Handle kernel serialization (#232)

* Handle kernel serialization
* Do not use typetag in WASM
* enable tests for serialization
* Update serde feature deps

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

.github/CONTRIBUTING.md

@@ -37,8 +37,6 @@ $ rust-code-analysis-cli -p src/algorithm/neighbour/fastpair.rs --ls 22 --le 213
 ```
 * find more information about what happens in your binary with [`twiggy`](https://rustwasm.github.io/twiggy/install.html). This need a compiled binary so create a brief `main {}` function using `smartcore` and then point `twiggy` to that file.
 
-* Please take a look to the output of a profiler to spot most evident performance problems, see [this guide about using a profiler](http://www.codeofview.com/fix-rs/2017/01/24/how-to-optimize-rust-programs-on-linux/).
-
 ## Issue Report Process
 
 1. Go to the project's issues.
@@ -70,15 +68,3 @@ $ rust-code-analysis-cli -p src/algorithm/neighbour/fastpair.rs --ls 22 --le 213
 * **PRs on develop**: any change should be PRed first in `development`
 
 * **testing**:  everything should work and be tested as defined in the workflow. If any is failing for non-related reasons, annotate the test failure in the PR comment.
-
-
-## Suggestions for debugging
-1. Install `lldb` for your platform
-2. Run `rust-lldb target/debug/libsmartcore.rlib` in your command-line
-3. In lldb, set up some breakpoints using `b func_name` or `b src/path/to/file.rs:linenumber`
-4. In lldb, run a single test with `r the_name_of_your_test`
-
-Display variables in scope: `frame variable <name>` 
-
-Execute expression: `p <expr>`
-

.github/workflows/ci.yml

@@ -23,7 +23,6 @@ jobs:
           ]
     env:
       TZ: "/usr/share/zoneinfo/your/location"
-      RUST_BACKTRACE: "1"
     steps:
       - uses: actions/checkout@v3
       - name: Cache .cargo and target
@@ -37,7 +36,7 @@ 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

.github/workflows/coverage.yml

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

CHANGELOG.md

@@ -4,12 +4,6 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.4.0] - 2023-04-05
-
-## Added
-- WARNING: Breaking changes!
-- `DenseMatrix` constructor now returns `Result` to avoid user instantiating inconsistent rows/cols count. Their return values need to be unwrapped with `unwrap()`, see tests
-
 ## [0.3.0] - 2022-11-09 
 
 ## Added

Cargo.toml

@@ -2,7 +2,7 @@
 name = "smartcore"
 description = "Machine Learning in Rust."
 homepage = "https://smartcorelib.org"
-version = "0.4.0"
+version = "0.3.0"
 authors = ["smartcore Developers"]
 edition = "2021"
 license = "Apache-2.0"
@@ -42,13 +42,13 @@ std_rand = ["rand/std_rng", "rand/std"]
 js = ["getrandom/js"]
 
 [target.'cfg(target_arch = "wasm32")'.dependencies]
-getrandom = { version = "0.2.8", optional = true }
+getrandom = { version = "*", optional = true }
 
 [target.'cfg(all(target_arch = "wasm32", not(target_os = "wasi")))'.dev-dependencies]
 wasm-bindgen-test = "0.3"
 
 [dev-dependencies]
-itertools = "0.13.0"
+itertools = "*"
 serde_json = "1.0"
 bincode = "1.3.1"
 

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 v0.5 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).

smartcore.iml

@@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="RUST_MODULE" version="4">
+  <component name="NewModuleRootManager" inherit-compiler-output="true">
+    <exclude-output />
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$/src" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/examples" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/tests" isTestSource="true" />
+      <sourceFolder url="file://$MODULE_DIR$/benches" isTestSource="true" />
+      <excludeFolder url="file://$MODULE_DIR$/target" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

src/algorithm/neighbour/bbd_tree.rs

@@ -40,11 +40,11 @@ impl BBDTreeNode {
 
 impl BBDTree {
     pub fn new<T: Number, M: Array2<T>>(data: &M) -> BBDTree {
-        let nodes: Vec<BBDTreeNode> = Vec::new();
+        let nodes = Vec::new();
 
         let (n, _) = data.shape();
 
-        let index = (0..n).collect::<Vec<usize>>();
+        let index = (0..n).collect::<Vec<_>>();
 
         let mut tree = BBDTree {
             nodes,
@@ -343,8 +343,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
 
         let tree = BBDTree::new(&data);
 

src/algorithm/neighbour/cover_tree.rs

@@ -124,7 +124,7 @@ impl<T: Debug + PartialEq, D: Distance<T>> CoverTree<T, D> {
         current_cover_set.push((d, &self.root));
 
         let mut heap = HeapSelection::with_capacity(k);
-        heap.add(f64::MAX);
+        heap.add(std::f64::MAX);
 
         let mut empty_heap = true;
         if !self.identical_excluded || self.get_data_value(self.root.idx) != p {
@@ -145,7 +145,7 @@ impl<T: Debug + PartialEq, D: Distance<T>> CoverTree<T, D> {
                     }
 
                     let upper_bound = if empty_heap {
-                        f64::INFINITY
+                        std::f64::INFINITY
                     } else {
                         *heap.peek()
                     };
@@ -291,7 +291,7 @@ impl<T: Debug + PartialEq, D: Distance<T>> CoverTree<T, D> {
         } else {
             let max_dist = self.max(point_set);
             let next_scale = (max_scale - 1).min(self.get_scale(max_dist));
-            if next_scale == i64::MIN {
+            if next_scale == std::i64::MIN {
                 let mut children: Vec<Node> = Vec::new();
                 let mut leaf = self.new_leaf(p);
                 children.push(leaf);
@@ -435,7 +435,7 @@ impl<T: Debug + PartialEq, D: Distance<T>> CoverTree<T, D> {
 
     fn get_scale(&self, d: f64) -> i64 {
         if d == 0f64 {
-            i64::MIN
+            std::i64::MIN
         } else {
             (self.inv_log_base * d.ln()).ceil() as i64
         }

src/algorithm/neighbour/fastpair.rs

@@ -17,7 +17,7 @@
 ///     &[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);
 /// let closest_pair: PairwiseDistance<f64> = fastpair.unwrap().closest_pair();
 /// ```
@@ -52,8 +52,10 @@ pub struct FastPair<'a, T: RealNumber + FloatNumber, M: Array2<T>> {
 }
 
 impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> FastPair<'a, T, M> {
+    ///
     /// Constructor
-    /// Instantiate and initialize the algorithm
+    /// Instantiate and inizialise the algorithm
+    ///
     pub fn new(m: &'a M) -> Result<Self, Failed> {
         if m.shape().0 < 3 {
             return Err(Failed::because(
@@ -72,8 +74,10 @@ impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> FastPair<'a, T, M> {
         Ok(init)
     }
 
+    ///
     /// Initialise `FastPair` by passing a `Array2`.
     /// Build a FastPairs data-structure from a set of (new) points.
+    ///
     fn init(&mut self) {
         // basic measures
         let len = self.samples.shape().0;
@@ -154,7 +158,9 @@ impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> FastPair<'a, T, M> {
         self.neighbours = neighbours;
     }
 
+    ///
     /// Find closest pair by scanning list of nearest neighbors.
+    ///
     #[allow(dead_code)]
     pub fn closest_pair(&self) -> PairwiseDistance<T> {
         let mut a = self.neighbours[0]; // Start with first point
@@ -211,10 +217,10 @@ mod tests_fastpair {
     use super::*;
     use crate::linalg::basic::{arrays::Array, matrix::DenseMatrix};
 
+    ///
     /// Brute force algorithm, used only for comparison and testing
-    pub fn closest_pair_brute(
-        fastpair: &FastPair<'_, f64, DenseMatrix<f64>>,
-    ) -> PairwiseDistance<f64> {
+    ///
+    pub fn closest_pair_brute(fastpair: &FastPair<f64, DenseMatrix<f64>>) -> PairwiseDistance<f64> {
         use itertools::Itertools;
         let m = fastpair.samples.shape().0;
 
@@ -254,8 +260,8 @@ mod tests_fastpair {
         let distances = fastpair.distances;
         let neighbours = fastpair.neighbours;
 
-        assert!(!distances.is_empty());
-        assert!(!neighbours.is_empty());
+        assert!(distances.len() != 0);
+        assert!(neighbours.len() != 0);
 
         assert_eq!(10, neighbours.len());
         assert_eq!(10, distances.len());
@@ -265,24 +271,28 @@ mod tests_fastpair {
     fn dataset_has_at_least_three_points() {
         // Create a dataset which consists of only two points:
         // A(0.0, 0.0) and B(1.0, 1.0).
-        let dataset = DenseMatrix::<f64>::from_2d_array(&[&[0.0, 0.0], &[1.0, 1.0]]).unwrap();
+        let dataset = DenseMatrix::<f64>::from_2d_array(&[&[0.0, 0.0], &[1.0, 1.0]]);
 
         // 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);
-        assert!(fastpair.is_err());
+        let _fastpair = FastPair::new(&dataset);
 
-        if let Err(e) = fastpair {
-            let expected_error =
-                Failed::because(FailedError::FindFailed, "min number of rows should be 3");
-            assert_eq!(e, expected_error)
+        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);
+            }
         }
     }
 
     #[test]
     fn one_dimensional_dataset_minimal() {
-        let dataset = DenseMatrix::<f64>::from_2d_array(&[&[0.0], &[2.0], &[9.0]]).unwrap();
+        let dataset = DenseMatrix::<f64>::from_2d_array(&[&[0.0], &[2.0], &[9.0]]);
 
         let result = FastPair::new(&dataset);
         assert!(result.is_ok());
@@ -302,8 +312,7 @@ mod tests_fastpair {
 
     #[test]
     fn one_dimensional_dataset_2() {
-        let dataset =
-            DenseMatrix::<f64>::from_2d_array(&[&[27.0], &[0.0], &[9.0], &[2.0]]).unwrap();
+        let dataset = DenseMatrix::<f64>::from_2d_array(&[&[27.0], &[0.0], &[9.0], &[2.0]]);
 
         let result = FastPair::new(&dataset);
         assert!(result.is_ok());
@@ -338,8 +347,7 @@ mod tests_fastpair {
             &[6.9, 3.1, 4.9, 1.5],
             &[5.5, 2.3, 4.0, 1.3],
             &[6.5, 2.8, 4.6, 1.5],
-        ])
-        .unwrap();
+        ]);
         let fastpair = FastPair::new(&x);
         assert!(fastpair.is_ok());
 
@@ -512,8 +520,7 @@ mod tests_fastpair {
             &[6.9, 3.1, 4.9, 1.5],
             &[5.5, 2.3, 4.0, 1.3],
             &[6.5, 2.8, 4.6, 1.5],
-        ])
-        .unwrap();
+        ]);
         // compute
         let fastpair = FastPair::new(&x);
         assert!(fastpair.is_ok());
@@ -561,8 +568,7 @@ mod tests_fastpair {
             &[6.9, 3.1, 4.9, 1.5],
             &[5.5, 2.3, 4.0, 1.3],
             &[6.5, 2.8, 4.6, 1.5],
-        ])
-        .unwrap();
+        ]);
         // compute
         let fastpair = FastPair::new(&x);
         assert!(fastpair.is_ok());
@@ -576,7 +582,7 @@ mod tests_fastpair {
         };
         for p in dissimilarities.iter() {
             if p.distance.unwrap() < min_dissimilarity.distance.unwrap() {
-                min_dissimilarity = *p
+                min_dissimilarity = p.clone()
             }
         }
 

src/algorithm/neighbour/linear_search.rs

@@ -61,7 +61,7 @@ impl<T, D: Distance<T>> LinearKNNSearch<T, D> {
 
         for _ in 0..k {
             heap.add(KNNPoint {
-                distance: f64::INFINITY,
+                distance: std::f64::INFINITY,
                 index: None,
             });
         }
@@ -215,7 +215,7 @@ mod tests {
         };
 
         let point_inf = KNNPoint {
-            distance: f64::INFINITY,
+            distance: std::f64::INFINITY,
             index: Some(3),
         };
 

src/algorithm/neighbour/mod.rs

@@ -49,15 +49,20 @@ 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, Default)]
+#[derive(Debug, Clone)]
 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/algorithm/sort/heap_select.rs

@@ -133,7 +133,7 @@ mod tests {
     #[test]
     fn test_add1() {
         let mut heap = HeapSelection::with_capacity(3);
-        heap.add(f64::INFINITY);
+        heap.add(std::f64::INFINITY);
         heap.add(-5f64);
         heap.add(4f64);
         heap.add(-1f64);
@@ -151,7 +151,7 @@ mod tests {
     #[test]
     fn test_add2() {
         let mut heap = HeapSelection::with_capacity(3);
-        heap.add(f64::INFINITY);
+        heap.add(std::f64::INFINITY);
         heap.add(0.0);
         heap.add(8.4852);
         heap.add(5.6568);

src/algorithm/sort/quick_sort.rs

@@ -3,7 +3,6 @@ use num_traits::Num;
 pub trait QuickArgSort {
     fn quick_argsort_mut(&mut self) -> Vec<usize>;
 
-    #[allow(dead_code)]
     fn quick_argsort(&self) -> Vec<usize>;
 }
 

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::*;
@@ -315,7 +315,8 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
                         }
                     }
 
-                    while let Some(neighbor) = neighbors.pop() {
+                    while !neighbors.is_empty() {
+                        let neighbor = neighbors.pop().unwrap();
                         let index = neighbor.0;
 
                         if y[index] == outlier {
@@ -442,8 +443,7 @@ mod tests {
             &[2.2, 1.2],
             &[1.8, 0.8],
             &[3.0, 5.0],
-        ])
-        .unwrap();
+        ]);
 
         let expected_labels = vec![1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 0];
 
@@ -488,8 +488,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
 
         let dbscan = DBSCAN::fit(&x, Default::default()).unwrap();
 
@@ -512,6 +511,6 @@ mod tests {
             .and_then(|dbscan| dbscan.predict(&x))
             .unwrap();
 
-        println!("{labels:?}");
+        println!("{:?}", labels);
     }
 }

src/cluster/kmeans.rs

@@ -41,7 +41,7 @@
 //!            &[4.9, 2.4, 3.3, 1.0],
 //!            &[6.6, 2.9, 4.6, 1.3],
 //!            &[5.2, 2.7, 3.9, 1.4],
-//!            ]).unwrap();
+//!            ]);
 //!
 //! let kmeans = KMeans::fit(&x, KMeansParameters::default().with_k(2)).unwrap(); // Fit to data, 2 clusters
 //! let y_hat: Vec<u8> = kmeans.predict(&x).unwrap(); // use the same points for prediction
@@ -96,7 +96,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> PartialEq for KMeans<
                     return false;
                 }
                 for j in 0..self.centroids[i].len() {
-                    if (self.centroids[i][j] - other.centroids[i][j]).abs() > f64::EPSILON {
+                    if (self.centroids[i][j] - other.centroids[i][j]).abs() > std::f64::EPSILON {
                         return false;
                     }
                 }
@@ -249,7 +249,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> Predictor<X, Y>
 
 impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y> {
     /// Fit algorithm to _NxM_ matrix where _N_ is number of samples and _M_ is number of features.
-    /// * `data` - training instances to cluster
+    /// * `data` - training instances to cluster    
     /// * `parameters` - cluster parameters
     pub fn fit(data: &X, parameters: KMeansParameters) -> Result<KMeans<TX, TY, X, Y>, Failed> {
         let bbd = BBDTree::new(data);
@@ -270,7 +270,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
 
         let (n, d) = data.shape();
 
-        let mut distortion = f64::MAX;
+        let mut distortion = std::f64::MAX;
         let mut y = KMeans::<TX, TY, X, Y>::kmeans_plus_plus(data, parameters.k, parameters.seed);
         let mut size = vec![0; parameters.k];
         let mut centroids = vec![vec![0f64; d]; parameters.k];
@@ -331,7 +331,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
         let mut row = vec![0f64; x.shape().1];
 
         for i in 0..n {
-            let mut min_dist = f64::MAX;
+            let mut min_dist = std::f64::MAX;
             let mut best_cluster = 0;
 
             for j in 0..self.k {
@@ -361,7 +361,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
             .cloned()
             .collect();
 
-        let mut d = vec![f64::MAX; n];
+        let mut d = vec![std::f64::MAX; n];
         let mut row = vec![TX::zero(); data.shape().1];
 
         for j in 1..k {
@@ -424,7 +424,7 @@ mod tests {
     )]
     #[test]
     fn invalid_k() {
-        let x = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6]]).unwrap();
+        let x = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6]]);
 
         assert!(KMeans::<i32, i32, DenseMatrix<i32>, Vec<i32>>::fit(
             &x,
@@ -492,15 +492,14 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
 
         let kmeans = KMeans::fit(&x, Default::default()).unwrap();
 
         let y: Vec<usize> = kmeans.predict(&x).unwrap();
 
-        for (i, _y_i) in y.iter().enumerate() {
-            assert_eq!({ y[i] }, kmeans._y[i]);
+        for i in 0..y.len() {
+            assert_eq!(y[i] as usize, kmeans._y[i]);
         }
     }
 
@@ -532,8 +531,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
 
         let kmeans: KMeans<f32, f32, DenseMatrix<f32>, Vec<f32>> =
             KMeans::fit(&x, Default::default()).unwrap();

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(),
@@ -40,7 +40,7 @@ pub fn load_dataset() -> Dataset<f32, u32> {
         target: y,
         num_samples,
         num_features,
-        feature_names: [
+        feature_names: vec![
             "Age", "Sex", "BMI", "BP", "S1", "S2", "S3", "S4", "S5", "S6",
         ]
         .iter()

src/dataset/digits.rs

@@ -16,7 +16,7 @@ use crate::dataset::Dataset;
 pub fn load_dataset() -> Dataset<f32, f32> {
     let (x, y, num_samples, num_features) = match deserialize_data(std::include_bytes!("digits.xy"))
     {
-        Err(why) => panic!("Can't deserialize digits.xy. {why}"),
+        Err(why) => panic!("Can't deserialize digits.xy. {}", why),
         Ok((x, y, num_samples, num_features)) => (x, y, num_samples, num_features),
     };
 
@@ -25,14 +25,16 @@ pub fn load_dataset() -> Dataset<f32, f32> {
         target: y,
         num_samples,
         num_features,
-        feature_names: ["sepal length (cm)",
+        feature_names: vec![
+            "sepal length (cm)",
             "sepal width (cm)",
             "petal length (cm)",
-            "petal width (cm)"]
+            "petal width (cm)",
+        ]
         .iter()
         .map(|s| s.to_string())
         .collect(),
-        target_names: ["setosa", "versicolor", "virginica"]
+        target_names: vec!["setosa", "versicolor", "virginica"]
             .iter()
             .map(|s| s.to_string())
             .collect(),

src/dataset/iris.rs

@@ -22,7 +22,7 @@ use crate::dataset::Dataset;
 pub fn load_dataset() -> Dataset<f32, u32> {
     let (x, y, num_samples, num_features): (Vec<f32>, Vec<u32>, usize, usize) =
         match deserialize_data(std::include_bytes!("iris.xy")) {
-            Err(why) => panic!("Can't deserialize iris.xy. {why}"),
+            Err(why) => panic!("Can't deserialize iris.xy. {}", why),
             Ok((x, y, num_samples, num_features)) => (
                 x,
                 y.into_iter().map(|x| x as u32).collect(),
@@ -36,7 +36,7 @@ pub fn load_dataset() -> Dataset<f32, u32> {
         target: y,
         num_samples,
         num_features,
-        feature_names: [
+        feature_names: vec![
             "sepal length (cm)",
             "sepal width (cm)",
             "petal length (cm)",
@@ -45,7 +45,7 @@ pub fn load_dataset() -> Dataset<f32, u32> {
         .iter()
         .map(|s| s.to_string())
         .collect(),
-        target_names: ["setosa", "versicolor", "virginica"]
+        target_names: vec!["setosa", "versicolor", "virginica"]
             .iter()
             .map(|s| s.to_string())
             .collect(),

src/dataset/mod.rs

@@ -78,7 +78,7 @@ pub(crate) fn serialize_data<X: Number + RealNumber, Y: RealNumber>(
                 .collect();
             file.write_all(&y)?;
         }
-        Err(why) => panic!("couldn't create {filename}: {why}"),
+        Err(why) => panic!("couldn't create {}: {}", filename, why),
     }
     Ok(())
 }

src/decomposition/pca.rs

@@ -35,7 +35,7 @@
 //!                     &[4.9, 2.4, 3.3, 1.0],
 //!                     &[6.6, 2.9, 4.6, 1.3],
 //!                     &[5.2, 2.7, 3.9, 1.4],
-//!                     ]).unwrap();
+//!                     ]);
 //!
 //! let pca = PCA::fit(&iris, PCAParameters::default().with_n_components(2)).unwrap(); // Reduce number of features to 2
 //!
@@ -231,7 +231,8 @@ impl<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable
 
         if parameters.n_components > n {
             return Err(Failed::fit(&format!(
-                "Number of components, n_components should be <= number of attributes ({n})"
+                "Number of components, n_components should be <= number of attributes ({})",
+                n
             )));
         }
 
@@ -373,20 +374,21 @@ mod tests {
         let parameters = PCASearchParameters {
             n_components: vec![2, 4],
             use_correlation_matrix: vec![true, false],
+            ..Default::default()
         };
         let mut iter = parameters.into_iter();
         let next = iter.next().unwrap();
         assert_eq!(next.n_components, 2);
-        assert!(next.use_correlation_matrix);
+        assert_eq!(next.use_correlation_matrix, true);
         let next = iter.next().unwrap();
         assert_eq!(next.n_components, 4);
-        assert!(next.use_correlation_matrix);
+        assert_eq!(next.use_correlation_matrix, true);
         let next = iter.next().unwrap();
         assert_eq!(next.n_components, 2);
-        assert!(!next.use_correlation_matrix);
+        assert_eq!(next.use_correlation_matrix, false);
         let next = iter.next().unwrap();
         assert_eq!(next.n_components, 4);
-        assert!(!next.use_correlation_matrix);
+        assert_eq!(next.use_correlation_matrix, false);
         assert!(iter.next().is_none());
     }
 
@@ -443,7 +445,6 @@ mod tests {
             &[2.6, 53.0, 66.0, 10.8],
             &[6.8, 161.0, 60.0, 15.6],
         ])
-        .unwrap()
     }
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
@@ -458,8 +459,7 @@ mod tests {
             &[0.9952, 0.0588],
             &[0.0463, 0.9769],
             &[0.0752, 0.2007],
-        ])
-        .unwrap();
+        ]);
 
         let pca = PCA::fit(&us_arrests, Default::default()).unwrap();
 
@@ -502,8 +502,7 @@ mod tests {
                 -0.974080592182491,
                 0.0723250196376097,
             ],
-        ])
-        .unwrap();
+        ]);
 
         let expected_projection = DenseMatrix::from_2d_array(&[
             &[-64.8022, -11.448, 2.4949, -2.4079],
@@ -556,8 +555,7 @@ mod tests {
             &[91.5446, -22.9529, 0.402, -0.7369],
             &[118.1763, 5.5076, 2.7113, -0.205],
             &[10.4345, -5.9245, 3.7944, 0.5179],
-        ])
-        .unwrap();
+        ]);
 
         let expected_eigenvalues: Vec<f64> = vec![
             343544.6277001563,
@@ -574,8 +572,8 @@ mod tests {
             epsilon = 1e-4
         ));
 
-        for (i, pca_eigenvalues_i) in pca.eigenvalues.iter().enumerate() {
-            assert!((pca_eigenvalues_i.abs() - expected_eigenvalues[i].abs()).abs() < 1e-8);
+        for i in 0..pca.eigenvalues.len() {
+            assert!((pca.eigenvalues[i].abs() - expected_eigenvalues[i].abs()).abs() < 1e-8);
         }
 
         let us_arrests_t = pca.transform(&us_arrests).unwrap();
@@ -620,8 +618,7 @@ mod tests {
                 -0.0881962972508558,
                 -0.0096011588898465,
             ],
-        ])
-        .unwrap();
+        ]);
 
         let expected_projection = DenseMatrix::from_2d_array(&[
             &[0.9856, -1.1334, 0.4443, -0.1563],
@@ -674,8 +671,7 @@ mod tests {
             &[-2.1086, -1.4248, -0.1048, -0.1319],
             &[-2.0797, 0.6113, 0.1389, -0.1841],
             &[-0.6294, -0.321, 0.2407, 0.1667],
-        ])
-        .unwrap();
+        ]);
 
         let expected_eigenvalues: Vec<f64> = vec![
             2.480241579149493,
@@ -698,8 +694,8 @@ mod tests {
             epsilon = 1e-4
         ));
 
-        for (i, pca_eigenvalues_i) in pca.eigenvalues.iter().enumerate() {
-            assert!((pca_eigenvalues_i.abs() - expected_eigenvalues[i].abs()).abs() < 1e-8);
+        for i in 0..pca.eigenvalues.len() {
+            assert!((pca.eigenvalues[i].abs() - expected_eigenvalues[i].abs()).abs() < 1e-8);
         }
 
         let us_arrests_t = pca.transform(&us_arrests).unwrap();
@@ -738,7 +734,7 @@ mod tests {
     //         &[4.9, 2.4, 3.3, 1.0],
     //         &[6.6, 2.9, 4.6, 1.3],
     //         &[5.2, 2.7, 3.9, 1.4],
-    //     ]).unwrap();
+    //     ]);
 
     //     let pca = PCA::fit(&iris, Default::default()).unwrap();
 

src/decomposition/svd.rs

@@ -32,7 +32,7 @@
 //!                     &[4.9, 2.4, 3.3, 1.0],
 //!                     &[6.6, 2.9, 4.6, 1.3],
 //!                     &[5.2, 2.7, 3.9, 1.4],
-//!                     ]).unwrap();
+//!                     ]);
 //!
 //! let svd = SVD::fit(&iris, SVDParameters::default().
