Merge branch 'development' into kmeans-with-fastpair

.github/workflows/ci.yml

@@ -36,7 +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

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 = "0.3.2"
 authors = ["smartcore Developers"]
 edition = "2021"
 license = "Apache-2.0"
@@ -48,7 +48,7 @@ getrandom = { version = "0.2.8", optional = true }
 wasm-bindgen-test = "0.3"
 
 [dev-dependencies]
-itertools = "0.13.0"
+itertools = "0.10.5"
 serde_json = "1.0"
 bincode = "1.3.1"
 

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
@@ -173,6 +179,21 @@ impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> FastPair<'a, T, M> {
         }
     }
 
+    ///
+    /// Return order dissimilarities from closest to furthest
+    ///
+    #[allow(dead_code)]
+    pub fn ordered_pairs(&self) -> std::vec::IntoIter<&PairwiseDistance<T>> {
+        // improvement: implement this to return `impl Iterator<Item = &PairwiseDistance<T>>`
+        // need to implement trait `Iterator` for `Vec<&PairwiseDistance<T>>`
+        let mut distances = self
+            .distances
+            .values()
+            .collect::<Vec<&PairwiseDistance<T>>>();
+        distances.sort_by(|a, b| a.partial_cmp(b).unwrap());
+        distances.into_iter()
+    }
+
     //
     // Compute distances from input to all other points in data-structure.
     // input is the row index of the sample matrix
@@ -211,7 +232,9 @@ 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> {
         use itertools::Itertools;
         let m = fastpair.samples.shape().0;
@@ -263,7 +286,7 @@ 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
@@ -280,7 +303,7 @@ mod tests_fastpair {
 
     #[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());
@@ -300,8 +323,7 @@ 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());
@@ -336,8 +358,7 @@ 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());
 
@@ -510,8 +531,7 @@ 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());
@@ -559,8 +579,7 @@ 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());
@@ -586,4 +605,39 @@ mod tests_fastpair {
 
         assert_eq!(closest, min_dissimilarity);
     }
+
+    #[test]
+    fn fastpair_ordered_pairs() {
+        let x = DenseMatrix::<f64>::from_2d_array(&[
+            &[5.1, 3.5, 1.4, 0.2],
+            &[4.9, 3.0, 1.4, 0.2],
+            &[4.7, 3.2, 1.3, 0.2],
+            &[4.6, 3.1, 1.5, 0.2],
+            &[5.0, 3.6, 1.4, 0.2],
+            &[5.4, 3.9, 1.7, 0.4],
+            &[4.9, 3.1, 1.5, 0.1],
+            &[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],
+            &[4.6, 3.4, 1.4, 0.3],
+            &[5.0, 3.4, 1.5, 0.2],
+            &[4.4, 2.9, 1.4, 0.2],
+        ]);
+        let fastpair = FastPair::new(&x).unwrap();
+
+        let ordered = fastpair.ordered_pairs();
+
+        let mut previous: f64 = -1.0;
+        for p in ordered {
+            if previous == -1.0 {
+                previous = p.distance.unwrap();
+            } else {
+                let current = p.distance.unwrap();
+                assert!(current >= previous);
+                previous = current;
+            }
+        }
+    }
 }

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/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

@@ -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();
 

