fix: cargo fmt

benches/distance.rs

@@ -2,14 +2,16 @@
 extern crate criterion;
 extern crate smartcore;
 
-use criterion::Criterion;
 use criterion::black_box;
+use criterion::Criterion;
 use smartcore::math::distance::*;
 
 fn criterion_benchmark(c: &mut Criterion) {
     let a = vec![1., 2., 3.];
 
-    c.bench_function("Euclidean Distance", move |b| b.iter(|| Distances::euclidian().distance(black_box(&a), black_box(&a))));
+    c.bench_function("Euclidean Distance", move |b| {
+        b.iter(|| Distances::euclidian().distance(black_box(&a), black_box(&a)))
+    });
 }
 
 criterion_group!(benches, criterion_benchmark);

src/algorithm/mod.rs

@@ -1,2 +1,2 @@
-pub mod sort;
 pub mod neighbour;
+pub mod sort;

src/algorithm/neighbour/bbd_tree.rs

@@ -1,14 +1,14 @@
 use std::fmt::Debug;
 
-use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
 use crate::math::distance::euclidian::*;
+use crate::math::num::FloatExt;
 
 #[derive(Debug)]
 pub struct BBDTree<T: FloatExt> {
     nodes: Vec<BBDTreeNode<T>>,
     index: Vec<usize>,
-    root: usize
+    root: usize,
 }
 
 #[derive(Debug)]
@@ -20,7 +20,7 @@ struct BBDTreeNode<T: FloatExt> {
     sum: Vec<T>,
     cost: T,
     lower: Option<usize>,
-    upper: Option<usize>
+    upper: Option<usize>,
 }
 
 impl<T: FloatExt> BBDTreeNode<T> {
@@ -33,7 +33,7 @@ impl<T: FloatExt> BBDTreeNode<T> {
             sum: vec![T::zero(); d],
             cost: T::zero(),
             lower: Option::None,
-            upper: Option::None
+            upper: Option::None,
         }
     }
 }
@@ -52,7 +52,7 @@ impl<T: FloatExt> BBDTree<T> {
         let mut tree = BBDTree {
             nodes: nodes,
             index: index,
-            root: 0
+            root: 0,
         };
 
         let root = tree.build_node(data, 0, n);
@@ -62,7 +62,13 @@ impl<T: FloatExt> BBDTree<T> {
         tree
     }
 
-    pub(in crate) fn clustering(&self, centroids: &Vec<Vec<T>>, sums: &mut Vec<Vec<T>>, counts: &mut Vec<usize>, membership: &mut Vec<usize>) -> T {
+    pub(in crate) fn clustering(
+        &self,
+        centroids: &Vec<Vec<T>>,
+        sums: &mut Vec<Vec<T>>,
+        counts: &mut Vec<usize>,
+        membership: &mut Vec<usize>,
+    ) -> T {
         let k = centroids.len();
 
         counts.iter_mut().for_each(|x| *x = 0);
@@ -72,17 +78,36 @@ impl<T: FloatExt> BBDTree<T> {
             sums[i].iter_mut().for_each(|x| *x = T::zero());
         }
 
-        self.filter(self.root, centroids, &candidates, k, sums, counts, membership)
+        self.filter(
+            self.root,
+            centroids,
+            &candidates,
+            k,
+            sums,
+            counts,
+            membership,
+        )
     }
 
-    fn filter(&self, node: usize, centroids: &Vec<Vec<T>>, candidates: &Vec<usize>, k: usize, sums: &mut Vec<Vec<T>>, counts: &mut Vec<usize>, membership: &mut Vec<usize>) -> T{
+    fn filter(
+        &self,
+        node: usize,
+        centroids: &Vec<Vec<T>>,
+        candidates: &Vec<usize>,
+        k: usize,
+        sums: &mut Vec<Vec<T>>,
+        counts: &mut Vec<usize>,
+        membership: &mut Vec<usize>,
+    ) -> T {
         let d = centroids[0].len();
 
         // Determine which mean the node mean is closest to
-        let mut min_dist = Euclidian::squared_distance(&self.nodes[node].center, &centroids[candidates[0]]);
+        let mut min_dist =
+            Euclidian::squared_distance(&self.nodes[node].center, &centroids[candidates[0]]);
         let mut closest = candidates[0];
         for i in 1..k {
-            let dist = Euclidian::squared_distance(&self.nodes[node].center, &centroids[candidates[i]]);
+            let dist =
+                Euclidian::squared_distance(&self.nodes[node].center, &centroids[candidates[i]]);
             if dist < min_dist {
                 min_dist = dist;
                 closest = candidates[i];
@@ -96,7 +121,13 @@ impl<T: FloatExt> BBDTree<T> {
             let mut newk = 0;
 
             for i in 0..k {
-                if !BBDTree::prune(&self.nodes[node].center, &self.nodes[node].radius, &centroids, closest, candidates[i]) {
+                if !BBDTree::prune(
+                    &self.nodes[node].center,
+                    &self.nodes[node].radius,
+                    &centroids,
+                    closest,
+                    candidates[i],
+                ) {
                     new_candidates[newk] = candidates[i];
                     newk += 1;
                 }
@@ -104,8 +135,23 @@ impl<T: FloatExt> BBDTree<T> {
 
             // Recurse if there's at least two
             if newk > 1 {
-                let result = self.filter(self.nodes[node].lower.unwrap(), centroids, &mut new_candidates, newk, sums, counts, membership) + 
+                let result = self.filter(
-                            self.filter(self.nodes[node].upper.unwrap(), centroids, &mut new_candidates, newk, sums, counts, membership);
+                    self.nodes[node].lower.unwrap(),
+                    centroids,
+                    &mut new_candidates,
+                    newk,
+                    sums,
+                    counts,
+                    membership,
+                ) + self.filter(
+                    self.nodes[node].upper.unwrap(),
+                    centroids,
+                    &mut new_candidates,
+                    newk,
+                    sums,
+                    counts,
+                    membership,
+                );
                 return result;
             }
         }
@@ -123,10 +169,15 @@ impl<T: FloatExt> BBDTree<T> {
         }
 
         BBDTree::node_cost(&self.nodes[node], &centroids[closest])
-        
     }
 
-    fn prune(center: &Vec<T>, radius: &Vec<T>, centroids: &Vec<Vec<T>>, best_index: usize, test_index: usize) -> bool {
+    fn prune(
+        center: &Vec<T>,
+        radius: &Vec<T>,
+        centroids: &Vec<Vec<T>>,
+        best_index: usize,
+        test_index: usize,
+    ) -> bool {
         if best_index == test_index {
             return false;
         }
@@ -247,7 +298,8 @@ impl<T: FloatExt> BBDTree<T> {
 
         // Calculate the new sum and opt cost
         for i in 0..d {
-            node.sum[i] = self.nodes[node.lower.unwrap()].sum[i] + self.nodes[node.upper.unwrap()].sum[i];
+            node.sum[i] =
+                self.nodes[node.lower.unwrap()].sum[i] + self.nodes[node.upper.unwrap()].sum[i];
         }
 
         let mut mean = vec![T::zero(); d];
@@ -255,7 +307,8 @@ impl<T: FloatExt> BBDTree<T> {
             mean[i] = node.sum[i] / T::from(node.count).unwrap();
         }
 
-        node.cost = BBDTree::node_cost(&self.nodes[node.lower.unwrap()], &mean) + BBDTree::node_cost(&self.nodes[node.upper.unwrap()], &mean);
+        node.cost = BBDTree::node_cost(&self.nodes[node.lower.unwrap()], &mean)
+            + BBDTree::node_cost(&self.nodes[node.upper.unwrap()], &mean);
 
         self.add_node(node)
     }
@@ -284,7 +337,6 @@ mod tests {
 
     #[test]
     fn fit_predict_iris() {
-
         let data = DenseMatrix::from_array(&[
             &[5.1, 3.5, 1.4, 0.2],
             &[4.9, 3.0, 1.4, 0.2],
@@ -305,19 +357,14 @@ mod tests {
             &[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]]);        
+            &[5.2, 2.7, 3.9, 1.4],
+        ]);
 
         let tree = BBDTree::new(&data);
 
-        let centroids = vec![
+        let centroids = vec![vec![4.86, 3.22, 1.61, 0.29], vec![6.23, 2.92, 4.48, 1.42]];
-            vec![4.86, 3.22, 1.61, 0.29],
-            vec![6.23, 2.92, 4.48, 1.42]
-        ];
 
-        let mut sums = vec![
+        let mut sums = vec![vec![0f64; 4], vec![0f64; 4]];
-            vec![0f64; 4],
-            vec![0f64; 4]
-        ];
 
         let mut counts = vec![11, 9];
 
@@ -328,7 +375,5 @@ mod tests {
         assert!((sums[0][0] - 48.6).abs() < 1e-2);
         assert!((sums[1][3] - 13.8).abs() < 1e-2);
         assert_eq!(membership[17], 1);
-            
     }
-
 }

src/algorithm/neighbour/cover_tree.rs

@@ -1,41 +1,37 @@
-use std::collections::{HashMap, HashSet};
-use std::iter::FromIterator;
-use std::fmt::Debug;
 use core::hash::{Hash, Hasher};
+use std::collections::{HashMap, HashSet};
+use std::fmt::Debug;
+use std::iter::FromIterator;
 
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
-use crate::math::distance::Distance;
 use crate::algorithm::sort::heap_select::HeapSelect;
+use crate::math::distance::Distance;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
-pub struct CoverTree<T, F: FloatExt, D: Distance<T, F>>
+pub struct CoverTree<T, F: FloatExt, D: Distance<T, F>> {
-{
     base: F,
     max_level: i8,
     min_level: i8,
     distance: D,
-    nodes: Vec<Node<T>>
+    nodes: Vec<Node<T>>,
 }
 
-impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D> 
+impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D> {
-{
-
     pub fn new(mut data: Vec<T>, distance: D) -> CoverTree<T, F, D> {
         let mut tree = CoverTree {
             base: F::two(),
             max_level: 100,
             min_level: 100,
             distance: distance,
-            nodes: Vec::new()
+            nodes: Vec::new(),
         };
 
         let p = tree.new_node(None, data.remove(0));
         tree.construct(p, data, Vec::new(), 10);
 
         tree
-        
     }
 
     pub fn insert(&mut self, p: T) {
@@ -44,14 +40,14 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
         } else {
             let mut parent: Option<NodeId> = Option::None;
             let mut p_i = 0;
-            let mut qi_p_ds = vec!((self.root(), self.distance.distance(&p, &self.root().data)));
+            let mut qi_p_ds = vec![(self.root(), self.distance.distance(&p, &self.root().data))];
             let mut i = self.max_level;
             loop {
                 let i_d = self.base.powf(F::from(i).unwrap());
                 let q_p_ds = self.get_children_dist(&p, &qi_p_ds, i);
                 let d_p_q = self.min_by_distance(&q_p_ds);
                 if d_p_q < F::epsilon() {
-                    return
+                    return;
                 } else if d_p_q > i_d {
                     break;
                 }
@@ -73,30 +69,40 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
     pub fn new_node(&mut self, parent: Option<NodeId>, data: T) -> NodeId {
         let next_index = self.nodes.len();
         let node_id = NodeId { index: next_index };
-        self.nodes.push(
+        self.nodes.push(Node {
-            Node {
             index: node_id,
             data: data,
             parent: parent,
-                children: HashMap::new()                               
+            children: HashMap::new(),
         });
         node_id
     }
 
     pub fn find(&self, p: &T, k: usize) -> Vec<usize> {
-        let mut qi_p_ds = vec!((self.root(), self.distance.distance(&p, &self.root().data)));
+        let mut qi_p_ds = vec![(self.root(), self.distance.distance(&p, &self.root().data))];
         for i in (self.min_level..self.max_level + 1).rev() {
             let i_d = self.base.powf(F::from(i).unwrap());
             let mut q_p_ds = self.get_children_dist(&p, &qi_p_ds, i);
             let d_p_q = self.min_k_by_distance(&mut q_p_ds, k);
-            qi_p_ds = q_p_ds.into_iter().filter(|(_, d)| d <= &(d_p_q + i_d)).collect();
+            qi_p_ds = q_p_ds
+                .into_iter()
+                .filter(|(_, d)| d <= &(d_p_q + i_d))
+                .collect();
         }
         qi_p_ds.sort_by(|(_, d1), (_, d2)| d1.partial_cmp(d2).unwrap());
-        qi_p_ds[..usize::min(qi_p_ds.len(), k)].iter().map(|(n, _)| n.index.index).collect()
+        qi_p_ds[..usize::min(qi_p_ds.len(), k)]
+            .iter()
+            .map(|(n, _)| n.index.index)
+            .collect()
     }
 
-    fn split(&self, p_id: NodeId, r: F, s1: &mut Vec<T>, s2: Option<&mut Vec<T>>) -> (Vec<T>, Vec<T>){
+    fn split(
-        
+        &self,
+        p_id: NodeId,
+        r: F,
+        s1: &mut Vec<T>,
+        s2: Option<&mut Vec<T>>,
+    ) -> (Vec<T>, Vec<T>) {
         let mut my_near = (Vec::new(), Vec::new());
 
         my_near = self.split_remove_s(p_id, r, s1, my_near);
@@ -105,12 +111,16 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
             my_near = self.split_remove_s(p_id, r, s, my_near);
         }
 
-        return my_near
+        return my_near;
-
     }
 
-    fn split_remove_s(&self, p_id: NodeId, r: F, s: &mut Vec<T>, mut my_near: (Vec<T>, Vec<T>)) -> (Vec<T>, Vec<T>){
+    fn split_remove_s(
-
+        &self,
+        p_id: NodeId,
+        r: F,
+        s: &mut Vec<T>,
+        mut my_near: (Vec<T>, Vec<T>),
+    ) -> (Vec<T>, Vec<T>) {
         if s.len() > 0 {
             let p = &self.nodes.get(p_id.index).unwrap().data;
             let mut i = 0;
@@ -126,11 +136,16 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
             }
         }
 
-        return my_near
+        return my_near;
     }
 
-    fn construct<'b>(&mut self, p: NodeId, mut near: Vec<T>, mut far: Vec<T>, i: i8) -> (NodeId, Vec<T>) {        
+    fn construct<'b>(
-
+        &mut self,
+        p: NodeId,
+        mut near: Vec<T>,
+        mut far: Vec<T>,
+        i: i8,
+    ) -> (NodeId, Vec<T>) {
         if near.len() < 1 {
             self.min_level = std::cmp::min(self.min_level, i);
             return (p, far);
@@ -140,39 +155,57 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
             while near.len() > 0 {
                 let q_data = near.remove(0);
                 let nn = self.new_node(Some(p), q_data);
-                let (my, n) = self.split(nn, self.base.powf(F::from(i-1).unwrap()), &mut near, Some(&mut far));                
+                let (my, n) = self.split(
+                    nn,
+                    self.base.powf(F::from(i - 1).unwrap()),
+                    &mut near,
+                    Some(&mut far),
+                );
                 let (child, mut unused) = self.construct(nn, my, n, i - 1);
                 self.add_child(pi, child, i);
-                let new_near_far = self.split(p, self.base.powf(F::from(i).unwrap()), &mut unused, None);
+                let new_near_far =
+                    self.split(p, self.base.powf(F::from(i).unwrap()), &mut unused, None);
                 near.extend(new_near_far.0);
                 far.extend(new_near_far.1);
             }
             self.min_level = std::cmp::min(self.min_level, i);
             return (pi, far);
         }
-
     }
 
     fn add_child(&mut self, parent: NodeId, node: NodeId, i: i8) {
-        self.nodes.get_mut(parent.index).unwrap().children.insert(i, node);
+        self.nodes
+            .get_mut(parent.index)
+            .unwrap()
+            .children
+            .insert(i, node);
     }
 
     fn root(&self) -> &Node<T> {
         self.nodes.first().unwrap()
     }
 
-    fn get_children_dist<'b>(&'b self, p: &T, qi_p_ds: &Vec<(&'b Node<T>, F)>, i: i8) -> Vec<(&'b Node<T>, F)> {
+    fn get_children_dist<'b>(
-
+        &'b self,
+        p: &T,
+        qi_p_ds: &Vec<(&'b Node<T>, F)>,
+        i: i8,
+    ) -> Vec<(&'b Node<T>, F)> {
         let mut children = Vec::<(&'b Node<T>, F)>::new();
 
         children.extend(qi_p_ds.iter().cloned());
 