 //!         with_n_components(2)).unwrap(); // Reduce number of features to 2
@@ -180,7 +180,8 @@ impl<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable
 
         if parameters.n_components >= p {
             return Err(Failed::fit(&format!(
-                "Number of components, n_components should be < number of attributes ({p})"
+                "Number of components, n_components should be < number of attributes ({})",
+                p
             )));
         }
 
@@ -201,7 +202,8 @@ impl<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable
         let (p_c, k) = self.components.shape();
         if p_c != p {
             return Err(Failed::transform(&format!(
-                "Can not transform a {n}x{p} matrix into {n}x{k} matrix, incorrect input dimentions"
+                "Can not transform a {}x{} matrix into {}x{} matrix, incorrect input dimentions",
+                n, p, n, k
             )));
         }
 
@@ -225,6 +227,7 @@ mod tests {
     fn search_parameters() {
         let parameters = SVDSearchParameters {
             n_components: vec![10, 100],
+            ..Default::default()
         };
         let mut iter = parameters.into_iter();
         let next = iter.next().unwrap();
@@ -292,8 +295,7 @@ mod tests {
             &[5.7, 81.0, 39.0, 9.3],
             &[2.6, 53.0, 66.0, 10.8],
             &[6.8, 161.0, 60.0, 15.6],
-        ])
-        .unwrap();
+        ]);
 
         let expected = DenseMatrix::from_2d_array(&[
             &[243.54655757, -18.76673788],
@@ -301,8 +303,7 @@ mod tests {
             &[305.93972467, -15.39087376],
             &[197.28420365, -11.66808306],
             &[293.43187394, 1.91163633],
-        ])
-        .unwrap();
+        ]);
         let svd = SVD::fit(&x, Default::default()).unwrap();
 
         let x_transformed = svd.transform(&x).unwrap();
@@ -343,7 +344,7 @@ mod tests {
     //         &[4.9, 2.4, 3.3, 1.0],
     //         &[6.6, 2.9, 4.6, 1.3],
     //         &[5.2, 2.7, 3.9, 1.4],
-    //     ]).unwrap();
+    //     ]);
 
     //     let svd = SVD::fit(&iris, Default::default()).unwrap();
 

src/ensemble/random_forest_classifier.rs

@@ -33,7 +33,7 @@
 //!              &[4.9, 2.4, 3.3, 1.0],
 //!              &[6.6, 2.9, 4.6, 1.3],
 //!              &[5.2, 2.7, 3.9, 1.4],
-//!         ]).unwrap();
+//!         ]);
 //! let y = vec![
 //!              0, 0, 0, 0, 0, 0, 0, 0,
 //!              1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
@@ -55,9 +55,7 @@ use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::{Failed, FailedError};
-use crate::linalg::basic::arrays::MutArray;
 use crate::linalg::basic::arrays::{Array1, Array2};
-use crate::linalg::basic::matrix::DenseMatrix;
 use crate::numbers::basenum::Number;
 use crate::numbers::floatnum::FloatNumber;
 
@@ -456,12 +454,8 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
         y: &Y,
         parameters: RandomForestClassifierParameters,
     ) -> Result<RandomForestClassifier<TX, TY, X, Y>, Failed> {
-        let (x_nrows, num_attributes) = x.shape();
+        let (_, num_attributes) = x.shape();
         let y_ncols = y.shape();
-        if x_nrows != y_ncols {
-            return Err(Failed::fit("Number of rows in X should = len(y)"));
-        }
-
         let mut yi: Vec<usize> = vec![0; y_ncols];
         let classes = y.unique();
 
@@ -604,76 +598,11 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
         }
         samples
     }
-
-    /// Predict class probabilities for X.
-    ///
-    /// The predicted class probabilities of an input sample are computed as
-    /// the mean predicted class probabilities of the trees in the forest.
-    /// The class probability of a single tree is the fraction of samples of
-    /// the same class in a leaf.
-    ///
-    /// # Arguments
-    ///
-    /// * `x` - The input samples. A matrix of shape (n_samples, n_features).
-    ///
-    /// # Returns
-    ///
-    /// * `Result<DenseMatrix<f64>, Failed>` - The class probabilities of the input samples.
-    ///   The order of the classes corresponds to that in the attribute `classes_`.
-    ///   The matrix has shape (n_samples, n_classes).
-    ///
-    /// # Errors
-    ///
-    /// Returns a `Failed` error if:
-    /// * The model has not been fitted yet.
-    /// * The input `x` is not compatible with the model's expected input.
-    /// * Any of the tree predictions fail.
-    ///
-    /// # Examples
-    ///
-    /// ```
-    /// use smartcore::ensemble::random_forest_classifier::RandomForestClassifier;
-    /// use smartcore::linalg::basic::matrix::DenseMatrix;
-    /// use smartcore::linalg::basic::arrays::Array;
-    ///
-    /// let x = DenseMatrix::from_2d_array(&[
-    ///     &[5.1, 3.5, 1.4, 0.2],
-    ///     &[4.9, 3.0, 1.4, 0.2],
-    ///     &[7.0, 3.2, 4.7, 1.4],
-    /// ]).unwrap();
-    /// let y = vec![0, 0, 1];
-    ///
-    /// let forest = RandomForestClassifier::fit(&x, &y, Default::default()).unwrap();
-    /// let probas = forest.predict_proba(&x).unwrap();
-    ///
-    /// assert_eq!(probas.shape(), (3, 2));
-    /// ```
-    pub fn predict_proba(&self, x: &X) -> Result<DenseMatrix<f64>, Failed> {
-        let (n_samples, _) = x.shape();
-        let n_classes = self.classes.as_ref().unwrap().len();
-        let mut probas = DenseMatrix::<f64>::zeros(n_samples, n_classes);
-
-        for tree in self.trees.as_ref().unwrap().iter() {
-            let tree_predictions: Y = tree.predict(x).unwrap();
-
-            for (i, &class_idx) in tree_predictions.iterator(0).enumerate() {
-                let class_ = class_idx.to_usize().unwrap();
-                probas.add_element_mut((i, class_), 1.0);
-            }
-        }
-
-        let n_trees: f64 = self.trees.as_ref().unwrap().len() as f64;
-        probas.mul_scalar_mut(1.0 / n_trees);
-
-        Ok(probas)
-    }
 }
 
 #[cfg(test)]
 mod tests {
     use super::*;
-    use crate::ensemble::random_forest_classifier::RandomForestClassifier;
-    use crate::linalg::basic::arrays::Array;
     use crate::linalg::basic::matrix::DenseMatrix;
     use crate::metrics::*;
 
@@ -727,8 +656,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
         let y = vec![0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 
         let classifier = RandomForestClassifier::fit(
@@ -750,30 +678,6 @@ mod tests {
         assert!(accuracy(&y, &classifier.predict(&x).unwrap()) >= 0.95);
     }
 
-    #[test]
-    fn test_random_matrix_with_wrong_rownum() {
-        let x_rand: DenseMatrix<f64> = DenseMatrix::<f64>::rand(21, 200);
-
-        let y: Vec<u32> = vec![0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
-
-        let fail = RandomForestClassifier::fit(
-            &x_rand,
-            &y,
-            RandomForestClassifierParameters {
-                criterion: SplitCriterion::Gini,
-                max_depth: Option::None,
-                min_samples_leaf: 1,
-                min_samples_split: 2,
-                n_trees: 100,
-                m: Option::None,
-                keep_samples: false,
-                seed: 87,
-            },
-        );
-
-        assert!(fail.is_err());
-    }
-
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test
@@ -801,8 +705,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
         let y = vec![0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 
         let classifier = RandomForestClassifier::fit(
@@ -827,101 +730,6 @@ mod tests {
         );
     }
 
-    #[cfg_attr(
-        all(target_arch = "wasm32", not(target_os = "wasi")),
-        wasm_bindgen_test::wasm_bindgen_test
-    )]
-    #[test]
-    fn test_random_forest_predict_proba() {
-        use num_traits::FromPrimitive;
-        // Iris-like dataset (subset)
-        let x: DenseMatrix<f64> = 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],
-            &[7.0, 3.2, 4.7, 1.4],
-            &[6.4, 3.2, 4.5, 1.5],
-            &[6.9, 3.1, 4.9, 1.5],
-            &[5.5, 2.3, 4.0, 1.3],
-            &[6.5, 2.8, 4.6, 1.5],
-        ])
-        .unwrap();
-        let y: Vec<u32> = vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1];
-
-        let forest = RandomForestClassifier::fit(&x, &y, Default::default()).unwrap();
-        let probas = forest.predict_proba(&x).unwrap();
-
-        // Test shape
-        assert_eq!(probas.shape(), (10, 2));
-
-        let (pro_n_rows, _) = probas.shape();
-
-        // Test probability sum
-        for i in 0..pro_n_rows {
-            let row_sum: f64 = probas.get_row(i).sum();
-            assert!(
-                (row_sum - 1.0).abs() < 1e-6,
-                "Row probabilities should sum to 1"
-            );
-        }
-
-        // Test class prediction
-        let predictions: Vec<u32> = (0..pro_n_rows)
-            .map(|i| {
-                if probas.get((i, 0)) > probas.get((i, 1)) {
-                    0
-                } else {
-                    1
-                }
-            })
-            .collect();
-        let acc = accuracy(&y, &predictions);
-        assert!(acc > 0.8, "Accuracy should be high for the training set");
-
-        // Test probability values
-        // These values are approximate and based on typical random forest behavior
-        for i in 0..(pro_n_rows / 2) {
-            assert!(
-                f64::from_f32(0.6).unwrap().lt(probas.get((i, 0))),
-                "Class 0 samples should have high probability for class 0"
-            );
-            assert!(
-                f64::from_f32(0.4).unwrap().gt(probas.get((i, 1))),
-                "Class 0 samples should have low probability for class 1"
-            );
-        }
-
-        for i in (pro_n_rows / 2)..pro_n_rows {
-            assert!(
-                f64::from_f32(0.6).unwrap().lt(probas.get((i, 1))),
-                "Class 1 samples should have high probability for class 1"
-            );
-            assert!(
-                f64::from_f32(0.4).unwrap().gt(probas.get((i, 0))),
-                "Class 1 samples should have low probability for class 0"
-            );
-        }
-
-        // Test with new data
-        let x_new = DenseMatrix::from_2d_array(&[
-            &[5.0, 3.4, 1.5, 0.2], // Should be close to class 0
-            &[6.3, 3.3, 4.7, 1.6], // Should be close to class 1
-        ])
-        .unwrap();
-        let probas_new = forest.predict_proba(&x_new).unwrap();
-        assert_eq!(probas_new.shape(), (2, 2));
-        assert!(
-            probas_new.get((0, 0)) > probas_new.get((0, 1)),
-            "First sample should be predicted as class 0"
-        );
-        assert!(
-            probas_new.get((1, 1)) > probas_new.get((1, 0)),
-            "Second sample should be predicted as class 1"
-        );
-    }
-
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test
@@ -950,8 +758,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
         let y = vec![0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 
         let forest = RandomForestClassifier::fit(&x, &y, Default::default()).unwrap();

src/ensemble/random_forest_regressor.rs

@@ -29,7 +29,7 @@
 //!             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
 //!             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
 //!             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-//!         ]).unwrap();
+//!         ]);
 //! let y = vec![
 //!             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2,
 //!             104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9
@@ -399,10 +399,6 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
     ) -> Result<RandomForestRegressor<TX, TY, X, Y>, Failed> {
         let (n_rows, num_attributes) = x.shape();
 
-        if n_rows != y.shape() {
-            return Err(Failed::fit("Number of rows in X should = len(y)"));
-        }
-
         let mtry = parameters
             .m
             .unwrap_or((num_attributes as f64).sqrt().floor() as usize);
@@ -574,8 +570,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
         let y = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
             114.2, 115.7, 116.9,
@@ -600,32 +595,6 @@ mod tests {
         assert!(mean_absolute_error(&y, &y_hat) < 1.0);
     }
 
-    #[test]
-    fn test_random_matrix_with_wrong_rownum() {
-        let x_rand: DenseMatrix<f64> = DenseMatrix::<f64>::rand(17, 200);
-
-        let y = vec![
-            83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
-            114.2, 115.7, 116.9,
-        ];
-
-        let fail = RandomForestRegressor::fit(
-            &x_rand,
-            &y,
-            RandomForestRegressorParameters {
-                max_depth: Option::None,
-                min_samples_leaf: 1,
-                min_samples_split: 2,
-                n_trees: 1000,
-                m: Option::None,
-                keep_samples: false,
-                seed: 87,
-            },
-        );
-
-        assert!(fail.is_err());
-    }
-
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test
@@ -649,8 +618,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
         let y = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
             114.2, 115.7, 116.9,
@@ -704,8 +672,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
         let y = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
             114.2, 115.7, 116.9,

src/error/mod.rs

@@ -30,10 +30,8 @@ pub enum FailedError {
     DecompositionFailed,
     /// Can't solve for x
     SolutionFailed,
-    /// Error in input parameters
+    /// Erro in input
     ParametersError,
-    /// Invalid state error (should never happen)
-    InvalidStateError,
 }
 
 impl Failed {
@@ -66,22 +64,6 @@ impl Failed {
         }
     }
 
-    /// new instance of `FailedError::ParametersError`
-    pub fn input(msg: &str) -> Self {
-        Failed {
-            err: FailedError::ParametersError,
-            msg: msg.to_string(),
-        }
-    }
-
-    /// new instance of `FailedError::InvalidStateError`
-    pub fn invalid_state(msg: &str) -> Self {
-        Failed {
-            err: FailedError::InvalidStateError,
-            msg: msg.to_string(),
-        }
-    }
-
     /// new instance of `err`
     pub fn because(err: FailedError, msg: &str) -> Self {
         Failed {
@@ -115,9 +97,8 @@ impl fmt::Display for FailedError {
             FailedError::DecompositionFailed => "Decomposition failed",
             FailedError::SolutionFailed => "Can't find solution",
             FailedError::ParametersError => "Error in input, check parameters",
-            FailedError::InvalidStateError => "Invalid state, this should never happen", // useful in development phase of lib
         };
-        write!(f, "{failed_err_str}")
+        write!(f, "{}", failed_err_str)
     }
 }
 

src/lib.rs

@@ -3,8 +3,7 @@
     clippy::too_many_arguments,
     clippy::many_single_char_names,
     clippy::unnecessary_wraps,
-    clippy::upper_case_acronyms,
-    clippy::approx_constant
+    clippy::upper_case_acronyms
 )]
 #![warn(missing_docs)]
 #![warn(rustdoc::missing_doc_code_examples)]
@@ -64,7 +63,7 @@
 //!    &[3., 4.],
 //!    &[5., 6.],
 //!    &[7., 8.],
-//!    &[9., 10.]]).unwrap();
+//!    &[9., 10.]]);
 //! // Our classes are defined as a vector
 //! let y = vec![2, 2, 2, 3, 3];
 //!

src/linalg/basic/matrix.rs

@@ -19,8 +19,6 @@ use crate::linalg::traits::svd::SVDDecomposable;
 use crate::numbers::basenum::Number;
 use crate::numbers::realnum::RealNumber;
 
-use crate::error::Failed;
-
 /// Dense matrix
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
@@ -52,26 +50,26 @@ pub struct DenseMatrixMutView<'a, T: Debug + Display + Copy + Sized> {
 }
 
 impl<'a, T: Debug + Display + Copy + Sized> DenseMatrixView<'a, T> {
-    fn new(
-        m: &'a DenseMatrix<T>,
-        vrows: Range<usize>,
-        vcols: Range<usize>,
-    ) -> Result<Self, Failed> {
-        if m.is_valid_view(m.shape().0, m.shape().1, &vrows, &vcols) {
-            Err(Failed::input(
-                "The specified view is outside of the matrix range",
-            ))
+    fn new(m: &'a DenseMatrix<T>, rows: Range<usize>, cols: Range<usize>) -> Self {
+        let (start, end, stride) = if m.column_major {
+            (
+                rows.start + cols.start * m.nrows,
+                rows.end + (cols.end - 1) * m.nrows,
+                m.nrows,
+            )
         } else {
-            let (start, end, stride) =
-                m.stride_range(m.shape().0, m.shape().1, &vrows, &vcols, m.column_major);
-
-            Ok(DenseMatrixView {
-                values: &m.values[start..end],
-                stride,
-                nrows: vrows.end - vrows.start,
-                ncols: vcols.end - vcols.start,
-                column_major: m.column_major,
-            })
+            (
+                rows.start * m.ncols + cols.start,
+                (rows.end - 1) * m.ncols + cols.end,
+                m.ncols,
+            )
+        };
+        DenseMatrixView {
+            values: &m.values[start..end],
+            stride,
+            nrows: rows.end - rows.start,
+            ncols: cols.end - cols.start,
+            column_major: m.column_major,
         }
     }
 
@@ -91,7 +89,7 @@ impl<'a, T: Debug + Display + Copy + Sized> DenseMatrixView<'a, T> {
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixView<'_, T> {
+impl<'a, T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixView<'a, T> {
     fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
         writeln!(
             f,
@@ -104,26 +102,26 @@ impl<T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixView<'_, T>
 }
 
 impl<'a, T: Debug + Display + Copy + Sized> DenseMatrixMutView<'a, T> {
-    fn new(
-        m: &'a mut DenseMatrix<T>,
-        vrows: Range<usize>,
-        vcols: Range<usize>,
-    ) -> Result<Self, Failed> {
-        if m.is_valid_view(m.shape().0, m.shape().1, &vrows, &vcols) {
-            Err(Failed::input(
-                "The specified view is outside of the matrix range",
-            ))
+    fn new(m: &'a mut DenseMatrix<T>, rows: Range<usize>, cols: Range<usize>) -> Self {
+        let (start, end, stride) = if m.column_major {
+            (
+                rows.start + cols.start * m.nrows,
+                rows.end + (cols.end - 1) * m.nrows,
+                m.nrows,
+            )
         } else {
-            let (start, end, stride) =
-                m.stride_range(m.shape().0, m.shape().1, &vrows, &vcols, m.column_major);
-
-            Ok(DenseMatrixMutView {
-                values: &mut m.values[start..end],
-                stride,
-                nrows: vrows.end - vrows.start,
-                ncols: vcols.end - vcols.start,
-                column_major: m.column_major,
-            })
+            (
+                rows.start * m.ncols + cols.start,
+                (rows.end - 1) * m.ncols + cols.end,
+                m.ncols,
+            )
+        };
+        DenseMatrixMutView {
+            values: &mut m.values[start..end],
+            stride,
+            nrows: rows.end - rows.start,
+            ncols: cols.end - cols.start,
+            column_major: m.column_major,
         }
     }
 
@@ -142,7 +140,7 @@ impl<'a, T: Debug + Display + Copy + Sized> DenseMatrixMutView<'a, T> {
         }
     }
 
-    fn iter_mut<'b>(&'b mut self, axis: u8) -> Box<dyn Iterator<Item = &'b mut T> + 'b> {
+    fn iter_mut<'b>(&'b mut self, axis: u8) -> Box<dyn Iterator<Item = &mut T> + 'b> {
         let column_major = self.column_major;
         let stride = self.stride;
         let ptr = self.values.as_mut_ptr();
@@ -169,7 +167,7 @@ impl<'a, T: Debug + Display + Copy + Sized> DenseMatrixMutView<'a, T> {
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixMutView<'_, T> {
+impl<'a, T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixMutView<'a, T> {
     fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
         writeln!(
             f,
@@ -184,102 +182,42 @@ impl<T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixMutView<'_,
 impl<T: Debug + Display + Copy + Sized> DenseMatrix<T> {
     /// Create new instance of `DenseMatrix` without copying data.
     /// `values` should be in column-major order.
-    pub fn new(
-        nrows: usize,
-        ncols: usize,
-        values: Vec<T>,
-        column_major: bool,
-    ) -> Result<Self, Failed> {
-        let data_len = values.len();
-        if nrows * ncols != values.len() {
-            Err(Failed::input(&format!(
-                "The specified shape: (cols: {ncols}, rows: {nrows}) does not align with data len: {data_len}"
-            )))
-        } else {
-            Ok(DenseMatrix {
-                ncols,
-                nrows,
-                values,
-                column_major,
-            })
+    pub fn new(nrows: usize, ncols: usize, values: Vec<T>, column_major: bool) -> Self {
+        DenseMatrix {
+            ncols,
+            nrows,
+            values,
+            column_major,
         }
     }
 
     /// New instance of `DenseMatrix` from 2d array.
-    pub fn from_2d_array(values: &[&[T]]) -> Result<Self, Failed> {
+    pub fn from_2d_array(values: &[&[T]]) -> Self {
         DenseMatrix::from_2d_vec(&values.iter().map(|row| Vec::from(*row)).collect())
     }
 
     /// New instance of `DenseMatrix` from 2d vector.
-    #[allow(clippy::ptr_arg)]
-    pub fn from_2d_vec(values: &Vec<Vec<T>>) -> Result<Self, Failed> {
-        if values.is_empty() || values[0].is_empty() {
-            Err(Failed::input(
-                "The 2d vec provided is empty; cannot instantiate the matrix",
-            ))
-        } else {
-            let nrows = values.len();
-            let ncols = values
-                .first()
-                .unwrap_or_else(|| {
-                    panic!("Invalid state: Cannot create 2d matrix from an empty vector")
-                })
-                .len();
-            let mut m_values = Vec::with_capacity(nrows * ncols);
+    pub fn from_2d_vec(values: &Vec<Vec<T>>) -> Self {
+        let nrows = values.len();
+        let ncols = values
+            .first()
+            .unwrap_or_else(|| panic!("Cannot create 2d matrix from an empty vector"))
+            .len();
+        let mut m_values = Vec::with_capacity(nrows * ncols);
 
-            for c in 0..ncols {
-                for r in values.iter().take(nrows) {
-                    m_values.push(r[c])
-                }
+        for c in 0..ncols {
+            for r in values.iter().take(nrows) {
+                m_values.push(r[c])
             }
-
-            DenseMatrix::new(nrows, ncols, m_values, true)
         }
+
+        DenseMatrix::new(nrows, ncols, m_values, true)
     }
 
     /// Iterate over values of matrix
     pub fn iter(&self) -> Iter<'_, T> {
         self.values.iter()
     }
-
-    ///  Check if the size of the requested view is bounded to matrix rows/cols count
-    fn is_valid_view(
-        &self,
-        n_rows: usize,
-        n_cols: usize,
-        vrows: &Range<usize>,
-        vcols: &Range<usize>,
-    ) -> bool {
-        !(vrows.end <= n_rows
-            && vcols.end <= n_cols
-            && vrows.start <= n_rows
-            && vcols.start <= n_cols)
-    }
-
-    ///  Compute the range of the requested view: start, end, size of the slice
-    fn stride_range(
-        &self,
-        n_rows: usize,
-        n_cols: usize,
-        vrows: &Range<usize>,
-        vcols: &Range<usize>,
-        column_major: bool,
-    ) -> (usize, usize, usize) {
-        let (start, end, stride) = if column_major {
-            (
-                vrows.start + vcols.start * n_rows,
-                vrows.end + (vcols.end - 1) * n_rows,
-                n_rows,
-            )
-        } else {
-            (
-                vrows.start * n_cols + vcols.start,
-                (vrows.end - 1) * n_cols + vcols.end,
-                n_cols,
-            )
-        };
-        (start, end, stride)
-    }
 }
 
 impl<T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrix<T> {
@@ -366,7 +304,6 @@ where
 impl<T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrix<T> {
     fn get(&self, pos: (usize, usize)) -> &T {
         let (row, col) = pos;
-
         if row >= self.nrows || col >= self.ncols {
             panic!(
                 "Invalid index ({},{}) for {}x{} matrix",
@@ -446,15 +383,15 @@ impl<T: Debug + Display + Copy + Sized> MutArrayView2<T> for DenseMatrix<T> {}
 
 impl<T: Debug + Display + Copy + Sized> Array2<T> for DenseMatrix<T> {
     fn get_row<'a>(&'a self, row: usize) -> Box<dyn ArrayView1<T> + 'a> {
-        Box::new(DenseMatrixView::new(self, row..row + 1, 0..self.ncols).unwrap())
+        Box::new(DenseMatrixView::new(self, row..row + 1, 0..self.ncols))
     }
 
     fn get_col<'a>(&'a self, col: usize) -> Box<dyn ArrayView1<T> + 'a> {
-        Box::new(DenseMatrixView::new(self, 0..self.nrows, col..col + 1).unwrap())
+        Box::new(DenseMatrixView::new(self, 0..self.nrows, col..col + 1))
     }
 
     fn slice<'a>(&'a self, rows: Range<usize>, cols: Range<usize>) -> Box<dyn ArrayView2<T> + 'a> {
-        Box::new(DenseMatrixView::new(self, rows, cols).unwrap())
+        Box::new(DenseMatrixView::new(self, rows, cols))
     }
 
     fn slice_mut<'a>(
@@ -465,17 +402,15 @@ impl<T: Debug + Display + Copy + Sized> Array2<T> for DenseMatrix<T> {
     where
         Self: Sized,
     {
-        Box::new(DenseMatrixMutView::new(self, rows, cols).unwrap())
+        Box::new(DenseMatrixMutView::new(self, rows, cols))
     }
 
-    // private function so for now assume infalible
     fn fill(nrows: usize, ncols: usize, value: T) -> Self {
-        DenseMatrix::new(nrows, ncols, vec![value; nrows * ncols], true).unwrap()
+        DenseMatrix::new(nrows, ncols, vec![value; nrows * ncols], true)
     }
 
-    // private function so for now assume infalible
     fn from_iterator<I: Iterator<Item = T>>(iter: I, nrows: usize, ncols: usize, axis: u8) -> Self {
-        DenseMatrix::new(nrows, ncols, iter.collect(), axis != 0).unwrap()
+        DenseMatrix::new(nrows, ncols, iter.collect(), axis != 0)
     }
 
     fn transpose(&self) -> Self {
@@ -493,12 +428,12 @@ impl<T: Number + RealNumber> EVDDecomposable<T> for DenseMatrix<T> {}
 impl<T: Number + RealNumber> LUDecomposable<T> for DenseMatrix<T> {}
 impl<T: Number + RealNumber> SVDDecomposable<T> for DenseMatrix<T> {}
 
-impl<T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrixView<'_, T> {
+impl<'a, T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrixView<'a, T> {
     fn get(&self, pos: (usize, usize)) -> &T {
         if self.column_major {
-            &self.values[pos.0 + pos.1 * self.stride]
+            &self.values[(pos.0 + pos.1 * self.stride)]
         } else {
-            &self.values[pos.0 * self.stride + pos.1]
+            &self.values[(pos.0 * self.stride + pos.1)]
         }
     }
 
@@ -515,7 +450,7 @@ impl<T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrix
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> Array<T, usize> for DenseMatrixView<'_, T> {