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
@@ -62,7 +62,7 @@ use serde::{Deserialize, Serialize};
 use crate::algorithm::neighbour::bbd_tree::BBDTree;
 use crate::api::{Predictor, UnsupervisedEstimator};
 use crate::error::Failed;
-use crate::linalg::basic::arrays::{Array1, Array2};
+use crate::linalg::basic::arrays::{Array1, Array2, Array};
 use crate::metrics::distance::euclidian::*;
 use crate::numbers::basenum::Number;
 use crate::rand_custom::get_rng_impl;
@@ -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];
@@ -322,6 +322,109 @@ 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    
+    /// * `parameters` - cluster parameters
+    /// * `centroids` - starting centroids
+    pub fn fit_with_centroids(
+        data: &X,
+        parameters: KMeansParameters,
+        centroids: Vec<Vec<f64>>,
+    ) -> Result<KMeans<TX, TY, X, Y>, Failed> {
+
+        // TODO: reuse existing methods in `crate::metrics`
+        fn euclidean_distance(point1: &Vec<f64>, point2: &Vec<f64>) -> f64 {
+            let mut dist = 0.0;
+            for i in 0..point1.len() {
+                dist += (point1[i] - point2[i]).powi(2);
+            }
+            dist.sqrt()
+        }
+
+        fn closest_centroid(point: &Vec<f64>, centroids: &Vec<Vec<f64>>) -> usize {
+            let mut closest_idx = 0;
+            let mut closest_dist = std::f64::MAX;
+            for (i, centroid) in centroids.iter().enumerate() {
+                let dist = euclidean_distance(point, centroid);
+                if dist < closest_dist {
+                    closest_dist = dist;
+                    closest_idx = i;
+                }
+            }
+            closest_idx
+        }
+
+        let bbd = BBDTree::new(data);
+    
+        if centroids.len() != parameters.k {
+            return Err(Failed::fit(&format!(
+                "number of centroids ({}) must be equal to k ({})",
+                centroids.len(),
+                parameters.k
+            )));
+        }
+    
+        let mut y = vec![0; data.shape().0];
+        for i in 0..data.shape().0 {
+            y[i] = closest_centroid(
+                &Vec::from_iterator(data.get_row(i).iterator(0).map(|e| e.to_f64().unwrap()),
+                data.shape().1), &centroids
+            );
+        }
+    
+        let mut size = vec![0; parameters.k];
+        let mut new_centroids = vec![vec![0f64; data.shape().1]; parameters.k];
+    
+        for i in 0..data.shape().0 {
+            size[y[i]] += 1;
+        }
+    
+        for i in 0..data.shape().0 {
+            for j in 0..data.shape().1 {
+                new_centroids[y[i]][j] += data.get((i, j)).to_f64().unwrap();
+            }
+        }
+    
+        for i in 0..parameters.k {
+            for j in 0..data.shape().1 {
+                new_centroids[i][j] /= size[i] as f64;
+            }
+        }
+    
+        let mut sums = vec![vec![0f64; data.shape().1]; parameters.k];
+        let mut distortion = std::f64::MAX;
+    
+        for _ in 1..=parameters.max_iter {
+            let dist = bbd.clustering(&new_centroids, &mut sums, &mut size, &mut y);
+            for i in 0..parameters.k {
+                if size[i] > 0 {
+                    for j in 0..data.shape().1 {
+                        new_centroids[i][j] = sums[i][j] / size[i] as f64;
+                    }
+                }
+            }
+    
+            if distortion <= dist {
+                break;
+            } else {
+                distortion = dist;
+            }
+        }
+    
+        Ok(KMeans {
+            k: parameters.k,
+            _y: y,
+            size,
+            _distortion: distortion,
+            centroids: new_centroids,
+            _phantom_tx: PhantomData,
+            _phantom_ty: PhantomData,
+            _phantom_x: PhantomData,
+            _phantom_y: PhantomData,
+        })
+    }
+
+
     /// Predict clusters for `x`
     /// * `x` - matrix with new data to transform of size _KxM_ , where _K_ is number of new samples and _M_ is number of features.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
@@ -331,7 +434,7 @@ 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 +464,7 @@ 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 {
@@ -417,6 +520,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
 mod tests {
     use super::*;
     use crate::linalg::basic::matrix::DenseMatrix;
+    use crate::algorithm::neighbour::fastpair;
 
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
@@ -424,7 +528,7 @@ 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,
@@ -492,8 +596,7 @@ 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::fit(&x, Default::default()).unwrap();
 