-        let q: Vec<&Node<T>> = qi_p_ds.iter().flat_map(|(n, _)| self.get_child(n, i)).collect();        
+        let q: Vec<&Node<T>> = qi_p_ds
+            .iter()
+            .flat_map(|(n, _)| self.get_child(n, i))
+            .collect();
 
-        children.extend(q.into_iter().map(|n| (n, self.distance.distance(&n.data, &p))));
+        children.extend(
+            q.into_iter()
+                .map(|n| (n, self.distance.distance(&n.data, &p))),
+        );
 
         children
-        
     }
 
     fn min_k_by_distance(&self, q_p_ds: &mut Vec<(&Node<T>, F)>, k: usize) -> F {
@@ -185,15 +218,24 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
     }
 
     fn min_by_distance(&self, q_p_ds: &Vec<(&Node<T>, F)>) -> F {
-        q_p_ds.into_iter().min_by(|(_, d1), (_, d2)| d1.partial_cmp(d2).unwrap()).unwrap().1        
+        q_p_ds
+            .into_iter()
+            .min_by(|(_, d1), (_, d2)| d1.partial_cmp(d2).unwrap())
+            .unwrap()
+            .1
     }
 
     fn get_child(&self, node: &Node<T>, i: i8) -> Option<&Node<T>> {
-        node.children.get(&i).and_then(|n_id| self.nodes.get(n_id.index))       
+        node.children
+            .get(&i)
+            .and_then(|n_id| self.nodes.get(n_id.index))
     }
 
     #[allow(dead_code)]
-    fn check_invariant(&self, invariant: fn(&CoverTree<T, F, D>, &Vec<&Node<T>>, &Vec<&Node<T>>, i8) -> ()) {
+    fn check_invariant(
+        &self,
+        invariant: fn(&CoverTree<T, F, D>, &Vec<&Node<T>>, &Vec<&Node<T>>, i8) -> (),
+    ) {
         let mut current_nodes: Vec<&Node<T>> = Vec::new();
         current_nodes.push(self.root());
         for i in (self.min_level..self.max_level + 1).rev() {
@@ -206,16 +248,27 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
     }
 
     #[allow(dead_code)]
-    fn nesting_invariant(_: &CoverTree<T, F, D>, nodes: &Vec<&Node<T>>, next_nodes: &Vec<&Node<T>>, _: i8) {   
+    fn nesting_invariant(
+        _: &CoverTree<T, F, D>,
+        nodes: &Vec<&Node<T>>,
+        next_nodes: &Vec<&Node<T>>,
+        _: i8,
+    ) {
         let nodes_set: HashSet<&Node<T>> = HashSet::from_iter(nodes.into_iter().map(|n| *n));
-        let next_nodes_set: HashSet<&Node<T>> = HashSet::from_iter(next_nodes.into_iter().map(|n| *n));        
+        let next_nodes_set: HashSet<&Node<T>> =
+            HashSet::from_iter(next_nodes.into_iter().map(|n| *n));
         for n in nodes_set.iter() {
             assert!(next_nodes_set.contains(n), "Nesting invariant of the cover tree is not satisfied. Set of nodes [{:?}] is not a subset of [{:?}]", nodes_set, next_nodes_set);
         }
     }
 
     #[allow(dead_code)]
-    fn covering_tree(tree: &CoverTree<T, F, D>, nodes: &Vec<&Node<T>>, next_nodes: &Vec<&Node<T>>, i: i8) {        
+    fn covering_tree(
+        tree: &CoverTree<T, F, D>,
+        nodes: &Vec<&Node<T>>,
+        next_nodes: &Vec<&Node<T>>,
+        i: i8,
+    ) {
         let mut p_selected: Vec<&Node<T>> = Vec::new();
         for p in next_nodes {
             for q in nodes {
@@ -223,7 +276,10 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
                     p_selected.push(*p);
                 }
             }
-            let c = p_selected.iter().filter(|q| p.parent.map(|p| q.index == p).unwrap_or(false)).count();            
+            let c = p_selected
+                .iter()
+                .filter(|q| p.parent.map(|p| q.index == p).unwrap_or(false))
+                .count();
             assert!(c <= 1);
         }
     }
@@ -233,12 +289,14 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
         for p in nodes {
             for q in nodes {
                 if p != q {
-                    assert!(tree.distance.distance(&p.data, &q.data) > tree.base.powf(F::from(i).unwrap()));
+                    assert!(
+                        tree.distance.distance(&p.data, &q.data)
+                            > tree.base.powf(F::from(i).unwrap())
+                    );
                 }
             }
         }
     }
-
 }
 
 #[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
@@ -251,7 +309,7 @@ struct Node<T> {
     index: NodeId,
     data: T,
     children: HashMap<i8, NodeId>,
-    parent: Option<NodeId>
+    parent: Option<NodeId>,
 }
 
 impl<T> PartialEq for Node<T> {
@@ -287,10 +345,10 @@ mod tests {
 
     #[test]
     fn cover_tree_test() {
-        let data = vec!(1, 2, 3, 4, 5, 6, 7, 8, 9);          
+        let data = vec![1, 2, 3, 4, 5, 6, 7, 8, 9];
 
         let mut tree = CoverTree::new(data, SimpleDistance {});
-        for d in vec!(10, 11, 12, 13, 14, 15, 16, 17, 18, 19) {
+        for d in vec![10, 11, 12, 13, 14, 15, 16, 17, 18, 19] {
             tree.insert(d);
         }
 
@@ -308,12 +366,11 @@ mod tests {
 
     #[test]
     fn test_invariants() {
-        let data = vec!(1, 2, 3, 4, 5, 6, 7, 8, 9);          
+        let data = vec![1, 2, 3, 4, 5, 6, 7, 8, 9];
 
         let tree = CoverTree::new(data, SimpleDistance {});
         tree.check_invariant(CoverTree::nesting_invariant);
         tree.check_invariant(CoverTree::covering_tree);
         tree.check_invariant(CoverTree::separation);
     }
-
 }

src/algorithm/neighbour/linear_search.rs

@@ -1,16 +1,16 @@
+use serde::{Deserialize, Serialize};
 use std::cmp::{Ordering, PartialOrd};
 use std::marker::PhantomData;
-use serde::{Serialize, Deserialize};
 
-use crate::math::num::FloatExt;
-use crate::math::distance::Distance;
 use crate::algorithm::sort::heap_select::HeapSelect;
+use crate::math::distance::Distance;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct LinearKNNSearch<T, F: FloatExt, D: Distance<T, F>> {
     distance: D,
     data: Vec<T>,
-    f: PhantomData<F>
+    f: PhantomData<F>,
 }
 
 impl<T, F: FloatExt, D: Distance<T, F>> LinearKNNSearch<T, F, D> {
@@ -18,7 +18,7 @@ impl<T, F: FloatExt, D: Distance<T, F>> LinearKNNSearch<T, F, D> {
         LinearKNNSearch {
             data: data,
             distance: distance,
-            f: PhantomData
+            f: PhantomData,
         }
     }
 
@@ -32,12 +32,11 @@ impl<T, F: FloatExt, D: Distance<T, F>> LinearKNNSearch<T, F, D> {
         for _ in 0..k {
             heap.add(KNNPoint {
                 distance: F::infinity(),
-                index: None
+                index: None,
             });
         }
 
         for i in 0..self.data.len() {
-
             let d = self.distance.distance(&from, &self.data[i]);
             let datum = heap.peek_mut();
             if d < datum.distance {
@@ -56,7 +55,7 @@ impl<T, F: FloatExt, D: Distance<T, F>> LinearKNNSearch<T, F, D> {
 #[derive(Debug)]
 struct KNNPoint<F: FloatExt> {
     distance: F,
-    index: Option<usize>
+    index: Option<usize>,
 }
 
 impl<F: FloatExt> PartialOrd for KNNPoint<F> {
@@ -88,13 +87,19 @@ mod tests {
 
     #[test]
     fn knn_find() {
-        let data1 = vec!(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
+        let data1 = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
 
         let algorithm1 = LinearKNNSearch::new(data1, SimpleDistance {});
 
         assert_eq!(vec!(1, 2, 0), algorithm1.find(&2, 3));
 
-        let data2 = vec!(vec![1., 1.], vec![2., 2.], vec![3., 3.], vec![4., 4.], vec![5., 5.]);
+        let data2 = vec![
+            vec![1., 1.],
+            vec![2., 2.],
+            vec![3., 3.],
+            vec![4., 4.],
+            vec![5., 5.],
+        ];
 
         let algorithm2 = LinearKNNSearch::new(data2, Distances::euclidian());
 
@@ -105,22 +110,22 @@ mod tests {
     fn knn_point_eq() {
         let point1 = KNNPoint {
             distance: 10.,
-            index: Some(0)
+            index: Some(0),
         };
 
         let point2 = KNNPoint {
             distance: 100.,
-            index: Some(1)
+            index: Some(1),
         };
 
         let point3 = KNNPoint {
             distance: 10.,
-            index: Some(2)
+            index: Some(2),
         };
 
         let point_inf = KNNPoint {
             distance: std::f64::INFINITY,
-            index: Some(3)
+            index: Some(3),
         };
 
         assert!(point2 > point1);

src/algorithm/neighbour/mod.rs

@@ -1,3 +1,3 @@
+pub mod bbd_tree;
 pub mod cover_tree;
 pub mod linear_search;
-pub mod bbd_tree;

src/algorithm/sort/heap_select.rs

@@ -5,17 +5,16 @@ pub struct HeapSelect<T: PartialOrd> {
     k: usize,
     n: usize,
     sorted: bool,
-    heap: Vec<T>
+    heap: Vec<T>,
 }
 
 impl<'a, T: PartialOrd> HeapSelect<T> {
-
     pub fn with_capacity(k: usize) -> HeapSelect<T> {
         HeapSelect {
             k: k,
             n: 0,
             sorted: false,
-            heap: Vec::<T>::new()
+            heap: Vec::<T>::new(),
         }
     }
 
@@ -63,7 +62,6 @@ impl<'a, T: PartialOrd> HeapSelect<T> {
             self.heap.swap(k, j);
             k = j;
         }
-
     }
 
     pub fn get(self) -> Vec<T> {
@@ -95,7 +93,6 @@ impl<'a, T: PartialOrd> HeapSelect<T> {
             inc /= 3
         }
     }
-
 }
 
 #[cfg(test)]
@@ -150,5 +147,4 @@ mod tests {
         HeapSelect::shuffle_sort(&mut v3, 3);
         assert_eq!(vec![3, 4, 5, 2, 1], v3);
     }
-
 }

src/algorithm/sort/quick_sort.rs

@@ -5,7 +5,6 @@ pub trait QuickArgSort {
 }
 
 impl<T: Float> QuickArgSort for Vec<T> {
-    
     fn quick_argsort(&mut self) -> Vec<usize> {
         let stack_size = 64;
         let mut jstack = -1;
@@ -112,7 +111,13 @@ mod tests {
         let mut arr1 = vec![0.3, 0.1, 0.2, 0.4, 0.9, 0.5, 0.7, 0.6, 0.8];
         assert_eq!(vec![1, 2, 0, 3, 5, 7, 6, 8, 4], arr1.quick_argsort());
 
-        let mut arr2 = vec![0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4];
+        let mut arr2 = vec![
-        assert_eq!(vec![9, 7, 1, 8, 0, 2, 4, 3, 6, 5, 17, 18, 15, 13, 19, 10, 14, 11, 12, 16], arr2.quick_argsort());                
+            0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6,
+            1.0, 1.3, 1.4,
+        ];
+        assert_eq!(
+            vec![9, 7, 1, 8, 0, 2, 4, 3, 6, 5, 17, 18, 15, 13, 19, 10, 14, 11, 12, 16],
+            arr2.quick_argsort()
+        );
     }
 }

src/cluster/kmeans.rs

@@ -1,15 +1,15 @@
 extern crate rand;
 
 use rand::Rng;
-use std::iter::Sum;
 use std::fmt::Debug;
+use std::iter::Sum;
 
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
+use crate::algorithm::neighbour::bbd_tree::BBDTree;
 use crate::linalg::Matrix;
 use crate::math::distance::euclidian::*;
-use crate::algorithm::neighbour::bbd_tree::BBDTree;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct KMeans<T: FloatExt> {
@@ -17,24 +17,25 @@ pub struct KMeans<T: FloatExt> {
     y: Vec<usize>,
     size: Vec<usize>,
     distortion: T,
-    centroids: Vec<Vec<T>>
+    centroids: Vec<Vec<T>>,
 }
 
 impl<T: FloatExt> PartialEq for KMeans<T> {
     fn eq(&self, other: &Self) -> bool {
-        if self.k != other.k ||
+        if self.k != other.k
-            self.size != other.size ||
+            || self.size != other.size
-            self.centroids.len() != other.centroids.len() {
+            || self.centroids.len() != other.centroids.len()
+        {
             false
         } else {
             let n_centroids = self.centroids.len();
             for i in 0..n_centroids {
                 if self.centroids[i].len() != other.centroids[i].len() {
-                    return false
+                    return false;
                 }
                 for j in 0..self.centroids[i].len() {
                     if (self.centroids[i][j] - other.centroids[i][j]).abs() > T::epsilon() {
-                        return false
+                        return false;
                     }
                 }
             }
@@ -45,20 +46,17 @@ impl<T: FloatExt> PartialEq for KMeans<T> {
 
 #[derive(Debug, Clone)]
 pub struct KMeansParameters {
-    pub max_iter: usize
+    pub max_iter: usize,
 }
 
 impl Default for KMeansParameters {
     fn default() -> Self {
-        KMeansParameters {
+        KMeansParameters { max_iter: 100 }
-            max_iter: 100
-        }
     }
 }
 
 impl<T: FloatExt + Sum> KMeans<T> {
     pub fn new<M: Matrix<T>>(data: &M, k: usize, parameters: KMeansParameters) -> KMeans<T> {
-
         let bbd = BBDTree::new(data);
 
         if k < 2 {
@@ -66,7 +64,10 @@ impl<T: FloatExt + Sum> KMeans<T>{
         }
 
         if parameters.max_iter <= 0 {
-            panic!("Invalid maximum number of iterations: {}", parameters.max_iter);
+            panic!(
+                "Invalid maximum number of iterations: {}",
+                parameters.max_iter
+            );
         }
 
         let (n, d) = data.shape();
@@ -108,7 +109,6 @@ impl<T: FloatExt + Sum> KMeans<T>{
             } else {
                 distortion = dist;
             }
-            
         }
 
         KMeans {
@@ -116,7 +116,7 @@ impl<T: FloatExt + Sum> KMeans<T>{
             y: y,
             size: size,
             distortion: distortion,
-            centroids: centroids
+            centroids: centroids,
         }
     }
 
@@ -125,7 +125,6 @@ impl<T: FloatExt + Sum> KMeans<T>{
         let mut result = M::zeros(1, n);
 
         for i in 0..n {
-
             let mut min_dist = T::max_value();
             let mut best_cluster = 0;
 