+impl<'a, T: Debug + Display + Copy + Sized> Array<T, usize> for DenseMatrixView<'a, T> {
     fn get(&self, i: usize) -> &T {
         if self.nrows == 1 {
             if self.column_major {
@@ -553,16 +488,16 @@ impl<T: Debug + Display + Copy + Sized> Array<T, usize> for DenseMatrixView<'_,
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for DenseMatrixView<'_, T> {}
+impl<'a, T: Debug + Display + Copy + Sized> ArrayView2<T> for DenseMatrixView<'a, T> {}
 
-impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for DenseMatrixView<'_, T> {}
+impl<'a, T: Debug + Display + Copy + Sized> ArrayView1<T> for DenseMatrixView<'a, T> {}
 
-impl<T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrixMutView<'_, T> {
+impl<'a, T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrixMutView<'a, T> {
     fn get(&self, pos: (usize, usize)) -> &T {
         if self.column_major {
-            &self.values[pos.0 + pos.1 * self.stride]
+            &self.values[(pos.0 + pos.1 * self.stride)]
         } else {
-            &self.values[pos.0 * self.stride + pos.1]
+            &self.values[(pos.0 * self.stride + pos.1)]
         }
     }
 
@@ -579,12 +514,14 @@ impl<T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrix
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)> for DenseMatrixMutView<'_, T> {
+impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
+    for DenseMatrixMutView<'a, T>
+{
     fn set(&mut self, pos: (usize, usize), x: T) {
         if self.column_major {
-            self.values[pos.0 + pos.1 * self.stride] = x;
+            self.values[(pos.0 + pos.1 * self.stride)] = x;
         } else {
-            self.values[pos.0 * self.stride + pos.1] = x;
+            self.values[(pos.0 * self.stride + pos.1)] = x;
         }
     }
 
@@ -593,89 +530,29 @@ impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)> for DenseMat
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> MutArrayView2<T> for DenseMatrixMutView<'_, T> {}
+impl<'a, T: Debug + Display + Copy + Sized> MutArrayView2<T> for DenseMatrixMutView<'a, T> {}
 
-impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for DenseMatrixMutView<'_, T> {}
+impl<'a, T: Debug + Display + Copy + Sized> ArrayView2<T> for DenseMatrixMutView<'a, T> {}
 
 impl<T: RealNumber> MatrixStats<T> for DenseMatrix<T> {}
 
 impl<T: RealNumber> MatrixPreprocessing<T> for DenseMatrix<T> {}
 
 #[cfg(test)]
-#[warn(clippy::reversed_empty_ranges)]
 mod tests {
     use super::*;
     use approx::relative_eq;
 
-    #[test]
-    fn test_instantiate_from_2d() {
-        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]);
-        assert!(x.is_ok());
-    }
-    #[test]
-    fn test_instantiate_from_2d_empty() {
-        let input: &[&[f64]] = &[&[]];
-        let x = DenseMatrix::from_2d_array(input);
-        assert!(x.is_err());
-    }
-    #[test]
-    fn test_instantiate_from_2d_empty2() {
-        let input: &[&[f64]] = &[&[], &[]];
-        let x = DenseMatrix::from_2d_array(input);
-        assert!(x.is_err());
-    }
-    #[test]
-    fn test_instantiate_ok_view1() {
-        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
-        let v = DenseMatrixView::new(&x, 0..2, 0..2);
-        assert!(v.is_ok());
-    }
-    #[test]
-    fn test_instantiate_ok_view2() {
-        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
-        let v = DenseMatrixView::new(&x, 0..3, 0..3);
-        assert!(v.is_ok());
-    }
-    #[test]
-    fn test_instantiate_ok_view3() {
-        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
-        let v = DenseMatrixView::new(&x, 2..3, 0..3);
-        assert!(v.is_ok());
-    }
-    #[test]
-    fn test_instantiate_ok_view4() {
-        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
-        let v = DenseMatrixView::new(&x, 3..3, 0..3);
-        assert!(v.is_ok());
-    }
-    #[test]
-    fn test_instantiate_err_view1() {
-        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
-        let v = DenseMatrixView::new(&x, 3..4, 0..3);
-        assert!(v.is_err());
-    }
-    #[test]
-    fn test_instantiate_err_view2() {
-        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
-        let v = DenseMatrixView::new(&x, 0..3, 3..4);
-        assert!(v.is_err());
-    }
-    #[test]
-    fn test_instantiate_err_view3() {
-        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
-        let v = DenseMatrixView::new(&x, 0..3, 4..3);
-        assert!(v.is_err());
-    }
     #[test]
     fn test_display() {
-        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
+        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]);
 
         println!("{}", &x);
     }
 
     #[test]
     fn test_get_row_col() {
-        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
+        let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]);
 
         assert_eq!(15.0, x.get_col(1).sum());
         assert_eq!(15.0, x.get_row(1).sum());
@@ -684,7 +561,7 @@ mod tests {
 
     #[test]
     fn test_row_major() {
-        let mut x = DenseMatrix::new(2, 3, vec![1, 2, 3, 4, 5, 6], false).unwrap();
+        let mut x = DenseMatrix::new(2, 3, vec![1, 2, 3, 4, 5, 6], false);
 
         assert_eq!(5, *x.get_col(1).get(1));
         assert_eq!(7, x.get_col(1).sum());
@@ -698,22 +575,21 @@ mod tests {
 
     #[test]
     fn test_get_slice() {
-        let x = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6], &[7, 8, 9], &[10, 11, 12]])
-            .unwrap();
+        let x = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6], &[7, 8, 9], &[10, 11, 12]]);
 
         assert_eq!(
             vec![4, 5, 6],
             DenseMatrix::from_slice(&(*x.slice(1..2, 0..3))).values
         );
-        let second_row: Vec<i32> = x.slice(1..2, 0..3).iterator(0).copied().collect();
+        let second_row: Vec<i32> = x.slice(1..2, 0..3).iterator(0).map(|x| *x).collect();
         assert_eq!(vec![4, 5, 6], second_row);
-        let second_col: Vec<i32> = x.slice(0..3, 1..2).iterator(0).copied().collect();
+        let second_col: Vec<i32> = x.slice(0..3, 1..2).iterator(0).map(|x| *x).collect();
         assert_eq!(vec![2, 5, 8], second_col);
     }
 
     #[test]
     fn test_iter_mut() {
-        let mut x = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6], &[7, 8, 9]]).unwrap();
+        let mut x = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6], &[7, 8, 9]]);
 
         assert_eq!(vec![1, 4, 7, 2, 5, 8, 3, 6, 9], x.values);
         // add +2 to some elements
@@ -749,8 +625,7 @@ mod tests {
     #[test]
     fn test_str_array() {
         let mut x =
-            DenseMatrix::from_2d_array(&[&["1", "2", "3"], &["4", "5", "6"], &["7", "8", "9"]])
-                .unwrap();
+            DenseMatrix::from_2d_array(&[&["1", "2", "3"], &["4", "5", "6"], &["7", "8", "9"]]);
 
         assert_eq!(vec!["1", "4", "7", "2", "5", "8", "3", "6", "9"], x.values);
         x.iterator_mut(0).for_each(|v| *v = "str");
@@ -762,20 +637,20 @@ mod tests {
 
     #[test]
     fn test_transpose() {
-        let x = DenseMatrix::<&str>::from_2d_array(&[&["1", "2", "3"], &["4", "5", "6"]]).unwrap();
+        let x = DenseMatrix::<&str>::from_2d_array(&[&["1", "2", "3"], &["4", "5", "6"]]);
 
         assert_eq!(vec!["1", "4", "2", "5", "3", "6"], x.values);
-        assert!(x.column_major);
+        assert!(x.column_major == true);
 
         // transpose
         let x = x.transpose();
         assert_eq!(vec!["1", "4", "2", "5", "3", "6"], x.values);
-        assert!(!x.column_major); // should change column_major
+        assert!(x.column_major == false); // should change column_major
     }
 
     #[test]
     fn test_from_iterator() {
-        let data = [1, 2, 3, 4, 5, 6];
+        let data = vec![1, 2, 3, 4, 5, 6];
 
         let m = DenseMatrix::from_iterator(data.iter(), 2, 3, 0);
 
@@ -784,25 +659,25 @@ mod tests {
             vec![1, 2, 3, 4, 5, 6],
             m.values.iter().map(|e| **e).collect::<Vec<i32>>()
         );
-        assert!(!m.column_major);
+        assert!(m.column_major == false);
     }
 
     #[test]
     fn test_take() {
-        let a = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6]]).unwrap();
-        let b = DenseMatrix::from_2d_array(&[&[1, 2], &[3, 4], &[5, 6]]).unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6]]);
+        let b = DenseMatrix::from_2d_array(&[&[1, 2], &[3, 4], &[5, 6]]);
 
-        println!("{a}");
+        println!("{}", a);
         // take column 0 and 2
         assert_eq!(vec![1, 3, 4, 6], a.take(&[0, 2], 1).values);
-        println!("{b}");
+        println!("{}", b);
         // take rows 0 and 2
         assert_eq!(vec![1, 2, 5, 6], b.take(&[0, 2], 0).values);
     }
 
     #[test]
     fn test_mut() {
-        let a = DenseMatrix::from_2d_array(&[&[1.3, -2.1, 3.4], &[-4., -5.3, 6.1]]).unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[1.3, -2.1, 3.4], &[-4., -5.3, 6.1]]);
 
         let a = a.abs();
         assert_eq!(vec![1.3, 4.0, 2.1, 5.3, 3.4, 6.1], a.values);
@@ -813,29 +688,26 @@ mod tests {
 
     #[test]
     fn test_reshape() {
-        let a = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6], &[7, 8, 9], &[10, 11, 12]])
-            .unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6], &[7, 8, 9], &[10, 11, 12]]);
 
         let a = a.reshape(2, 6, 0);
         assert_eq!(vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], a.values);
-        assert!(a.ncols == 6 && a.nrows == 2 && !a.column_major);
+        assert!(a.ncols == 6 && a.nrows == 2 && a.column_major == false);
 
         let a = a.reshape(3, 4, 1);
         assert_eq!(vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], a.values);
-        assert!(a.ncols == 4 && a.nrows == 3 && a.column_major);
+        assert!(a.ncols == 4 && a.nrows == 3 && a.column_major == true);
     }
 
     #[test]
     fn test_eq() {
-        let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.]]).unwrap();
-        let b = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.]]);
+        let b = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]);
         let c = DenseMatrix::from_2d_array(&[
             &[1. + f32::EPSILON, 2., 3.],
             &[4., 5., 6. + f32::EPSILON],
-        ])
-        .unwrap();
-        let d = DenseMatrix::from_2d_array(&[&[1. + 0.5, 2., 3.], &[4., 5., 6. + f32::EPSILON]])
-            .unwrap();
+        ]);
+        let d = DenseMatrix::from_2d_array(&[&[1. + 0.5, 2., 3.], &[4., 5., 6. + f32::EPSILON]]);
 
         assert!(!relative_eq!(a, b));
         assert!(!relative_eq!(a, d));

src/linalg/basic/vector.rs

@@ -15,25 +15,6 @@ pub struct VecView<'a, T: Debug + Display + Copy + Sized> {
     ptr: &'a [T],
 }
 
-impl<T: Debug + Display + Copy + Sized> Array<T, usize> for &[T] {
-    fn get(&self, i: usize) -> &T {
-        &self[i]
-    }
-
-    fn shape(&self) -> usize {
-        self.len()
-    }
-
-    fn is_empty(&self) -> bool {
-        self.len() > 0
-    }
-
-    fn iterator<'b>(&'b self, axis: u8) -> Box<dyn Iterator<Item = &'b T> + 'b> {
-        assert!(axis == 0, "For one dimensional array `axis` should == 0");
-        Box::new(self.iter())
-    }
-}
-
 impl<T: Debug + Display + Copy + Sized> Array<T, usize> for Vec<T> {
     fn get(&self, i: usize) -> &T {
         &self[i]
@@ -55,7 +36,6 @@ impl<T: Debug + Display + Copy + Sized> Array<T, usize> for Vec<T> {
 
 impl<T: Debug + Display + Copy + Sized> MutArray<T, usize> for Vec<T> {
     fn set(&mut self, i: usize, x: T) {
-        // NOTE: this panics in case of out of bounds index
         self[i] = x
     }
 
@@ -66,7 +46,6 @@ impl<T: Debug + Display + Copy + Sized> MutArray<T, usize> for Vec<T> {
 }
 
 impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for Vec<T> {}
-impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for &[T] {}
 
 impl<T: Debug + Display + Copy + Sized> MutArrayView1<T> for Vec<T> {}
 
@@ -119,7 +98,7 @@ impl<T: Debug + Display + Copy + Sized> Array1<T> for Vec<T> {
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> Array<T, usize> for VecMutView<'_, T> {
+impl<'a, T: Debug + Display + Copy + Sized> Array<T, usize> for VecMutView<'a, T> {
     fn get(&self, i: usize) -> &T {
         &self.ptr[i]
     }
@@ -138,7 +117,7 @@ impl<T: Debug + Display + Copy + Sized> Array<T, usize> for VecMutView<'_, T> {
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> MutArray<T, usize> for VecMutView<'_, T> {
+impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, usize> for VecMutView<'a, T> {
     fn set(&mut self, i: usize, x: T) {
         self.ptr[i] = x;
     }
@@ -149,10 +128,10 @@ impl<T: Debug + Display + Copy + Sized> MutArray<T, usize> for VecMutView<'_, T>
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for VecMutView<'_, T> {}
-impl<T: Debug + Display + Copy + Sized> MutArrayView1<T> for VecMutView<'_, T> {}
+impl<'a, T: Debug + Display + Copy + Sized> ArrayView1<T> for VecMutView<'a, T> {}
+impl<'a, T: Debug + Display + Copy + Sized> MutArrayView1<T> for VecMutView<'a, T> {}
 
-impl<T: Debug + Display + Copy + Sized> Array<T, usize> for VecView<'_, T> {
+impl<'a, T: Debug + Display + Copy + Sized> Array<T, usize> for VecView<'a, T> {
     fn get(&self, i: usize) -> &T {
         &self.ptr[i]
     }
@@ -171,7 +150,7 @@ impl<T: Debug + Display + Copy + Sized> Array<T, usize> for VecView<'_, T> {
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for VecView<'_, T> {}
+impl<'a, T: Debug + Display + Copy + Sized> ArrayView1<T> for VecView<'a, T> {}
 
 #[cfg(test)]
 mod tests {
@@ -181,8 +160,8 @@ mod tests {
     fn dot_product<T: Number, V: Array1<T>>(v: &V) -> T {
         let vv = V::zeros(10);
         let v_s = vv.slice(0..3);
-
-        v_s.dot(v)
+        let dot = v_s.dot(v);
+        dot
     }
 
     fn vector_ops<T: Number + PartialOrd, V: Array1<T>>(_: &V) -> T {
@@ -212,7 +191,7 @@ mod tests {
 
     #[test]
     fn test_len() {
-        let x = [1, 2, 3];
+        let x = vec![1, 2, 3];
         assert_eq!(3, x.len());
     }
 
@@ -237,7 +216,7 @@ mod tests {
     #[test]
     fn test_mut_iterator() {
         let mut x = vec![1, 2, 3];
-        x.iterator_mut(0).for_each(|v| *v *= 2);
+        x.iterator_mut(0).for_each(|v| *v = *v * 2);
         assert_eq!(vec![2, 4, 6], x);
     }
 

src/linalg/ndarray/matrix.rs

@@ -68,7 +68,7 @@ impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayBase<OwnedRepr<T>
 
 impl<T: Debug + Display + Copy + Sized> MutArrayView2<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
 
-impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)> for ArrayView<'_, T, Ix2> {
+impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)> for ArrayView<'a, T, Ix2> {
     fn get(&self, pos: (usize, usize)) -> &T {
         &self[[pos.0, pos.1]]
     }
@@ -144,9 +144,11 @@ impl<T: Number + RealNumber> EVDDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2>
 impl<T: Number + RealNumber> LUDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
 impl<T: Number + RealNumber> SVDDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
 
-impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayView<'_, T, Ix2> {}
+impl<'a, T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayView<'a, T, Ix2> {}
 
-impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)> for ArrayViewMut<'_, T, Ix2> {
+impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)>
+    for ArrayViewMut<'a, T, Ix2>
+{
     fn get(&self, pos: (usize, usize)) -> &T {
         &self[[pos.0, pos.1]]
     }
@@ -173,7 +175,9 @@ impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)> for ArrayVi
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)> for ArrayViewMut<'_, T, Ix2> {
+impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
+    for ArrayViewMut<'a, T, Ix2>
+{
     fn set(&mut self, pos: (usize, usize), x: T) {
         self[[pos.0, pos.1]] = x
     }
@@ -191,9 +195,9 @@ impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)> for ArrayVie
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> MutArrayView2<T> for ArrayViewMut<'_, T, Ix2> {}
+impl<'a, T: Debug + Display + Copy + Sized> MutArrayView2<T> for ArrayViewMut<'a, T, Ix2> {}
 
-impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayViewMut<'_, T, Ix2> {}
+impl<'a, T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayViewMut<'a, T, Ix2> {}
 
 #[cfg(test)]
 mod tests {
@@ -213,7 +217,7 @@ mod tests {
     fn test_iterator() {
         let a = arr2(&[[1, 2, 3], [4, 5, 6]]);
 
-        let v: Vec<i32> = a.iterator(0).copied().collect();
+        let v: Vec<i32> = a.iterator(0).map(|&v| v).collect();
         assert_eq!(v, vec!(1, 2, 3, 4, 5, 6));
     }
 
@@ -232,7 +236,7 @@ mod tests {
         let x = arr2(&[[1, 2, 3], [4, 5, 6]]);
         let x_slice = Array2::slice(&x, 0..2, 1..2);
         assert_eq!((2, 1), x_slice.shape());
-        let v: Vec<i32> = x_slice.iterator(0).copied().collect();
+        let v: Vec<i32> = x_slice.iterator(0).map(|&v| v).collect();
         assert_eq!(v, [2, 5]);
     }
 
@@ -241,11 +245,11 @@ mod tests {
         let x = arr2(&[[1, 2, 3], [4, 5, 6]]);
         let x_slice = Array2::slice(&x, 0..2, 0..3);
         assert_eq!(
-            x_slice.iterator(0).copied().collect::<Vec<i32>>(),
+            x_slice.iterator(0).map(|&v| v).collect::<Vec<i32>>(),
             vec![1, 2, 3, 4, 5, 6]
         );
         assert_eq!(
-            x_slice.iterator(1).copied().collect::<Vec<i32>>(),
+            x_slice.iterator(1).map(|&v| v).collect::<Vec<i32>>(),
             vec![1, 4, 2, 5, 3, 6]
         );
     }
@@ -275,8 +279,8 @@ mod tests {
     fn test_c_from_iterator() {
         let data = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
         let a: NDArray2<i32> = Array2::from_iterator(data.clone().into_iter(), 4, 3, 0);
-        println!("{a}");
+        println!("{}", a);
         let a: NDArray2<i32> = Array2::from_iterator(data.into_iter(), 4, 3, 1);
-        println!("{a}");
+        println!("{}", a);
     }
 }

src/linalg/ndarray/vector.rs

@@ -41,7 +41,7 @@ impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for ArrayBase<OwnedRepr<T>
 
 impl<T: Debug + Display + Copy + Sized> MutArrayView1<T> for ArrayBase<OwnedRepr<T>, Ix1> {}
 
-impl<T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayView<'_, T, Ix1> {
+impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayView<'a, T, Ix1> {
     fn get(&self, i: usize) -> &T {
         &self[i]
     }
@@ -60,9 +60,9 @@ impl<T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayView<'_, T,
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for ArrayView<'_, T, Ix1> {}
+impl<'a, T: Debug + Display + Copy + Sized> ArrayView1<T> for ArrayView<'a, T, Ix1> {}
 
-impl<T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayViewMut<'_, T, Ix1> {
+impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayViewMut<'a, T, Ix1> {
     fn get(&self, i: usize) -> &T {
         &self[i]
     }
@@ -81,7 +81,7 @@ impl<T: Debug + Display + Copy + Sized> BaseArray<T, usize> for ArrayViewMut<'_,
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> MutArray<T, usize> for ArrayViewMut<'_, T, Ix1> {
+impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, usize> for ArrayViewMut<'a, T, Ix1> {
     fn set(&mut self, i: usize, x: T) {
         self[i] = x;
     }
@@ -92,8 +92,8 @@ impl<T: Debug + Display + Copy + Sized> MutArray<T, usize> for ArrayViewMut<'_,
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> ArrayView1<T> for ArrayViewMut<'_, T, Ix1> {}
-impl<T: Debug + Display + Copy + Sized> MutArrayView1<T> for ArrayViewMut<'_, T, Ix1> {}
+impl<'a, T: Debug + Display + Copy + Sized> ArrayView1<T> for ArrayViewMut<'a, T, Ix1> {}
+impl<'a, T: Debug + Display + Copy + Sized> MutArrayView1<T> for ArrayViewMut<'a, T, Ix1> {}
 
 impl<T: Debug + Display + Copy + Sized> Array1<T> for ArrayBase<OwnedRepr<T>, Ix1> {
     fn slice<'a>(&'a self, range: Range<usize>) -> Box<dyn ArrayView1<T> + 'a> {
@@ -152,7 +152,7 @@ mod tests {
     fn test_iterator() {
         let a = arr1(&[1, 2, 3]);
 
-        let v: Vec<i32> = a.iterator(0).copied().collect();
+        let v: Vec<i32> = a.iterator(0).map(|&v| v).collect();
         assert_eq!(v, vec!(1, 2, 3));
     }
 

src/linalg/traits/cholesky.rs

@@ -15,7 +15,7 @@
 //!                 &[25., 15., -5.],
 //!                 &[15., 18., 0.],
 //!                 &[-5., 0., 11.]
-//!         ]).unwrap();
+//!         ]);
 //!
 //! let cholesky = A.cholesky().unwrap();
 //! let lower_triangular: DenseMatrix<f64> = cholesky.L();
@@ -175,14 +175,11 @@ mod tests {
     )]
     #[test]
     fn cholesky_decompose() {
-        let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]])
-            .unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]]);
         let l =
-            DenseMatrix::from_2d_array(&[&[5.0, 0.0, 0.0], &[3.0, 3.0, 0.0], &[-1.0, 1.0, 3.0]])
-                .unwrap();
+            DenseMatrix::from_2d_array(&[&[5.0, 0.0, 0.0], &[3.0, 3.0, 0.0], &[-1.0, 1.0, 3.0]]);
         let u =
-            DenseMatrix::from_2d_array(&[&[5.0, 3.0, -1.0], &[0.0, 3.0, 1.0], &[0.0, 0.0, 3.0]])
-                .unwrap();
+            DenseMatrix::from_2d_array(&[&[5.0, 3.0, -1.0], &[0.0, 3.0, 1.0], &[0.0, 0.0, 3.0]]);
         let cholesky = a.cholesky().unwrap();
 
         assert!(relative_eq!(cholesky.L().abs(), l.abs(), epsilon = 1e-4));
@@ -200,10 +197,9 @@ mod tests {
     )]
     #[test]
     fn cholesky_solve_mut() {
-        let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]])
-            .unwrap();
-        let b = DenseMatrix::from_2d_array(&[&[40., 51., 28.]]).unwrap();
-        let expected = DenseMatrix::from_2d_array(&[&[1.0, 2.0, 3.0]]).unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]]);
+        let b = DenseMatrix::from_2d_array(&[&[40., 51., 28.]]);
+        let expected = DenseMatrix::from_2d_array(&[&[1.0, 2.0, 3.0]]);
 
         let cholesky = a.cholesky().unwrap();
 

src/linalg/traits/evd.rs

@@ -19,7 +19,7 @@
 //!                  &[0.9000, 0.4000, 0.7000],
 //!                  &[0.4000, 0.5000, 0.3000],
 //!                  &[0.7000, 0.3000, 0.8000],
-//!         ]).unwrap();
+//!         ]);
 //!
 //! let evd = A.evd(true).unwrap();
 //! let eigenvectors: DenseMatrix<f64> = evd.V;
@@ -66,7 +66,7 @@ pub trait EVDDecomposable<T: Number + RealNumber>: Array2<T> {
     fn evd_mut(mut self, symmetric: bool) -> Result<EVD<T, Self>, Failed> {
         let (nrows, ncols) = self.shape();
         if ncols != nrows {
-            panic!("Matrix is not square: {nrows} x {ncols}");
+            panic!("Matrix is not square: {} x {}", nrows, ncols);
         }
 
         let n = nrows;
@@ -820,8 +820,7 @@ mod tests {
             &[0.9000, 0.4000, 0.7000],
             &[0.4000, 0.5000, 0.3000],
             &[0.7000, 0.3000, 0.8000],
-        ])
-        .unwrap();
+        ]);
 
         let eigen_values: Vec<f64> = vec![1.7498382, 0.3165784, 0.1335834];
 
@@ -829,8 +828,7 @@ mod tests {
             &[0.6881997, -0.07121225, 0.7220180],
             &[0.3700456, 0.89044952, -0.2648886],
             &[0.6240573, -0.44947578, -0.6391588],
-        ])
-        .unwrap();
+        ]);
 
         let evd = A.evd(true).unwrap();
 
@@ -839,9 +837,11 @@ mod tests {
             evd.V.abs(),
             epsilon = 1e-4
         ));
-        for (i, eigen_values_i) in eigen_values.iter().enumerate() {
-            assert!((eigen_values_i - evd.d[i]).abs() < 1e-4);
-            assert!((0f64 - evd.e[i]).abs() < f64::EPSILON);
+        for i in 0..eigen_values.len() {
+            assert!((eigen_values[i] - evd.d[i]).abs() < 1e-4);
+        }
+        for i in 0..eigen_values.len() {
+            assert!((0f64 - evd.e[i]).abs() < std::f64::EPSILON);
         }
     }
     #[cfg_attr(
@@ -854,8 +854,7 @@ mod tests {
             &[0.9000, 0.4000, 0.7000],
             &[0.4000, 0.5000, 0.3000],
             &[0.8000, 0.3000, 0.8000],
-        ])
-        .unwrap();
+        ]);
 
         let eigen_values: Vec<f64> = vec![1.79171122, 0.31908143, 0.08920735];
 
@@ -863,8 +862,7 @@ mod tests {
             &[0.7178958, 0.05322098, 0.6812010],
             &[0.3837711, -0.84702111, -0.1494582],
             &[0.6952105, 0.43984484, -0.7036135],
-        ])
-        .unwrap();
+        ]);
 
         let evd = A.evd(false).unwrap();
 
@@ -873,9 +871,11 @@ mod tests {
             evd.V.abs(),
             epsilon = 1e-4
         ));
-        for (i, eigen_values_i) in eigen_values.iter().enumerate() {
-            assert!((eigen_values_i - evd.d[i]).abs() < 1e-4);
-            assert!((0f64 - evd.e[i]).abs() < f64::EPSILON);
+        for i in 0..eigen_values.len() {
+            assert!((eigen_values[i] - evd.d[i]).abs() < 1e-4);
+        }
+        for i in 0..eigen_values.len() {
+            assert!((0f64 - evd.e[i]).abs() < std::f64::EPSILON);
         }
     }
     #[cfg_attr(
@@ -889,8 +889,7 @@ mod tests {
             &[4.0, -1.0, 1.0, 1.0],
             &[1.0, 1.0, 3.0, -2.0],
             &[1.0, 1.0, 4.0, -1.0],
-        ])
-        .unwrap();
+        ]);
 
         let eigen_values_d: Vec<f64> = vec![0.0, 2.0, 2.0, 0.0];
         let eigen_values_e: Vec<f64> = vec![2.2361, 0.9999, -0.9999, -2.2361];
@@ -900,8 +899,7 @@ mod tests {
             &[-0.6707, 0.1059, 0.901, 0.6289],
             &[0.9159, -0.1378, 0.3816, 0.0806],
             &[0.6707, 0.1059, 0.901, -0.6289],
-        ])
-        .unwrap();
+        ]);
 
         let evd = A.evd(false).unwrap();
 
@@ -910,11 +908,11 @@ mod tests {
             evd.V.abs(),
             epsilon = 1e-4
         ));
-        for (i, eigen_values_d_i) in eigen_values_d.iter().enumerate() {
-            assert!((eigen_values_d_i - evd.d[i]).abs() < 1e-4);
+        for i in 0..eigen_values_d.len() {
+            assert!((eigen_values_d[i] - evd.d[i]).abs() < 1e-4);
         }
-        for (i, eigen_values_e_i) in eigen_values_e.iter().enumerate() {
-            assert!((eigen_values_e_i - evd.e[i]).abs() < 1e-4);
+        for i in 0..eigen_values_e.len() {
+            assert!((eigen_values_e[i] - evd.e[i]).abs() < 1e-4);
         }
     }
 }

src/linalg/traits/high_order.rs

@@ -12,9 +12,9 @@ pub trait HighOrderOperations<T: Number>: Array2<T> {
     /// use smartcore::linalg::traits::high_order::HighOrderOperations;
     /// use smartcore::linalg::basic::arrays::Array2;
     ///
-    /// let a = DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.]]).unwrap();
-    /// let b = DenseMatrix::from_2d_array(&[&[5., 6.], &[7., 8.], &[9., 10.]]).unwrap();
-    /// let expected = DenseMatrix::from_2d_array(&[&[71., 80.], &[92., 104.]]).unwrap();
+    /// let a = DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.]]);
+    /// let b = DenseMatrix::from_2d_array(&[&[5., 6.], &[7., 8.], &[9., 10.]]);
+    /// let expected = DenseMatrix::from_2d_array(&[&[71., 80.], &[92., 104.]]);
     ///
     /// assert_eq!(a.ab(true, &b, false), expected);
     /// ```

src/linalg/traits/lu.rs

@@ -18,7 +18,7 @@
 //!                  &[1., 2., 3.],
 //!                  &[0., 1., 5.],
 //!                  &[5., 6., 0.]
-//!         ]).unwrap();
+//!         ]);
 //!
