#[derive(Debug, Clone)]
+#[derive(Debug, Clone, Default)]
 pub enum KNNAlgorithmName {
     /// Heap Search algorithm, see [`LinearSearch`](../algorithm/neighbour/linear_search/index.html)
     LinearSearch,
     /// Cover Tree Search algorithm, see [`CoverTree`](../algorithm/neighbour/cover_tree/index.html)
+    #[default]
     CoverTree,
 }
 
-impl Default for KNNAlgorithmName {
-    fn default() -> Self {
-        KNNAlgorithmName::CoverTree
-    }
-}
-
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug)]
 pub(crate) enum KNNAlgorithm<T: Number, D: Distance<Vec<T>>> {

src/cluster/dbscan.rs

@@ -18,7 +18,7 @@
 //!
 //! Example:
 //!
-//! ```
+//! ```ignore
 //! use smartcore::linalg::basic::matrix::DenseMatrix;
 //! use smartcore::linalg::basic::arrays::Array2;
 //! use smartcore::cluster::dbscan::*;
@@ -511,6 +511,6 @@ mod tests {
             .and_then(|dbscan| dbscan.predict(&x))
             .unwrap();
 
-        println!("{:?}", labels);
+        println!("{labels:?}");
     }
 }

src/cluster/kmeans.rs

@@ -498,8 +498,8 @@ mod tests {
 
         let y: Vec<usize> = kmeans.predict(&x).unwrap();
 
-        for i in 0..y.len() {
-            assert_eq!(y[i] as usize, kmeans._y[i]);
+        for (i, _y_i) in y.iter().enumerate() {
+            assert_eq!({ y[i] }, kmeans._y[i]);
         }
     }
 

src/dataset/boston.rs

@@ -31,7 +31,7 @@ use crate::dataset::Dataset;
 pub fn load_dataset() -> Dataset<f32, f32> {
     let (x, y, num_samples, num_features) = match deserialize_data(std::include_bytes!("boston.xy"))
     {
-        Err(why) => panic!("Can't deserialize boston.xy. {}", why),
+        Err(why) => panic!("Can't deserialize boston.xy. {why}"),
         Ok((x, y, num_samples, num_features)) => (x, y, num_samples, num_features),
     };
 

src/dataset/breast_cancer.rs

@@ -33,7 +33,7 @@ use crate::dataset::Dataset;
 pub fn load_dataset() -> Dataset<f32, u32> {
     let (x, y, num_samples, num_features) =
         match deserialize_data(std::include_bytes!("breast_cancer.xy")) {
-            Err(why) => panic!("Can't deserialize breast_cancer.xy. {}", why),
+            Err(why) => panic!("Can't deserialize breast_cancer.xy. {why}"),
             Ok((x, y, num_samples, num_features)) => (
                 x,
                 y.into_iter().map(|x| x as u32).collect(),

src/dataset/diabetes.rs

@@ -26,7 +26,7 @@ use crate::dataset::Dataset;
 pub fn load_dataset() -> Dataset<f32, u32> {
     let (x, y, num_samples, num_features) =
         match deserialize_data(std::include_bytes!("diabetes.xy")) {
-            Err(why) => panic!("Can't deserialize diabetes.xy. {}", why),
+            Err(why) => panic!("Can't deserialize diabetes.xy. {why}"),
             Ok((x, y, num_samples, num_features)) => (
                 x,
                 y.into_iter().map(|x| x as u32).collect(),