@@ -193,10 +192,8 @@ impl<T: FloatExt + Sum> KMeans<T>{
 
         y
     }
-    
 }
 
-
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -224,7 +221,8 @@ mod tests {
             &[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]]);                
+            &[5.2, 2.7, 3.9, 1.4],
+        ]);
 
         let kmeans = KMeans::new(&x, 2, Default::default());
 
@@ -233,7 +231,6 @@ mod tests {
         for i in 0..y.len() {
             assert_eq!(y[i] as usize, kmeans.y[i]);
         }
-        
     }
 
     #[test]
@@ -258,14 +255,14 @@ mod tests {
             &[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]]);                
+            &[5.2, 2.7, 3.9, 1.4],
+        ]);
 
         let kmeans = KMeans::new(&x, 2, Default::default());
 
-        let deserialized_kmeans: KMeans<f64> = serde_json::from_str(&serde_json::to_string(&kmeans).unwrap()).unwrap();
+        let deserialized_kmeans: KMeans<f64> =
+            serde_json::from_str(&serde_json::to_string(&kmeans).unwrap()).unwrap();
 
         assert_eq!(kmeans, deserialized_kmeans);
-        
     }
-    
 }

src/common/mod.rs

@@ -1,13 +1,18 @@
-use num_traits::{Num, ToPrimitive, FromPrimitive, Zero, One};
+use ndarray::ScalarOperand;
-use ndarray::{ScalarOperand};
+use num_traits::{FromPrimitive, Num, One, ToPrimitive, Zero};
-use std::hash::Hash;
 use std::fmt::Debug;
+use std::hash::Hash;
 
 pub trait AnyNumber: Num + ScalarOperand + ToPrimitive + FromPrimitive {}
 
-pub trait Nominal: PartialEq + Zero + One + Eq + Hash + ToPrimitive + FromPrimitive + Debug + 'static + Clone{}
+pub trait Nominal:
-
+    PartialEq + Zero + One + Eq + Hash + ToPrimitive + FromPrimitive + Debug + 'static + Clone
+{
+}
 
 impl<T> AnyNumber for T where T: Num + ScalarOperand + ToPrimitive + FromPrimitive {}
 
-impl<T> Nominal for T where T: PartialEq + Zero + One + Eq + Hash + ToPrimitive + Debug + FromPrimitive + 'static + Clone {}
+impl<T> Nominal for T where
+    T: PartialEq + Zero + One + Eq + Hash + ToPrimitive + Debug + FromPrimitive + 'static + Clone
+{
+}

src/decomposition/pca.rs

@@ -1,9 +1,9 @@
 use std::fmt::Debug;
 
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
+use crate::linalg::Matrix;
 use crate::math::num::FloatExt;
-use crate::linalg::{Matrix};
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct PCA<T: FloatExt, M: Matrix<T>> {
@@ -11,42 +11,41 @@ pub struct PCA<T: FloatExt, M: Matrix<T>> {
     eigenvalues: Vec<T>,
     projection: M,
     mu: Vec<T>,
-    pmu: Vec<T>
+    pmu: Vec<T>,
 }
 
 impl<T: FloatExt, M: Matrix<T>> PartialEq for PCA<T, M> {
     fn eq(&self, other: &Self) -> bool {
-        if self.eigenvectors != other.eigenvectors ||
+        if self.eigenvectors != other.eigenvectors
-        self.eigenvalues.len() != other.eigenvalues.len() {
+            || self.eigenvalues.len() != other.eigenvalues.len()
-            return false
+        {
+            return false;
         } else {
             for i in 0..self.eigenvalues.len() {
                 if (self.eigenvalues[i] - other.eigenvalues[i]).abs() > T::epsilon() {
-                    return false
+                    return false;
                 }
             }
-            return true
+            return true;
         }
     }
 }
 
 #[derive(Debug, Clone)]
 pub struct PCAParameters {
-    use_correlation_matrix: bool 
+    use_correlation_matrix: bool,
 }
 
 impl Default for PCAParameters {
     fn default() -> Self {
         PCAParameters {
-            use_correlation_matrix: false           
+            use_correlation_matrix: false,
         }
     }
 }
 
 impl<T: FloatExt, M: Matrix<T>> PCA<T, M> {
-
     pub fn new(data: &M, n_components: usize, parameters: PCAParameters) -> PCA<T, M> {
-
         let (m, n) = data.shape();
 
         let mu = data.column_mean();
@@ -63,7 +62,6 @@ impl<T: FloatExt, M: Matrix<T>> PCA<T, M> {
         let mut eigenvectors;
 
         if m > n && !parameters.use_correlation_matrix {
-
             let svd = x.svd();
             eigenvalues = svd.s;
             for i in 0..eigenvalues.len() {
@@ -114,13 +112,11 @@ impl<T: FloatExt, M: Matrix<T>> PCA<T, M> {
                     }
                 }
             } else {
-
                 let evd = cov.evd(true);
 
                 eigenvalues = evd.d;
 
                 eigenvectors = evd.V;
-
             }
         }
 
@@ -143,7 +139,7 @@ impl<T: FloatExt, M: Matrix<T>> PCA<T, M> {
             eigenvalues: eigenvalues,
             projection: projection.transpose(),
             mu: mu,
-            pmu: pmu
+            pmu: pmu,
         }
     }
 
@@ -151,7 +147,11 @@ impl<T: FloatExt, M: Matrix<T>> PCA<T, M> {
         let (nrows, ncols) = x.shape();
         let (_, n_components) = self.projection.shape();
         if ncols != self.mu.len() {
-            panic!("Invalid input vector size: {}, expected: {}", ncols, self.mu.len());
+            panic!(
+                "Invalid input vector size: {}, expected: {}",
+                ncols,
+                self.mu.len()
+            );
         }
 
         let mut x_transformed = x.dot(&self.projection);
@@ -162,7 +162,6 @@ impl<T: FloatExt, M: Matrix<T>> PCA<T, M> {
         }
         x_transformed
     }
-    
 }
 
 #[cfg(test)]
@@ -221,19 +220,39 @@ mod tests {
             &[4.0, 145.0, 73.0, 26.2],
             &[5.7, 81.0, 39.0, 9.3],
             &[2.6, 53.0, 66.0, 10.8],
-            &[6.8, 161.0, 60.0, 15.6]])
+            &[6.8, 161.0, 60.0, 15.6],
+        ])
     }
 
     #[test]
     fn decompose_covariance() {
-        
         let us_arrests = us_arrests_data();
 
         let expected_eigenvectors = DenseMatrix::from_array(&[
-                &[-0.0417043206282872, -0.0448216562696701, -0.0798906594208108, -0.994921731246978],
+            &[
-                &[-0.995221281426497, -0.058760027857223, 0.0675697350838043, 0.0389382976351601],
+                -0.0417043206282872,
-                &[-0.0463357461197108, 0.97685747990989, 0.200546287353866, -0.0581691430589319],
+                -0.0448216562696701,
-                &[-0.075155500585547, 0.200718066450337, -0.974080592182491, 0.0723250196376097]
+                -0.0798906594208108,
+                -0.994921731246978,
+            ],
+            &[
+                -0.995221281426497,
+                -0.058760027857223,
+                0.0675697350838043,
+                0.0389382976351601,
+            ],
+            &[
+                -0.0463357461197108,
+                0.97685747990989,
+                0.200546287353866,
+                -0.0581691430589319,
+            ],
+            &[
+                -0.075155500585547,
+                0.200718066450337,
+                -0.974080592182491,
+                0.0723250196376097,
+            ],
         ]);
 
         let expected_projection = DenseMatrix::from_array(&[
@@ -286,14 +305,22 @@ mod tests {
             &[25.0758, 9.968, -4.7811, 2.6911],
             &[91.5446, -22.9529, 0.402, -0.7369],
             &[118.1763, 5.5076, 2.7113, -0.205],
-                &[10.4345, -5.9245, 3.7944, 0.5179]
+            &[10.4345, -5.9245, 3.7944, 0.5179],
         ]);
 
-            let expected_eigenvalues: Vec<f64> = vec![343544.6277001563, 9897.625949808047, 2063.519887011604, 302.04806302399646];
+        let expected_eigenvalues: Vec<f64> = vec![
+            343544.6277001563,
+            9897.625949808047,
+            2063.519887011604,
+            302.04806302399646,
+        ];
 
         let pca = PCA::new(&us_arrests, 4, Default::default());
 
-            assert!(pca.eigenvectors.abs().approximate_eq(&expected_eigenvectors.abs(), 1e-4));
+        assert!(pca
+            .eigenvectors
+            .abs()
+            .approximate_eq(&expected_eigenvectors.abs(), 1e-4));
 
         for i in 0..pca.eigenvalues.len() {
             assert_eq!(pca.eigenvalues[i].abs(), expected_eigenvalues[i].abs());
@@ -301,20 +328,40 @@ mod tests {
 
         let us_arrests_t = pca.transform(&us_arrests);
 
-            assert!(us_arrests_t.abs().approximate_eq(&expected_projection.abs(), 1e-4));
+        assert!(us_arrests_t
-        
+            .abs()
+            .approximate_eq(&expected_projection.abs(), 1e-4));
     }
 
     #[test]
     fn decompose_correlation() {
-        
         let us_arrests = us_arrests_data();
 
         let expected_eigenvectors = DenseMatrix::from_array(&[
-                &[0.124288601688222, -0.0969866877028367, 0.0791404742697482, -0.150572299008293],
+            &[
-                &[0.00706888610512014, -0.00227861130898090, 0.00325028101296307, 0.00901099154845273],
+                0.124288601688222,
-                &[0.0194141494466002, 0.060910660326921, 0.0263806464184195, -0.0093429458365566],
+                -0.0969866877028367,
-                &[0.0586084532558777, 0.0180450999787168, -0.0881962972508558, -0.0096011588898465]
+                0.0791404742697482,
+                -0.150572299008293,
+            ],
+            &[
+                0.00706888610512014,
+                -0.00227861130898090,
+                0.00325028101296307,
+                0.00901099154845273,
+            ],
+            &[
+                0.0194141494466002,
+                0.060910660326921,
+                0.0263806464184195,
+                -0.0093429458365566,
+            ],
+            &[
+                0.0586084532558777,
+                0.0180450999787168,
+                -0.0881962972508558,
+                -0.0096011588898465,
+            ],
         ]);
 
         let expected_projection = DenseMatrix::from_array(&[
@@ -367,14 +414,28 @@ mod tests {
             &[-0.2169, 0.9701, -0.6249, 0.2208],
             &[-2.1086, -1.4248, -0.1048, -0.1319],
             &[-2.0797, 0.6113, 0.1389, -0.1841],
-                &[-0.6294, -0.321, 0.2407, 0.1667]
+            &[-0.6294, -0.321, 0.2407, 0.1667],
         ]);
 
-            let expected_eigenvalues: Vec<f64> = vec![2.480241579149493, 0.9897651525398419, 0.35656318058083064, 0.1734300877298357];
+        let expected_eigenvalues: Vec<f64> = vec![
+            2.480241579149493,
+            0.9897651525398419,
+            0.35656318058083064,
+            0.1734300877298357,
+        ];
 
-            let pca = PCA::new(&us_arrests, 4, PCAParameters{use_correlation_matrix: true});
+        let pca = PCA::new(
+            &us_arrests,
+            4,
+            PCAParameters {
+                use_correlation_matrix: true,
+            },
+        );
 
-            assert!(pca.eigenvectors.abs().approximate_eq(&expected_eigenvectors.abs(), 1e-4));
+        assert!(pca
+            .eigenvectors
+            .abs()
+            .approximate_eq(&expected_eigenvectors.abs(), 1e-4));
 
         for i in 0..pca.eigenvalues.len() {
             assert_eq!(pca.eigenvalues[i].abs(), expected_eigenvalues[i].abs());
@@ -382,8 +443,9 @@ mod tests {
 
         let us_arrests_t = pca.transform(&us_arrests);
 
-            assert!(us_arrests_t.abs().approximate_eq(&expected_projection.abs(), 1e-4));
+        assert!(us_arrests_t
-        
+            .abs()
+            .approximate_eq(&expected_projection.abs(), 1e-4));
     }
 
     #[test]
@@ -408,14 +470,14 @@ mod tests {
             &[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]]);                
+            &[5.2, 2.7, 3.9, 1.4],
+        ]);
 
         let pca = PCA::new(&iris, 4, Default::default());
 
-        let deserialized_pca: PCA<f64, DenseMatrix<f64>> = serde_json::from_str(&serde_json::to_string(&pca).unwrap()).unwrap();
+        let deserialized_pca: PCA<f64, DenseMatrix<f64>> =
+            serde_json::from_str(&serde_json::to_string(&pca).unwrap()).unwrap();
 
         assert_eq!(pca, deserialized_pca);
-        
     }
-    
 }

src/ensemble/random_forest_classifier.rs

@@ -4,11 +4,13 @@ use std::default::Default;
 use std::fmt::Debug;
 
 use rand::Rng;
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
-use crate::tree::decision_tree_classifier::{DecisionTreeClassifier, DecisionTreeClassifierParameters, SplitCriterion, which_max};
+use crate::math::num::FloatExt;
+use crate::tree::decision_tree_classifier::{
+    which_max, DecisionTreeClassifier, DecisionTreeClassifierParameters, SplitCriterion,
+};
 
 #[derive(Serialize, Deserialize, Debug, Clone)]
 pub struct RandomForestClassifierParameters {
@@ -17,30 +19,29 @@ pub struct RandomForestClassifierParameters {
     pub min_samples_leaf: usize,
     pub min_samples_split: usize,
     pub n_trees: u16,
-    pub mtry: Option<usize>
+    pub mtry: Option<usize>,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct RandomForestClassifier<T: FloatExt> {
     parameters: RandomForestClassifierParameters,
     trees: Vec<DecisionTreeClassifier<T>>,
-    classes: Vec<T>
+    classes: Vec<T>,
 }
 
 impl<T: FloatExt> PartialEq for RandomForestClassifier<T> {
     fn eq(&self, other: &Self) -> bool {
-        if self.classes.len() != other.classes.len() ||
+        if self.classes.len() != other.classes.len() || self.trees.len() != other.trees.len() {
-            self.trees.len() != other.trees.len() {
+            return false;
-                return false
         } else {
             for i in 0..self.classes.len() {
                 if (self.classes[i] - other.classes[i]).abs() > T::epsilon() {
-                    return false
+                    return false;
                 }
             }
             for i in 0..self.trees.len() {
                 if self.trees[i] != other.trees[i] {
-                    return false
+                    return false;
                 }
             }
             true
@@ -56,14 +57,17 @@ impl Default for RandomForestClassifierParameters {
             min_samples_leaf: 1,
             min_samples_split: 2,
             n_trees: 100,
-            mtry: Option::None
+            mtry: Option::None,
         }
     }
 }
 
 impl<T: FloatExt> RandomForestClassifier<T> {
-
+    pub fn fit<M: Matrix<T>>(
-    pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, parameters: RandomForestClassifierParameters) -> RandomForestClassifier<T> {        
+        x: &M,
+        y: &M::RowVector,
+        parameters: RandomForestClassifierParameters,
+    ) -> RandomForestClassifier<T> {
         let (_, num_attributes) = x.shape();
         let y_m = M::from_row_vector(y.clone());
         let (_, y_ncols) = y_m.shape();
@@ -75,7 +79,13 @@ impl<T: FloatExt> RandomForestClassifier<T> {
             yi[i] = classes.iter().position(|c| yc == *c).unwrap();
         }
 