 //! let lu = A.lu().unwrap();
 //! let lower: DenseMatrix<f64> = lu.L();
@@ -126,7 +126,7 @@ impl<T: Number + RealNumber, M: Array2<T>> LU<T, M> {
         let (m, n) = self.LU.shape();
 
         if m != n {
-            panic!("Matrix is not square: {m}x{n}");
+            panic!("Matrix is not square: {}x{}", m, n);
         }
 
         let mut inv = M::zeros(n, n);
@@ -143,7 +143,10 @@ impl<T: Number + RealNumber, M: Array2<T>> LU<T, M> {
         let (b_m, b_n) = b.shape();
 
         if b_m != m {
-            panic!("Row dimensions do not agree: A is {m} x {n}, but B is {b_m} x {b_n}");
+            panic!(
+                "Row dimensions do not agree: A is {} x {}, but B is {} x {}",
+                m, n, b_m, b_n
+            );
         }
 
         if self.singular {
@@ -263,13 +266,13 @@ mod tests {
     )]
     #[test]
     fn decompose() {
-        let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]).unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]);
         let expected_L =
-            DenseMatrix::from_2d_array(&[&[1., 0., 0.], &[0., 1., 0.], &[0.2, 0.8, 1.]]).unwrap();
+            DenseMatrix::from_2d_array(&[&[1., 0., 0.], &[0., 1., 0.], &[0.2, 0.8, 1.]]);
         let expected_U =
-            DenseMatrix::from_2d_array(&[&[5., 6., 0.], &[0., 1., 5.], &[0., 0., -1.]]).unwrap();
+            DenseMatrix::from_2d_array(&[&[5., 6., 0.], &[0., 1., 5.], &[0., 0., -1.]]);
         let expected_pivot =
-            DenseMatrix::from_2d_array(&[&[0., 0., 1.], &[0., 1., 0.], &[1., 0., 0.]]).unwrap();
+            DenseMatrix::from_2d_array(&[&[0., 0., 1.], &[0., 1., 0.], &[1., 0., 0.]]);
         let lu = a.lu().unwrap();
         assert!(relative_eq!(lu.L(), expected_L, epsilon = 1e-4));
         assert!(relative_eq!(lu.U(), expected_U, epsilon = 1e-4));
@@ -281,10 +284,9 @@ mod tests {
     )]
     #[test]
     fn inverse() {
-        let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]).unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]);
         let expected =
-            DenseMatrix::from_2d_array(&[&[-6.0, 3.6, 1.4], &[5.0, -3.0, -1.0], &[-1.0, 0.8, 0.2]])
-                .unwrap();
+            DenseMatrix::from_2d_array(&[&[-6.0, 3.6, 1.4], &[5.0, -3.0, -1.0], &[-1.0, 0.8, 0.2]]);
         let a_inv = a.lu().and_then(|lu| lu.inverse()).unwrap();
         assert!(relative_eq!(a_inv, expected, epsilon = 1e-4));
     }

src/linalg/traits/qr.rs

@@ -13,7 +13,7 @@
 //!                 &[0.9, 0.4, 0.7],
 //!                 &[0.4, 0.5, 0.3],
 //!                 &[0.7, 0.3, 0.8]
-//!         ]).unwrap();
+//!         ]);
 //!
 //! let qr = A.qr().unwrap();
 //! let orthogonal: DenseMatrix<f64> = qr.Q();
@@ -102,7 +102,10 @@ impl<T: Number + RealNumber, M: Array2<T>> QR<T, M> {
         let (b_nrows, b_ncols) = b.shape();
 
         if b_nrows != m {
-            panic!("Row dimensions do not agree: A is {m} x {n}, but B is {b_nrows} x {b_ncols}");
+            panic!(
+                "Row dimensions do not agree: A is {} x {}, but B is {} x {}",
+                m, n, b_nrows, b_ncols
+            );
         }
 
         if self.singular {
@@ -201,20 +204,17 @@ mod tests {
     )]
     #[test]
     fn decompose() {
-        let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]])
-            .unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
         let q = DenseMatrix::from_2d_array(&[
             &[-0.7448, 0.2436, 0.6212],
             &[-0.331, -0.9432, -0.027],
             &[-0.5793, 0.2257, -0.7832],
-        ])
-        .unwrap();
+        ]);
         let r = DenseMatrix::from_2d_array(&[
             &[-1.2083, -0.6373, -1.0842],
             &[0.0, -0.3064, 0.0682],
             &[0.0, 0.0, -0.1999],
-        ])
-        .unwrap();
+        ]);
         let qr = a.qr().unwrap();
         assert!(relative_eq!(qr.Q().abs(), q.abs(), epsilon = 1e-4));
         assert!(relative_eq!(qr.R().abs(), r.abs(), epsilon = 1e-4));
@@ -226,15 +226,13 @@ mod tests {
     )]
     #[test]
     fn qr_solve_mut() {
-        let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]])
-            .unwrap();
-        let b = DenseMatrix::from_2d_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]).unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
+        let b = DenseMatrix::from_2d_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]);
         let expected_w = DenseMatrix::from_2d_array(&[
             &[-0.2027027, -1.2837838],
             &[0.8783784, 2.2297297],
             &[0.4729730, 0.6621622],
-        ])
-        .unwrap();
+        ]);
         let w = a.qr_solve_mut(b).unwrap();
         assert!(relative_eq!(w, expected_w, epsilon = 1e-2));
     }

src/linalg/traits/stats.rs

@@ -136,12 +136,13 @@ pub trait MatrixPreprocessing<T: RealNumber>: MutArrayView2<T> + Clone {
     /// ```rust
     /// use smartcore::linalg::basic::matrix::DenseMatrix;
     /// use smartcore::linalg::traits::stats::MatrixPreprocessing;
-    /// let mut a = DenseMatrix::from_2d_array(&[&[0., 2., 3.], &[-5., -6., -7.]]).unwrap();
-    /// let expected = DenseMatrix::from_2d_array(&[&[0., 1., 1.],&[0., 0., 0.]]).unwrap();
+    /// let mut a = DenseMatrix::from_2d_array(&[&[0., 2., 3.], &[-5., -6., -7.]]);
+    /// let expected = DenseMatrix::from_2d_array(&[&[0., 1., 1.],&[0., 0., 0.]]);
     /// a.binarize_mut(0.);
     ///
     /// assert_eq!(a, expected);
     /// ```
+
     fn binarize_mut(&mut self, threshold: T) {
         let (nrows, ncols) = self.shape();
         for row in 0..nrows {
@@ -158,8 +159,8 @@ pub trait MatrixPreprocessing<T: RealNumber>: MutArrayView2<T> + Clone {
     /// ```rust
     /// use smartcore::linalg::basic::matrix::DenseMatrix;
     /// use smartcore::linalg::traits::stats::MatrixPreprocessing;
-    /// let a = DenseMatrix::from_2d_array(&[&[0., 2., 3.], &[-5., -6., -7.]]).unwrap();
-    /// let expected = DenseMatrix::from_2d_array(&[&[0., 1., 1.],&[0., 0., 0.]]).unwrap();
+    /// let a = DenseMatrix::from_2d_array(&[&[0., 2., 3.], &[-5., -6., -7.]]);
+    /// let expected = DenseMatrix::from_2d_array(&[&[0., 1., 1.],&[0., 0., 0.]]);
     ///
     /// assert_eq!(a.binarize(0.), expected);
     /// ```
@@ -185,8 +186,7 @@ mod tests {
             &[1., 2., 3., 1., 2.],
             &[4., 5., 6., 3., 4.],
             &[7., 8., 9., 5., 6.],
-        ])
-        .unwrap();
+        ]);
         let expected_0 = vec![4., 5., 6., 3., 4.];
         let expected_1 = vec![1.8, 4.4, 7.];
 
@@ -196,7 +196,7 @@ mod tests {
 
     #[test]
     fn test_var() {
-        let m = DenseMatrix::from_2d_array(&[&[1., 2., 3., 4.], &[5., 6., 7., 8.]]).unwrap();
+        let m = DenseMatrix::from_2d_array(&[&[1., 2., 3., 4.], &[5., 6., 7., 8.]]);
         let expected_0 = vec![4., 4., 4., 4.];
         let expected_1 = vec![1.25, 1.25];
 
@@ -211,13 +211,12 @@ mod tests {
         let m = DenseMatrix::from_2d_array(&[
             &[0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25],
             &[0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25],
-        ])
-        .unwrap();
+        ]);
         let expected_0 = vec![0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0];
         let expected_1 = vec![1.25, 1.25];
 
-        assert!(m.var(0).approximate_eq(&expected_0, f64::EPSILON));
-        assert!(m.var(1).approximate_eq(&expected_1, f64::EPSILON));
+        assert!(m.var(0).approximate_eq(&expected_0, std::f64::EPSILON));
+        assert!(m.var(1).approximate_eq(&expected_1, std::f64::EPSILON));
         assert_eq!(
             m.mean(0),
             vec![0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25]
@@ -231,8 +230,7 @@ mod tests {
             &[1., 2., 3., 1., 2.],
             &[4., 5., 6., 3., 4.],
             &[7., 8., 9., 5., 6.],
-        ])
-        .unwrap();
+        ]);
         let expected_0 = vec![
             2.449489742783178,
             2.449489742783178,
@@ -253,10 +251,10 @@ mod tests {
     #[test]
     fn test_scale() {
         let m: DenseMatrix<f64> =
-            DenseMatrix::from_2d_array(&[&[1., 2., 3., 4.], &[5., 6., 7., 8.]]).unwrap();
+            DenseMatrix::from_2d_array(&[&[1., 2., 3., 4.], &[5., 6., 7., 8.]]);
 
         let expected_0: DenseMatrix<f64> =
-            DenseMatrix::from_2d_array(&[&[-1., -1., -1., -1.], &[1., 1., 1., 1.]]).unwrap();
+            DenseMatrix::from_2d_array(&[&[-1., -1., -1., -1.], &[1., 1., 1., 1.]]);
         let expected_1: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
             &[
                 -1.3416407864998738,
@@ -270,8 +268,7 @@ mod tests {
                 0.4472135954999579,
                 1.3416407864998738,
             ],
-        ])
-        .unwrap();
+        ]);
 
         assert_eq!(m.mean(0), vec![3.0, 4.0, 5.0, 6.0]);
         assert_eq!(m.mean(1), vec![2.5, 6.5]);
@@ -289,7 +286,7 @@ mod tests {
         }
 
         {
-            let mut m = m;
+            let mut m = m.clone();
             m.standard_scale_mut(&m.mean(1), &m.std(1), 1);
             assert_eq!(&m, &expected_1);
         }

src/linalg/traits/svd.rs

@@ -17,7 +17,7 @@
 //!                 &[0.9, 0.4, 0.7],
 //!                 &[0.4, 0.5, 0.3],
 //!                 &[0.7, 0.3, 0.8]
-//!         ]).unwrap();
+//!         ]);
 //!
 //! let svd = A.svd().unwrap();
 //! let u: DenseMatrix<f64> = svd.U;
@@ -48,9 +48,11 @@ pub struct SVD<T: Number + RealNumber, M: SVDDecomposable<T>> {
     pub V: M,
     /// Singular values of the original matrix
     pub s: Vec<T>,
+    ///
     m: usize,
+    ///
     n: usize,
-    /// Tolerance
+    ///
     tol: T,
 }
 
@@ -487,8 +489,7 @@ mod tests {
             &[0.9000, 0.4000, 0.7000],
             &[0.4000, 0.5000, 0.3000],
             &[0.7000, 0.3000, 0.8000],
-        ])
-        .unwrap();
+        ]);
 
         let s: Vec<f64> = vec![1.7498382, 0.3165784, 0.1335834];
 
@@ -496,22 +497,20 @@ mod tests {
             &[0.6881997, -0.07121225, 0.7220180],
             &[0.3700456, 0.89044952, -0.2648886],
             &[0.6240573, -0.44947578, -0.639158],
-        ])
-        .unwrap();
+        ]);
 
         let V = DenseMatrix::from_2d_array(&[
             &[0.6881997, -0.07121225, 0.7220180],
             &[0.3700456, 0.89044952, -0.2648886],
             &[0.6240573, -0.44947578, -0.6391588],
-        ])
-        .unwrap();
+        ]);
 
         let svd = A.svd().unwrap();
 
         assert!(relative_eq!(V.abs(), svd.V.abs(), epsilon = 1e-4));
         assert!(relative_eq!(U.abs(), svd.U.abs(), epsilon = 1e-4));
-        for (i, s_i) in s.iter().enumerate() {
-            assert!((s_i - svd.s[i]).abs() < 1e-4);
+        for i in 0..s.len() {
+            assert!((s[i] - svd.s[i]).abs() < 1e-4);
         }
     }
     #[cfg_attr(
@@ -578,8 +577,7 @@ mod tests {
                 -0.2158704,
                 -0.27529472,
             ],
-        ])
-        .unwrap();
+        ]);
 
         let s: Vec<f64> = vec![
             3.8589375, 3.4396766, 2.6487176, 2.2317399, 1.5165054, 0.8109055, 0.2706515,
@@ -649,8 +647,7 @@ mod tests {
                 0.73034065,
                 -0.43965505,
             ],
-        ])
-        .unwrap();
+        ]);
 
         let V = DenseMatrix::from_2d_array(&[
             &[
@@ -710,15 +707,14 @@ mod tests {
                 0.1654796,
                 -0.32346758,
             ],
-        ])
-        .unwrap();
+        ]);
 
         let svd = A.svd().unwrap();
 
         assert!(relative_eq!(V.abs(), svd.V.abs(), epsilon = 1e-4));
         assert!(relative_eq!(U.abs(), svd.U.abs(), epsilon = 1e-4));
-        for (i, s_i) in s.iter().enumerate() {
-            assert!((s_i - svd.s[i]).abs() < 1e-4);
+        for i in 0..s.len() {
+            assert!((s[i] - svd.s[i]).abs() < 1e-4);
         }
     }
     #[cfg_attr(
@@ -727,11 +723,10 @@ mod tests {
     )]
     #[test]
     fn solve() {
-        let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]])
-            .unwrap();
-        let b = DenseMatrix::from_2d_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]).unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
+        let b = DenseMatrix::from_2d_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]);
         let expected_w =
-            DenseMatrix::from_2d_array(&[&[-0.20, -1.28], &[0.87, 2.22], &[0.47, 0.66]]).unwrap();
+            DenseMatrix::from_2d_array(&[&[-0.20, -1.28], &[0.87, 2.22], &[0.47, 0.66]]);
         let w = a.svd_solve_mut(b).unwrap();
         assert!(relative_eq!(w, expected_w, epsilon = 1e-2));
     }
@@ -742,8 +737,7 @@ mod tests {
     )]
     #[test]
     fn decompose_restore() {
-        let a =
-            DenseMatrix::from_2d_array(&[&[1.0, 2.0, 3.0, 4.0], &[5.0, 6.0, 7.0, 8.0]]).unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[1.0, 2.0, 3.0, 4.0], &[5.0, 6.0, 7.0, 8.0]]);
         let svd = a.svd().unwrap();
         let u: &DenseMatrix<f32> = &svd.U; //U
         let v: &DenseMatrix<f32> = &svd.V; // V

src/linear/bg_solver.rs

@@ -12,8 +12,7 @@
 //! pub struct BGSolver {}
 //! impl<'a, T: FloatNumber, X: Array2<T>> BiconjugateGradientSolver<'a, T, X> for BGSolver {}
 //!
-//! let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0.,
-//! 11.]]).unwrap();
+//! let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]]);
 //! let b = vec![40., 51., 28.];
 //! let expected = vec![1.0, 2.0, 3.0];
 //! let mut x = Vec::zeros(3);
@@ -27,9 +26,9 @@ use crate::error::Failed;
 use crate::linalg::basic::arrays::{Array, Array1, Array2, ArrayView1, MutArrayView1};
 use crate::numbers::floatnum::FloatNumber;
 
-/// Trait for Biconjugate Gradient Solver
+///
 pub trait BiconjugateGradientSolver<'a, T: FloatNumber, X: Array2<T>> {
-    /// Solve Ax = b
+    ///
     fn solve_mut(
         &self,
         a: &'a X,
@@ -109,7 +108,7 @@ pub trait BiconjugateGradientSolver<'a, T: FloatNumber, X: Array2<T>> {
         Ok(err)
     }
 
-    /// solve preconditioner
+    ///
     fn solve_preconditioner(&self, a: &'a X, b: &[T], x: &mut [T]) {
         let diag = Self::diag(a);
         let n = diag.len();
@@ -133,7 +132,7 @@ pub trait BiconjugateGradientSolver<'a, T: FloatNumber, X: Array2<T>> {
         y.copy_from(&x.xa(true, a));
     }
 
-    /// Extract the diagonal from a matrix
+    ///
     fn diag(a: &X) -> Vec<T> {
         let (nrows, ncols) = a.shape();
         let n = nrows.min(ncols);
@@ -159,10 +158,9 @@ mod tests {
 
     #[test]
     fn bg_solver() {
-        let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]])
-            .unwrap();
+        let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]]);
         let b = vec![40., 51., 28.];
-        let expected = [1.0, 2.0, 3.0];
+        let expected = vec![1.0, 2.0, 3.0];
 
         let mut x = Vec::zeros(3);
 

src/linear/elastic_net.rs

@@ -38,7 +38,7 @@
 //!               &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
 //!               &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
 //!               &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-//!          ]).unwrap();
+//!          ]);
 //!
 //! let y: Vec<f64> = vec![83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0,
 //!           100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9];
@@ -425,7 +425,10 @@ impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>>
 
         for (i, col_std_i) in col_std.iter().enumerate() {
             if (*col_std_i - TX::zero()).abs() < TX::epsilon() {
-                return Err(Failed::fit(&format!("Cannot rescale constant column {i}")));
+                return Err(Failed::fit(&format!(
+                    "Cannot rescale constant column {}",
+                    i
+                )));
             }
         }
 
@@ -511,8 +514,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<f64> = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
@@ -563,8 +565,7 @@ mod tests {
             &[17.0, 1918.0, 1.4054969025700674],
             &[18.0, 1929.0, 1.3271699396384906],
             &[19.0, 1915.0, 1.1373332337674806],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<f64> = vec![
             1.48, 2.72, 4.52, 5.72, 5.25, 4.07, 3.75, 4.75, 6.77, 4.72, 6.78, 6.79, 8.3, 7.42,
@@ -629,7 +630,7 @@ mod tests {
     //         &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
     //         &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
     //         &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-    //     ]).unwrap();
+    //     ]);
 
     //     let y = vec![
     //         83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,

src/linear/lasso.rs

@@ -356,7 +356,10 @@ impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> Las
 
         for (i, col_std_i) in col_std.iter().enumerate() {
             if (*col_std_i - TX::zero()).abs() < TX::epsilon() {
-                return Err(Failed::fit(&format!("Cannot rescale constant column {i}")));
+                return Err(Failed::fit(&format!(
+                    "Cannot rescale constant column {}",
+                    i
+                )));
             }
         }
 
@@ -418,8 +421,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<f64> = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,

src/linear/lasso_optimizer.rs

@@ -16,7 +16,7 @@ use crate::linalg::basic::arrays::{Array1, Array2, ArrayView1, MutArray, MutArra
 use crate::linear::bg_solver::BiconjugateGradientSolver;
 use crate::numbers::floatnum::FloatNumber;
 
-/// Interior Point Optimizer
+///
 pub struct InteriorPointOptimizer<T: FloatNumber, X: Array2<T>> {
     ata: X,
     d1: Vec<T>,
@@ -25,8 +25,9 @@ pub struct InteriorPointOptimizer<T: FloatNumber, X: Array2<T>> {
     prs: Vec<T>,
 }
 
+///
 impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
-    /// Initialize a new Interior Point Optimizer
+    ///
     pub fn new(a: &X, n: usize) -> InteriorPointOptimizer<T, X> {
         InteriorPointOptimizer {
             ata: a.ab(true, a, false),
@@ -37,7 +38,7 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
         }
     }
 
-    /// Run the optimization
+    ///
     pub fn optimize(
         &mut self,
         x: &X,
@@ -100,7 +101,7 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
 
             // CALCULATE DUALITY GAP
             let xnu = nu.xa(false, x);
-            let max_xnu = xnu.norm(f64::INFINITY);
+            let max_xnu = xnu.norm(std::f64::INFINITY);
             if max_xnu > lambda_f64 {
                 let lnu = T::from_f64(lambda_f64 / max_xnu).unwrap();
                 nu.mul_scalar_mut(lnu);
@@ -207,6 +208,7 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
         Ok(w)
     }
 
+    ///
     fn sumlogneg(f: &X) -> T {
         let (n, _) = f.shape();
         let mut sum = T::zero();
@@ -218,9 +220,11 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
     }
 }
 
+///
 impl<'a, T: FloatNumber, X: Array2<T>> BiconjugateGradientSolver<'a, T, X>
     for InteriorPointOptimizer<T, X>
 {
+    ///
     fn solve_preconditioner(&self, a: &'a X, b: &[T], x: &mut [T]) {
         let (_, p) = a.shape();
 
@@ -230,6 +234,7 @@ impl<'a, T: FloatNumber, X: Array2<T>> BiconjugateGradientSolver<'a, T, X>
         }
     }
 
+    ///
     fn mat_vec_mul(&self, _: &X, x: &Vec<T>, y: &mut Vec<T>) {
         let (_, p) = self.ata.shape();
         let x_slice = Vec::from_slice(x.slice(0..p).as_ref());
@@ -241,6 +246,7 @@ impl<'a, T: FloatNumber, X: Array2<T>> BiconjugateGradientSolver<'a, T, X>
         }
     }
 
+    ///
     fn mat_t_vec_mul(&self, a: &X, x: &Vec<T>, y: &mut Vec<T>) {
         self.mat_vec_mul(a, x, y);
     }

src/linear/linear_regression.rs

@@ -40,7 +40,7 @@
 //!               &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
 //!               &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
 //!               &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-//!          ]).unwrap();
+//!          ]);
 //!
 //! let y: Vec<f64> = vec![83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0,
 //!           100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9];
@@ -341,8 +341,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<f64> = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8,
@@ -394,7 +393,7 @@ mod tests {
     //         &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
     //         &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
     //         &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-    //     ]).unwrap();
+    //     ]);
 
     //     let y = vec![
     //         83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,

src/linear/logistic_regression.rs

@@ -35,7 +35,7 @@
 //!           &[4.9, 2.4, 3.3, 1.0],
 //!           &[6.6, 2.9, 4.6, 1.3],
 //!           &[5.2, 2.7, 3.9, 1.4],
-//!           ]).unwrap();
+//!           ]);
 //! let y: Vec<i32> = vec![
 //!           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
 //! ];
@@ -71,14 +71,19 @@ use crate::optimization::line_search::Backtracking;
 use crate::optimization::FunctionOrder;
 
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
-#[derive(Debug, Clone, Eq, PartialEq, Default)]
+#[derive(Debug, Clone, Eq, PartialEq)]
 /// Solver options for Logistic regression. Right now only LBFGS solver is supported.
 pub enum LogisticRegressionSolverName {
     /// Limited-memory Broyden–Fletcher–Goldfarb–Shanno method, see [LBFGS paper](http://users.iems.northwestern.edu/~nocedal/lbfgsb.html)
-    #[default]
     LBFGS,
 }
 
+impl Default for LogisticRegressionSolverName {
+    fn default() -> Self {
+        LogisticRegressionSolverName::LBFGS
+    }
+}
+
 /// Logistic Regression parameters
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
@@ -183,11 +188,14 @@ pub struct LogisticRegression<
 }
 
 trait ObjectiveFunction<T: Number + FloatNumber, X: Array2<T>> {
+    ///
     fn f(&self, w_bias: &[T]) -> T;
 
+    ///
     #[allow(clippy::ptr_arg)]
     fn df(&self, g: &mut Vec<T>, w_bias: &Vec<T>);
 
+    ///
     #[allow(clippy::ptr_arg)]
     fn partial_dot(w: &[T], x: &X, v_col: usize, m_row: usize) -> T {
         let mut sum = T::zero();
@@ -258,8 +266,8 @@ impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y:
     }
 }
 
-impl<T: Number + FloatNumber, X: Array2<T>> ObjectiveFunction<T, X>
-    for BinaryObjectiveFunction<'_, T, X>
+impl<'a, T: Number + FloatNumber, X: Array2<T>> ObjectiveFunction<T, X>
+    for BinaryObjectiveFunction<'a, T, X>
 {
     fn f(&self, w_bias: &[T]) -> T {
         let mut f = T::zero();
@@ -313,8 +321,8 @@ struct MultiClassObjectiveFunction<'a, T: Number + FloatNumber, X: Array2<T>> {
     _phantom_t: PhantomData<T>,
 }
 
-impl<T: Number + FloatNumber + RealNumber, X: Array2<T>> ObjectiveFunction<T, X>
-    for MultiClassObjectiveFunction<'_, T, X>
+impl<'a, T: Number + FloatNumber + RealNumber, X: Array2<T>> ObjectiveFunction<T, X>
+    for MultiClassObjectiveFunction<'a, T, X>
 {
     fn f(&self, w_bias: &[T]) -> T {
         let mut f = T::zero();
@@ -413,7 +421,7 @@ impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y:
     /// Fits Logistic Regression to your data.
     /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
     /// * `y` - target class values
-    /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values.
+    /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values.    
     pub fn fit(
         x: &X,
         y: &Y,
@@ -441,7 +449,8 @@ impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y:
 
         match k.cmp(&2) {
             Ordering::Less => Err(Failed::fit(&format!(
-                "incorrect number of classes: {k}. Should be >= 2."