@@ -504,6 +607,78 @@ mod tests {
         }
     }
 
+    #[cfg_attr(
+        all(target_arch = "wasm32", not(target_os = "wasi")),
+        wasm_bindgen_test::wasm_bindgen_test
+    )]
+    #[test]
+    fn fit_with_centroids_predict() {
+        let x = DenseMatrix::from_2d_array(&[
+            &[5.1, 3.5, 1.4, 0.2],
+            &[4.9, 3.0, 1.4, 0.2],
+            &[4.7, 3.2, 1.3, 0.2],
+            &[4.6, 3.1, 1.5, 0.2],
+            &[5.0, 3.6, 1.4, 0.2],
+            &[5.4, 3.9, 1.7, 0.4],
+            &[4.6, 3.4, 1.4, 0.3],
+            &[5.0, 3.4, 1.5, 0.2],
+            &[4.4, 2.9, 1.4, 0.2],
+            &[4.9, 3.1, 1.5, 0.1],
+            &[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],
+            &[5.7, 2.8, 4.5, 1.3],
+            &[6.3, 3.3, 4.7, 1.6],
+            &[4.9, 2.4, 3.3, 1.0],
+            &[6.6, 2.9, 4.6, 1.3],
+            &[5.2, 2.7, 3.9, 1.4],
+        ]);
+
+        let parameters = KMeansParameters {
+            k: 3,
+            max_iter: 50,
+            ..Default::default()
+        };
+
+        // compute pairs
+        let fastpair = fastpair::FastPair::new(&x).unwrap();
+
+        // compute centroids for N closest pairs
+        let mut n: isize = 2;
+        let mut centroids = vec![vec![0f64; x.shape().1]; n as usize + 1];
+        for p in fastpair.ordered_pairs() {
+            if n == -1 {
+                break
+            }
+
+            centroids[n as usize] = {
+                let mut result: Vec<f64> = Vec::with_capacity(x.shape().1);
+                for val1 in x.get_row(p.node).iterator(0) {
+                    for val2 in x.get_row(p.neighbour.unwrap()).iterator(0) {
+                        let sum = val1 + val2;
+                        let avg = sum * 0.5f64;
+                        result.push(avg);
+                    }
+                }
+                result
+            };
+
+            n -= 1;
+        }
+
+
+        let kmeans = KMeans::fit_with_centroids(
+            &x, parameters, centroids).unwrap();
+
+        let y: Vec<usize> = kmeans.predict(&x).unwrap();
+
+        for (i, _y_i) in y.iter().enumerate() {
+            assert_eq!({ y[i] }, kmeans._y[i]);
+        }
+    }
+
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test
@@ -532,8 +707,7 @@ 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/diabetes.rs

@@ -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

@@ -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

@@ -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/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
 //!
@@ -443,7 +443,6 @@ 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 +457,7 @@ 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 +500,7 @@ mod tests {
                 -0.974080592182491,
                 0.0723250196376097,
             ],
-        ])
-        .unwrap();
+        ]);
 
         let expected_projection = DenseMatrix::from_2d_array(&[
             &[-64.8022, -11.448, 2.4949, -2.4079],
@@ -556,8 +553,7 @@ 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,
@@ -620,8 +616,7 @@ mod tests {
                 -0.0881962972508558,
                 -0.0096011588898465,
             ],
-        ])
-        .unwrap();
+        ]);
 
         let expected_projection = DenseMatrix::from_2d_array(&[
             &[0.9856, -1.1334, 0.4443, -0.1563],
@@ -674,8 +669,7 @@ 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,
@@ -738,7 +732,7 @@ 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
@@ -292,8 +292,7 @@ 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 +300,7 @@ 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 +341,7 @@ 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,
@@ -660,8 +660,7 @@ 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(
@@ -734,8 +733,7 @@ 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(
@@ -788,8 +786,7 @@ 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
@@ -574,8 +574,7 @@ 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,
@@ -649,8 +648,7 @@ 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 +702,7 @@ 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

@@ -32,8 +32,6 @@ pub enum FailedError {
     SolutionFailed,
     /// Error in input parameters
     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 {
@@ -115,7 +97,6 @@ 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}")
     }

src/lib.rs

@@ -64,7 +64,7 @@
 //!    &[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,
         }
     }
 
@@ -104,26 +102,26 @@ impl<'a, T: Debug + Display + Copy + Sized> fmt::Display for DenseMatrixView<'a,
 }
 
 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,
         }
     }
 
@@ -184,102 +182,42 @@ impl<'a, 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 {
@@ -608,75 +543,16 @@ 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());
@@ -685,7 +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());
@@ -699,8 +575,7 @@ 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],
@@ -714,7 +589,7 @@ mod tests {
 