-        let mtry = parameters.mtry.unwrap_or((T::from(num_attributes).unwrap()).sqrt().floor().to_usize().unwrap());        
+        let mtry = parameters.mtry.unwrap_or(
+            (T::from(num_attributes).unwrap())
+                .sqrt()
+                .floor()
+                .to_usize()
+                .unwrap(),
+        );
 
         let classes = y_m.unique();
         let k = classes.len();
@@ -87,7 +97,7 @@ impl<T: FloatExt> RandomForestClassifier<T> {
                 criterion: parameters.criterion.clone(),
                 max_depth: parameters.max_depth,
                 min_samples_leaf: parameters.min_samples_leaf,
-                min_samples_split: parameters.min_samples_split          
+                min_samples_split: parameters.min_samples_split,
             };
             let tree = DecisionTreeClassifier::fit_weak_learner(x, y, samples, mtry, params);
             trees.push(tree);
@@ -96,7 +106,7 @@ impl<T: FloatExt> RandomForestClassifier<T> {
         RandomForestClassifier {
             parameters: parameters,
             trees: trees,
-            classes
+            classes,
         }
     }
 
@@ -119,8 +129,7 @@ impl<T: FloatExt> RandomForestClassifier<T> {
             result[tree.predict_for_row(x, row)] += 1;
         }
 
-        return which_max(&result)
+        return which_max(&result);
-        
     }
 
     fn sample_with_replacement(y: &Vec<usize>, num_classes: usize) -> Vec<usize> {
@@ -146,7 +155,6 @@ impl<T: FloatExt> RandomForestClassifier<T> {
         }
         samples
     }
-
 }
 
 #[cfg(test)]
@@ -156,7 +164,6 @@ mod tests {
 
     #[test]
     fn fit_predict_iris() {
-
         let x = DenseMatrix::from_array(&[
             &[5.1, 3.5, 1.4, 0.2],
             &[4.9, 3.0, 1.4, 0.2],
@@ -177,20 +184,26 @@ mod tests {
             &[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]]);
+            &[5.2, 2.7, 3.9, 1.4],
-        let y = vec![0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.];
+        ]);
+        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(&x, &y, RandomForestClassifierParameters{
+        let classifier = RandomForestClassifier::fit(
+            &x,
+            &y,
+            RandomForestClassifierParameters {
                 criterion: SplitCriterion::Gini,
                 max_depth: None,
                 min_samples_leaf: 1,
                 min_samples_split: 2,
                 n_trees: 1000,
-            mtry: Option::None
+                mtry: Option::None,
-        });
+            },
+        );
 
         assert_eq!(y, classifier.predict(&x));
-            
     }
 
     #[test]
@@ -215,15 +228,17 @@ mod tests {
             &[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]]);
+            &[5.2, 2.7, 3.9, 1.4],
-        let y = vec![0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.];          
+        ]);
+        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());
 
-        let deserialized_forest: RandomForestClassifier<f64> = bincode::deserialize(&bincode::serialize(&forest).unwrap()).unwrap();
+        let deserialized_forest: RandomForestClassifier<f64> =
+            bincode::deserialize(&bincode::serialize(&forest).unwrap()).unwrap();
 
         assert_eq!(forest, deserialized_forest);
-        
     }
-
 }

src/ensemble/random_forest_regressor.rs

@@ -4,11 +4,13 @@ use std::default::Default;
 use std::fmt::Debug;
 
 use rand::Rng;
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
-use crate::tree::decision_tree_regressor::{DecisionTreeRegressor, DecisionTreeRegressorParameters};
+use crate::math::num::FloatExt;
+use crate::tree::decision_tree_regressor::{
+    DecisionTreeRegressor, DecisionTreeRegressorParameters,
+};
 
 #[derive(Serialize, Deserialize, Debug, Clone)]
 pub struct RandomForestRegressorParameters {
@@ -16,13 +18,13 @@ pub struct RandomForestRegressorParameters {
     pub min_samples_leaf: usize,
     pub min_samples_split: usize,
     pub n_trees: usize,
-    pub mtry: Option<usize>
+    pub mtry: Option<usize>,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct RandomForestRegressor<T: FloatExt> {
     parameters: RandomForestRegressorParameters,
-    trees: Vec<DecisionTreeRegressor<T>>    
+    trees: Vec<DecisionTreeRegressor<T>>,
 }
 
 impl Default for RandomForestRegressorParameters {
@@ -32,7 +34,7 @@ impl Default for RandomForestRegressorParameters {
             min_samples_leaf: 1,
             min_samples_split: 2,
             n_trees: 10,
-            mtry: Option::None
+            mtry: Option::None,
         }
     }
 }
@@ -40,11 +42,11 @@ impl Default for RandomForestRegressorParameters {
 impl<T: FloatExt> PartialEq for RandomForestRegressor<T> {
     fn eq(&self, other: &Self) -> bool {
         if self.trees.len() != other.trees.len() {
-                return false
+            return false;
         } else {
             for i in 0..self.trees.len() {
                 if self.trees[i] != other.trees[i] {
-                    return false
+                    return false;
                 }
             }
             true
@@ -53,11 +55,16 @@ impl<T: FloatExt> PartialEq for RandomForestRegressor<T> {
 }
 
 impl<T: FloatExt> RandomForestRegressor<T> {
-
+    pub fn fit<M: Matrix<T>>(
-    pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, parameters: RandomForestRegressorParameters) -> RandomForestRegressor<T> {        
+        x: &M,
+        y: &M::RowVector,
+        parameters: RandomForestRegressorParameters,
+    ) -> RandomForestRegressor<T> {
         let (n_rows, num_attributes) = x.shape();
 
-        let mtry = parameters.mtry.unwrap_or((num_attributes as f64).sqrt().floor() as usize);        
+        let mtry = parameters
+            .mtry
+            .unwrap_or((num_attributes as f64).sqrt().floor() as usize);
 
         let mut trees: Vec<DecisionTreeRegressor<T>> = Vec::new();
 
@@ -66,7 +73,7 @@ impl<T: FloatExt> RandomForestRegressor<T> {
             let params = DecisionTreeRegressorParameters {
                 max_depth: parameters.max_depth,
                 min_samples_leaf: parameters.min_samples_leaf,
-                min_samples_split: parameters.min_samples_split          
+                min_samples_split: parameters.min_samples_split,
             };
             let tree = DecisionTreeRegressor::fit_weak_learner(x, y, samples, mtry, params);
             trees.push(tree);
@@ -74,7 +81,7 @@ impl<T: FloatExt> RandomForestRegressor<T> {
 
         RandomForestRegressor {
             parameters: parameters,
-            trees: trees
+            trees: trees,
         }
     }
 
@@ -91,7 +98,6 @@ impl<T: FloatExt> RandomForestRegressor<T> {
     }
 
     fn predict_for_row<M: Matrix<T>>(&self, x: &M, row: usize) -> T {
-        
         let n_trees = self.trees.len();
 
         let mut result = T::zero();
@@ -101,7 +107,6 @@ impl<T: FloatExt> RandomForestRegressor<T> {
         }
 
         result / T::from(n_trees).unwrap()
-        
     }
 
     fn sample_with_replacement(nrows: usize) -> Vec<usize> {
@@ -113,7 +118,6 @@ impl<T: FloatExt> RandomForestRegressor<T> {
         }
         samples
     }
-
 }
 
 #[cfg(test)]
@@ -124,7 +128,6 @@ mod tests {
 
     #[test]
     fn fit_longley() {
-
         let x = DenseMatrix::from_array(&[
             &[234.289, 235.6, 159., 107.608, 1947., 60.323],
             &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
@@ -141,27 +144,37 @@ mod tests {
             &[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
             &[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]]);
+            &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        let y = vec![83.0,  88.5,  88.2,  89.5,  96.2,  98.1,  99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9];        
+        ]);
+        let y = vec![
+            83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
+            114.2, 115.7, 116.9,
+        ];
 
-        let expected_y: Vec<f64> = vec![85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.];
+        let expected_y: Vec<f64> = vec![
+            85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.,
+        ];
 
-        let y_hat = RandomForestRegressor::fit(&x, &y, 
+        let y_hat = RandomForestRegressor::fit(
-            RandomForestRegressorParameters{max_depth: None,
+            &x,
+            &y,
+            RandomForestRegressorParameters {
+                max_depth: None,
                 min_samples_leaf: 1,
                 min_samples_split: 2,
                 n_trees: 1000,
-                mtry: Option::None}).predict(&x);        
+                mtry: Option::None,
+            },
+        )
+        .predict(&x);
 
         for i in 0..y_hat.len() {
             assert!((y_hat[i] - expected_y[i]).abs() < 1.0);
         }
-            
     }
 
     #[test]
     fn my_fit_longley_ndarray() {
-
         let x = arr2(&[
             [234.289, 235.6, 159., 107.608, 1947., 60.323],
             [259.426, 232.5, 145.6, 108.632, 1948., 61.122],
@@ -178,22 +191,33 @@ mod tests {
             [482.704, 381.3, 255.2, 123.366, 1959., 68.655],
             [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]]);
+            [554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        let y = arr1(&[83.0,  88.5,  88.2,  89.5,  96.2,  98.1,  99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9]);             
+        ]);
+        let y = arr1(&[
+            83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
+            114.2, 115.7, 116.9,
+        ]);
 
-        let expected_y: Vec<f64> = vec![85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.];
+        let expected_y: Vec<f64> = vec![
+            85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.,
+        ];
 
-        let y_hat = RandomForestRegressor::fit(&x, &y, 
+        let y_hat = RandomForestRegressor::fit(
-            RandomForestRegressorParameters{max_depth: None,
+            &x,
+            &y,
+            RandomForestRegressorParameters {
+                max_depth: None,
                 min_samples_leaf: 1,
                 min_samples_split: 2,
                 n_trees: 1000,
-                mtry: Option::None}).predict(&x);                        
+                mtry: Option::None,
+            },
+        )
+        .predict(&x);
 
         for i in 0..y_hat.len() {
             assert!((y_hat[i] - expected_y[i]).abs() < 1.0);
         }
-            
     }
 
     #[test]
@@ -214,15 +238,18 @@ mod tests {
             &[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
             &[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]]);
+            &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        let y = vec![83.0,  88.5,  88.2,  89.5,  96.2,  98.1,  99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9];          
+        ]);
+        let y = vec![
+            83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
+            114.2, 115.7, 116.9,
+        ];
 
         let forest = RandomForestRegressor::fit(&x, &y, Default::default());
 
-        let deserialized_forest: RandomForestRegressor<f64> = bincode::deserialize(&bincode::serialize(&forest).unwrap()).unwrap();
+        let deserialized_forest: RandomForestRegressor<f64> =
+            bincode::deserialize(&bincode::serialize(&forest).unwrap()).unwrap();
 
         assert_eq!(forest, deserialized_forest);
-        
     }
-
 }

src/lib.rs

@@ -1,12 +1,12 @@
-pub mod linear;
-pub mod neighbors;
-pub mod ensemble;
-pub mod tree;
-pub mod cluster;
-pub mod decomposition;
-pub mod linalg;
-pub mod math;
 pub mod algorithm;
+pub mod cluster;
 pub mod common;
-pub mod optimization;
+pub mod decomposition;
+pub mod ensemble;
+pub mod linalg;
+pub mod linear;
+pub mod math;
 pub mod metrics;
+pub mod neighbors;
+pub mod optimization;
+pub mod tree;

src/linalg/evd.rs

@@ -1,29 +1,24 @@
 #![allow(non_snake_case)]
 
-use num::complex::Complex;
 use crate::linalg::BaseMatrix;
 use crate::math::num::FloatExt;
+use num::complex::Complex;
 use std::fmt::Debug;
 
 #[derive(Debug, Clone)]
 pub struct EVD<T: FloatExt, M: BaseMatrix<T>> {
     pub d: Vec<T>,
     pub e: Vec<T>,
-    pub V: M
+    pub V: M,
 }
 
 impl<T: FloatExt, M: BaseMatrix<T>> EVD<T, M> {
     pub fn new(V: M, d: Vec<T>, e: Vec<T>) -> EVD<T, M> {
-        EVD {
+        EVD { d: d, e: e, V: V }
-            d: d,
-            e: e,
-            V: V
-        }
     }
 }
 
 pub trait EVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
-    
     fn evd(&self, symmetric: bool) -> EVD<T, Self> {
         self.clone().evd_mut(symmetric)
     }
@@ -45,7 +40,6 @@ pub trait EVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
             tred2(&mut V, &mut d, &mut e);
             // Diagonalize.
             tql2(&mut V, &mut d, &mut e);
-
         } else {
             let scale = balance(&mut self);
 
@@ -60,16 +54,11 @@ pub trait EVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
             sort(&mut d, &mut e, &mut V);
         }
 
-        EVD {
+        EVD { V: V, d: d, e: e }
-            V: V,
-            d: d,
-            e: e
-        }
     }
 }
 
 fn tred2<T: FloatExt, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<T>) {
-    
     let (n, _) = V.shape();
     for i in 0..n {
         d[i] = V.get(n - 1, i);
@@ -527,7 +516,8 @@ fn hqr2<T: FloatExt, M: BaseMatrix<T>>(A: &mut M, V: &mut M, d: &mut Vec<T>, e:
                             break;
                         }
                         let u = A.get(m, m - 1).abs() * (q.abs() + r.abs());
-                        let v = p.abs() * (A.get(m - 1, m - 1).abs() + z.abs() + A.get(m + 1, m + 1).abs());
+                        let v = p.abs()
+                            * (A.get(m - 1, m - 1).abs() + z.abs() + A.get(m + 1, m + 1).abs());
                         if u <= T::epsilon() * v {
                             break;
                         }
@@ -609,7 +599,7 @@ fn hqr2<T: FloatExt, M: BaseMatrix<T>>(A: &mut M, V: &mut M, d: &mut Vec<T>, e:
             if l + 1 >= nn {
                 break;
             }
-        };
+        }
     }
 
     if anorm != T::zero() {
@@ -672,7 +662,8 @@ fn hqr2<T: FloatExt, M: BaseMatrix<T>>(A: &mut M, V: &mut M, d: &mut Vec<T>, e:
                     A.set(na, na, q / A.get(nn, na));
                     A.set(na, nn, -(A.get(nn, nn) - p) / A.get(nn, na));
                 } else {
-                    let temp = Complex::new(T::zero(), -A.get(na, nn)) / Complex::new(A.get(na, na) - p, q);                        
+                    let temp = Complex::new(T::zero(), -A.get(na, nn))
+                        / Complex::new(A.get(na, na) - p, q);
                     A.set(na, na, temp.re);
                     A.set(na, nn, temp.im);
                 }
@@ -700,19 +691,34 @@ fn hqr2<T: FloatExt, M: BaseMatrix<T>>(A: &mut M, V: &mut M, d: &mut Vec<T>, e:
                             } else {
                                 let x = A.get(i, i + 1);
                                 let y = A.get(i + 1, i);
-                                let mut vr = (d[i] - p).powf(T::two()) + (e[i]).powf(T::two()) - q * q;
+                                let mut vr =
+                                    (d[i] - p).powf(T::two()) + (e[i]).powf(T::two()) - q * q;
                                 let vi = T::two() * q * (d[i] - p);
                                 if vr == T::zero() && vi == T::zero() {
-                                    vr = T::epsilon() * anorm * (w.abs() + q.abs() + x.abs() + y.abs() + z.abs());
+                                    vr = T::epsilon()
+                                        * anorm
+                                        * (w.abs() + q.abs() + x.abs() + y.abs() + z.abs());
                                 }
-                                let temp = Complex::new(x * r - z * ra + q * sa, x * s - z * sa - q * ra) / Complex::new(vr, vi);
+                                let temp =
+                                    Complex::new(x * r - z * ra + q * sa, x * s - z * sa - q * ra)
+                                        / Complex::new(vr, vi);
                                 A.set(i, na, temp.re);
                                 A.set(i, nn, temp.im);
                                 if x.abs() > z.abs() + q.abs() {
-                                    A.set(i + 1, na, (-ra - w * A.get(i, na) + q * A.get(i, nn)) / x);
+                                    A.set(
-                                    A.set(i + 1, nn, (-sa - w * A.get(i, nn) - q * A.get(i, na)) / x);
+                                        i + 1,
+                                        na,
+                                        (-ra - w * A.get(i, na) + q * A.get(i, nn)) / x,
+                                    );
+                                    A.set(
+                                        i + 1,
+                                        nn,
+                                        (-sa - w * A.get(i, nn) - q * A.get(i, na)) / x,
+                                    );
                                 } else {
-                                    let temp = Complex::new(-r - y * A.get(i, na), -s - y * A.get(i, nn)) / Complex::new(z, q);
+                                    let temp =
+                                        Complex::new(-r - y * A.get(i, na), -s - y * A.get(i, nn))
+                                            / Complex::new(z, q);
                                     A.set(i + 1, na, temp.re);
                                     A.set(i + 1, nn, temp.im);
                                 }
@@ -787,18 +793,18 @@ mod tests {
 