+                "incorrect number of classes: {}. Should be >= 2.",
+                k
             ))),
             Ordering::Equal => {
                 let x0 = Vec::zeros(num_attributes + 1);
@@ -608,8 +617,7 @@ mod tests {
             &[10., -2.],
             &[8., 2.],
             &[9., 0.],
-        ])
-        .unwrap();
+        ]);
 
         let y = vec![0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 1];
 
@@ -626,21 +634,21 @@ mod tests {
         objective.df(&mut g, &vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
         objective.df(&mut g, &vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
 
-        assert!((g[0] + 33.000068218163484).abs() < f64::EPSILON);
+        assert!((g[0] + 33.000068218163484).abs() < std::f64::EPSILON);
 
-        let f = objective.f(&[1., 2., 3., 4., 5., 6., 7., 8., 9.]);
+        let f = objective.f(&vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
 
-        assert!((f - 408.0052230582765).abs() < f64::EPSILON);
+        assert!((f - 408.0052230582765).abs() < std::f64::EPSILON);
 
         let objective_reg = MultiClassObjectiveFunction {
             x: &x,
-            y,
+            y: y.clone(),
             k: 3,
             alpha: 1.0,
             _phantom_t: PhantomData,
         };
 
-        let f = objective_reg.f(&[1., 2., 3., 4., 5., 6., 7., 8., 9.]);
+        let f = objective_reg.f(&vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
         assert!((f - 487.5052).abs() < 1e-4);
 
         objective_reg.df(&mut g, &vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
@@ -669,8 +677,7 @@ mod tests {
             &[10., -2.],
             &[8., 2.],
             &[9., 0.],
-        ])
-        .unwrap();
+        ]);
 
         let y = vec![0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1];
 
@@ -686,22 +693,22 @@ mod tests {
         objective.df(&mut g, &vec![1., 2., 3.]);
         objective.df(&mut g, &vec![1., 2., 3.]);
 
-        assert!((g[0] - 26.051064349381285).abs() < f64::EPSILON);
-        assert!((g[1] - 10.239000702928523).abs() < f64::EPSILON);
-        assert!((g[2] - 3.869294270156324).abs() < f64::EPSILON);
+        assert!((g[0] - 26.051064349381285).abs() < std::f64::EPSILON);
+        assert!((g[1] - 10.239000702928523).abs() < std::f64::EPSILON);
+        assert!((g[2] - 3.869294270156324).abs() < std::f64::EPSILON);
 
-        let f = objective.f(&[1., 2., 3.]);
+        let f = objective.f(&vec![1., 2., 3.]);
 
-        assert!((f - 59.76994756647412).abs() < f64::EPSILON);
+        assert!((f - 59.76994756647412).abs() < std::f64::EPSILON);
 
         let objective_reg = BinaryObjectiveFunction {
             x: &x,
-            y,
+            y: y.clone(),
             alpha: 1.0,
             _phantom_t: PhantomData,
         };
 
-        let f = objective_reg.f(&[1., 2., 3.]);
+        let f = objective_reg.f(&vec![1., 2., 3.]);
         assert!((f - 62.2699).abs() < 1e-4);
 
         objective_reg.df(&mut g, &vec![1., 2., 3.]);
@@ -732,8 +739,7 @@ mod tests {
             &[10., -2.],
             &[8., 2.],
             &[9., 0.],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<i32> = vec![0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 1];
 
         let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
@@ -818,41 +824,37 @@ mod tests {
         assert!(reg_coeff_sum < coeff);
     }
 
-    //TODO: serialization for the new DenseMatrix needs to be implemented
-    #[cfg_attr(
-        all(target_arch = "wasm32", not(target_os = "wasi")),
-        wasm_bindgen_test::wasm_bindgen_test
-    )]
-    #[test]
-    #[cfg(feature = "serde")]
-    fn serde() {
-        let x: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
-            &[1., -5.],
-            &[2., 5.],
-            &[3., -2.],
-            &[1., 2.],
-            &[2., 0.],
-            &[6., -5.],
-            &[7., 5.],
-            &[6., -2.],
-            &[7., 2.],
-            &[6., 0.],
-            &[8., -5.],
-            &[9., 5.],
-            &[10., -2.],
-            &[8., 2.],
-            &[9., 0.],
-        ])
-        .unwrap();
-        let y: Vec<i32> = vec![0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 1];
+    // TODO: serialization for the new DenseMatrix needs to be implemented
+    // #[cfg_attr(all(target_arch = "wasm32", not(target_os = "wasi")), wasm_bindgen_test::wasm_bindgen_test)]
+    // #[test]
+    // #[cfg(feature = "serde")]
+    // fn serde() {
+    //     let x = DenseMatrix::from_2d_array(&[
+    //         &[1., -5.],
+    //         &[2., 5.],
+    //         &[3., -2.],
+    //         &[1., 2.],
+    //         &[2., 0.],
+    //         &[6., -5.],
+    //         &[7., 5.],
+    //         &[6., -2.],
+    //         &[7., 2.],
+    //         &[6., 0.],
+    //         &[8., -5.],
+    //         &[9., 5.],
+    //         &[10., -2.],
+    //         &[8., 2.],
+    //         &[9., 0.],
+    //     ]);
+    //     let y: Vec<i32> = vec![0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 1];
 
-        let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
+    //     let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
 
-        let deserialized_lr: LogisticRegression<f64, i32, DenseMatrix<f64>, Vec<i32>> =
-            serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
+    //     let deserialized_lr: LogisticRegression<f64, i32, DenseMatrix<f64>, Vec<i32>> =
+    //         serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
 
-        assert_eq!(lr, deserialized_lr);
-    }
+    //     assert_eq!(lr, deserialized_lr);
+    // }
 
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
@@ -881,8 +883,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<i32> = vec![0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 
         let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
@@ -895,7 +896,11 @@ mod tests {
 
         let y_hat = lr.predict(&x).unwrap();
 
-        let error: i32 = y.into_iter().zip(y_hat).map(|(a, b)| (a - b).abs()).sum();
+        let error: i32 = y
+            .into_iter()
+            .zip(y_hat.into_iter())
+            .map(|(a, b)| (a - b).abs())
+            .sum();
 
         assert!(error <= 1);
 
@@ -904,46 +909,4 @@ mod tests {
 
         assert!(reg_coeff_sum < coeff);
     }
-    #[cfg_attr(
-        all(target_arch = "wasm32", not(target_os = "wasi")),
-        wasm_bindgen_test::wasm_bindgen_test
-    )]
-    #[test]
-    fn lr_fit_predict_random() {
-        let x: DenseMatrix<f32> = DenseMatrix::rand(52181, 94);
-        let y1: Vec<i32> = vec![1; 2181];
-        let y2: Vec<i32> = vec![0; 50000];
-        let y: Vec<i32> = y1.into_iter().chain(y2).collect();
-
-        let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
-        let lr_reg = LogisticRegression::fit(
-            &x,
-            &y,
-            LogisticRegressionParameters::default().with_alpha(1.0),
-        )
-        .unwrap();
-
-        let y_hat = lr.predict(&x).unwrap();
-        let y_hat_reg = lr_reg.predict(&x).unwrap();
-
-        assert_eq!(y.len(), y_hat.len());
-        assert_eq!(y.len(), y_hat_reg.len());
-    }
-
-    #[test]
-    fn test_logit() {
-        let x: &DenseMatrix<f64> = &DenseMatrix::rand(52181, 94);
-        let y1: Vec<u32> = vec![1; 2181];
-        let y2: Vec<u32> = vec![0; 50000];
-        let y: &Vec<u32> = &(y1.into_iter().chain(y2).collect());
-        println!("y vec height: {:?}", y.len());
-        println!("x matrix shape: {:?}", x.shape());
-
-        let lr = LogisticRegression::fit(x, y, Default::default()).unwrap();
-        let y_hat = lr.predict(x).unwrap();
-
-        println!("y_hat shape: {:?}", y_hat.shape());
-
-        assert_eq!(y_hat.shape(), 52181);
-    }
 }

src/linear/ridge_regression.rs

@@ -40,7 +40,7 @@
 //!               &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
 //!               &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
 //!               &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-//!          ]).unwrap();
+//!          ]);
 //!
 //! let y: Vec<f64> = vec![83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0,
 //!           100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9];
@@ -71,16 +71,21 @@ use crate::numbers::basenum::Number;
 use crate::numbers::realnum::RealNumber;
 
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
-#[derive(Debug, Clone, Eq, PartialEq, Default)]
+#[derive(Debug, Clone, Eq, PartialEq)]
 /// Approach to use for estimation of regression coefficients. Cholesky is more efficient but SVD is more stable.
 pub enum RidgeRegressionSolverName {
     /// Cholesky decomposition, see [Cholesky](../../linalg/cholesky/index.html)
-    #[default]
     Cholesky,
     /// SVD decomposition, see [SVD](../../linalg/svd/index.html)
     SVD,
 }
 
+impl Default for RidgeRegressionSolverName {
+    fn default() -> Self {
+        RidgeRegressionSolverName::Cholesky
+    }
+}
+
 /// Ridge Regression parameters
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
@@ -379,7 +384,10 @@ impl<
 
         for (i, col_std_i) in col_std.iter().enumerate() {
             if (*col_std_i - TX::zero()).abs() < TX::epsilon() {
-                return Err(Failed::fit(&format!("Cannot rescale constant column {i}")));
+                return Err(Failed::fit(&format!(
+                    "Cannot rescale constant column {}",
+                    i
+                )));
             }
         }
 
@@ -455,8 +463,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<f64> = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
@@ -514,7 +521,7 @@ mod tests {
     //         &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
     //         &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
     //         &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-    //     ]).unwrap();
+    //     ]);
 
     //     let y = vec![
     //         83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,

src/metrics/cluster_hcv.rs

@@ -98,8 +98,8 @@ mod tests {
         let mut scores = HCVScore::new();
         scores.compute(&v1, &v2);
 
-        assert!((0.2548 - scores.homogeneity.unwrap()).abs() < 1e-4);
-        assert!((0.5440 - scores.completeness.unwrap()).abs() < 1e-4);
-        assert!((0.3471 - scores.v_measure.unwrap()).abs() < 1e-4);
+        assert!((0.2548 - scores.homogeneity.unwrap() as f64).abs() < 1e-4);
+        assert!((0.5440 - scores.completeness.unwrap() as f64).abs() < 1e-4);
+        assert!((0.3471 - scores.v_measure.unwrap() as f64).abs() < 1e-4);
     }
 }

src/metrics/cluster_helpers.rs

@@ -125,7 +125,7 @@ mod tests {
     fn entropy_test() {
         let v1 = vec![0, 0, 1, 1, 2, 0, 4];
 
-        assert!((1.2770 - entropy(&v1).unwrap()).abs() < 1e-4);
+        assert!((1.2770 - entropy(&v1).unwrap() as f64).abs() < 1e-4);
     }
 
     #[cfg_attr(

src/metrics/distance/mahalanobis.rs

@@ -25,7 +25,7 @@
 //!                   &[68., 590., 37.],
 //!                   &[69., 660., 46.],
 //!                   &[73., 600., 55.],
-//! ]).unwrap();
+//! ]);
 //!
 //! let a = data.mean_by(0);
 //! let b = vec![66., 640., 44.];
@@ -151,8 +151,7 @@ mod tests {
             &[68., 590., 37.],
             &[69., 660., 46.],
             &[73., 600., 55.],
-        ])
-        .unwrap();
+        ]);
 
         let a = data.mean_by(0);
         let b = vec![66., 640., 44.];

src/metrics/f1.rs

@@ -95,8 +95,8 @@ mod tests {
         let score1: f64 = F1::new_with(beta).get_score(&y_true, &y_pred);
         let score2: f64 = F1::new_with(beta).get_score(&y_true, &y_true);
 
-        println!("{score1:?}");
-        println!("{score2:?}");
+        println!("{:?}", score1);
+        println!("{:?}", score2);
 
         assert!((score1 - 0.57142857).abs() < 1e-8);
         assert!((score2 - 1.0).abs() < 1e-8);

src/metrics/mod.rs

@@ -37,7 +37,7 @@
 //!             &[4.9, 2.4, 3.3, 1.0],
 //!             &[6.6, 2.9, 4.6, 1.3],
 //!             &[5.2, 2.7, 3.9, 1.4],
-//!   ]).unwrap();
+//!   ]);
 //! let y: Vec<i8> = vec![
 //!             0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
 //!   ];

src/model_selection/hyper_tuning/grid_search.rs

@@ -3,9 +3,9 @@
 use crate::{
     api::{Predictor, SupervisedEstimator},
     error::{Failed, FailedError},
-    linalg::basic::arrays::{Array1, Array2},
-    numbers::basenum::Number,
+    linalg::basic::arrays::{Array2, Array1},
     numbers::realnum::RealNumber,
+    numbers::basenum::Number,
 };
 
 use crate::model_selection::{cross_validate, BaseKFold, CrossValidationResult};

src/model_selection/kfold.rs

@@ -213,17 +213,17 @@ mod tests {
 
         for t in &test_masks[0][0..11] {
             // TODO: this can be prob done better
-            assert!(*t)
+            assert_eq!(*t, true)
         }
         for t in &test_masks[0][11..22] {
-            assert!(!*t)
+            assert_eq!(*t, false)
         }
 
         for t in &test_masks[1][0..11] {
-            assert!(!*t)
+            assert_eq!(*t, false)
         }
         for t in &test_masks[1][11..22] {
-            assert!(*t)
+            assert_eq!(*t, true)
         }
     }
 
@@ -283,7 +283,9 @@ mod tests {
             (vec![0, 1, 2, 3, 7, 8, 9], vec![4, 5, 6]),
             (vec![0, 1, 2, 3, 4, 5, 6], vec![7, 8, 9]),
         ];
-        for ((train, test), (expected_train, expected_test)) in k.split(&x).zip(expected) {
+        for ((train, test), (expected_train, expected_test)) in
+            k.split(&x).into_iter().zip(expected)
+        {
             assert_eq!(test, expected_test);
             assert_eq!(train, expected_train);
         }
@@ -305,7 +307,9 @@ mod tests {
             (vec![0, 1, 2, 3, 7, 8, 9], vec![4, 5, 6]),
             (vec![0, 1, 2, 3, 4, 5, 6], vec![7, 8, 9]),
         ];
-        for ((train, test), (expected_train, expected_test)) in k.split(&x).zip(expected) {
+        for ((train, test), (expected_train, expected_test)) in
+            k.split(&x).into_iter().zip(expected)
+        {
             assert_eq!(test.len(), expected_test.len());
             assert_eq!(train.len(), expected_train.len());
         }

src/model_selection/mod.rs

@@ -36,7 +36,7 @@
 //!           &[4.9, 2.4, 3.3, 1.0],
 //!           &[6.6, 2.9, 4.6, 1.3],
 //!           &[5.2, 2.7, 3.9, 1.4],
-//!           ]).unwrap();
+//!           ]);
 //! let y: Vec<f64> = vec![
 //!           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
 //! ];
@@ -84,7 +84,7 @@
 //!           &[4.9, 2.4, 3.3, 1.0],
 //!           &[6.6, 2.9, 4.6, 1.3],
 //!           &[5.2, 2.7, 3.9, 1.4],
-//!           ]).unwrap();
+//!           ]);
 //! let y: Vec<i32> = vec![
 //!           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
 //! ];
@@ -169,7 +169,7 @@ pub fn train_test_split<
     let n_test = ((n as f32) * test_size) as usize;
 
     if n_test < 1 {
-        panic!("number of sample is too small {n}");
+        panic!("number of sample is too small {}", n);
     }
 
     let mut indices: Vec<usize> = (0..n).collect();
@@ -396,8 +396,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 
         let cv = KFold {
@@ -442,8 +441,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
         let y = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
             114.2, 115.7, 116.9,
@@ -491,8 +489,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<f64> = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
             114.2, 115.7, 116.9,
@@ -542,8 +539,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<i32> = vec![0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 
         let cv = KFold::default().with_n_splits(3);
@@ -557,6 +553,6 @@ mod tests {
             &accuracy,
         )
         .unwrap();
-        println!("{results:?}");
+        println!("{:?}", results);
     }
 }

src/naive_bayes/bernoulli.rs

@@ -19,14 +19,14 @@
 //!           &[0, 1, 0, 0, 1, 0],
 //!           &[0, 1, 0, 1, 0, 0],
 //!           &[0, 1, 1, 0, 0, 1],
-//! ]).unwrap();
+//! ]);
 //! let y: Vec<u32> = vec![0, 0, 0, 1];
 //!
 //! let nb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
 //!
 //! // Testing data point is:
 //! // Chinese Chinese Chinese Tokyo Japan
-//! let x_test = DenseMatrix::from_2d_array(&[&[0, 1, 1, 0, 0, 1]]).unwrap();
+//! let x_test = DenseMatrix::from_2d_array(&[&[0, 1, 1, 0, 0, 1]]);
 //! let y_hat = nb.predict(&x_test).unwrap();
 //! ```
 //!
@@ -258,7 +258,7 @@ impl<TY: Number + Ord + Unsigned> BernoulliNBDistribution<TY> {
     /// * `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 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>>(
@@ -271,18 +271,21 @@ impl<TY: Number + Ord + Unsigned> BernoulliNBDistribution<TY> {
         let y_samples = y.shape();
         if y_samples != n_samples {
             return Err(Failed::fit(&format!(
-                "Size of x should equal size of y; |x|=[{n_samples}], |y|=[{y_samples}]"
+                "Size of x should equal size of y; |x|=[{}], |y|=[{}]",
+                n_samples, y_samples
             )));
         }
 
         if n_samples == 0 {
             return Err(Failed::fit(&format!(
-                "Size of x and y should greater than 0; |x|=[{n_samples}]"
+                "Size of x and y should greater than 0; |x|=[{}]",
+                n_samples
             )));
         }
         if alpha < 0f64 {
             return Err(Failed::fit(&format!(
-                "Alpha should be greater than 0; |alpha|=[{alpha}]"
+                "Alpha should be greater than 0; |alpha|=[{}]",
+                alpha
             )));
         }
 
@@ -315,7 +318,8 @@ impl<TY: Number + Ord + Unsigned> BernoulliNBDistribution<TY> {
                 feature_in_class_counter[class_index][idx] +=
                     row_i.to_usize().ok_or_else(|| {
                         Failed::fit(&format!(
-                            "Elements of the matrix should be 1.0 or 0.0 |found|=[{row_i}]"
+                            "Elements of the matrix should be 1.0 or 0.0 |found|=[{}]",
+                            row_i
                         ))
                     })?;
             }
@@ -402,10 +406,10 @@ impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Arr
 {
     /// Fits BernoulliNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of
-    ///   features.
+    /// features.
     /// * `y` - vector with target values (classes) of length N.
     /// * `parameters` - additional parameters like class priors, alpha for smoothing and
-    ///   binarizing threshold.
+    /// binarizing threshold.
     pub fn fit(x: &X, y: &Y, parameters: BernoulliNBParameters<TX>) -> Result<Self, Failed> {
         let distribution = if let Some(threshold) = parameters.binarize {
             BernoulliNBDistribution::fit(
@@ -427,7 +431,6 @@ impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Arr
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
-    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         if let Some(threshold) = self.binarize {
@@ -528,8 +531,7 @@ mod tests {
             &[0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
             &[0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
             &[0.0, 1.0, 1.0, 0.0, 0.0, 1.0],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![0, 0, 0, 1];
         let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
 
@@ -560,7 +562,7 @@ mod tests {
 
         // Testing data point is:
         //  Chinese Chinese Chinese Tokyo Japan
-        let x_test = DenseMatrix::from_2d_array(&[&[0.0, 1.0, 1.0, 0.0, 0.0, 1.0]]).unwrap();
+        let x_test = DenseMatrix::from_2d_array(&[&[0.0, 1.0, 1.0, 0.0, 0.0, 1.0]]);
         let y_hat = bnb.predict(&x_test).unwrap();
 
         assert_eq!(y_hat, &[1]);
@@ -588,8 +590,7 @@ mod tests {
             &[2, 0, 3, 3, 1, 2, 0, 2, 4, 1],
             &[2, 4, 0, 4, 2, 4, 1, 3, 1, 4],
             &[0, 2, 2, 3, 4, 0, 4, 4, 4, 4],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![2, 2, 0, 0, 0, 2, 1, 1, 0, 1, 0, 0, 2, 0, 2];
         let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
 
@@ -646,8 +647,7 @@ mod tests {
             &[0, 1, 0, 0, 1, 0],
             &[0, 1, 0, 1, 0, 0],
             &[0, 1, 1, 0, 0, 1],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![0, 0, 0, 1];
 
         let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();

src/naive_bayes/categorical.rs

@@ -24,7 +24,7 @@
 //!              &[3, 4, 2, 4],
 //!              &[0, 3, 1, 2],
 //!              &[0, 4, 1, 2],
-//!          ]).unwrap();
+//!          ]);
 //! let y: Vec<u32> = vec![0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0];
 //!
 //! let nb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
@@ -95,7 +95,7 @@ impl<T: Number + Unsigned> PartialEq for CategoricalNBDistribution<T> {
                         return false;
                     }
                     for (a_i_j, b_i_j) in a_i.iter().zip(b_i.iter()) {
-                        if (*a_i_j - *b_i_j).abs() > f64::EPSILON {
+                        if (*a_i_j - *b_i_j).abs() > std::f64::EPSILON {
                             return false;
                         }
                     }
@@ -158,7 +158,8 @@ impl<T: Number + Unsigned> CategoricalNBDistribution<T> {
     pub fn fit<X: Array2<T>, Y: Array1<T>>(x: &X, y: &Y, alpha: f64) -> Result<Self, Failed> {
         if alpha < 0f64 {
             return Err(Failed::fit(&format!(
-                "alpha should be >= 0, alpha=[{alpha}]"
+                "alpha should be >= 0, alpha=[{}]",
+                alpha
             )));
         }
 
@@ -166,13 +167,15 @@ impl<T: Number + Unsigned> CategoricalNBDistribution<T> {
         let y_samples = y.shape();
         if y_samples != n_samples {
             return Err(Failed::fit(&format!(
-                "Size of x should equal size of y; |x|=[{n_samples}], |y|=[{y_samples}]"
+                "Size of x should equal size of y; |x|=[{}], |y|=[{}]",
+                n_samples, y_samples
             )));
         }
 
         if n_samples == 0 {
             return Err(Failed::fit(&format!(
-                "Size of x and y should greater than 0; |x|=[{n_samples}]"
+                "Size of x and y should greater than 0; |x|=[{}]",
+                n_samples
             )));
         }
         let y: Vec<usize> = y.iterator(0).map(|y_i| y_i.to_usize().unwrap()).collect();
@@ -199,7 +202,8 @@ impl<T: Number + Unsigned> CategoricalNBDistribution<T> {
                 .max()
                 .ok_or_else(|| {
                     Failed::fit(&format!(
-                        "Failed to get the categories for feature = {feature}"
+                        "Failed to get the categories for feature = {}",
+                        feature
                     ))
                 })?;
             n_categories.push(feature_max + 1);
@@ -363,7 +367,7 @@ impl<T: Number + Unsigned, X: Array2<T>, Y: Array1<T>> Predictor<X, Y> for Categ
 impl<T: Number + Unsigned, X: Array2<T>, Y: Array1<T>> CategoricalNB<T, X, Y> {
     /// Fits CategoricalNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of
-    ///   features.
+    /// features.
     /// * `y` - vector with target values (classes) of length N.
     /// * `parameters` - additional parameters like alpha for smoothing
     pub fn fit(x: &X, y: &Y, parameters: CategoricalNBParameters) -> Result<Self, Failed> {
@@ -375,7 +379,6 @@ impl<T: Number + Unsigned, X: Array2<T>, Y: Array1<T>> CategoricalNB<T, X, Y> {
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
-    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         self.inner.as_ref().unwrap().predict(x)
@@ -426,6 +429,7 @@ mod tests {
     fn search_parameters() {
         let parameters = CategoricalNBSearchParameters {
             alpha: vec![1., 2.],
+            ..Default::default()
         };
         let mut iter = parameters.into_iter();
         let next = iter.next().unwrap();
@@ -456,8 +460,7 @@ mod tests {
             &[1, 1, 1, 1],
             &[1, 2, 0, 0],
             &[2, 1, 1, 1],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0];
 
         let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
@@ -515,7 +518,7 @@ mod tests {
             ]
         );
 
-        let x_test = DenseMatrix::from_2d_array(&[&[0, 2, 1, 0], &[2, 2, 0, 0]]).unwrap();
+        let x_test = DenseMatrix::from_2d_array(&[&[0, 2, 1, 0], &[2, 2, 0, 0]]);
         let y_hat = cnb.predict(&x_test).unwrap();
         assert_eq!(y_hat, vec![0, 1]);
     }
@@ -541,8 +544,7 @@ mod tests {
             &[3, 4, 2, 4],
             &[0, 3, 1, 2],
             &[0, 4, 1, 2],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0];
 
         let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
@@ -574,8 +576,7 @@ mod tests {
             &[3, 4, 2, 4],
             &[0, 3, 1, 2],
             &[0, 4, 1, 2],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<u32> = vec![0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0];
         let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();

src/naive_bayes/gaussian.rs

@@ -16,7 +16,7 @@
 //!              &[ 1.,  1.],
 //!              &[ 2.,  1.],
 //!              &[ 3.,  2.],
-//!          ]).unwrap();
+//!          ]);
 //! let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
 //!
 //! let nb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
@@ -175,7 +175,7 @@ impl<TY: Number + Ord + Unsigned> GaussianNBDistribution<TY> {
     /// * `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 are adjusted according to the data.
     pub fn fit<TX: Number + RealNumber, X: Array2<TX>, Y: Array1<TY>>(
         x: &X,
         y: &Y,
@@ -185,13 +185,15 @@ impl<TY: Number + Ord + Unsigned> GaussianNBDistribution<TY> {
         let y_samples = y.shape();
         if y_samples != n_samples {
             return Err(Failed::fit(&format!(
-                "Size of x should equal size of y; |x|=[{n_samples}], |y|=[{y_samples}]"
+                "Size of x should equal size of y; |x|=[{}], |y|=[{}]",
+                n_samples, y_samples
             )));
         }
 
         if n_samples == 0 {
             return Err(Failed::fit(&format!(
-                "Size of x and y should greater than 0; |x|=[{n_samples}]"
+                "Size of x and y should greater than 0; |x|=[{}]",
+                n_samples
             )));
         }
         let (class_labels, indices) = y.unique_with_indices();
@@ -317,7 +319,7 @@ impl<TX: Number + RealNumber, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Arr
 {
     /// Fits GaussianNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of
-    ///   features.
+    /// features.
     /// * `y` - vector with target values (classes) of length N.
     /// * `parameters` - additional parameters like class priors.
     pub fn fit(x: &X, y: &Y, parameters: GaussianNBParameters) -> Result<Self, Failed> {
@@ -328,7 +330,6 @@ impl<TX: Number + RealNumber, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Arr
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
-    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         self.inner.as_ref().unwrap().predict(x)
@@ -374,6 +375,7 @@ mod tests {
     fn search_parameters() {
         let parameters = GaussianNBSearchParameters {
             priors: vec![Some(vec![1.]), Some(vec![2.])],
+            ..Default::default()
         };
         let mut iter = parameters.into_iter();
         let next = iter.next().unwrap();
@@ -396,8 +398,7 @@ mod tests {
             &[1., 1.],
             &[2., 1.],
             &[3., 2.],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
 
         let gnb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
@@ -437,8 +438,7 @@ mod tests {
             &[1., 1.],
             &[2., 1.],
             &[3., 2.],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
 
         let priors = vec![0.3, 0.7];
@@ -465,8 +465,7 @@ mod tests {
             &[1., 1.],
             &[2., 1.],
             &[3., 2.],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
 
         let gnb = GaussianNB::fit(&x, &y, Default::default()).unwrap();

src/naive_bayes/mod.rs

@@ -40,7 +40,7 @@ use crate::linalg::basic::arrays::{Array1, Array2, ArrayView1};
 use crate::numbers::basenum::Number;
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
-use std::{cmp::Ordering, marker::PhantomData};
+use std::marker::PhantomData;
 
 /// Distribution used in the Naive Bayes classifier.
 pub(crate) trait NBDistribution<X: Number, Y: Number>: Clone {
@@ -89,14 +89,14 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: NBDistribution<TX,
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
-    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         let y_classes = self.distribution.classes();
-        let predictions = x
-            .row_iter()
-            .map(|row| {
-                y_classes
+        let (rows, _) = x.shape();
+        let predictions = (0..rows)
+            .map(|row_index| {
+                let row = x.get_row(row_index);
+                let (prediction, _probability) = y_classes
                     .iter()
                     .enumerate()
                     .map(|(class_index, class)| {
@@ -106,26 +106,11 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: NBDistribution<TX,
                                 + self.distribution.prior(class_index).ln(),
                         )
                     })
-                    // For some reason, the max_by method cannot use NaNs for finding the maximum value, it panics.
-                    // NaN must be considered as minimum values,
-                    // therefore it's like NaNs would not be considered for choosing the maximum value.
-                    // So we need to handle this case for avoiding panicking by using `Option::unwrap`.