     #[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
@@ -750,8 +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");
@@ -763,7 +637,7 @@ 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);
@@ -776,7 +650,7 @@ mod tests {
 
     #[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);
 
@@ -790,8 +664,8 @@ mod tests {
 
     #[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}");
         // take column 0 and 2
@@ -803,7 +677,7 @@ mod tests {
 
     #[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);
@@ -814,8 +688,7 @@ 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);
@@ -828,15 +701,13 @@ mod tests {
 
     #[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

@@ -55,7 +55,6 @@ 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
     }
 
@@ -212,7 +211,7 @@ mod tests {
 
     #[test]
     fn test_len() {
-        let x = [1, 2, 3];
+        let x = vec![1, 2, 3];
         assert_eq!(3, x.len());
     }
 

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;
@@ -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();
 
@@ -841,7 +839,7 @@ mod tests {
         ));
         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);
+            assert!((0f64 - evd.e[i]).abs() < std::f64::EPSILON);
         }
     }
     #[cfg_attr(
@@ -854,8 +852,7 @@ 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 +860,7 @@ 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();
 
@@ -875,7 +871,7 @@ mod tests {
         ));
         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);
+            assert!((0f64 - evd.e[i]).abs() < std::f64::EPSILON);
         }
     }
     #[cfg_attr(
@@ -889,8 +885,7 @@ 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 +895,7 @@ 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();
 

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();
@@ -263,13 +263,13 @@ 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 +281,9 @@ 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();
@@ -201,20 +201,17 @@ 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 +223,13 @@ 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

@@ -136,8 +136,8 @@ 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);
@@ -159,8 +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);
     /// ```
@@ -186,8 +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.];
 
@@ -197,7 +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];
 
@@ -212,13 +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]
@@ -232,8 +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,
@@ -254,10 +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,
@@ -271,8 +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]);

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];
 
@@ -496,15 +497,13 @@ 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();
 
@@ -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(&[
             &[
@@ -710,8 +707,7 @@ mod tests {
                 0.1654796,
                 -0.32346758,
             ],
-        ])
-        .unwrap();
+        ]);
 
         let svd = A.svd().unwrap();
 
@@ -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];
@@ -511,8 +511,7 @@ 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 +562,7 @@ 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 +627,7 @@ 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

@@ -418,8 +418,7 @@ 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,
 //! ];
@@ -183,11 +183,14 @@ 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();
@@ -413,7 +416,7 @@ 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,
@@ -608,8 +611,7 @@ 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];
 
@@ -626,11 +628,11 @@ 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.]);
 
-        assert!((f - 408.0052230582765).abs() < f64::EPSILON);
+        assert!((f - 408.0052230582765).abs() < std::f64::EPSILON);
 
         let objective_reg = MultiClassObjectiveFunction {
             x: &x,
@@ -669,8 +671,7 @@ 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];
 
@@ -686,13 +687,13 @@ 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.]);
 
-        assert!((f - 59.76994756647412).abs() < f64::EPSILON);
+        assert!((f - 59.76994756647412).abs() < std::f64::EPSILON);
 
         let objective_reg = BinaryObjectiveFunction {
             x: &x,
@@ -732,8 +733,7 @@ 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 +818,37 @@ 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 +877,7 @@ 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 +890,11 @@ 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 +903,4 @@ 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];
@@ -455,8 +455,7 @@ 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 +513,7 @@ 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/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/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/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,
 //! ];
@@ -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);

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>>(
@@ -402,10 +402,10 @@ 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 +427,6 @@ 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 +527,7 @@ 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 +558,7 @@ 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 +586,7 @@ 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 +643,7 @@ 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;
                         }
                     }
@@ -363,7 +363,7 @@ 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 +375,6 @@ 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)
@@ -456,8 +455,7 @@ 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 +513,7 @@ 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 +539,7 @@ 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 +571,7 @@ 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,
@@ -317,7 +317,7 @@ 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 +328,6 @@ 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)
@@ -396,8 +395,7 @@ 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 +435,7 @@ 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 +462,7 @@ 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<'d> NBDistribution<i32, i32> for TestDistribution<'d> {
-        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,
@@ -345,10 +345,10 @@ 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 +358,6 @@ 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 +433,7 @@ 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 +467,7 @@ 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 +495,7 @@ 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 +554,7 @@ 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,
@@ -261,7 +261,6 @@ 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 +311,7 @@ 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 +325,7 @@ 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 +336,7 @@ 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 +348,7 @@ 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,
@@ -246,7 +250,6 @@ 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 +295,9 @@ 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 +311,7 @@ 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 +322,9 @@ 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 +341,7 @@ 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/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>,