     #[test]
     fn decompose_symmetric() {
-
         let A = DenseMatrix::from_array(&[
             &[0.9000, 0.4000, 0.7000],
             &[0.4000, 0.5000, 0.3000],
-            &[0.7000, 0.3000, 0.8000]]);
+            &[0.7000, 0.3000, 0.8000],
+        ]);
 
         let eigen_values: Vec<f64> = vec![1.7498382, 0.3165784, 0.1335834];
 
         let eigen_vectors = DenseMatrix::from_array(&[
             &[0.6881997, -0.07121225, 0.7220180],
             &[0.3700456, 0.89044952, -0.2648886],
-            &[0.6240573, -0.44947578, -0.6391588]
+            &[0.6240573, -0.44947578, -0.6391588],
         ]);
 
         let evd = A.evd(true);
@@ -810,23 +816,22 @@ mod tests {
         for i in 0..eigen_values.len() {
             assert!((0f64 - evd.e[i]).abs() < std::f64::EPSILON);
         }
-
     }
 
     #[test]
     fn decompose_asymmetric() {
-
         let A = DenseMatrix::from_array(&[
             &[0.9000, 0.4000, 0.7000],
             &[0.4000, 0.5000, 0.3000],
-            &[0.8000, 0.3000, 0.8000]]);
+            &[0.8000, 0.3000, 0.8000],
+        ]);
 
         let eigen_values: Vec<f64> = vec![1.79171122, 0.31908143, 0.08920735];
 
         let eigen_vectors = DenseMatrix::from_array(&[
             &[0.7178958, 0.05322098, 0.6812010],
             &[0.3837711, -0.84702111, -0.1494582],
-            &[0.6952105,  0.43984484, -0.7036135]
+            &[0.6952105, 0.43984484, -0.7036135],
         ]);
 
         let evd = A.evd(false);
@@ -838,17 +843,16 @@ mod tests {
         for i in 0..eigen_values.len() {
             assert!((0f64 - evd.e[i]).abs() < std::f64::EPSILON);
         }
-
     }
 
     #[test]
     fn decompose_complex() {
-
         let A = DenseMatrix::from_array(&[
             &[3.0, -2.0, 1.0, 1.0],
             &[4.0, -1.0, 1.0, 1.0],
             &[1.0, 1.0, 3.0, -2.0],
-            &[1.0, 1.0, 4.0, -1.0]]);
+            &[1.0, 1.0, 4.0, -1.0],
+        ]);
 
         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];
@@ -857,7 +861,7 @@ mod tests {
             &[-0.9159, -0.1378, 0.3816, -0.0806],
             &[-0.6707, 0.1059, 0.901, 0.6289],
             &[0.9159, -0.1378, 0.3816, 0.0806],
-            &[0.6707, 0.1059, 0.901, -0.6289]
+            &[0.6707, 0.1059, 0.901, -0.6289],
         ]);
 
         let evd = A.evd(false);
@@ -869,7 +873,5 @@ mod tests {
         for i in 0..eigen_values_e.len() {
             assert!((eigen_values_e[i] - evd.e[i]).abs() < 1e-4);
         }
-
     }
-    
 }

src/linalg/lu.rs

@@ -3,8 +3,8 @@
 use std::fmt::Debug;
 use std::marker::PhantomData;
 
-use crate::math::num::FloatExt;
 use crate::linalg::BaseMatrix;
+use crate::math::num::FloatExt;
 
 #[derive(Debug, Clone)]
 pub struct LU<T: FloatExt, M: BaseMatrix<T>> {
@@ -12,12 +12,11 @@ pub struct LU<T: FloatExt, M: BaseMatrix<T>> {
     pivot: Vec<usize>,
     pivot_sign: i8,
     singular: bool,
-    phantom: PhantomData<T>
+    phantom: PhantomData<T>,
 }
 
 impl<T: FloatExt, M: BaseMatrix<T>> LU<T, M> {
     pub fn new(LU: M, pivot: Vec<usize>, pivot_sign: i8) -> LU<T, M> {
-
         let (_, n) = LU.shape();
 
         let mut singular = false;
@@ -33,7 +32,7 @@ impl<T: FloatExt, M: BaseMatrix<T>> LU<T, M> {
             pivot: pivot,
             pivot_sign: pivot_sign,
             singular: singular,
-            phantom: PhantomData
+            phantom: PhantomData,
         }
     }
 
@@ -106,7 +105,10 @@ impl<T: FloatExt, M: BaseMatrix<T>> LU<T, M> {
         let (b_m, b_n) = b.shape();
 
         if b_m != m {
-            panic!("Row dimensions do not agree: A is {} x {}, but B is {} x {}", m, n, b_m, b_n);
+            panic!(
+                "Row dimensions do not agree: A is {} x {}, but B is {} x {}",
+                m, n, b_m, b_n
+            );
         }
 
         if self.singular {
@@ -148,19 +150,15 @@ impl<T: FloatExt, M: BaseMatrix<T>> LU<T, M> {
         }
 
         b
-
     }
-
 }
 
 pub trait LUDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
-
     fn lu(&self) -> LU<T, Self> {
         self.clone().lu_mut()
     }
 
     fn lu_mut(mut self) -> LU<T, Self> {
-
         let (m, n) = self.shape();
 
         let mut piv = vec![0; m];
@@ -172,7 +170,6 @@ pub trait LUDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
         let mut LUcolj = vec![T::zero(); m];
 
         for j in 0..n {
-
             for i in 0..m {
                 LUcolj[i] = self.get(i, j);
             }
@@ -214,13 +211,10 @@ pub trait LUDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
         }
 
         LU::new(self, piv, pivsign)
-
     }
 
     fn lu_solve_mut(self, b: Self) -> Self {
-
         self.lu_mut().solve(b)
-
     }
 }
 
@@ -231,11 +225,11 @@ mod tests {
 
     #[test]
     fn decompose() {
-
         let a = DenseMatrix::from_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]);
         let expected_L = DenseMatrix::from_array(&[&[1., 0., 0.], &[0., 1., 0.], &[0.2, 0.8, 1.]]);
         let expected_U = DenseMatrix::from_array(&[&[5., 6., 0.], &[0., 1., 5.], &[0., 0., -1.]]);
-            let expected_pivot = DenseMatrix::from_array(&[&[0., 0., 1.], &[0., 1., 0.], &[1., 0., 0.]]);
+        let expected_pivot =
+            DenseMatrix::from_array(&[&[0., 0., 1.], &[0., 1., 0.], &[1., 0., 0.]]);
         let lu = a.lu();
         assert!(lu.L().approximate_eq(&expected_L, 1e-4));
         assert!(lu.U().approximate_eq(&expected_U, 1e-4));
@@ -244,9 +238,9 @@ mod tests {
 
     #[test]
     fn inverse() {
-
         let a = DenseMatrix::from_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]);
-            let expected = DenseMatrix::from_array(&[&[-6.0, 3.6, 1.4], &[5.0, -3.0, -1.0], &[-1.0, 0.8, 0.2]]);
+        let expected =
+            DenseMatrix::from_array(&[&[-6.0, 3.6, 1.4], &[5.0, -3.0, -1.0], &[-1.0, 0.8, 0.2]]);
         let a_inv = a.lu().inverse();
         println!("{}", a_inv);
         assert!(a_inv.approximate_eq(&expected, 1e-4));

src/linalg/mod.rs

@@ -1,23 +1,22 @@
-pub mod naive;
-pub mod qr;
-pub mod svd;
 pub mod evd;
 pub mod lu;
-pub mod ndarray_bindings;
+pub mod naive;
 pub mod nalgebra_bindings;
+pub mod ndarray_bindings;
+pub mod qr;
+pub mod svd;
 
-use std::ops::Range;
 use std::fmt::{Debug, Display};
 use std::marker::PhantomData;
+use std::ops::Range;
 
 use crate::math::num::FloatExt;
-use svd::SVDDecomposableMatrix;
 use evd::EVDDecomposableMatrix;
-use qr::QRDecomposableMatrix;
 use lu::LUDecomposableMatrix;
+use qr::QRDecomposableMatrix;
+use svd::SVDDecomposableMatrix;
 
 pub trait BaseVector<T: FloatExt>: Clone + Debug {
-
     fn get(&self, i: usize) -> T;
 
     fn set(&mut self, i: usize, x: T);
@@ -26,7 +25,6 @@ pub trait BaseVector<T: FloatExt>: Clone + Debug {
 }
 
 pub trait BaseMatrix<T: FloatExt>: Clone + Debug {
-
     type RowVector: BaseVector<T> + Clone + Debug;
 
     fn from_row_vector(vec: Self::RowVector) -> Self;
@@ -186,17 +184,25 @@ pub trait BaseMatrix<T: FloatExt>: Clone + Debug {
     fn unique(&self) -> Vec<T>;
 
     fn cov(&self) -> Self;
-
 }
 
-pub trait Matrix<T: FloatExt>: BaseMatrix<T> + SVDDecomposableMatrix<T> + EVDDecomposableMatrix<T> + QRDecomposableMatrix<T> + LUDecomposableMatrix<T> + PartialEq + Display {}
+pub trait Matrix<T: FloatExt>:
+    BaseMatrix<T>
+    + SVDDecomposableMatrix<T>
+    + EVDDecomposableMatrix<T>
+    + QRDecomposableMatrix<T>
+    + LUDecomposableMatrix<T>
+    + PartialEq
+    + Display
+{
+}
 
 pub fn row_iter<F: FloatExt, M: BaseMatrix<F>>(m: &M) -> RowIter<F, M> {
     RowIter {
         m: m,
         pos: 0,
         max_pos: m.shape().0,
-        phantom: PhantomData
+        phantom: PhantomData,
     }
 }
 
@@ -204,11 +210,10 @@ pub struct RowIter<'a, T: FloatExt, M: BaseMatrix<T>> {
     m: &'a M,
     pos: usize,
     max_pos: usize,
-    phantom: PhantomData<&'a T>
+    phantom: PhantomData<&'a T>,
 }
 
 impl<'a, T: FloatExt, M: BaseMatrix<T>> Iterator for RowIter<'a, T, M> {
-
     type Item = Vec<T>;
 
     fn next(&mut self) -> Option<Vec<T>> {
@@ -221,5 +226,4 @@ impl<'a, T: FloatExt, M: BaseMatrix<T>> Iterator for RowIter<'a, T, M> {
         self.pos += 1;
         res
     }
-
 }

src/linalg/naive/dense_matrix.rs

@@ -1,19 +1,19 @@
 extern crate num;
-use std::ops::Range;
 use std::fmt;
 use std::fmt::Debug;
 use std::marker::PhantomData;
+use std::ops::Range;
 
-use serde::{Serialize, Deserialize};
+use serde::de::{Deserializer, MapAccess, SeqAccess, Visitor};
-use serde::ser::{Serializer, SerializeStruct};
+use serde::ser::{SerializeStruct, Serializer};
-use serde::de::{Deserializer, Visitor, SeqAccess, MapAccess};
+use serde::{Deserialize, Serialize};
 
+use crate::linalg::evd::EVDDecomposableMatrix;
+use crate::linalg::lu::LUDecomposableMatrix;
+use crate::linalg::qr::QRDecomposableMatrix;
+use crate::linalg::svd::SVDDecomposableMatrix;
 use crate::linalg::Matrix;
 pub use crate::linalg::{BaseMatrix, BaseVector};
-use crate::linalg::svd::SVDDecomposableMatrix;
-use crate::linalg::evd::EVDDecomposableMatrix;
-use crate::linalg::qr::QRDecomposableMatrix;
-use crate::linalg::lu::LUDecomposableMatrix;
 use crate::math::num::FloatExt;
 
 impl<T: FloatExt> BaseVector<T> for Vec<T> {
@@ -31,30 +31,32 @@ impl<T: FloatExt> BaseVector<T> for Vec<T> {
 
 #[derive(Debug, Clone)]
 pub struct DenseMatrix<T: FloatExt> {
-
     ncols: usize,
     nrows: usize,
-    values: Vec<T> 
+    values: Vec<T>,
-
 }
 
 impl<T: FloatExt> fmt::Display for DenseMatrix<T> {
     fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
         let mut rows: Vec<Vec<f64>> = Vec::new();
         for r in 0..self.nrows {
-            rows.push(self.get_row_as_vec(r).iter().map(|x| (x.to_f64().unwrap() * 1e4).round() / 1e4 ).collect());
+            rows.push(
+                self.get_row_as_vec(r)
+                    .iter()
+                    .map(|x| (x.to_f64().unwrap() * 1e4).round() / 1e4)
+                    .collect(),
+            );
         }
         write!(f, "{:?}", rows)
     }
 }
 
 impl<T: FloatExt> DenseMatrix<T> {
-    
     fn new(nrows: usize, ncols: usize, values: Vec<T>) -> Self {
         DenseMatrix {
             ncols: ncols,
             nrows: nrows,
-            values: values
+            values: values,
         }
     }
 
@@ -64,11 +66,14 @@ impl<T: FloatExt> DenseMatrix<T> {
 
     pub fn from_vec(values: &Vec<Vec<T>>) -> DenseMatrix<T> {
         let nrows = values.len();
-        let ncols = values.first().unwrap_or_else(|| panic!("Cannot create 2d matrix from an empty vector")).len();
+        let ncols = values
+            .first()
+            .unwrap_or_else(|| panic!("Cannot create 2d matrix from an empty vector"))
+            .len();
         let mut m = DenseMatrix {
             ncols: ncols,
             nrows: nrows,
-            values: vec![T::zero(); ncols*nrows]
+            values: vec![T::zero(); ncols * nrows],
         };
         for row in 0..nrows {
             for col in 0..ncols {
@@ -86,7 +91,7 @@ impl<T: FloatExt> DenseMatrix<T> {
         DenseMatrix {
             ncols: values.len(),
             nrows: 1,
-            values: values
+            values: values,
         }
     }
 