-                    .max_by(|(_, p1), (_, p2)| match p1.partial_cmp(p2) {
-                        Some(ordering) => ordering,
-                        None => {
-                            if p1.is_nan() {
-                                Ordering::Less
-                            } else if p2.is_nan() {
-                                Ordering::Greater
-                            } else {
-                                Ordering::Equal
-                            }
-                        }
-                    })
-                    .map(|(prediction, _probability)| *prediction)
-                    .ok_or_else(|| Failed::predict("Failed to predict, there is no result"))
+                    .max_by(|(_, p1), (_, p2)| p1.partial_cmp(p2).unwrap())
+                    .unwrap();
+                *prediction
             })
-            .collect::<Result<Vec<TY>, Failed>>()?;
+            .collect::<Vec<TY>>();
         let y_hat = Y::from_vec_slice(&predictions);
         Ok(y_hat)
     }
@@ -134,63 +119,3 @@ pub mod bernoulli;
 pub mod categorical;
 pub mod gaussian;
 pub mod multinomial;
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-    use crate::linalg::basic::arrays::Array;
-    use crate::linalg::basic::matrix::DenseMatrix;
-    use num_traits::float::Float;
-
-    type Model<'d> = BaseNaiveBayes<i32, i32, DenseMatrix<i32>, Vec<i32>, TestDistribution<'d>>;
-
-    #[derive(Debug, PartialEq, Clone)]
-    struct TestDistribution<'d>(&'d Vec<i32>);
-
-    impl NBDistribution<i32, i32> for TestDistribution<'_> {
-        fn prior(&self, _class_index: usize) -> f64 {
-            1.
-        }
-
-        fn log_likelihood<'a>(
-            &'a self,
-            class_index: usize,
-            _j: &'a Box<dyn ArrayView1<i32> + 'a>,
-        ) -> f64 {
-            match self.0.get(class_index) {
-                &v @ 2 | &v @ 10 | &v @ 20 => v as f64,
-                _ => f64::nan(),
-            }
-        }
-
-        fn classes(&self) -> &Vec<i32> {
-            self.0
-        }
-    }
-
-    #[test]
-    fn test_predict() {
-        let matrix = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6], &[7, 8, 9]]).unwrap();
-
-        let val = vec![];
-        match Model::fit(TestDistribution(&val)).unwrap().predict(&matrix) {
-            Ok(_) => panic!("Should return error in case of empty classes"),
-            Err(err) => assert_eq!(
-                err.to_string(),
-                "Predict failed: Failed to predict, there is no result"
-            ),
-        }
-
-        let val = vec![1, 2, 3];
-        match Model::fit(TestDistribution(&val)).unwrap().predict(&matrix) {
-            Ok(r) => assert_eq!(r, vec![2, 2, 2]),
-            Err(_) => panic!("Should success in normal case with NaNs"),
-        }
-
-        let val = vec![20, 2, 10];
-        match Model::fit(TestDistribution(&val)).unwrap().predict(&matrix) {
-            Ok(r) => assert_eq!(r, vec![20, 20, 20]),
-            Err(_) => panic!("Should success in normal case without NaNs"),
-        }
-    }
-}

src/naive_bayes/multinomial.rs

@@ -20,13 +20,13 @@
 //!                   &[0, 2, 0, 0, 1, 0],
 //!                   &[0, 1, 0, 1, 0, 0],
 //!                   &[0, 1, 1, 0, 0, 1],
-//!         ]).unwrap();
+//!         ]);
 //! let y: Vec<u32> = vec![0, 0, 0, 1];
 //! let nb = MultinomialNB::fit(&x, &y, Default::default()).unwrap();
 //!
 //! // Testing data point is:
 //! //  Chinese Chinese Chinese Tokyo Japan
-//! let x_test = DenseMatrix::from_2d_array(&[&[0, 3, 1, 0, 0, 1]]).unwrap();
+//! let x_test = DenseMatrix::from_2d_array(&[&[0, 3, 1, 0, 0, 1]]);
 //! let y_hat = nb.predict(&x_test).unwrap();
 //! ```
 //!
@@ -208,7 +208,7 @@ impl<TY: Number + Ord + Unsigned> MultinomialNBDistribution<TY> {
     /// * `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 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,
@@ -220,18 +220,21 @@ impl<TY: Number + Ord + Unsigned> MultinomialNBDistribution<TY> {
         let y_samples = y.shape();
         if y_samples != n_samples {
             return Err(Failed::fit(&format!(
-                "Size of x should equal size of y; |x|=[{n_samples}], |y|=[{y_samples}]"
+                "Size of x should equal size of y; |x|=[{}], |y|=[{}]",
+                n_samples, y_samples
             )));
         }
 
         if n_samples == 0 {
             return Err(Failed::fit(&format!(
-                "Size of x and y should greater than 0; |x|=[{n_samples}]"
+                "Size of x and y should greater than 0; |x|=[{}]",
+                n_samples
             )));
         }
         if alpha < 0f64 {
             return Err(Failed::fit(&format!(
-                "Alpha should be greater than 0; |alpha|=[{alpha}]"
+                "Alpha should be greater than 0; |alpha|=[{}]",
+                alpha
             )));
         }
 
@@ -263,7 +266,8 @@ impl<TY: Number + Ord + Unsigned> MultinomialNBDistribution<TY> {
                 feature_in_class_counter[class_index][idx] +=
                     row_i.to_usize().ok_or_else(|| {
                         Failed::fit(&format!(
-                            "Elements of the matrix should be convertible to usize |found|=[{row_i}]"
+                            "Elements of the matrix should be convertible to usize |found|=[{}]",
+                            row_i
                         ))
                     })?;
             }
@@ -345,10 +349,10 @@ impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array
 {
     /// Fits MultinomialNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of
-    ///   features.
+    /// features.
     /// * `y` - vector with target values (classes) of length N.
     /// * `parameters` - additional parameters like class priors, alpha for smoothing and
-    ///   binarizing threshold.
+    /// binarizing threshold.
     pub fn fit(x: &X, y: &Y, parameters: MultinomialNBParameters) -> Result<Self, Failed> {
         let distribution =
             MultinomialNBDistribution::fit(x, y, parameters.alpha, parameters.priors)?;
@@ -358,7 +362,6 @@ impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
-    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         self.inner.as_ref().unwrap().predict(x)
@@ -434,8 +437,7 @@ mod tests {
             &[0, 2, 0, 0, 1, 0],
             &[0, 1, 0, 1, 0, 0],
             &[0, 1, 1, 0, 0, 1],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![0, 0, 0, 1];
         let mnb = MultinomialNB::fit(&x, &y, Default::default()).unwrap();
 
@@ -469,7 +471,7 @@ mod tests {
 
         // Testing data point is:
         //  Chinese Chinese Chinese Tokyo Japan
-        let x_test = DenseMatrix::<u32>::from_2d_array(&[&[0, 3, 1, 0, 0, 1]]).unwrap();
+        let x_test = DenseMatrix::<u32>::from_2d_array(&[&[0, 3, 1, 0, 0, 1]]);
         let y_hat = mnb.predict(&x_test).unwrap();
 
         assert_eq!(y_hat, &[0]);
@@ -497,8 +499,7 @@ mod tests {
             &[2, 0, 3, 3, 1, 2, 0, 2, 4, 1],
             &[2, 4, 0, 4, 2, 4, 1, 3, 1, 4],
             &[0, 2, 2, 3, 4, 0, 4, 4, 4, 4],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![2, 2, 0, 0, 0, 2, 1, 1, 0, 1, 0, 0, 2, 0, 2];
         let nb = MultinomialNB::fit(&x, &y, Default::default()).unwrap();
 
@@ -557,8 +558,7 @@ mod tests {
             &[0, 1, 0, 0, 1, 0],
             &[0, 1, 0, 1, 0, 0],
             &[0, 1, 1, 0, 0, 1],
-        ])
-        .unwrap();
+        ]);
         let y = vec![0, 0, 0, 1];
 
         let mnb = MultinomialNB::fit(&x, &y, Default::default()).unwrap();

src/neighbors/knn_classifier.rs

@@ -22,7 +22,7 @@
 //!     &[3., 4.],
 //!     &[5., 6.],
 //!     &[7., 8.],
-//! &[9., 10.]]).unwrap();
+//! &[9., 10.]]);
 //! let y = vec![2, 2, 2, 3, 3]; //your class labels
 //!
 //! let knn = KNNClassifier::fit(&x, &y, Default::default()).unwrap();
@@ -211,7 +211,7 @@ impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec
 {
     /// Fits KNN classifier 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
+    /// * `y` - vector with target values (classes) of length N    
     /// * `parameters` - additional parameters like search algorithm and k
     pub fn fit(
         x: &X,
@@ -236,7 +236,8 @@ impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec
 
         if x_n != y_n {
             return Err(Failed::fit(&format!(
-                "Size of x should equal size of y; |x|=[{x_n}], |y|=[{y_n}]"
+                "Size of x should equal size of y; |x|=[{}], |y|=[{}]",
+                x_n, y_n
             )));
         }
 
@@ -261,7 +262,6 @@ impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
-    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         let mut result = Y::zeros(x.shape().0);
@@ -312,8 +312,7 @@ mod tests {
     #[test]
     fn knn_fit_predict() {
         let x =
-            DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]])
-                .unwrap();
+            DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
         let y = vec![2, 2, 2, 3, 3];
         let knn = KNNClassifier::fit(&x, &y, Default::default()).unwrap();
         let y_hat = knn.predict(&x).unwrap();
@@ -327,7 +326,7 @@ mod tests {
     )]
     #[test]
     fn knn_fit_predict_weighted() {
-        let x = DenseMatrix::from_2d_array(&[&[1.], &[2.], &[3.], &[4.], &[5.]]).unwrap();
+        let x = DenseMatrix::from_2d_array(&[&[1.], &[2.], &[3.], &[4.], &[5.]]);
         let y = vec![2, 2, 2, 3, 3];
         let knn = KNNClassifier::fit(
             &x,
@@ -338,9 +337,7 @@ mod tests {
                 .with_weight(KNNWeightFunction::Distance),
         )
         .unwrap();
-        let y_hat = knn
-            .predict(&DenseMatrix::from_2d_array(&[&[4.1]]).unwrap())
-            .unwrap();
+        let y_hat = knn.predict(&DenseMatrix::from_2d_array(&[&[4.1]])).unwrap();
         assert_eq!(vec![3], y_hat);
     }
 
@@ -352,8 +349,7 @@ mod tests {
     #[cfg(feature = "serde")]
     fn serde() {
         let x =
-            DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]])
-                .unwrap();
+            DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
         let y = vec![2, 2, 2, 3, 3];
 
         let knn = KNNClassifier::fit(&x, &y, Default::default()).unwrap();

src/neighbors/knn_regressor.rs

@@ -24,7 +24,7 @@
 //!     &[2., 2.],
 //!     &[3., 3.],
 //!     &[4., 4.],
-//!     &[5., 5.]]).unwrap();
+//!     &[5., 5.]]);
 //! let y = vec![1., 2., 3., 4., 5.]; //your target values
 //!
 //! let knn = KNNRegressor::fit(&x, &y, Default::default()).unwrap();
@@ -88,21 +88,25 @@ pub struct KNNRegressor<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D:
 impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
     KNNRegressor<TX, TY, X, Y, D>
 {
+    ///
     fn y(&self) -> &Y {
         self.y.as_ref().unwrap()
     }
 
+    ///
     fn knn_algorithm(&self) -> &KNNAlgorithm<TX, D> {
         self.knn_algorithm
             .as_ref()
             .expect("Missing parameter: KNNAlgorithm")
     }
 
+    ///
     fn weight(&self) -> &KNNWeightFunction {
         self.weight.as_ref().expect("Missing parameter: weight")
     }
 
     #[allow(dead_code)]
+    ///
     fn k(&self) -> usize {
         self.k.unwrap()
     }
@@ -203,7 +207,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
 {
     /// Fits KNN regressor to a NxM matrix where N is number of samples and M is number of features.
     /// * `x` - training data
-    /// * `y` - vector with real values
+    /// * `y` - vector with real values    
     /// * `parameters` - additional parameters like search algorithm and k
     pub fn fit(
         x: &X,
@@ -220,7 +224,8 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
 
         if x_n != y_n {
             return Err(Failed::fit(&format!(
-                "Size of x should equal size of y; |x|=[{x_n}], |y|=[{y_n}]"
+                "Size of x should equal size of y; |x|=[{}], |y|=[{}]",
+                x_n, y_n
             )));
         }
 
@@ -246,7 +251,6 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
 
     /// Predict the target for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
-    ///
     /// Returns a vector of size N with estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         let mut result = Y::zeros(x.shape().0);
@@ -292,10 +296,9 @@ mod tests {
     #[test]
     fn knn_fit_predict_weighted() {
         let x =
-            DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]])
-                .unwrap();
+            DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
         let y: Vec<f64> = vec![1., 2., 3., 4., 5.];
-        let y_exp = [1., 2., 3., 4., 5.];
+        let y_exp = vec![1., 2., 3., 4., 5.];
         let knn = KNNRegressor::fit(
             &x,
             &y,
@@ -309,7 +312,7 @@ mod tests {
         let y_hat = knn.predict(&x).unwrap();
         assert_eq!(5, Vec::len(&y_hat));
         for i in 0..y_hat.len() {
-            assert!((y_hat[i] - y_exp[i]).abs() < f64::EPSILON);
+            assert!((y_hat[i] - y_exp[i]).abs() < std::f64::EPSILON);
         }
     }
 
@@ -320,10 +323,9 @@ mod tests {
     #[test]
     fn knn_fit_predict_uniform() {
         let x =
-            DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]])
-                .unwrap();
+            DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
         let y: Vec<f64> = vec![1., 2., 3., 4., 5.];
-        let y_exp = [2., 2., 3., 4., 4.];
+        let y_exp = vec![2., 2., 3., 4., 4.];
         let knn = KNNRegressor::fit(&x, &y, Default::default()).unwrap();
         let y_hat = knn.predict(&x).unwrap();
         assert_eq!(5, Vec::len(&y_hat));
@@ -340,8 +342,7 @@ mod tests {
     #[cfg(feature = "serde")]
     fn serde() {
         let x =
-            DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]])
-                .unwrap();
+            DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
         let y = vec![1., 2., 3., 4., 5.];
 
         let knn = KNNRegressor::fit(&x, &y, Default::default()).unwrap();

src/neighbors/mod.rs

@@ -49,15 +49,20 @@ pub type KNNAlgorithmName = crate::algorithm::neighbour::KNNAlgorithmName;
 
 /// Weight function that is used to determine estimated value.
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
-#[derive(Debug, Clone, Default)]
+#[derive(Debug, Clone)]
 pub enum KNNWeightFunction {
     /// All k nearest points are weighted equally
-    #[default]
     Uniform,
     /// k nearest points are weighted by the inverse of their distance. Closer neighbors will have a greater influence than neighbors which are further away.
     Distance,
 }
 
+impl Default for KNNWeightFunction {
+    fn default() -> Self {
+        KNNWeightFunction::Uniform
+    }
+}
+
 impl KNNWeightFunction {
     fn calc_weights(&self, distances: Vec<f64>) -> std::vec::Vec<f64> {
         match *self {

src/numbers/realnum.rs

@@ -2,13 +2,9 @@
 //! Most algorithms in `smartcore` rely on basic linear algebra operations like dot product, matrix decomposition and other subroutines that are defined for a set of real numbers, ℝ.
 //! This module defines real number and some useful functions that are used in [Linear Algebra](../../linalg/index.html) module.
 
-use rand::rngs::SmallRng;
-use rand::{Rng, SeedableRng};
-
 use num_traits::Float;
 
 use crate::numbers::basenum::Number;
-use crate::rand_custom::get_rng_impl;
 
 /// Defines real number
 /// <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_CHTML"></script>
@@ -67,12 +63,8 @@ impl RealNumber for f64 {
     }
 
     fn rand() -> f64 {
-        let mut small_rng = get_rng_impl(None);
-
-        let mut rngs: Vec<SmallRng> = (0..3)
-            .map(|_| SmallRng::from_rng(&mut small_rng).unwrap())
-            .collect();
-        rngs[0].gen::<f64>()
+        // TODO: to be implemented, see issue smartcore#214
+        1.0
     }
 
     fn two() -> Self {
@@ -116,12 +108,7 @@ impl RealNumber for f32 {
     }
 
     fn rand() -> f32 {
-        let mut small_rng = get_rng_impl(None);
-
-        let mut rngs: Vec<SmallRng> = (0..3)
-            .map(|_| SmallRng::from_rng(&mut small_rng).unwrap())
-            .collect();
-        rngs[0].gen::<f32>()
+        1.0
     }
 
     fn two() -> Self {
@@ -162,14 +149,4 @@ mod tests {
     fn f64_from_string() {
         assert_eq!(f64::from_str("1.111111111").unwrap(), 1.111111111)
     }
-
-    #[test]
-    fn f64_rand() {
-        f64::rand();
-    }
-
-    #[test]
-    fn f32_rand() {
-        f32::rand();
-    }
 }

src/optimization/first_order/gradient_descent.rs

@@ -1,3 +1,5 @@
+// TODO: missing documentation
+
 use std::default::Default;
 
 use crate::linalg::basic::arrays::Array1;
@@ -6,27 +8,30 @@ use crate::optimization::first_order::{FirstOrderOptimizer, OptimizerResult};
 use crate::optimization::line_search::LineSearchMethod;
 use crate::optimization::{DF, F};
 
-/// Gradient Descent optimization algorithm
+///
 pub struct GradientDescent {
-    /// Maximum number of iterations
+    ///
     pub max_iter: usize,
-    /// Relative tolerance for the gradient norm
+    ///
     pub g_rtol: f64,
-    /// Absolute tolerance for the gradient norm
+    ///
     pub g_atol: f64,
 }
 
+///
 impl Default for GradientDescent {
     fn default() -> Self {
         GradientDescent {
             max_iter: 10000,
-            g_rtol: f64::EPSILON.sqrt(),
-            g_atol: f64::EPSILON,
+            g_rtol: std::f64::EPSILON.sqrt(),
+            g_atol: std::f64::EPSILON,
         }
     }
 }
 
+///
 impl<T: FloatNumber> FirstOrderOptimizer<T> for GradientDescent {
+    ///
     fn optimize<'a, X: Array1<T>, LS: LineSearchMethod<T>>(
         &self,
         f: &'a F<'_, T, X>,
@@ -108,13 +113,12 @@ mod tests {
             g[1] = 200. * (x[1] - x[0].powf(2.));
         };
 
-        let ls: Backtracking<f64> = Backtracking::<f64> {
-            order: FunctionOrder::THIRD,
-            ..Default::default()
-        };
+        let mut ls: Backtracking<f64> = Default::default();
+        ls.order = FunctionOrder::THIRD;
         let optimizer: GradientDescent = Default::default();
 
         let result = optimizer.optimize(&f, &df, &x0, &ls);
+        println!("{:?}", result);
 
         assert!((result.f_x - 0.0).abs() < 1e-5);
         assert!((result.x[0] - 1.0).abs() < 1e-2);

src/optimization/first_order/lbfgs.rs

@@ -11,29 +11,31 @@ use crate::optimization::first_order::{FirstOrderOptimizer, OptimizerResult};
 use crate::optimization::line_search::LineSearchMethod;
 use crate::optimization::{DF, F};
 
-/// Limited-memory BFGS optimization algorithm
+///
 pub struct LBFGS {
-    /// Maximum number of iterations
+    ///
     pub max_iter: usize,
-    /// TODO: Add documentation
+    ///
     pub g_rtol: f64,
-    /// TODO: Add documentation
+    ///
     pub g_atol: f64,
-    /// TODO: Add documentation
+    ///
     pub x_atol: f64,
-    /// TODO: Add documentation
+    ///
     pub x_rtol: f64,
-    /// TODO: Add documentation
+    ///
     pub f_abstol: f64,
-    /// TODO: Add documentation
+    ///
     pub f_reltol: f64,
-    /// TODO: Add documentation
+    ///
     pub successive_f_tol: usize,
-    /// TODO: Add documentation
+    ///
     pub m: usize,
 }
 
+///
 impl Default for LBFGS {
+    ///
     fn default() -> Self {
         LBFGS {
             max_iter: 1000,
@@ -49,7 +51,9 @@ impl Default for LBFGS {
     }
 }
 
+///
 impl LBFGS {
+    ///
     fn two_loops<T: FloatNumber + RealNumber, X: Array1<T>>(&self, state: &mut LBFGSState<T, X>) {
         let lower = state.iteration.max(self.m) - self.m;
         let upper = state.iteration;
@@ -91,6 +95,7 @@ impl LBFGS {
         state.s.mul_scalar_mut(-T::one());
     }
 
+    ///
     fn init_state<T: FloatNumber + RealNumber, X: Array1<T>>(&self, x: &X) -> LBFGSState<T, X> {
         LBFGSState {
             x: x.clone(),
@@ -114,6 +119,7 @@ impl LBFGS {
         }
     }
 
+    ///
     fn update_state<'a, T: FloatNumber + RealNumber, X: Array1<T>, LS: LineSearchMethod<T>>(
         &self,
         f: &'a F<'_, T, X>,
@@ -155,6 +161,7 @@ impl LBFGS {
         df(&mut state.x_df, &state.x);
     }
 
+    ///
     fn assess_convergence<T: FloatNumber, X: Array1<T>>(
         &self,
         state: &mut LBFGSState<T, X>,
@@ -166,7 +173,7 @@ impl LBFGS {
         }
 
         if state.x.max_diff(&state.x_prev)
-            <= T::from_f64(self.x_rtol * state.x.norm(f64::INFINITY)).unwrap()
+            <= T::from_f64(self.x_rtol * state.x.norm(std::f64::INFINITY)).unwrap()
         {
             x_converged = true;
         }
@@ -181,16 +188,17 @@ impl LBFGS {
             state.counter_f_tol += 1;
         }
 
-        if state.x_df.norm(f64::INFINITY) <= self.g_atol {
+        if state.x_df.norm(std::f64::INFINITY) <= self.g_atol {
             g_converged = true;
         }
 
         g_converged || x_converged || state.counter_f_tol > self.successive_f_tol
     }
 
-    fn update_hessian<T: FloatNumber, X: Array1<T>>(
+    ///
+    fn update_hessian<'a, T: FloatNumber, X: Array1<T>>(
         &self,
-        _: &DF<'_, X>,
+        _: &'a DF<'_, X>,
         state: &mut LBFGSState<T, X>,
     ) {
         state.dg = state.x_df.sub(&state.x_df_prev);
@@ -204,6 +212,7 @@ impl LBFGS {
     }
 }
 
+///
 #[derive(Debug)]
 struct LBFGSState<T: FloatNumber, X: Array1<T>> {
     x: X,
@@ -225,7 +234,9 @@ struct LBFGSState<T: FloatNumber, X: Array1<T>> {
     alpha: T,
 }
 
+///
 impl<T: FloatNumber + RealNumber> FirstOrderOptimizer<T> for LBFGS {
+    ///
     fn optimize<'a, X: Array1<T>, LS: LineSearchMethod<T>>(
         &self,
         f: &F<'_, T, X>,
@@ -237,7 +248,7 @@ impl<T: FloatNumber + RealNumber> FirstOrderOptimizer<T> for LBFGS {
 
         df(&mut state.x_df, x0);
 
-        let g_converged = state.x_df.norm(f64::INFINITY) < self.g_atol;
+        let g_converged = state.x_df.norm(std::f64::INFINITY) < self.g_atol;
         let mut converged = g_converged;
         let stopped = false;
 
@@ -280,15 +291,13 @@ mod tests {
             g[0] = -2. * (1. - x[0]) - 400. * (x[1] - x[0].powf(2.)) * x[0];
             g[1] = 200. * (x[1] - x[0].powf(2.));
         };
-        let ls: Backtracking<f64> = Backtracking::<f64> {
-            order: FunctionOrder::THIRD,
-            ..Default::default()
-        };
+        let mut ls: Backtracking<f64> = Default::default();
+        ls.order = FunctionOrder::THIRD;
         let optimizer: LBFGS = Default::default();
 
         let result = optimizer.optimize(&f, &df, &x0, &ls);
 
-        assert!((result.f_x - 0.0).abs() < f64::EPSILON);
+        assert!((result.f_x - 0.0).abs() < std::f64::EPSILON);
         assert!((result.x[0] - 1.0).abs() < 1e-8);
         assert!((result.x[1] - 1.0).abs() < 1e-8);
         assert!(result.iterations <= 24);

src/optimization/first_order/mod.rs

@@ -1,6 +1,6 @@
-/// Gradient descent optimization algorithm
+///
 pub mod gradient_descent;
-/// Limited-memory BFGS optimization algorithm
+///
 pub mod lbfgs;
 
 use std::clone::Clone;
@@ -11,9 +11,9 @@ use crate::numbers::floatnum::FloatNumber;
 use crate::optimization::line_search::LineSearchMethod;
 use crate::optimization::{DF, F};
 
-/// First-order optimization is a class of algorithms that use the first derivative of a function to find optimal solutions.
+///
 pub trait FirstOrderOptimizer<T: FloatNumber> {
-    /// run first order optimization
+    ///
     fn optimize<'a, X: Array1<T>, LS: LineSearchMethod<T>>(
         &self,
         f: &F<'_, T, X>,
@@ -23,13 +23,13 @@ pub trait FirstOrderOptimizer<T: FloatNumber> {
     ) -> OptimizerResult<T, X>;
 }
 
-/// Result of optimization
+///
 #[derive(Debug, Clone)]
 pub struct OptimizerResult<T: FloatNumber, X: Array1<T>> {
-    /// Solution
+    ///
     pub x: X,
-    /// f(x) value
+    ///
     pub f_x: T,
-    /// number of iterations
+    ///
     pub iterations: usize,
 }

src/optimization/line_search.rs

@@ -1,9 +1,11 @@
+// TODO: missing documentation
+
 use crate::optimization::FunctionOrder;
 use num_traits::Float;
 
-/// Line search optimization.
+///
 pub trait LineSearchMethod<T: Float> {
-    /// Find alpha that satisfies strong Wolfe conditions.
+    ///
     fn search(
         &self,
         f: &(dyn Fn(T) -> T),
@@ -14,31 +16,32 @@ pub trait LineSearchMethod<T: Float> {
     ) -> LineSearchResult<T>;
 }
 
-/// Line search result
+///
 #[derive(Debug, Clone)]
 pub struct LineSearchResult<T: Float> {
-    /// Alpha value
+    ///
     pub alpha: T,
-    /// f(alpha) value
+    ///
     pub f_x: T,
 }
 
-/// Backtracking line search method.