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;
         }
@@ -181,13 +188,14 @@ 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>>(
         &self,
         _: &DF<'_, X>,
@@ -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;
 
@@ -288,7 +299,7 @@ mod tests {
 
         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();
@@ -240,16 +240,14 @@ 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 +259,14 @@ 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 +277,7 @@ 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);
@@ -338,8 +334,7 @@ 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);

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;
@@ -197,18 +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]
+                ]))
             )
         }
 
@@ -290,15 +287,13 @@ 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}");
             assert!(transformed_values.approximate_eq(
                 &DenseMatrix::from_2d_array(&[
@@ -306,8 +301,7 @@ mod tests {
                     &[-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
             ))
         }
@@ -316,10 +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"
             )
         }
@@ -334,8 +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 {
@@ -358,8 +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();
@@ -369,18 +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
@@ -398,9 +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]]))
             )
         }
 
@@ -420,8 +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]]))
             )
         }
 
@@ -440,8 +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();
@@ -454,18 +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/readers/csv.rs

@@ -238,8 +238,7 @@ 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 +261,7 @@ 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]

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];
 //!
@@ -957,8 +957,7 @@ 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,
@@ -984,8 +983,7 @@ 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 +992,7 @@ 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 +1045,7 @@ 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,
@@ -1098,8 +1094,7 @@ 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,

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];
@@ -640,8 +640,7 @@ 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,
@@ -689,8 +688,7 @@ 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/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];
 //!
@@ -116,7 +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>,
@@ -160,13 +159,11 @@ pub enum SplitCriterion {
 #[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
@@ -197,12 +194,12 @@ 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,
             }
@@ -403,16 +400,14 @@ 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,
         }
     }
 }
@@ -512,7 +507,6 @@ 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,
@@ -584,7 +578,7 @@ 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();
 
@@ -599,7 +593,6 @@ 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,
@@ -613,7 +606,7 @@ 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,
@@ -650,7 +643,7 @@ 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 {
@@ -685,7 +678,16 @@ 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 {
@@ -694,15 +696,7 @@ 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<_>>();
 
@@ -711,7 +705,14 @@ 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()
@@ -723,6 +724,7 @@ 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];
@@ -758,7 +760,6 @@ 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)
@@ -803,7 +804,9 @@ 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;
@@ -824,9 +827,9 @@ 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);
 
@@ -860,33 +863,6 @@ 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
-    }
 }
 
 #[cfg(test)]
@@ -923,14 +899,14 @@ mod tests {
     )]
     #[test]
     fn gini_impurity() {
-        assert!((impurity(&SplitCriterion::Gini, &[7, 3], 10) - 0.42).abs() < f64::EPSILON);
+        assert!((impurity(&SplitCriterion::Gini, &[7, 3], 10) - 0.42).abs() < std::f64::EPSILON);
         assert!(
             (impurity(&SplitCriterion::Entropy, &[7, 3], 10) - 0.8812908992306927).abs()
-                < f64::EPSILON
+                < std::f64::EPSILON
         );
         assert!(
             (impurity(&SplitCriterion::ClassificationError, &[7, 3], 10) - 0.3).abs()
-                < f64::EPSILON
+                < std::f64::EPSILON
         );
     }
 
@@ -962,8 +938,7 @@ 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!(
@@ -1030,8 +1005,7 @@ 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!(
@@ -1042,43 +1016,6 @@ 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
@@ -1107,8 +1044,7 @@ 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

@@ -39,7 +39,7 @@
 //!             &[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 +311,15 @@ 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,
             }
@@ -478,7 +478,7 @@ 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 +515,7 @@ 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 +640,9 @@ 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 +753,7 @@ 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 +767,7 @@ 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 +788,7 @@ 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 +834,7 @@ 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,