@@ -103,7 +108,6 @@ impl<T: FloatExt> DenseMatrix<T> {
     pub fn get_raw_values(&self) -> &Vec<T> {
         &self.values
     }
-    
 }
 
 impl<'de, T: FloatExt + fmt::Debug + Deserialize<'de>> Deserialize<'de> for DenseMatrix<T> {
@@ -111,13 +115,16 @@ impl<'de, T: FloatExt + fmt::Debug + Deserialize<'de>> Deserialize<'de> for Dens
     where
         D: Deserializer<'de>,
     {
-
         #[derive(Deserialize)]
         #[serde(field_identifier, rename_all = "lowercase")]
-        enum Field { NRows, NCols, Values }        
+        enum Field {
+            NRows,
+            NCols,
+            Values,
+        }
 
         struct DenseMatrixVisitor<T: FloatExt + fmt::Debug> {
-            t: PhantomData<T>      
+            t: PhantomData<T>,
         }
 
         impl<'a, T: FloatExt + fmt::Debug + Deserialize<'a>> Visitor<'a> for DenseMatrixVisitor<T> {
@@ -131,11 +138,14 @@ impl<'de, T: FloatExt + fmt::Debug + Deserialize<'de>> Deserialize<'de> for Dens
             where
                 V: SeqAccess<'a>,
             {
-                let nrows = seq.next_element()?
+                let nrows = seq
+                    .next_element()?
                     .ok_or_else(|| serde::de::Error::invalid_length(0, &self))?;
-                let ncols = seq.next_element()?
+                let ncols = seq
+                    .next_element()?
                     .ok_or_else(|| serde::de::Error::invalid_length(1, &self))?;
-                let values = seq.next_element()?
+                let values = seq
+                    .next_element()?
                     .ok_or_else(|| serde::de::Error::invalid_length(2, &self))?;
                 Ok(DenseMatrix::new(nrows, ncols, values))
             }
@@ -177,16 +187,19 @@ impl<'de, T: FloatExt + fmt::Debug + Deserialize<'de>> Deserialize<'de> for Dens
         }
 
         const FIELDS: &'static [&'static str] = &["nrows", "ncols", "values"];
-        deserializer.deserialize_struct("DenseMatrix", FIELDS, DenseMatrixVisitor {
+        deserializer.deserialize_struct(
-            t: PhantomData            
+            "DenseMatrix",
-        })
+            FIELDS,
+            DenseMatrixVisitor { t: PhantomData },
+        )
     }
 }
 
 impl<T: FloatExt + fmt::Debug + Serialize> Serialize for DenseMatrix<T> {
-
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
-    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error> where
+    where
-    S: Serializer { 
+        S: Serializer,
+    {
         let (nrows, ncols) = self.shape();
         let mut state = serializer.serialize_struct("DenseMatrix", 3)?;
         state.serialize_field("nrows", &nrows)?;
@@ -209,7 +222,7 @@ impl<T: FloatExt> Matrix<T> for DenseMatrix<T> {}
 impl<T: FloatExt> PartialEq for DenseMatrix<T> {
     fn eq(&self, other: &Self) -> bool {
         if self.ncols != other.ncols || self.nrows != other.nrows {
-            return false
+            return false;
         }
 
         let len = self.values.len();
@@ -236,7 +249,6 @@ impl<T: FloatExt> Into<Vec<T>> for DenseMatrix<T> {
 }
 
 impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
-    
     type RowVector = Vec<T>;
 
     fn from_row_vector(vec: Self::RowVector) -> Self {
@@ -249,7 +261,10 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
 
     fn get(&self, row: usize, col: usize) -> T {
         if row >= self.nrows || col >= self.ncols {
-            panic!("Invalid index ({},{}) for {}x{} matrix", row, col, self.nrows, self.ncols);
+            panic!(
+                "Invalid index ({},{}) for {}x{} matrix",
+                row, col, self.nrows, self.ncols
+            );
         }
         self.values[col * self.nrows + row]
     }
@@ -343,7 +358,6 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
     }
 
     fn dot(&self, other: &Self) -> Self {
-
         if self.ncols != other.nrows {
             panic!("Number of rows of A should equal number of columns of B");
         }
@@ -380,7 +394,6 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
     }
 
     fn slice(&self, rows: Range<usize>, cols: Range<usize>) -> Self {
-
         let ncols = cols.len();
         let nrows = rows.len();
 
@@ -397,13 +410,13 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
 
     fn approximate_eq(&self, other: &Self, error: T) -> bool {
         if self.ncols != other.ncols || self.nrows != other.nrows {
-            return false
+            return false;
         }
 
         for c in 0..self.ncols {
             for r in 0..self.nrows {
                 if (self.get(r, c) - other.get(r, c)).abs() > error {
-                    return false
+                    return false;
                 }
             }
         }
@@ -487,7 +500,7 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
         let mut m = DenseMatrix {
             ncols: self.nrows,
             nrows: self.ncols,
-            values: vec![T::zero(); self.ncols * self.nrows]
+            values: vec![T::zero(); self.ncols * self.nrows],
         };
         for c in 0..self.ncols {
             for r in 0..self.nrows {
@@ -495,17 +508,14 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
             }
         }
         m
-
     }
 
     fn rand(nrows: usize, ncols: usize) -> Self {
-        let values: Vec<T> = (0..nrows*ncols).map(|_| {
+        let values: Vec<T> = (0..nrows * ncols).map(|_| T::rand()).collect();
-            T::rand()
-        }).collect();
         DenseMatrix {
             ncols: ncols,
             nrows: nrows,
-            values: values
+            values: values,
         }
     }
 
@@ -520,13 +530,17 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
     }
 
     fn norm(&self, p: T) -> T {
-
         if p.is_infinite() && p.is_sign_positive() {
-            self.values.iter().map(|x| x.abs()).fold(T::neg_infinity(), |a, b| a.max(b))
+            self.values
+                .iter()
+                .map(|x| x.abs())
+                .fold(T::neg_infinity(), |a, b| a.max(b))
         } else if p.is_infinite() && p.is_sign_negative() {
-            self.values.iter().map(|x| x.abs()).fold(T::infinity(), |a, b| a.min(b))
+            self.values
+                .iter()
+                .map(|x| x.abs())
+                .fold(T::infinity(), |a, b| a.min(b))
         } else {
-
             let mut norm = T::zero();
 
             for xi in self.values.iter() {
@@ -589,7 +603,10 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
 
     fn reshape(&self, nrows: usize, ncols: usize) -> Self {
         if self.nrows * self.ncols != nrows * ncols {
-            panic!("Can't reshape {}x{} matrix into {}x{}.", self.nrows, self.ncols, nrows, ncols);
+            panic!(
+                "Can't reshape {}x{} matrix into {}x{}.",
+                self.nrows, self.ncols, nrows, ncols
+            );
         }
         let mut dst = DenseMatrix::zeros(nrows, ncols);
         let mut dst_r = 0;
@@ -609,9 +626,11 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
     }
 
     fn copy_from(&mut self, other: &Self) {
-
         if self.nrows != other.nrows || self.ncols != other.ncols {
-            panic!("Can't copy {}x{} matrix into {}x{}.", self.nrows, self.ncols, other.nrows, other.ncols);
+            panic!(
+                "Can't copy {}x{} matrix into {}x{}.",
+                self.nrows, self.ncols, other.nrows, other.ncols
+            );
         }
 
         for i in 0..self.values.len() {
@@ -632,7 +651,6 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
             max_diff = max_diff.max((self.values[i] - other.values[i]).abs());
         }
         max_diff
-
     }
 
     fn sum(&self) -> T {
@@ -644,7 +662,11 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
     }
 
     fn softmax_mut(&mut self) {
-        let max = self.values.iter().map(|x| x.abs()).fold(T::neg_infinity(), |a, b| a.max(b));
+        let max = self
+            .values
+            .iter()
+            .map(|x| x.abs())
+            .fold(T::neg_infinity(), |a, b| a.max(b));
         let mut z = T::zero();
         for r in 0..self.nrows {
             for c in 0..self.ncols {
@@ -668,7 +690,6 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
     }
 
     fn argmax(&self) -> Vec<usize> {
-
         let mut res = vec![0usize; self.nrows];
 
         for r in 0..self.nrows {
@@ -685,7 +706,6 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
         }
 
         res
-
     }
 
     fn unique(&self) -> Vec<T> {
@@ -696,7 +716,6 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
     }
 
     fn cov(&self) -> Self {
-
         let (m, n) = self.shape();
 
         let mu = self.column_mean();
@@ -722,7 +741,6 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
 
         cov
     }
-
 }
 
 #[cfg(test)]
@@ -731,109 +749,71 @@ mod tests {
 
     #[test]
     fn from_to_row_vec() {
-
         let vec = vec![1., 2., 3.];
-        assert_eq!(DenseMatrix::from_row_vector(vec.clone()), DenseMatrix::new(1, 3, vec![1., 2., 3.]));
+        assert_eq!(
-        assert_eq!(DenseMatrix::from_row_vector(vec.clone()).to_row_vector(), vec![1., 2., 3.]);
+            DenseMatrix::from_row_vector(vec.clone()),
-
+            DenseMatrix::new(1, 3, vec![1., 2., 3.])
+        );
+        assert_eq!(
+            DenseMatrix::from_row_vector(vec.clone()).to_row_vector(),
+            vec![1., 2., 3.]
+        );
     }
 
     #[test]
     fn h_stack() {
-
+        let a = DenseMatrix::from_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]);
-            let a = DenseMatrix::from_array(
+        let b = DenseMatrix::from_array(&[&[1., 2., 3.], &[4., 5., 6.]]);
-                &[
+        let expected = DenseMatrix::from_array(&[
-                    &[1., 2., 3.],
-                    &[4., 5., 6.],
-                    &[7., 8., 9.]]);
-            let b = DenseMatrix::from_array(
-                &[
-                    &[1., 2., 3.],
-                    &[4., 5., 6.]]);
-            let expected = DenseMatrix::from_array(
-                &[
             &[1., 2., 3.],
             &[4., 5., 6.],
             &[7., 8., 9.],
             &[1., 2., 3.],
-                    &[4., 5., 6.]]);
+            &[4., 5., 6.],
+        ]);
         let result = a.h_stack(&b);
         assert_eq!(result, expected);
     }
 
     #[test]
     fn v_stack() {
-
+        let a = DenseMatrix::from_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]);
-            let a = DenseMatrix::from_array(
+        let b = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.]]);
-                &[
+        let expected = DenseMatrix::from_array(&[
-                    &[1., 2., 3.],
-                    &[4., 5., 6.],
-                    &[7., 8., 9.]]);
-            let b = DenseMatrix::from_array(
-                &[
-                    &[1., 2.],
-                    &[3., 4.],
-                    &[5., 6.]]);
-            let expected = DenseMatrix::from_array(
-                &[
             &[1., 2., 3., 1., 2.],
             &[4., 5., 6., 3., 4.],
-                    &[7., 8., 9., 5., 6.]]);
+            &[7., 8., 9., 5., 6.],
+        ]);
         let result = a.v_stack(&b);
         assert_eq!(result, expected);
     }
 
     #[test]
     fn dot() {
-
+        let a = DenseMatrix::from_array(&[&[1., 2., 3.], &[4., 5., 6.]]);
-            let a = DenseMatrix::from_array(
+        let b = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.]]);
-                &[
+        let expected = DenseMatrix::from_array(&[&[22., 28.], &[49., 64.]]);
-                    &[1., 2., 3.],
-                    &[4., 5., 6.]]);
-            let b = DenseMatrix::from_array(
-                &[
-                    &[1., 2.],
-                    &[3., 4.],
-                    &[5., 6.]]);
-            let expected = DenseMatrix::from_array(
-                &[
-                    &[22., 28.], 
-                    &[49., 64.]]);
         let result = a.dot(&b);
         assert_eq!(result, expected);
     }
 
     #[test]
     fn slice() {
-
+        let m = DenseMatrix::from_array(&[
-            let m = DenseMatrix::from_array(
-                &[
             &[1., 2., 3., 1., 2.],
             &[4., 5., 6., 3., 4.],
-                    &[7., 8., 9., 5., 6.]]);
+            &[7., 8., 9., 5., 6.],
-            let expected = DenseMatrix::from_array(
+        ]);
-                &[
+        let expected = DenseMatrix::from_array(&[&[2., 3.], &[5., 6.]]);
-                    &[2., 3.], 
-                    &[5., 6.]]);
         let result = m.slice(0..2, 1..3);
         assert_eq!(result, expected);
     }
 
-
     #[test]
     fn approximate_eq() {
-            let m = DenseMatrix::from_array(
+        let m = DenseMatrix::from_array(&[&[2., 3.], &[5., 6.]]);
-                &[
+        let m_eq = DenseMatrix::from_array(&[&[2.5, 3.0], &[5., 5.5]]);
-                    &[2., 3.], 
+        let m_neq = DenseMatrix::from_array(&[&[3.0, 3.0], &[5., 6.5]]);
-                    &[5., 6.]]);
-            let m_eq = DenseMatrix::from_array(
-                &[
-                    &[2.5, 3.0], 
-                    &[5., 5.5]]);
-            let m_neq = DenseMatrix::from_array(
-                &[
-                    &[3.0, 3.0], 
-                    &[5., 6.5]]);            
         assert!(m.approximate_eq(&m_eq, 0.5));
         assert!(!m.approximate_eq(&m_neq, 0.5));
     }
@@ -873,7 +853,6 @@ mod tests {
 
     #[test]
     fn norm() {
-
         let v = DenseMatrix::vector_from_array(&[3., -2., 6.]);
         assert_eq!(v.norm(1.), 11.);
         assert_eq!(v.norm(2.), 7.);
@@ -883,7 +862,6 @@ mod tests {
 
     #[test]
     fn softmax_mut() {
-
         let mut prob: DenseMatrix<f64> = DenseMatrix::vector_from_array(&[1., 2., 3.]);
         prob.softmax_mut();
         assert!((prob.get(0, 0) - 0.09).abs() < 0.01);
@@ -893,20 +871,14 @@ mod tests {
 
     #[test]
     fn col_mean() {
-        let a = DenseMatrix::from_array(&[
+        let a = DenseMatrix::from_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]);
-                       &[1., 2., 3.],
-                       &[4., 5., 6.], 
-                       &[7., 8., 9.]]);  
         let res = a.column_mean();
         assert_eq!(res, vec![4., 5., 6.]);
     }
 
     #[test]
     fn eye() {
-        let a = DenseMatrix::from_array(&[
+        let a = DenseMatrix::from_array(&[&[1., 0., 0.], &[0., 1., 0.], &[0., 0., 1.]]);
-                       &[1., 0., 0.],
-                       &[0., 1., 0.], 
-                       &[0., 0., 1.]]);  
         let res = DenseMatrix::eye(3);
         assert_eq!(res, a);
     }
@@ -914,28 +886,42 @@ mod tests {
     #[test]
     fn to_from_json() {
         let a = DenseMatrix::from_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
-            let deserialized_a: DenseMatrix<f64> = serde_json::from_str(&serde_json::to_string(&a).unwrap()).unwrap();
+        let deserialized_a: DenseMatrix<f64> =
+            serde_json::from_str(&serde_json::to_string(&a).unwrap()).unwrap();
         assert_eq!(a, deserialized_a);
     }
 
     #[test]
     fn to_from_bincode() {
         let a = DenseMatrix::from_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
-            let deserialized_a: DenseMatrix<f64> = bincode::deserialize(&bincode::serialize(&a).unwrap()).unwrap();
+        let deserialized_a: DenseMatrix<f64> =
+            bincode::deserialize(&bincode::serialize(&a).unwrap()).unwrap();
         assert_eq!(a, deserialized_a);
     }
 
     #[test]
     fn to_string() {
         let a = DenseMatrix::from_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
-            assert_eq!(format!("{}", a), "[[0.9, 0.4, 0.7], [0.4, 0.5, 0.3], [0.7, 0.3, 0.8]]");            
+        assert_eq!(
+            format!("{}", a),
+            "[[0.9, 0.4, 0.7], [0.4, 0.5, 0.3], [0.7, 0.3, 0.8]]"
+        );
     }
 