+///
 pub struct Backtracking<T: Float> {
-    /// TODO: Add documentation
+    ///
     pub c1: T,
-    /// Maximum number of iterations for Backtracking single run
+    ///
     pub max_iterations: usize,
-    /// TODO: Add documentation
+    ///
     pub max_infinity_iterations: usize,
-    /// TODO: Add documentation
+    ///
     pub phi: T,
-    /// TODO: Add documentation
+    ///
     pub plo: T,
-    /// function order
+    ///
     pub order: FunctionOrder,
 }
 
+///
 impl<T: Float> Default for Backtracking<T> {
     fn default() -> Self {
         Backtracking {
@@ -52,7 +55,9 @@ impl<T: Float> Default for Backtracking<T> {
     }
 }
 
+///
 impl<T: Float> LineSearchMethod<T> for Backtracking<T> {
+    ///
     fn search(
         &self,
         f: &(dyn Fn(T) -> T),

src/optimization/mod.rs

@@ -1,19 +1,21 @@
-/// first order optimization algorithms
+// TODO: missing documentation
+
+///
 pub mod first_order;
-/// line search algorithms
+///
 pub mod line_search;
 
-/// Function f(x) = y
+///
 pub type F<'a, T, X> = dyn for<'b> Fn(&'b X) -> T + 'a;
-/// Function df(x)
+///
 pub type DF<'a, X> = dyn for<'b> Fn(&'b mut X, &'b X) + 'a;
 
-/// Function order
+///
 #[allow(clippy::upper_case_acronyms)]
 #[derive(Debug, PartialEq, Eq)]
 pub enum FunctionOrder {
-    /// Second order
+    ///
     SECOND,
-    /// Third order
+    ///
     THIRD,
 }

src/preprocessing/categorical.rs

@@ -12,7 +12,7 @@
 //!         &[1.5, 2.0, 1.5, 4.0],
 //!         &[1.5, 1.0, 1.5, 5.0],
 //!         &[1.5, 2.0, 1.5, 6.0],
-//!   ]).unwrap();
+//!   ]);
 //! let encoder_params = OneHotEncoderParams::from_cat_idx(&[1, 3]);
 //! // Infer number of categories from data and return a reusable encoder
 //! let encoder = OneHotEncoder::fit(&data, encoder_params).unwrap();
@@ -132,7 +132,8 @@ impl OneHotEncoder {
                     data.copy_col_as_vec(idx, &mut col_buf);
                     if !validate_col_is_categorical(&col_buf) {
                         let msg = format!(
-                            "Column {idx} of data matrix containts non categorizable (integer) values"
+                            "Column {} of data matrix containts non categorizable (integer) values",
+                            idx
                         );
                         return Err(Failed::fit(&msg[..]));
                     }
@@ -181,7 +182,7 @@ impl OneHotEncoder {
                 match oh_vec {
                     None => {
                         // Since we support T types, bad value in a series causes in to be invalid
-                        let msg = format!("At least one value in column {old_cidx} doesn't conform to category definition");
+                        let msg = format!("At least one value in column {} doesn't conform to category definition", old_cidx);
                         return Err(Failed::transform(&msg[..]));
                     }
                     Some(v) => {
@@ -240,16 +241,14 @@ mod tests {
             &[2.0, 1.5, 4.0],
             &[1.0, 1.5, 5.0],
             &[2.0, 1.5, 6.0],
-        ])
-        .unwrap();
+        ]);
 
         let oh_enc = DenseMatrix::from_2d_array(&[
             &[1.0, 0.0, 1.5, 1.0, 0.0, 0.0, 0.0],
             &[0.0, 1.0, 1.5, 0.0, 1.0, 0.0, 0.0],
             &[1.0, 0.0, 1.5, 0.0, 0.0, 1.0, 0.0],
             &[0.0, 1.0, 1.5, 0.0, 0.0, 0.0, 1.0],
-        ])
-        .unwrap();
+        ]);
 
         (orig, oh_enc)
     }
@@ -261,16 +260,14 @@ mod tests {
             &[1.5, 2.0, 1.5, 4.0],
             &[1.5, 1.0, 1.5, 5.0],
             &[1.5, 2.0, 1.5, 6.0],
-        ])
-        .unwrap();
+        ]);
 
         let oh_enc = DenseMatrix::from_2d_array(&[
             &[1.5, 1.0, 0.0, 1.5, 1.0, 0.0, 0.0, 0.0],
             &[1.5, 0.0, 1.0, 1.5, 0.0, 1.0, 0.0, 0.0],
             &[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],
-        ])
-        .unwrap();
+        ]);
 
         (orig, oh_enc)
     }
@@ -281,7 +278,7 @@ mod tests {
     )]
     #[test]
     fn hash_encode_f64_series() {
-        let series = [3.0, 1.0, 2.0, 1.0];
+        let series = vec![3.0, 1.0, 2.0, 1.0];
         let hashable_series: Vec<CategoricalFloat> =
             series.iter().map(|v| v.to_category()).collect();
         let enc = CategoryMapper::from_positional_category_vec(hashable_series);
@@ -338,11 +335,14 @@ mod tests {
             &[2.0, 1.5, 4.0],
             &[1.0, 1.5, 5.0],
             &[2.0, 1.5, 6.0],
-        ])
-        .unwrap();
+        ]);
 
         let params = OneHotEncoderParams::from_cat_idx(&[1]);
-        let result = OneHotEncoder::fit(&m, params);
-        assert!(result.is_err());
+        match OneHotEncoder::fit(&m, params) {
+            Err(_) => {
+                assert!(true);
+            }
+            _ => assert!(false),
+        }
     }
 }

src/preprocessing/numerical.rs

@@ -11,7 +11,7 @@
 //!     vec![0.0, 0.0],
 //!     vec![1.0, 1.0],
 //!     vec![1.0, 1.0],
-//! ]).unwrap();
+//! ]);
 //!
 //! let standard_scaler =
 //! numerical::StandardScaler::fit(&data, numerical::StandardScalerParameters::default())
@@ -24,7 +24,7 @@
 //!         vec![-1.0, -1.0],
 //!         vec![1.0, 1.0],
 //!         vec![1.0, 1.0],
-//!     ]).unwrap()
+//!     ])
 //! );
 //! ```
 use std::marker::PhantomData;
@@ -172,14 +172,18 @@ where
     T: Number + RealNumber,
     M: Array2<T>,
 {
-    columns.first().cloned().map(|output_matrix| {
-        columns
-            .iter()
-            .skip(1)
-            .fold(output_matrix, |current_matrix, new_colum| {
-                current_matrix.h_stack(new_colum)
-            })
-    })
+    if let Some(output_matrix) = columns.first().cloned() {
+        return Some(
+            columns
+                .iter()
+                .skip(1)
+                .fold(output_matrix, |current_matrix, new_colum| {
+                    current_matrix.h_stack(new_colum)
+                }),
+        );
+    } else {
+        None
+    }
 }
 
 #[cfg(test)]
@@ -193,18 +197,15 @@ mod tests {
         fn combine_three_columns() {
             assert_eq!(
                 build_matrix_from_columns(vec![
-                    DenseMatrix::from_2d_vec(&vec![vec![1.0], vec![1.0], vec![1.0],]).unwrap(),
-                    DenseMatrix::from_2d_vec(&vec![vec![2.0], vec![2.0], vec![2.0],]).unwrap(),
-                    DenseMatrix::from_2d_vec(&vec![vec![3.0], vec![3.0], vec![3.0],]).unwrap()
+                    DenseMatrix::from_2d_vec(&vec![vec![1.0], vec![1.0], vec![1.0],]),
+                    DenseMatrix::from_2d_vec(&vec![vec![2.0], vec![2.0], vec![2.0],]),
+                    DenseMatrix::from_2d_vec(&vec![vec![3.0], vec![3.0], vec![3.0],])
                 ]),
-                Some(
-                    DenseMatrix::from_2d_vec(&vec![
-                        vec![1.0, 2.0, 3.0],
-                        vec![1.0, 2.0, 3.0],
-                        vec![1.0, 2.0, 3.0]
-                    ])
-                    .unwrap()
-                )
+                Some(DenseMatrix::from_2d_vec(&vec![
+                    vec![1.0, 2.0, 3.0],
+                    vec![1.0, 2.0, 3.0],
+                    vec![1.0, 2.0, 3.0]
+                ]))
             )
         }
 
@@ -286,24 +287,21 @@ mod tests {
         /// sklearn.
         #[test]
         fn fit_transform_random_values() {
-            let transformed_values = fit_transform_with_default_standard_scaler(
-                &DenseMatrix::from_2d_array(&[
+            let transformed_values =
+                fit_transform_with_default_standard_scaler(&DenseMatrix::from_2d_array(&[
                     &[0.1004222429, 0.2194113576, 0.9310663354, 0.3313593793],
                     &[0.2045493861, 0.1683865411, 0.5071506765, 0.7257355264],
                     &[0.5708488802, 0.1846414616, 0.9590802982, 0.5591871046],
                     &[0.8387612750, 0.5754861361, 0.5537109852, 0.1077646442],
-                ])
-                .unwrap(),
-            );
-            println!("{transformed_values}");
+                ]));
+            println!("{}", transformed_values);
             assert!(transformed_values.approximate_eq(
                 &DenseMatrix::from_2d_array(&[
                     &[-1.1154020653, -0.4031985330, 0.9284605204, -0.4271473866],
                     &[-0.7615464283, -0.7076698384, -1.1075452562, 1.2632979631],
                     &[0.4832504303, -0.6106747444, 1.0630075435, 0.5494084257],
                     &[1.3936980634, 1.7215431158, -0.8839228078, -1.3855590021],
-                ])
-                .unwrap(),
+                ]),
                 1.0
             ))
         }
@@ -312,10 +310,13 @@ mod tests {
         #[test]
         fn fit_transform_with_zero_variance() {
             assert_eq!(
-                fit_transform_with_default_standard_scaler(
-                    &DenseMatrix::from_2d_array(&[&[1.0], &[1.0], &[1.0], &[1.0]]).unwrap()
-                ),
-                DenseMatrix::from_2d_array(&[&[0.0], &[0.0], &[0.0], &[0.0]]).unwrap(),
+                fit_transform_with_default_standard_scaler(&DenseMatrix::from_2d_array(&[
+                    &[1.0],
+                    &[1.0],
+                    &[1.0],
+                    &[1.0]
+                ])),
+                DenseMatrix::from_2d_array(&[&[0.0], &[0.0], &[0.0], &[0.0]]),
                 "When scaling values with zero variance, zero is expected as return value"
             )
         }
@@ -330,8 +331,7 @@ mod tests {
                         &[1.0, 2.0, 5.0],
                         &[1.0, 1.0, 1.0],
                         &[1.0, 2.0, 5.0]
-                    ])
-                    .unwrap(),
+                    ]),
                     StandardScalerParameters::default(),
                 ),
                 Ok(StandardScaler {
@@ -354,8 +354,7 @@ mod tests {
                     &[0.2045493861, 0.1683865411, 0.5071506765, 0.7257355264],
                     &[0.5708488802, 0.1846414616, 0.9590802982, 0.5591871046],
                     &[0.8387612750, 0.5754861361, 0.5537109852, 0.1077646442],
-                ])
-                .unwrap(),
+                ]),
                 StandardScalerParameters::default(),
             )
             .unwrap();
@@ -365,18 +364,17 @@ mod tests {
                 vec![0.42864544605, 0.2869813741, 0.737752073825, 0.431011663625],
             );
 
-            assert!(&DenseMatrix::<f64>::from_2d_vec(&vec![fitted_scaler.stds])
-                .unwrap()
-                .approximate_eq(
+            assert!(
+                &DenseMatrix::<f64>::from_2d_vec(&vec![fitted_scaler.stds]).approximate_eq(
                     &DenseMatrix::from_2d_array(&[&[
                         0.29426447500954,
                         0.16758497615485,
                         0.20820945786863,
                         0.23329718831165
-                    ],])
-                    .unwrap(),
+                    ],]),
                     0.00000000000001
-                ))
+                )
+            )
         }
 
         /// If `with_std` is set to `false` the values should not be
@@ -394,9 +392,8 @@ mod tests {
             };
 
             assert_eq!(
-                standard_scaler
-                    .transform(&DenseMatrix::from_2d_array(&[&[0.0, 2.0], &[2.0, 4.0]]).unwrap()),
-                Ok(DenseMatrix::from_2d_array(&[&[-1.0, -1.0], &[1.0, 1.0]]).unwrap())
+                standard_scaler.transform(&DenseMatrix::from_2d_array(&[&[0.0, 2.0], &[2.0, 4.0]])),
+                Ok(DenseMatrix::from_2d_array(&[&[-1.0, -1.0], &[1.0, 1.0]]))
             )
         }
 
@@ -416,8 +413,8 @@ mod tests {
 
             assert_eq!(
                 standard_scaler
-                    .transform(&DenseMatrix::from_2d_array(&[&[0.0, 9.0], &[4.0, 12.0]]).unwrap()),
-                Ok(DenseMatrix::from_2d_array(&[&[0.0, 3.0], &[2.0, 4.0]]).unwrap())
+                    .transform(&DenseMatrix::from_2d_array(&[&[0.0, 9.0], &[4.0, 12.0]])),
+                Ok(DenseMatrix::from_2d_array(&[&[0.0, 3.0], &[2.0, 4.0]]))
             )
         }
 
@@ -436,8 +433,7 @@ mod tests {
                     &[0.2045493861, 0.1683865411, 0.5071506765, 0.7257355264],
                     &[0.5708488802, 0.1846414616, 0.9590802982, 0.5591871046],
                     &[0.8387612750, 0.5754861361, 0.5537109852, 0.1077646442],
-                ])
-                .unwrap(),
+                ]),
                 StandardScalerParameters::default(),
             )
             .unwrap();
@@ -450,18 +446,17 @@ mod tests {
                 vec![0.42864544605, 0.2869813741, 0.737752073825, 0.431011663625],
             );
 
-            assert!(&DenseMatrix::from_2d_vec(&vec![deserialized_scaler.stds])
-                .unwrap()
-                .approximate_eq(
+            assert!(
+                &DenseMatrix::from_2d_vec(&vec![deserialized_scaler.stds]).approximate_eq(
                     &DenseMatrix::from_2d_array(&[&[
                         0.29426447500954,
                         0.16758497615485,
                         0.20820945786863,
                         0.23329718831165
-                    ],])
-                    .unwrap(),
+                    ],]),
                     0.00000000000001
-                ))
+                )
+            )
         }
     }
 }

src/preprocessing/series_encoder.rs

@@ -206,7 +206,7 @@ mod tests {
     #[test]
     fn from_categories() {
         let fake_categories: Vec<usize> = vec![1, 2, 3, 4, 5, 3, 5, 3, 1, 2, 4];
-        let it = fake_categories.iter().copied();
+        let it = fake_categories.iter().map(|&a| a);
         let enc = CategoryMapper::<usize>::fit_to_iter(it);
         let oh_vec: Vec<f64> = match enc.get_one_hot(&1) {
             None => panic!("Wrong categories"),
@@ -218,8 +218,8 @@ mod tests {
 
     fn build_fake_str_enc<'a>() -> CategoryMapper<&'a str> {
         let fake_category_pos = vec!["background", "dog", "cat"];
-
-        CategoryMapper::<&str>::from_positional_category_vec(fake_category_pos)
+        let enc = CategoryMapper::<&str>::from_positional_category_vec(fake_category_pos);
+        enc
     }
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
@@ -275,7 +275,7 @@ mod tests {
         let lab = enc.invert_one_hot(res).unwrap();
         assert_eq!(lab, "dog");
         if let Err(e) = enc.invert_one_hot(vec![0.0, 0.0, 0.0]) {
-            let pos_entries = "Expected a single positive entry, 0 entires found".to_string();
+            let pos_entries = format!("Expected a single positive entry, 0 entires found");
             assert_eq!(e, Failed::transform(&pos_entries[..]));
         };
     }

src/readers/csv.rs

@@ -30,7 +30,7 @@ pub struct CSVDefinition<'a> {
     /// What seperates the fields in your csv-file?
     field_seperator: &'a str,
 }
-impl Default for CSVDefinition<'_> {
+impl<'a> Default for CSVDefinition<'a> {
     fn default() -> Self {
         Self {
             n_rows_header: 1,
@@ -83,7 +83,7 @@ where
     Matrix: Array2<T>,
 {
     let csv_text = read_string_from_source(source)?;
-    let rows: Vec<Vec<T>> = extract_row_vectors_from_csv_text(
+    let rows: Vec<Vec<T>> = extract_row_vectors_from_csv_text::<T, RowVector, Matrix>(
         &csv_text,
         &definition,
         detect_row_format(&csv_text, &definition)?,
@@ -103,7 +103,12 @@ where
 
 /// Given a string containing the contents of a csv file, extract its value
 /// into row-vectors.
-fn extract_row_vectors_from_csv_text<'a, T: Number + RealNumber + std::str::FromStr>(
+fn extract_row_vectors_from_csv_text<
+    'a,
+    T: Number + RealNumber + std::str::FromStr,
+    RowVector: Array1<T>,
+    Matrix: Array2<T>,
+>(
     csv_text: &'a str,
     definition: &'a CSVDefinition<'_>,
     row_format: CSVRowFormat<'_>,
@@ -162,7 +167,7 @@ where
 }
 
 /// Ensure that a string containing a csv row conforms to a specified row format.
-fn validate_csv_row(row: &str, row_format: &CSVRowFormat<'_>) -> Result<(), ReadingError> {
+fn validate_csv_row<'a>(row: &'a str, row_format: &CSVRowFormat<'_>) -> Result<(), ReadingError> {
     let actual_number_of_fields = row.split(row_format.field_seperator).count();
     if row_format.n_fields == actual_number_of_fields {
         Ok(())
@@ -203,7 +208,7 @@ where
     match value_string.parse::<T>().ok() {
         Some(value) => Ok(value),
         None => Err(ReadingError::InvalidField {
-            msg: format!("Value '{value_string}' could not be read.",),
+            msg: format!("Value '{}' could not be read.", value_string,),
         }),
     }
 }
@@ -238,8 +243,7 @@ mod tests {
                     &[5.1, 3.5, 1.4, 0.2],
                     &[4.9, 3.0, 1.4, 0.2],
                     &[4.7, 3.2, 1.3, 0.2],
-                ])
-                .unwrap())
+                ]))
             )
         }
         #[test]
@@ -262,7 +266,7 @@ mod tests {
                     &[5.1, 3.5, 1.4, 0.2],
                     &[4.9, 3.0, 1.4, 0.2],
                     &[4.7, 3.2, 1.3, 0.2],
-                ]).unwrap())
+                ]))
             )
         }
         #[test]
@@ -301,11 +305,12 @@ mod tests {
     }
     mod extract_row_vectors_from_csv_text {
         use super::super::{extract_row_vectors_from_csv_text, CSVDefinition, CSVRowFormat};
+        use crate::linalg::basic::matrix::DenseMatrix;
 
         #[test]
         fn read_default_csv() {
             assert_eq!(
-                extract_row_vectors_from_csv_text::<f64>(
+                extract_row_vectors_from_csv_text::<f64, Vec<_>, DenseMatrix<_>>(
                     "column 1, column 2, column3\n1.0,2.0,3.0\n4.0,5.0,6.0",
                     &CSVDefinition::default(),
                     CSVRowFormat {

src/svm/mod.rs

@@ -56,7 +56,7 @@ pub struct Kernels;
 impl Kernels {
     /// Return a default linear
     pub fn linear() -> LinearKernel {
-        LinearKernel
+        LinearKernel::default()
     }
     /// Return a default RBF
     pub fn rbf() -> RBFKernel {
@@ -292,7 +292,7 @@ mod tests {
             .unwrap()
             .abs();
 
-        assert!((4913f64 - result).abs() < f64::EPSILON);
+        assert!((4913f64 - result) < std::f64::EPSILON);
     }
 
     #[cfg_attr(

src/svm/svc.rs

@@ -53,7 +53,7 @@
 //!            &[4.9, 2.4, 3.3, 1.0],
 //!            &[6.6, 2.9, 4.6, 1.3],
 //!            &[5.2, 2.7, 3.9, 1.4],
-//!         ]).unwrap();
+//!         ]);
 //! let y = vec![ -1, -1, -1, -1, -1, -1, -1, -1,
 //!            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 //!
@@ -322,26 +322,19 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX> + 'a, Y: Array
         let (n, _) = x.shape();
         let mut y_hat: Vec<TX> = Array1::zeros(n);
 
-        let mut row = Vec::with_capacity(n);
         for i in 0..n {
-            row.clear();
-            row.extend(x.get_row(i).iterator(0).copied());
-            let row_pred: TX = self.predict_for_row(&row);
+            let row_pred: TX =
+                self.predict_for_row(Vec::from_iterator(x.get_row(i).iterator(0).copied(), n));
             y_hat.set(i, row_pred);
         }
 
         Ok(y_hat)
     }
 
-    fn predict_for_row(&self, x: &[TX]) -> TX {
+    fn predict_for_row(&self, x: Vec<TX>) -> TX {
         let mut f = self.b.unwrap();
 
-        let xi: Vec<_> = x.iter().map(|e| e.to_f64().unwrap()).collect();
         for i in 0..self.instances.as_ref().unwrap().len() {
-            let xj: Vec<_> = self.instances.as_ref().unwrap()[i]
-                .iter()
-                .map(|e| e.to_f64().unwrap())
-                .collect();
             f += self.w.as_ref().unwrap()[i]
                 * TX::from(
                     self.parameters
@@ -350,7 +343,13 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX> + 'a, Y: Array
                         .kernel
                         .as_ref()
                         .unwrap()
-                        .apply(&xi, &xj)
+                        .apply(
+                            &x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                            &self.instances.as_ref().unwrap()[i]
+                                .iter()
+                                .map(|e| e.to_f64().unwrap())
+                                .collect(),
+                        )
                         .unwrap(),
                 )
                 .unwrap();
@@ -360,8 +359,8 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX> + 'a, Y: Array
     }
 }
 
-impl<TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> PartialEq
-    for SVC<'_, TX, TY, X, Y>
+impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> PartialEq
+    for SVC<'a, TX, TY, X, Y>
 {
     fn eq(&self, other: &Self) -> bool {
         if (self.b.unwrap().sub(other.b.unwrap())).abs() > TX::epsilon() * TX::two()
@@ -473,12 +472,14 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
         let tol = self.parameters.tol;
         let good_enough = TX::from_i32(1000).unwrap();
 
-        let mut x = Vec::with_capacity(n);
         for _ in 0..self.parameters.epoch {
             for i in self.permutate(n) {
-                x.clear();
-                x.extend(self.x.get_row(i).iterator(0).take(n).copied());
-                self.process(i, &x, *self.y.get(i), &mut cache);
+                self.process(
+                    i,
+                    Vec::from_iterator(self.x.get_row(i).iterator(0).copied(), n),
+                    *self.y.get(i),
+                    &mut cache,
+                );
                 loop {
                     self.reprocess(tol, &mut cache);
                     self.find_min_max_gradient();
@@ -510,17 +511,24 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
         let mut cp = 0;
         let mut cn = 0;
 
-        let mut x = Vec::with_capacity(n);
         for i in self.permutate(n) {
-            x.clear();
-            x.extend(self.x.get_row(i).iterator(0).take(n).copied());
             if *self.y.get(i) == TY::one() && cp < few {
-                if self.process(i, &x, *self.y.get(i), cache) {
+                if self.process(
+                    i,
+                    Vec::from_iterator(self.x.get_row(i).iterator(0).copied(), n),
+                    *self.y.get(i),
+                    cache,
+                ) {
                     cp += 1;
                 }
             } else if *self.y.get(i) == TY::from(-1).unwrap()
                 && cn < few
-                && self.process(i, &x, *self.y.get(i), cache)
+                && self.process(
+                    i,
+                    Vec::from_iterator(self.x.get_row(i).iterator(0).copied(), n),
+                    *self.y.get(i),
+                    cache,
+                )
             {
                 cn += 1;
             }
@@ -531,7 +539,7 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
         }
     }
 
-    fn process(&mut self, i: usize, x: &[TX], y: TY, cache: &mut Cache<TX, TY, X, Y>) -> bool {
+    fn process(&mut self, i: usize, x: Vec<TX>, y: TY, cache: &mut Cache<TX, TY, X, Y>) -> bool {
         for j in 0..self.sv.len() {
             if self.sv[j].index == i {
                 return true;
@@ -543,14 +551,15 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
         let mut cache_values: Vec<((usize, usize), TX)> = Vec::new();
 
         for v in self.sv.iter() {
-            let xi: Vec<_> = v.x.iter().map(|e| e.to_f64().unwrap()).collect();
-            let xj: Vec<_> = x.iter().map(|e| e.to_f64().unwrap()).collect();
             let k = self
                 .parameters
                 .kernel
                 .as_ref()
                 .unwrap()
-                .apply(&xi, &xj)
+                .apply(
+                    &v.x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                    &x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                )
                 .unwrap();
             cache_values.push(((i, v.index), TX::from(k).unwrap()));
             g -= v.alpha * k;
@@ -569,7 +578,7 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
             cache.insert(v.0, v.1.to_f64().unwrap());
         }
 
-        let x_f64: Vec<_> = x.iter().map(|e| e.to_f64().unwrap()).collect();
+        let x_f64 = x.iter().map(|e| e.to_f64().unwrap()).collect();
         let k_v = self
             .parameters
             .kernel
@@ -692,10 +701,8 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                 let km = sv1.k;
                 let gm = sv1.grad;
                 let mut best = 0f64;
-                let xi: Vec<_> = sv1.x.iter().map(|e| e.to_f64().unwrap()).collect();
                 for i in 0..self.sv.len() {
                     let v = &self.sv[i];
-                    let xj: Vec<_> = v.x.iter().map(|e| e.to_f64().unwrap()).collect();
                     let z = v.grad - gm;
                     let k = cache.get(
                         sv1,
@@ -704,7 +711,10 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                             .kernel
                             .as_ref()
                             .unwrap()
-                            .apply(&xi, &xj)
+                            .apply(
+                                &sv1.x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                                &v.x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                            )
                             .unwrap(),
                     );
                     let mut curv = km + v.k - 2f64 * k;
@@ -722,12 +732,6 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                     }
                 }
 
-                let xi: Vec<_> = self.sv[idx_1]
-                    .x
-                    .iter()
-                    .map(|e| e.to_f64().unwrap())
-                    .collect::<Vec<_>>();
-
                 idx_2.map(|idx_2| {
                     (
                         idx_1,
@@ -738,12 +742,16 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                                 .as_ref()
                                 .unwrap()
                                 .apply(
-                                    &xi,
+                                    &self.sv[idx_1]
+                                        .x
+                                        .iter()
+                                        .map(|e| e.to_f64().unwrap())
+                                        .collect(),
                                     &self.sv[idx_2]
                                         .x
                                         .iter()
                                         .map(|e| e.to_f64().unwrap())
-                                        .collect::<Vec<_>>(),
+                                        .collect(),
                                 )
                                 .unwrap()
                         }),
@@ -757,11 +765,8 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                 let km = sv2.k;
                 let gm = sv2.grad;
                 let mut best = 0f64;
-
-                let xi: Vec<_> = sv2.x.iter().map(|e| e.to_f64().unwrap()).collect();
                 for i in 0..self.sv.len() {
                     let v = &self.sv[i];
-                    let xj: Vec<_> = v.x.iter().map(|e| e.to_f64().unwrap()).collect();
                     let z = gm - v.grad;
                     let k = cache.get(
                         sv2,
@@ -770,7 +775,10 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                             .kernel
                             .as_ref()
                             .unwrap()
-                            .apply(&xi, &xj)
+                            .apply(
+                                &sv2.x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                                &v.x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                            )
                             .unwrap(),
                     );
                     let mut curv = km + v.k - 2f64 * k;
@@ -789,12 +797,6 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                     }
                 }
 
-                let xj: Vec<_> = self.sv[idx_2]
-                    .x
-                    .iter()
-                    .map(|e| e.to_f64().unwrap())
-                    .collect();
-
                 idx_1.map(|idx_1| {
                     (
                         idx_1,
@@ -809,8 +811,12 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                                         .x
                                         .iter()
                                         .map(|e| e.to_f64().unwrap())
-                                        .collect::<Vec<_>>(),
-                                    &xj,
+                                        .collect(),
+                                    &self.sv[idx_2]
+                                        .x
+                                        .iter()
+                                        .map(|e| e.to_f64().unwrap())
+                                        .collect(),
                                 )
                                 .unwrap()
                         }),
@@ -829,12 +835,12 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                             .x
                             .iter()
                             .map(|e| e.to_f64().unwrap())
-                            .collect::<Vec<_>>(),
+                            .collect(),
                         &self.sv[idx_2]
                             .x
                             .iter()
                             .map(|e| e.to_f64().unwrap())
-                            .collect::<Vec<_>>(),
+                            .collect(),
                     )
                     .unwrap(),
             )),
@@ -889,10 +895,7 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
         self.sv[v1].alpha -= step.to_f64().unwrap();
         self.sv[v2].alpha += step.to_f64().unwrap();
 
-        let xi_v1: Vec<_> = self.sv[v1].x.iter().map(|e| e.to_f64().unwrap()).collect();
-        let xi_v2: Vec<_> = self.sv[v2].x.iter().map(|e| e.to_f64().unwrap()).collect();
         for i in 0..self.sv.len() {
-            let xj: Vec<_> = self.sv[i].x.iter().map(|e| e.to_f64().unwrap()).collect();
             let k2 = cache.get(
                 &self.sv[v2],
                 &self.sv[i],
@@ -900,7 +903,10 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                     .kernel
                     .as_ref()
                     .unwrap()
-                    .apply(&xi_v2, &xj)
+                    .apply(
+                        &self.sv[v2].x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                        &self.sv[i].x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                    )
                     .unwrap(),
             );
             let k1 = cache.get(
@@ -910,7 +916,10 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
                     .kernel
                     .as_ref()
                     .unwrap()
-                    .apply(&xi_v1, &xj)
+                    .apply(
+                        &self.sv[v1].x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                        &self.sv[i].x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                    )
                     .unwrap(),
             );
             self.sv[i].grad -= step.to_f64().unwrap() * (k2 - k1);
@@ -957,8 +966,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<i32> = vec![
             -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
@@ -975,7 +983,11 @@ mod tests {
             .unwrap();
         let acc = accuracy(&y, &(y_hat.iter().map(|e| e.to_i32().unwrap()).collect()));
 
-        assert!(acc >= 0.9, "accuracy ({acc}) is not larger or equal to 0.9");
+        assert!(
+            acc >= 0.9,
+            "accuracy ({}) is not larger or equal to 0.9",
+            acc
+        );
     }
 
     #[cfg_attr(
@@ -984,8 +996,7 @@ mod tests {
     )]
     #[test]
     fn svc_fit_decision_function() {
-        let x = DenseMatrix::from_2d_array(&[&[4.0, 0.0], &[0.0, 4.0], &[8.0, 0.0], &[0.0, 8.0]])
-            .unwrap();
+        let x = DenseMatrix::from_2d_array(&[&[4.0, 0.0], &[0.0, 4.0], &[8.0, 0.0], &[0.0, 8.0]]);
 
         let x2 = DenseMatrix::from_2d_array(&[
             &[3.0, 3.0],
@@ -994,8 +1005,7 @@ mod tests {
             &[10.0, 10.0],
             &[1.0, 1.0],
             &[0.0, 0.0],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<i32> = vec![-1, -1, 1, 1];
 
@@ -1048,8 +1058,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<i32> = vec![
             -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
@@ -1067,7 +1076,11 @@ mod tests {
 
         let acc = accuracy(&y, &(y_hat.iter().map(|e| e.to_i32().unwrap()).collect()));
 
-        assert!(acc >= 0.9, "accuracy ({acc}) is not larger or equal to 0.9");
+        assert!(
+            acc >= 0.9,
+            "accuracy ({}) is not larger or equal to 0.9",
+            acc
+        );
     }
 
     #[cfg_attr(
@@ -1098,8 +1111,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<i32> = vec![
             -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
@@ -1110,7 +1122,7 @@ mod tests {
         let svc = SVC::fit(&x, &y, &params).unwrap();
 
         // serialization
-        let deserialized_svc: SVC<'_, f64, i32, _, _> =
+        let deserialized_svc: SVC<f64, i32, _, _> =
             serde_json::from_str(&serde_json::to_string(&svc).unwrap()).unwrap();
 
         assert_eq!(svc, deserialized_svc);

src/svm/svr.rs

@@ -44,7 +44,7 @@
 //!               &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
 //!               &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
 //!               &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-//!          ]).unwrap();
+//!          ]);
 //!