     #[test]
     fn cov() {
-            let a = DenseMatrix::from_array(&[&[64.0, 580.0, 29.0], &[66.0, 570.0, 33.0], &[68.0, 590.0, 37.0], &[69.0, 660.0, 46.0], &[73.0, 600.0, 55.0]]);
+        let a = DenseMatrix::from_array(&[
-            let expected = DenseMatrix::from_array(&[&[11.5, 50.0, 34.75], &[50.0, 1250.0, 205.0], &[34.75, 205.0, 110.0]]);            
+            &[64.0, 580.0, 29.0],
+            &[66.0, 570.0, 33.0],
+            &[68.0, 590.0, 37.0],
+            &[69.0, 660.0, 46.0],
+            &[73.0, 600.0, 55.0],
+        ]);
+        let expected = DenseMatrix::from_array(&[
+            &[11.5, 50.0, 34.75],
+            &[50.0, 1250.0, 205.0],
+            &[34.75, 205.0, 110.0],
+        ]);
         assert_eq!(a.cov(), expected);
     }
-
 }

src/linalg/nalgebra_bindings.rs

@@ -1,15 +1,15 @@
-use std::ops::{Range, AddAssign, SubAssign, MulAssign, DivAssign};
 use std::iter::Sum;
+use std::ops::{AddAssign, DivAssign, MulAssign, Range, SubAssign};
 
-use nalgebra::{MatrixMN, DMatrix, Matrix, Scalar, Dynamic, U1, VecStorage};
+use nalgebra::{DMatrix, Dynamic, Matrix, MatrixMN, Scalar, VecStorage, U1};
 
-use crate::math::num::FloatExt;
-use crate::linalg::{BaseMatrix, BaseVector};
-use crate::linalg::Matrix as SmartCoreMatrix;
-use crate::linalg::svd::SVDDecomposableMatrix;
 use crate::linalg::evd::EVDDecomposableMatrix;
-use crate::linalg::qr::QRDecomposableMatrix;
 use crate::linalg::lu::LUDecomposableMatrix;
+use crate::linalg::qr::QRDecomposableMatrix;
+use crate::linalg::svd::SVDDecomposableMatrix;
+use crate::linalg::Matrix as SmartCoreMatrix;
+use crate::linalg::{BaseMatrix, BaseVector};
+use crate::math::num::FloatExt;
 
 impl<T: FloatExt + 'static> BaseVector<T> for MatrixMN<T, U1, Dynamic> {
     fn get(&self, i: usize) -> T {
@@ -24,7 +24,8 @@ impl<T: FloatExt + 'static> BaseVector<T> for MatrixMN<T, U1, Dynamic> {
     }
 }
 
-impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> BaseMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>>
+impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static>
+    BaseMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>>
 {
     type RowVector = MatrixMN<T, U1, Dynamic>;
 
@@ -171,9 +172,7 @@ impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum
     }
 
     fn rand(nrows: usize, ncols: usize) -> Self {
-        DMatrix::from_iterator(nrows, ncols, (0..nrows*ncols).map(|_| {
+        DMatrix::from_iterator(nrows, ncols, (0..nrows * ncols).map(|_| T::rand()))
-            T::rand()
-        }))
     }
 
     fn norm2(&self) -> T {
@@ -200,7 +199,6 @@ impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum
                 }
             })
         } else {
-
             let mut norm = T::zero();
 
             for xi in self.iter() {
@@ -212,7 +210,6 @@ impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum
     }
 
     fn column_mean(&self) -> Vec<T> {
-
         let mut res = Vec::new();
 
         for column in self.column_iter() {
@@ -282,7 +279,10 @@ impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum
     }
 
     fn softmax_mut(&mut self) {
-        let max = self.iter().map(|x| x.abs()).fold(T::neg_infinity(), |a, b| a.max(b));
+        let max = self
+            .iter()
+            .map(|x| x.abs())
+            .fold(T::neg_infinity(), |a, b| a.max(b));
         let mut z = T::zero();
         for r in 0..self.nrows() {
             for c in 0..self.ncols() {
@@ -322,7 +322,6 @@ impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum
         }
 
         res
-
     }
 
     fn unique(&self) -> Vec<T> {
@@ -335,35 +334,49 @@ impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum
     fn cov(&self) -> Self {
         panic!("Not implemented");
     }
-
 }
 
-impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> SVDDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>> {}
+impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static>
+    SVDDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>>
+{
+}
 
-impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> EVDDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>> {}
+impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static>
+    EVDDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>>
+{
+}
 
-impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> QRDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>> {}
+impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static>
+    QRDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>>
+{
+}
 
-impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> LUDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>> {}
+impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static>
+    LUDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>>
+{
+}
 
-impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> SmartCoreMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>> {}
+impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static>
+    SmartCoreMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>>
+{
+}
 
 #[cfg(test)]
 mod tests {
     use super::*;
-    use nalgebra::{Matrix2x3, DMatrix, RowDVector};
+    use nalgebra::{DMatrix, Matrix2x3, RowDVector};
 
     #[test]
     fn vec_len() {
-        let v = RowDVector::from_vec(vec!(1.,  2.,  3.));
+        let v = RowDVector::from_vec(vec![1., 2., 3.]);
         assert_eq!(3, v.len());
     }
 
     #[test]
     fn get_set_vector() {
-        let mut v = RowDVector::from_vec(vec!(1., 2., 3., 4.));
+        let mut v = RowDVector::from_vec(vec![1., 2., 3., 4.]);
 
-        let expected = RowDVector::from_vec(vec!(1., 5., 3., 4.));
+        let expected = RowDVector::from_vec(vec![1., 5., 3., 4.]);
 
         v.set(1, 5.);
 
@@ -373,14 +386,9 @@ mod tests {
 
     #[test]
     fn get_set_dynamic() {
-        let mut m = DMatrix::from_row_slice(
+        let mut m = DMatrix::from_row_slice(2, 3, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
-            2,
-            3,
-            &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
-        );
 
-        let expected = Matrix2x3::new(1., 2., 3., 4.,
+        let expected = Matrix2x3::new(1., 2., 3., 4., 10., 6.);
-            10., 6.);
 
         m.set(1, 1, 10.);
 
@@ -390,11 +398,7 @@ mod tests {
 
     #[test]
     fn zeros() {
-        let expected = DMatrix::from_row_slice(
+        let expected = DMatrix::from_row_slice(2, 2, &[0., 0., 0., 0.]);
-            2,
-            2,
-            &[0., 0., 0., 0.],
-        );
 
         let m: DMatrix<f64> = BaseMatrix::zeros(2, 2);
 
@@ -403,11 +407,7 @@ mod tests {
 
     #[test]
     fn ones() {
-        let expected = DMatrix::from_row_slice(
+        let expected = DMatrix::from_row_slice(2, 2, &[1., 1., 1., 1.]);
-            2,
-            2,
-            &[1., 1., 1., 1.],
-        );
 
         let m: DMatrix<f64> = BaseMatrix::ones(2, 2);
 
@@ -432,17 +432,9 @@ mod tests {
 
     #[test]
     fn scalar_add_sub_mul_div() {
-        let mut m = DMatrix::from_row_slice(
+        let mut m = DMatrix::from_row_slice(2, 3, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
-            2,
-            3,
-            &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
-        );        
 
-        let expected = DMatrix::from_row_slice(
+        let expected = DMatrix::from_row_slice(2, 3, &[0.6, 0.8, 1., 1.2, 1.4, 1.6]);
-            2,
-            3,
-            &[0.6, 0.8, 1., 1.2, 1.4, 1.6],
-        );
 
         m.add_scalar_mut(3.0);
         m.sub_scalar_mut(1.0);
@@ -453,25 +445,13 @@ mod tests {
 
     #[test]
     fn add_sub_mul_div() {
-        let mut m = DMatrix::from_row_slice(
+        let mut m = DMatrix::from_row_slice(2, 2, &[1.0, 2.0, 3.0, 4.0]);
-            2,
-            2,
-            &[1.0, 2.0, 3.0, 4.0],
-        );
 
-        let a = DMatrix::from_row_slice(
+        let a = DMatrix::from_row_slice(2, 2, &[1.0, 2.0, 3.0, 4.0]);
-            2,
-            2,
-            &[1.0, 2.0, 3.0, 4.0],
-        );
 
         let b: DMatrix<f64> = BaseMatrix::fill(2, 2, 10.);
 
-        let expected = DMatrix::from_row_slice(
+        let expected = DMatrix::from_row_slice(2, 2, &[0.1, 0.6, 1.5, 2.8]);
-            2,
-            2,
-            &[0.1, 0.6, 1.5, 2.8],
-        );        
 
         m.add_mut(&a);
         m.mul_mut(&a);
@@ -483,7 +463,7 @@ mod tests {
 
     #[test]
     fn to_from_row_vector() {
-        let v = RowDVector::from_vec(vec!(1., 2., 3., 4.));
+        let v = RowDVector::from_vec(vec![1., 2., 3., 4.]);
         let expected = v.clone();
         let m: DMatrix<f64> = BaseMatrix::from_row_vector(v);
         assert_eq!(m.to_row_vector(), expected);
@@ -491,11 +471,7 @@ mod tests {
 
     #[test]
     fn get_row_col_as_vec() {
-        let m = DMatrix::from_row_slice(
+        let m = DMatrix::from_row_slice(3, 3, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]);
-            3,
-            3,
-            &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0],
-        );  
 
         assert_eq!(m.get_row_as_vec(1), vec!(4., 5., 6.));
         assert_eq!(m.get_col_as_vec(1), vec!(2., 5., 8.));
@@ -503,28 +479,16 @@ mod tests {
 
     #[test]
     fn to_raw_vector() {
-        let m = DMatrix::from_row_slice(
+        let m = DMatrix::from_row_slice(2, 3, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
-            2,
-            3,
-            &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
-        );  
 
         assert_eq!(m.to_raw_vector(), vec!(1., 2., 3., 4., 5., 6.));
     }
 
     #[test]
     fn element_add_sub_mul_div() {
-        let mut m = DMatrix::from_row_slice(
+        let mut m = DMatrix::from_row_slice(2, 2, &[1.0, 2.0, 3.0, 4.0]);
-            2,
-            2,
-            &[1.0, 2.0, 3.0, 4.0],
-        );        
 
-        let expected = DMatrix::from_row_slice(
+        let expected = DMatrix::from_row_slice(2, 2, &[4., 1., 6., 0.4]);
-            2,
-            2,
-            &[4., 1., 6., 0.4],
-        );
 
         m.add_element_mut(0, 0, 3.0);
         m.sub_element_mut(0, 1, 1.0);
@@ -535,23 +499,21 @@ mod tests {
 
     #[test]
     fn vstack_hstack() {
-
         let m1 = DMatrix::from_row_slice(2, 3, &[1., 2., 3., 4., 5., 6.]);
         let m2 = DMatrix::from_row_slice(2, 1, &[7., 8.]);
 
         let m3 = DMatrix::from_row_slice(1, 4, &[9., 10., 11., 12.]);
 
-        let expected = DMatrix::from_row_slice(3, 4, &[1., 2., 3., 7., 4., 5., 6., 8., 9., 10., 11., 12.]);        
+        let expected =
+            DMatrix::from_row_slice(3, 4, &[1., 2., 3., 7., 4., 5., 6., 8., 9., 10., 11., 12.]);
 
         let result = m1.v_stack(&m2).h_stack(&m3);
 
         assert_eq!(result, expected);
-
     }
 
     #[test]
     fn dot() {
-
         let a = DMatrix::from_row_slice(2, 3, &[1., 2., 3., 4., 5., 6.]);
         let b = DMatrix::from_row_slice(3, 2, &[1., 2., 3., 4., 5., 6.]);
         let expected = DMatrix::from_row_slice(2, 2, &[22., 28., 49., 64.]);
@@ -568,8 +530,11 @@ mod tests {
 
     #[test]
     fn slice() {
-
+        let a = DMatrix::from_row_slice(
-            let a = DMatrix::from_row_slice(3, 5, &[1., 2., 3., 1., 2., 4., 5., 6., 3., 4., 7., 8., 9., 5., 6.]);
+            3,
+            5,
+            &[1., 2., 3., 1., 2., 4., 5., 6., 3., 4., 7., 8., 9., 5., 6.],
+        );
         let expected = DMatrix::from_row_slice(2, 2, &[2., 3., 5., 6.]);
         let result = BaseMatrix::slice(&a, 0..2, 1..3);
         assert_eq!(result, expected);
@@ -578,7 +543,11 @@ mod tests {
     #[test]
     fn approximate_eq() {
         let a = DMatrix::from_row_slice(3, 3, &[1., 2., 3., 4., 5., 6., 7., 8., 9.]);
-        let noise = DMatrix::from_row_slice(3, 3, &[1e-5, 2e-5, 3e-5, 4e-5, 5e-5, 6e-5, 7e-5, 8e-5, 9e-5]);        
+        let noise = DMatrix::from_row_slice(
+            3,
+            3,
+            &[1e-5, 2e-5, 3e-5, 4e-5, 5e-5, 6e-5, 7e-5, 8e-5, 9e-5],
+        );
         assert!(a.approximate_eq(&(&noise + &a), 1e-4));
         assert!(!a.approximate_eq(&(&noise + &a), 1e-5));
     }
@@ -695,5 +664,4 @@ mod tests {
         assert_eq!(res.len(), 7);
         assert_eq!(res, vec![-7., -6., -2., 1., 2., 3., 4.]);
     }
-    
 }

src/linalg/ndarray_bindings.rs

@@ -1,20 +1,20 @@
-use std::ops::Range;
 use std::iter::Sum;
 use std::ops::AddAssign;
-use std::ops::SubAssign;
-use std::ops::MulAssign;
 use std::ops::DivAssign;
+use std::ops::MulAssign;
+use std::ops::Range;
+use std::ops::SubAssign;
 
-use ndarray::{Array, ArrayBase, OwnedRepr, Ix2, Ix1, Axis, stack, s};
 use ndarray::ScalarOperand;
+use ndarray::{s, stack, Array, ArrayBase, Axis, Ix1, Ix2, OwnedRepr};
 
-use crate::math::num::FloatExt;
-use crate::linalg::{BaseMatrix, BaseVector};
-use crate::linalg::Matrix;
-use crate::linalg::svd::SVDDecomposableMatrix;
 use crate::linalg::evd::EVDDecomposableMatrix;
-use crate::linalg::qr::QRDecomposableMatrix;
 use crate::linalg::lu::LUDecomposableMatrix;
+use crate::linalg::qr::QRDecomposableMatrix;
+use crate::linalg::svd::SVDDecomposableMatrix;
+use crate::linalg::Matrix;
+use crate::linalg::{BaseMatrix, BaseVector};
+use crate::math::num::FloatExt;
 
 impl<T: FloatExt> BaseVector<T> for ArrayBase<OwnedRepr<T>, Ix1> {
     fn get(&self, i: usize) -> T {
@@ -29,7 +29,8 @@ impl<T: FloatExt> BaseVector<T> for ArrayBase<OwnedRepr<T>, Ix1> {
     }
 }
 
-impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> BaseMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
+    BaseMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
 {
     type RowVector = ArrayBase<OwnedRepr<T>, Ix1>;
 
@@ -152,9 +153,7 @@ impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign
     }
 
     fn rand(nrows: usize, ncols: usize) -> Self {
-        let values: Vec<T> = (0..nrows*ncols).map(|_| {
+        let values: Vec<T> = (0..nrows * ncols).map(|_| T::rand()).collect();
-            T::rand()
-        }).collect();
         Array::from_shape_vec((nrows, ncols), values).unwrap()
     }
 