 //! let y: Vec<f64> = vec![83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0,
 //!           100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9];
@@ -248,20 +248,19 @@ impl<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> SVR<'
 
         let mut y_hat: Vec<T> = Vec::<T>::zeros(n);
 
-        let mut x_i = Vec::with_capacity(n);
         for i in 0..n {
-            x_i.clear();
-            x_i.extend(x.get_row(i).iterator(0).copied());
-            y_hat.set(i, self.predict_for_row(&x_i));
+            y_hat.set(
+                i,
+                self.predict_for_row(Vec::from_iterator(x.get_row(i).iterator(0).copied(), n)),
+            );
         }
 
         Ok(y_hat)
     }
 
-    pub(crate) fn predict_for_row(&self, x: &[T]) -> T {
+    pub(crate) fn predict_for_row(&self, x: Vec<T>) -> T {
         let mut f = self.b;
 
-        let xi: Vec<_> = x.iter().map(|e| e.to_f64().unwrap()).collect();
         for i in 0..self.instances.as_ref().unwrap().len() {
             f += self.w.as_ref().unwrap()[i]
                 * T::from(
@@ -271,7 +270,10 @@ impl<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> SVR<'
                         .kernel
                         .as_ref()
                         .unwrap()
-                        .apply(&xi, &self.instances.as_ref().unwrap()[i])
+                        .apply(
+                            &x.iter().map(|e| e.to_f64().unwrap()).collect(),
+                            &self.instances.as_ref().unwrap()[i],
+                        )
                         .unwrap(),
                 )
                 .unwrap()
@@ -281,8 +283,8 @@ impl<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> SVR<'
     }
 }
 
-impl<T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> PartialEq
-    for SVR<'_, T, X, Y>
+impl<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> PartialEq
+    for SVR<'a, T, X, Y>
 {
     fn eq(&self, other: &Self) -> bool {
         if (self.b - other.b).abs() > T::epsilon() * T::two()
@@ -640,8 +642,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<f64> = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
@@ -661,7 +662,7 @@ mod tests {
         .unwrap();
 
         let t = mean_squared_error(&y_hat, &y);
-        println!("{t:?}");
+        println!("{:?}", t);
         assert!(t < 2.5);
     }
 
@@ -689,8 +690,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
 
         let y: Vec<f64> = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
@@ -702,7 +702,7 @@ mod tests {
 
         let svr = SVR::fit(&x, &y, &params).unwrap();
 
-        let deserialized_svr: SVR<'_, f64, DenseMatrix<f64>, _> =
+        let deserialized_svr: SVR<f64, DenseMatrix<f64>, _> =
             serde_json::from_str(&serde_json::to_string(&svr).unwrap()).unwrap();
 
         assert_eq!(svr, deserialized_svr);

src/tree/decision_tree_classifier.rs

@@ -48,7 +48,7 @@
 //!            &[4.9, 2.4, 3.3, 1.0],
 //!            &[6.6, 2.9, 4.6, 1.3],
 //!            &[5.2, 2.7, 3.9, 1.4],
-//!         ]).unwrap();
+//!         ]);
 //! let y = vec![ 0, 0, 0, 0, 0, 0, 0, 0,
 //!            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 //!
@@ -77,9 +77,7 @@ use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
-use crate::linalg::basic::arrays::MutArray;
 use crate::linalg::basic::arrays::{Array1, Array2, MutArrayView1};
-use crate::linalg::basic::matrix::DenseMatrix;
 use crate::numbers::basenum::Number;
 use crate::rand_custom::get_rng_impl;
 
@@ -118,7 +116,6 @@ pub struct DecisionTreeClassifier<
     num_classes: usize,
     classes: Vec<TY>,
     depth: u16,
-    num_features: usize,
     _phantom_tx: PhantomData<TX>,
     _phantom_x: PhantomData<X>,
     _phantom_y: PhantomData<Y>,
@@ -140,17 +137,16 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         self.classes.as_ref()
     }
     /// Get depth of tree
-    pub fn depth(&self) -> u16 {
+    fn depth(&self) -> u16 {
         self.depth
     }
 }
 
 /// The function to measure the quality of a split.
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
-#[derive(Debug, Clone, Default)]
+#[derive(Debug, Clone)]
 pub enum SplitCriterion {
     /// [Gini index](../decision_tree_classifier/index.html)
-    #[default]
     Gini,
     /// [Entropy](../decision_tree_classifier/index.html)
     Entropy,
@@ -158,17 +154,21 @@ pub enum SplitCriterion {
     ClassificationError,
 }
 
+impl Default for SplitCriterion {
+    fn default() -> Self {
+        SplitCriterion::Gini
+    }
+}
+
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 struct Node {
     output: usize,
-    n_node_samples: usize,
     split_feature: usize,
     split_value: Option<f64>,
     split_score: Option<f64>,
     true_child: Option<usize>,
     false_child: Option<usize>,
-    impurity: Option<f64>,
 }
 
 impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> PartialEq
@@ -199,12 +199,12 @@ impl PartialEq for Node {
         self.output == other.output
             && self.split_feature == other.split_feature
             && match (self.split_value, other.split_value) {
-                (Some(a), Some(b)) => (a - b).abs() < f64::EPSILON,
+                (Some(a), Some(b)) => (a - b).abs() < std::f64::EPSILON,
                 (None, None) => true,
                 _ => false,
             }
             && match (self.split_score, other.split_score) {
-                (Some(a), Some(b)) => (a - b).abs() < f64::EPSILON,
+                (Some(a), Some(b)) => (a - b).abs() < std::f64::EPSILON,
                 (None, None) => true,
                 _ => false,
             }
@@ -405,16 +405,14 @@ impl Default for DecisionTreeClassifierSearchParameters {
 }
 
 impl Node {
-    fn new(output: usize, n_node_samples: usize) -> Self {
+    fn new(output: usize) -> Self {
         Node {
             output,
-            n_node_samples,
             split_feature: 0,
             split_value: Option::None,
             split_score: Option::None,
             true_child: Option::None,
             false_child: Option::None,
-            impurity: Option::None,
         }
     }
 }
@@ -514,7 +512,6 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
             num_classes: 0usize,
             classes: vec![],
             depth: 0u16,
-            num_features: 0usize,
             _phantom_tx: PhantomData,
             _phantom_x: PhantomData,
             _phantom_y: PhantomData,
@@ -546,10 +543,6 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         parameters: DecisionTreeClassifierParameters,
     ) -> Result<DecisionTreeClassifier<TX, TY, X, Y>, Failed> {
         let (x_nrows, num_attributes) = x.shape();
-        if x_nrows != y.shape() {
-            return Err(Failed::fit("Size of x should equal size of y"));
-        }
-
         let samples = vec![1; x_nrows];
         DecisionTreeClassifier::fit_weak_learner(x, y, samples, num_attributes, parameters)
     }
@@ -567,7 +560,8 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         let k = classes.len();
         if k < 2 {
             return Err(Failed::fit(&format!(
-                "Incorrect number of classes: {k}. Should be >= 2."
+                "Incorrect number of classes: {}. Should be >= 2.",
+                k
             )));
         }
 
@@ -586,7 +580,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
             count[yi[i]] += samples[i];
         }
 
-        let root = Node::new(which_max(&count), y_ncols);
+        let root = Node::new(which_max(&count));
         change_nodes.push(root);
         let mut order: Vec<Vec<usize>> = Vec::new();
 
@@ -601,7 +595,6 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
             num_classes: k,
             classes,
             depth: 0u16,
-            num_features: num_attributes,
             _phantom_tx: PhantomData,
             _phantom_x: PhantomData,
             _phantom_y: PhantomData,
@@ -615,7 +608,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
             visitor_queue.push_back(visitor);
         }
 
-        while tree.depth() < tree.parameters().max_depth.unwrap_or(u16::MAX) {
+        while tree.depth() < tree.parameters().max_depth.unwrap_or(std::u16::MAX) {
             match visitor_queue.pop_front() {
                 Some(node) => tree.split(node, mtry, &mut visitor_queue, &mut rng),
                 None => break,
@@ -652,7 +645,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
                     if node.true_child.is_none() && node.false_child.is_none() {
                         result = node.output;
                     } else if x.get((row, node.split_feature)).to_f64().unwrap()
-                        <= node.split_value.unwrap_or(f64::NAN)
+                        <= node.split_value.unwrap_or(std::f64::NAN)
                     {
                         queue.push_back(node.true_child.unwrap());
                     } else {
@@ -687,7 +680,16 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
             }
         }
 
+        if is_pure {
+            return false;
+        }
+
         let n = visitor.samples.iter().sum();
+
+        if n <= self.parameters().min_samples_split {
+            return false;
+        }
+
         let mut count = vec![0; self.num_classes];
         let mut false_count = vec![0; self.num_classes];
         for i in 0..n_rows {
@@ -696,15 +698,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
             }
         }
 
-        self.nodes[visitor.node].impurity = Some(impurity(&self.parameters().criterion, &count, n));
-
-        if is_pure {
-            return false;
-        }
-
-        if n <= self.parameters().min_samples_split {
-            return false;
-        }
+        let parent_impurity = impurity(&self.parameters().criterion, &count, n);
 
         let mut variables = (0..n_attr).collect::<Vec<_>>();
 
@@ -713,7 +707,14 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         }
 
         for variable in variables.iter().take(mtry) {
-            self.find_best_split(visitor, n, &count, &mut false_count, *variable);
+            self.find_best_split(
+                visitor,
+                n,
+                &count,
+                &mut false_count,
+                parent_impurity,
+                *variable,
+            );
         }
 
         self.nodes()[visitor.node].split_score.is_some()
@@ -725,6 +726,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         n: usize,
         count: &[usize],
         false_count: &mut [usize],
+        parent_impurity: f64,
         j: usize,
     ) {
         let mut true_count = vec![0; self.num_classes];
@@ -760,7 +762,6 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
 
                 let true_label = which_max(&true_count);
                 let false_label = which_max(false_count);
-                let parent_impurity = self.nodes()[visitor.node].impurity.unwrap();
                 let gain = parent_impurity
                     - tc as f64 / n as f64
                         * impurity(&self.parameters().criterion, &true_count, tc)
@@ -805,7 +806,9 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
                     .get((i, self.nodes()[visitor.node].split_feature))
                     .to_f64()
                     .unwrap()
-                    <= self.nodes()[visitor.node].split_value.unwrap_or(f64::NAN)
+                    <= self.nodes()[visitor.node]
+                        .split_value
+                        .unwrap_or(std::f64::NAN)
                 {
                     *true_sample = visitor.samples[i];
                     tc += *true_sample;
@@ -826,9 +829,9 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
 
         let true_child_idx = self.nodes().len();
 
-        self.nodes.push(Node::new(visitor.true_child_output, tc));
+        self.nodes.push(Node::new(visitor.true_child_output));
         let false_child_idx = self.nodes().len();
-        self.nodes.push(Node::new(visitor.false_child_output, fc));
+        self.nodes.push(Node::new(visitor.false_child_output));
         self.nodes[visitor.node].true_child = Some(true_child_idx);
         self.nodes[visitor.node].false_child = Some(false_child_idx);
 
@@ -862,104 +865,11 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
 
         true
     }
-
-    /// Compute feature importances for the fitted tree.
-    pub fn compute_feature_importances(&self, normalize: bool) -> Vec<f64> {
-        let mut importances = vec![0f64; self.num_features];
-
-        for node in self.nodes().iter() {
-            if node.true_child.is_none() && node.false_child.is_none() {
-                continue;
-            }
-            let left = &self.nodes()[node.true_child.unwrap()];
-            let right = &self.nodes()[node.false_child.unwrap()];
-
-            importances[node.split_feature] += node.n_node_samples as f64 * node.impurity.unwrap()
-                - left.n_node_samples as f64 * left.impurity.unwrap()
-                - right.n_node_samples as f64 * right.impurity.unwrap();
-        }
-        for item in importances.iter_mut() {
-            *item /= self.nodes()[0].n_node_samples as f64;
-        }
-        if normalize {
-            let sum = importances.iter().sum::<f64>();
-            for importance in importances.iter_mut() {
-                *importance /= sum;
-            }
-        }
-        importances
-    }
-
-    /// Predict class probabilities for the input samples.
-    ///
-    /// # Arguments
-    ///
-    /// * `x` - The input samples as a matrix where each row is a sample and each column is a feature.
-    ///
-    /// # Returns
-    ///
-    /// A `Result` containing a `DenseMatrix<f64>` where each row corresponds to a sample and each column
-    /// corresponds to a class. The values represent the probability of the sample belonging to each class.
-    ///
-    /// # Errors
-    ///
-    /// Returns an error if at least one row prediction process fails.
-    pub fn predict_proba(&self, x: &X) -> Result<DenseMatrix<f64>, Failed> {
-        let (n_samples, _) = x.shape();
-        let n_classes = self.classes().len();
-        let mut result = DenseMatrix::<f64>::zeros(n_samples, n_classes);
-
-        for i in 0..n_samples {
-            let probs = self.predict_proba_for_row(x, i)?;
-            for (j, &prob) in probs.iter().enumerate() {
-                result.set((i, j), prob);
-            }
-        }
-
-        Ok(result)
-    }
-
-    /// Predict class probabilities for a single input sample.
-    ///
-    /// # Arguments
-    ///
-    /// * `x` - The input matrix containing all samples.
-    /// * `row` - The index of the row in `x` for which to predict probabilities.
-    ///
-    /// # Returns
-    ///
-    /// A vector of probabilities, one for each class, representing the probability
-    /// of the input sample belonging to each class.
-    fn predict_proba_for_row(&self, x: &X, row: usize) -> Result<Vec<f64>, Failed> {
-        let mut node = 0;
-
-        while let Some(current_node) = self.nodes().get(node) {
-            if current_node.true_child.is_none() && current_node.false_child.is_none() {
-                // Leaf node reached
-                let mut probs = vec![0.0; self.classes().len()];
-                probs[current_node.output] = 1.0;
-                return Ok(probs);
-            }
-
-            let split_feature = current_node.split_feature;
-            let split_value = current_node.split_value.unwrap_or(f64::NAN);
-
-            if x.get((row, split_feature)).to_f64().unwrap() <= split_value {
-                node = current_node.true_child.unwrap();
-            } else {
-                node = current_node.false_child.unwrap();
-            }
-        }
-
-        // This should never happen if the tree is properly constructed
-        Err(Failed::predict("Nodes iteration did not reach leaf"))
-    }
 }
 
 #[cfg(test)]
 mod tests {
     use super::*;
-    use crate::linalg::basic::arrays::Array;
     use crate::linalg::basic::matrix::DenseMatrix;
 
     #[test]
@@ -991,60 +901,17 @@ mod tests {
     )]
     #[test]
     fn gini_impurity() {
-        assert!((impurity(&SplitCriterion::Gini, &[7, 3], 10) - 0.42).abs() < f64::EPSILON);
         assert!(
-            (impurity(&SplitCriterion::Entropy, &[7, 3], 10) - 0.8812908992306927).abs()
-                < f64::EPSILON
+            (impurity(&SplitCriterion::Gini, &vec![7, 3], 10) - 0.42).abs() < std::f64::EPSILON
         );
         assert!(
-            (impurity(&SplitCriterion::ClassificationError, &[7, 3], 10) - 0.3).abs()
-                < f64::EPSILON
+            (impurity(&SplitCriterion::Entropy, &vec![7, 3], 10) - 0.8812908992306927).abs()
+                < std::f64::EPSILON
+        );
+        assert!(
+            (impurity(&SplitCriterion::ClassificationError, &vec![7, 3], 10) - 0.3).abs()
+                < std::f64::EPSILON
         );
-    }
-
-    #[cfg_attr(
-        all(target_arch = "wasm32", not(target_os = "wasi")),
-        wasm_bindgen_test::wasm_bindgen_test
-    )]
-    #[test]
-    fn test_predict_proba() {
-        let x: DenseMatrix<f64> = 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],
-            &[7.0, 3.2, 4.7, 1.4],
-            &[6.4, 3.2, 4.5, 1.5],
-            &[6.9, 3.1, 4.9, 1.5],
-            &[5.5, 2.3, 4.0, 1.3],
-            &[6.5, 2.8, 4.6, 1.5],
-        ])
-        .unwrap();
-        let y: Vec<usize> = vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1];
-
-        let tree = DecisionTreeClassifier::fit(&x, &y, Default::default()).unwrap();
-        let probabilities = tree.predict_proba(&x).unwrap();
-
-        assert_eq!(probabilities.shape(), (10, 2));
-
-        for row in 0..10 {
-            let row_sum: f64 = probabilities.get_row(row).sum();
-            assert!(
-                (row_sum - 1.0).abs() < 1e-6,
-                "Row probabilities should sum to 1"
-            );
-        }
-
-        // Check if the first 5 samples have higher probability for class 0
-        for i in 0..5 {
-            assert!(probabilities.get((i, 0)) > probabilities.get((i, 1)));
-        }
-
-        // Check if the last 5 samples have higher probability for class 1
-        for i in 5..10 {
-            assert!(probabilities.get((i, 1)) > probabilities.get((i, 0)));
-        }
     }
 
     #[cfg_attr(
@@ -1075,8 +942,7 @@ mod tests {
             &[4.9, 2.4, 3.3, 1.0],
             &[6.6, 2.9, 4.6, 1.3],
             &[5.2, 2.7, 3.9, 1.4],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 
         assert_eq!(
@@ -1105,17 +971,6 @@ mod tests {
         );
     }
 
-    #[test]
-    fn test_random_matrix_with_wrong_rownum() {
-        let x_rand: DenseMatrix<f64> = DenseMatrix::<f64>::rand(21, 200);
-
-        let y: Vec<u32> = vec![0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
-
-        let fail = DecisionTreeClassifier::fit(&x_rand, &y, Default::default());
-
-        assert!(fail.is_err());
-    }
-
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test
@@ -1143,8 +998,7 @@ mod tests {
             &[0., 0., 1., 1.],
             &[0., 0., 0., 0.],
             &[0., 0., 0., 1.],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<u32> = vec![1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0];
 
         assert_eq!(
@@ -1155,43 +1009,6 @@ mod tests {
         );
     }
 
-    #[test]
-    fn test_compute_feature_importances() {
-        let x: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
-            &[1., 1., 1., 0.],
-            &[1., 1., 1., 0.],
-            &[1., 1., 1., 1.],
-            &[1., 1., 0., 0.],
-            &[1., 1., 0., 1.],
-            &[1., 0., 1., 0.],
-            &[1., 0., 1., 0.],
-            &[1., 0., 1., 1.],
-            &[1., 0., 0., 0.],
-            &[1., 0., 0., 1.],
-            &[0., 1., 1., 0.],
-            &[0., 1., 1., 0.],
-            &[0., 1., 1., 1.],
-            &[0., 1., 0., 0.],
-            &[0., 1., 0., 1.],
-            &[0., 0., 1., 0.],
-            &[0., 0., 1., 0.],
-            &[0., 0., 1., 1.],
-            &[0., 0., 0., 0.],
-            &[0., 0., 0., 1.],
-        ])
-        .unwrap();
-        let y: Vec<u32> = vec![1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0];
-        let tree = DecisionTreeClassifier::fit(&x, &y, Default::default()).unwrap();
-        assert_eq!(
-            tree.compute_feature_importances(false),
-            vec![0., 0., 0.21333333333333332, 0.26666666666666666]
-        );
-        assert_eq!(
-            tree.compute_feature_importances(true),
-            vec![0., 0., 0.4444444444444444, 0.5555555555555556]
-        );
-    }
-
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test
@@ -1220,8 +1037,7 @@ mod tests {
             &[0., 0., 1., 1.],
             &[0., 0., 0., 0.],
             &[0., 0., 0., 1.],
-        ])
-        .unwrap();
+        ]);
         let y = vec![1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0];
 
         let tree = DecisionTreeClassifier::fit(&x, &y, Default::default()).unwrap();

src/tree/decision_tree_regressor.rs

@@ -18,6 +18,7 @@
 //! Example:
 //!
 //! ```
+//! use rand::thread_rng;
 //! use smartcore::linalg::basic::matrix::DenseMatrix;
 //! use smartcore::tree::decision_tree_regressor::*;
 //!
@@ -39,7 +40,7 @@
 //!             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
 //!             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
 //!             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-//!        ]).unwrap();
+//!        ]);
 //! let y: Vec<f64> = vec![
 //!             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0,
 //!             101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9,
@@ -311,15 +312,15 @@ impl Node {
 
 impl PartialEq for Node {
     fn eq(&self, other: &Self) -> bool {
-        (self.output - other.output).abs() < f64::EPSILON
+        (self.output - other.output).abs() < std::f64::EPSILON
             && self.split_feature == other.split_feature
             && match (self.split_value, other.split_value) {
-                (Some(a), Some(b)) => (a - b).abs() < f64::EPSILON,
+                (Some(a), Some(b)) => (a - b).abs() < std::f64::EPSILON,
                 (None, None) => true,
                 _ => false,
             }
             && match (self.split_score, other.split_score) {
-                (Some(a), Some(b)) => (a - b).abs() < f64::EPSILON,
+                (Some(a), Some(b)) => (a - b).abs() < std::f64::EPSILON,
                 (None, None) => true,
                 _ => false,
             }
@@ -421,10 +422,6 @@ impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
         parameters: DecisionTreeRegressorParameters,
     ) -> Result<DecisionTreeRegressor<TX, TY, X, Y>, Failed> {
         let (x_nrows, num_attributes) = x.shape();
-        if x_nrows != y.shape() {
-            return Err(Failed::fit("Size of x should equal size of y"));
-        }
-
         let samples = vec![1; x_nrows];
         DecisionTreeRegressor::fit_weak_learner(x, y, samples, num_attributes, parameters)
     }
@@ -478,7 +475,7 @@ impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
             visitor_queue.push_back(visitor);
         }
 
-        while tree.depth() < tree.parameters().max_depth.unwrap_or(u16::MAX) {
+        while tree.depth() < tree.parameters().max_depth.unwrap_or(std::u16::MAX) {
             match visitor_queue.pop_front() {
                 Some(node) => tree.split(node, mtry, &mut visitor_queue, &mut rng),
                 None => break,
@@ -515,7 +512,7 @@ impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
                     if node.true_child.is_none() && node.false_child.is_none() {
                         result = node.output;
                     } else if x.get((row, node.split_feature)).to_f64().unwrap()
-                        <= node.split_value.unwrap_or(f64::NAN)
+                        <= node.split_value.unwrap_or(std::f64::NAN)
                     {
                         queue.push_back(node.true_child.unwrap());
                     } else {
@@ -640,7 +637,9 @@ impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
                     .get((i, self.nodes()[visitor.node].split_feature))
                     .to_f64()
                     .unwrap()
-                    <= self.nodes()[visitor.node].split_value.unwrap_or(f64::NAN)
+                    <= self.nodes()[visitor.node]
+                        .split_value
+                        .unwrap_or(std::f64::NAN)
                 {
                     *true_sample = visitor.samples[i];
                     tc += *true_sample;
@@ -751,8 +750,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<f64> = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
             114.2, 115.7, 116.9,
@@ -766,7 +764,7 @@ mod tests {
             assert!((y_hat[i] - y[i]).abs() < 0.1);
         }
 
-        let expected_y = [
+        let expected_y = vec![
             87.3, 87.3, 87.3, 87.3, 98.9, 98.9, 98.9, 98.9, 98.9, 107.9, 107.9, 107.9, 114.85,
             114.85, 114.85, 114.85,
         ];
@@ -787,7 +785,7 @@ mod tests {
             assert!((y_hat[i] - expected_y[i]).abs() < 0.1);
         }
 
-        let expected_y = [
+        let expected_y = vec![
             83.0, 88.35, 88.35, 89.5, 97.15, 97.15, 99.5, 99.5, 101.2, 104.6, 109.6, 109.6, 113.4,
             113.4, 116.30, 116.30,
         ];
@@ -833,8 +831,7 @@ mod tests {
             &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
             &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
             &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        ])
-        .unwrap();
+        ]);
         let y: Vec<f64> = vec![
             83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
             114.2, 115.7, 116.9,