@@ -182,7 +181,6 @@ impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign
                 }
             })
         } else {
-
             let mut norm = T::zero();
 
             for xi in self.iter() {
@@ -247,7 +245,10 @@ impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign
     }
 
     fn softmax_mut(&mut self) {
-        let max = self.iter().map(|x| x.abs()).fold(T::neg_infinity(), |a, b| a.max(b));
+        let max = self
+            .iter()
+            .map(|x| x.abs())
+            .fold(T::neg_infinity(), |a, b| a.max(b));
         let mut z = T::zero();
         for r in 0..self.nrows() {
             for c in 0..self.ncols() {
@@ -289,7 +290,6 @@ impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign
         }
 
         res
-
     }
 
     fn unique(&self) -> Vec<T> {
@@ -302,18 +302,32 @@ impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign
     fn cov(&self) -> Self {
         panic!("Not implemented");
     }
-
 }
 
-impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> SVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
+    SVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
+{
+}
 
-impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> EVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
+    EVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
+{
+}
 
-impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> QRDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
+    QRDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
+{
+}
 
-impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> LUDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
+    LUDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
+{
+}
 
-impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> Matrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> Matrix<T>
+    for ArrayBase<OwnedRepr<T>, Ix2>
+{
+}
 
 #[cfg(test)]
 mod tests {
@@ -339,126 +353,98 @@ mod tests {
 
     #[test]
     fn from_to_row_vec() {
-
         let vec = arr1(&[1., 2., 3.]);
         assert_eq!(Array2::from_row_vector(vec.clone()), arr2(&[[1., 2., 3.]]));
-        assert_eq!(Array2::from_row_vector(vec.clone()).to_row_vector(), arr1(&[1., 2., 3.]));
+        assert_eq!(
-
+            Array2::from_row_vector(vec.clone()).to_row_vector(),
+            arr1(&[1., 2., 3.])
+        );
     }
 
     #[test]
     fn add_mut() {
-
+        let mut a1 = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
-        let mut a1 = arr2(&[[ 1.,  2.,  3.],
-                            [4., 5., 6.]]);
         let a2 = a1.clone();
         let a3 = a1.clone() + a2.clone();
         a1.add_mut(&a2);
 
         assert_eq!(a1, a3);
-
     }
 
     #[test]
     fn sub_mut() {
-
+        let mut a1 = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
-        let mut a1 = arr2(&[[ 1.,  2.,  3.],
-                            [4., 5., 6.]]);
         let a2 = a1.clone();
         let a3 = a1.clone() - a2.clone();
         a1.sub_mut(&a2);
 
         assert_eq!(a1, a3);
-
     }
 
     #[test]
     fn mul_mut() {
-
+        let mut a1 = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
-        let mut a1 = arr2(&[[ 1.,  2.,  3.],
-                            [4., 5., 6.]]);
         let a2 = a1.clone();
         let a3 = a1.clone() * a2.clone();
         a1.mul_mut(&a2);
 
         assert_eq!(a1, a3);
-
     }
 
     #[test]
     fn div_mut() {
-
+        let mut a1 = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
-        let mut a1 = arr2(&[[ 1.,  2.,  3.],
-                            [4., 5., 6.]]);
         let a2 = a1.clone();
         let a3 = a1.clone() / a2.clone();
         a1.div_mut(&a2);
 
         assert_eq!(a1, a3);
-
     }
 
     #[test]
     fn div_element_mut() {
-
+        let mut a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
-        let mut a = arr2(&[[ 1.,  2.,  3.],
-                            [4., 5., 6.]]);
         a.div_element_mut(1, 1, 5.);
 
         assert_eq!(BaseMatrix::get(&a, 1, 1), 1.);
-
     }
 
     #[test]
     fn mul_element_mut() {
-
+        let mut a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
-        let mut a = arr2(&[[ 1.,  2.,  3.],
-                            [4., 5., 6.]]);
         a.mul_element_mut(1, 1, 5.);
 
         assert_eq!(BaseMatrix::get(&a, 1, 1), 25.);
-
     }
 
     #[test]
     fn add_element_mut() {
-
+        let mut a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
-        let mut a = arr2(&[[ 1.,  2.,  3.],
-                            [4., 5., 6.]]);
         a.add_element_mut(1, 1, 5.);
 
         assert_eq!(BaseMatrix::get(&a, 1, 1), 10.);
-
     }
 
     #[test]
     fn sub_element_mut() {
-
+        let mut a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
-        let mut a = arr2(&[[ 1.,  2.,  3.],
-                            [4., 5., 6.]]);
         a.sub_element_mut(1, 1, 5.);
 
         assert_eq!(BaseMatrix::get(&a, 1, 1), 0.);
-
     }
 
     #[test]
     fn vstack_hstack() {
-
+        let a1 = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
-        let a1 = arr2(&[[1.,  2.,  3.],
-                        [4., 5., 6.]]);
         let a2 = arr2(&[[7.], [8.]]);
 
         let a3 = arr2(&[[9., 10., 11., 12.]]);
 
-        let expected = arr2(&[[1., 2., 3., 7.],
+        let expected = arr2(&[[1., 2., 3., 7.], [4., 5., 6., 8.], [9., 10., 11., 12.]]);
-                              [4., 5., 6., 8.],
-                              [9., 10., 11., 12.]]);
 
         let result = a1.v_stack(&a2).h_stack(&a3);
 
         assert_eq!(result, expected);
-
     }
 
     #[test]
@@ -482,17 +468,9 @@ mod tests {
 
     #[test]
     fn dot() {
-
+        let a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
-            let a = arr2(&[
+        let b = arr2(&[[1., 2.], [3., 4.], [5., 6.]]);
-                [1., 2., 3.],
+        let expected = arr2(&[[22., 28.], [49., 64.]]);
-                [4., 5., 6.]]);
-            let b = arr2(&[
-                 [1., 2.],
-                 [3., 4.],
-                 [5., 6.]]);
-            let expected = arr2(&[
-                    [22., 28.], 
-                    [49., 64.]]);            
         let result = BaseMatrix::dot(&a, &b);
         assert_eq!(result, expected);
     }
@@ -506,16 +484,12 @@ mod tests {
 
     #[test]
     fn slice() {
-
+        let a = arr2(&[
-            let a = arr2(
-                &[
             [1., 2., 3., 1., 2.],
             [4., 5., 6., 3., 4.],
-                    [7., 8., 9., 5., 6.]]);
+            [7., 8., 9., 5., 6.],
-            let expected = arr2(
+        ]);
-                &[
+        let expected = arr2(&[[2., 3.], [5., 6.]]);
-                    [2., 3.], 
-                    [5., 6.]]);
         let result = BaseMatrix::slice(&a, 0..2, 1..3);
         assert_eq!(result, expected);
     }
@@ -633,18 +607,14 @@ mod tests {
 
     #[test]
     fn col_mean() {
-        let a = arr2(&[[1., 2., 3.],
+        let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
-                       [4., 5., 6.], 
-                       [7., 8., 9.]]);  
         let res = a.column_mean();
         assert_eq!(res, vec![4., 5., 6.]);
     }
 
     #[test]
     fn eye() {
-        let a = arr2(&[[1., 0., 0.],
+        let a = arr2(&[[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]);
-                       [0., 1., 0.], 
-                       [0., 0., 1.]]);  
         let res: Array2<f64> = BaseMatrix::eye(3);
         assert_eq!(res, a);
     }
@@ -661,12 +631,8 @@ mod tests {
 
     #[test]
     fn approximate_eq() {
-        let a = arr2(&[[1., 2., 3.],
+        let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
-            [4., 5., 6.], 
+        let noise = arr2(&[[1e-5, 2e-5, 3e-5], [4e-5, 5e-5, 6e-5], [7e-5, 8e-5, 9e-5]]);
-            [7., 8., 9.]]);
-        let noise = arr2(&[[1e-5, 2e-5, 3e-5],
-            [4e-5, 5e-5, 6e-5], 
-            [7e-5, 8e-5, 9e-5]]);        
         assert!(a.approximate_eq(&(&noise + &a), 1e-4));
         assert!(!a.approximate_eq(&(&noise + &a), 1e-5));
     }

src/linalg/qr.rs

@@ -2,19 +2,18 @@
 
 use std::fmt::Debug;
 
-use crate::math::num::FloatExt;
 use crate::linalg::BaseMatrix;
+use crate::math::num::FloatExt;
 
 #[derive(Debug, Clone)]
 pub struct QR<T: FloatExt, M: BaseMatrix<T>> {
     QR: M,
     tau: Vec<T>,
-    singular: bool
+    singular: bool,
 }
 
 impl<T: FloatExt, M: BaseMatrix<T>> QR<T, M> {
     pub fn new(QR: M, tau: Vec<T>) -> QR<T, M> {
-
         let mut singular = false;
         for j in 0..tau.len() {
             if tau[j] == T::zero() {
@@ -26,7 +25,7 @@ impl<T: FloatExt, M: BaseMatrix<T>> QR<T, M> {
         QR {
             QR: QR,
             tau: tau,
-            singular: singular
+            singular: singular,
         }
     }
 
@@ -70,12 +69,14 @@ impl<T: FloatExt, M: BaseMatrix<T>> QR<T, M> {
     }
 
     fn solve(&self, mut b: M) -> M {
-
         let (m, n) = self.QR.shape();
         let (b_nrows, b_ncols) = b.shape();
 
         if b_nrows != m {
-            panic!("Row dimensions do not agree: A is {} x {}, but B is {} x {}", m, n, b_nrows, b_ncols);
+            panic!(
+                "Row dimensions do not agree: A is {} x {}, but B is {} x {}",
+                m, n, b_nrows, b_ncols
+            );
         }
 
         if self.singular {
@@ -108,18 +109,15 @@ impl<T: FloatExt, M: BaseMatrix<T>> QR<T, M> {
         }
 
         b
-
     }
 }
 
 pub trait QRDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
-
     fn qr(&self) -> QR<T, Self> {
         self.clone().qr_mut()
     }
 
     fn qr_mut(mut self) -> QR<T, Self> {
-
         let (m, n) = self.shape();
 
         let mut r_diagonal: Vec<T> = vec![T::zero(); n];
@@ -131,7 +129,6 @@ pub trait QRDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
             }
 
             if nrm.abs() > T::epsilon() {
-
                 if self.get(k, k) < T::zero() {
                     nrm = -nrm;
                 }
@@ -155,13 +152,10 @@ pub trait QRDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
         }
 
         QR::new(self, r_diagonal)
-
     }
 
     fn qr_solve_mut(self, b: Self) -> Self {
-
         self.qr_mut().solve(b)
-
     }
 }
 
@@ -172,16 +166,17 @@ mod tests {
 
     #[test]
     fn decompose() {
-
         let a = DenseMatrix::from_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
         let q = DenseMatrix::from_array(&[
             &[-0.7448, 0.2436, 0.6212],
             &[-0.331, -0.9432, -0.027],
-                &[-0.5793, 0.2257, -0.7832]]);
+            &[-0.5793, 0.2257, -0.7832],
+        ]);
         let r = DenseMatrix::from_array(&[
             &[-1.2083, -0.6373, -1.0842],
             &[0.0, -0.3064, 0.0682],
-                &[0.0, 0.0, -0.1999]]);
+            &[0.0, 0.0, -0.1999],
+        ]);
         let qr = a.qr();
         assert!(qr.Q().abs().approximate_eq(&q.abs(), 1e-4));
         assert!(qr.R().abs().approximate_eq(&r.abs(), 1e-4));
@@ -189,13 +184,12 @@ mod tests {
 
     #[test]
     fn qr_solve_mut() {
-
         let a = DenseMatrix::from_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
         let b = DenseMatrix::from_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]);
         let expected_w = DenseMatrix::from_array(&[
             &[-0.2027027, -1.2837838],
             &[0.8783784, 2.2297297],
-                &[0.4729730, 0.6621622]
+            &[0.4729730, 0.6621622],
         ]);
         let w = a.qr_solve_mut(b);
         assert!(w.approximate_eq(&expected_w, 1e-2));

src/linalg/svd.rs

@@ -12,11 +12,10 @@ pub struct SVD<T: FloatExt, M: SVDDecomposableMatrix<T>> {
     full: bool,
     m: usize,
     n: usize,
-    tol: T
+    tol: T,
 }
 
 pub trait SVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
-
     fn svd_solve_mut(self, b: Self) -> Self {
         self.svd_mut().solve(b)
     }
@@ -30,7 +29,6 @@ pub trait SVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
     }
 
     fn svd_mut(self) -> SVD<T, Self> {
-
         let mut U = self;
 
         let (m, n) = U.shape();
@@ -55,7 +53,6 @@ pub trait SVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
                 }
 
                 if scale.abs() > T::epsilon() {
-
                     for k in i..m {
                         U.div_element_mut(k, i, scale);
                         s = s + U.get(k, i) * U.get(k, i);
@@ -123,7 +120,6 @@ pub trait SVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
                 }
             }
 
-            
             anorm = T::max(anorm, w[i].abs() + rv1[i].abs());
         }
 
@@ -339,7 +335,6 @@ pub trait SVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
                 for k in 0..n {
                     v.set(k, j, sv[k]);
                 }
-
             }
             if inc <= 1 {
                 break;
@@ -369,7 +364,6 @@ pub trait SVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
         }
 
         SVD::new(U, v, w)
-
     }
 }
 
@@ -386,7 +380,7 @@ impl<T: FloatExt, M: SVDDecomposableMatrix<T>> SVD<T, M> {
             full: full,
             m: m,
             n: n,
-            tol: tol
+            tol: tol,
         }
     }
 
@@ -394,7 +388,11 @@ impl<T: FloatExt, M: SVDDecomposableMatrix<T>> SVD<T, M> {
         let p = b.shape().1;
 
         if self.U.shape().0 != b.shape().0 {
-            panic!("Dimensions do not agree. U.nrows should equal b.nrows but is {}, {}", self.U.shape().0, b.shape().0);
+            panic!(
+                "Dimensions do not agree. U.nrows should equal b.nrows but is {}, {}",
+                self.U.shape().0,
+                b.shape().0
+            );
         }
 
         for k in 0..p {
@@ -430,24 +428,24 @@ mod tests {
 
     #[test]
     fn decompose_symmetric() {
-
         let A = DenseMatrix::from_array(&[
             &[0.9000, 0.4000, 0.7000],
             &[0.4000, 0.5000, 0.3000],
-            &[0.7000, 0.3000, 0.8000]]);
+            &[0.7000, 0.3000, 0.8000],
+        ]);
 
         let s: Vec<f64> = vec![1.7498382, 0.3165784, 0.1335834];
 
         let U = DenseMatrix::from_array(&[
             &[0.6881997, -0.07121225, 0.7220180],
             &[0.3700456, 0.89044952, -0.2648886],
-            &[0.6240573, -0.44947578, -0.639158]
+            &[0.6240573, -0.44947578, -0.639158],
         ]);
 
         let V = DenseMatrix::from_array(&[
             &[0.6881997, -0.07121225, 0.7220180],
             &[0.3700456, 0.89044952, -0.2648886],
-            &[0.6240573, -0.44947578, -0.6391588]
+            &[0.6240573, -0.44947578, -0.6391588],
         ]);
 
         let svd = A.svd();
@@ -457,42 +455,198 @@ mod tests {
         for i in 0..s.len() {
             assert!((s[i] - svd.s[i]).abs() < 1e-4);
         }
-
     }
 
     #[test]
     fn decompose_asymmetric() {
-
         let A = DenseMatrix::from_array(&[
-            &[1.19720880, -1.8391378, 0.3019585, -1.1165701, -1.7210814, 0.4918882, -0.04247433],
+            &[
-            &[0.06605075, 1.0315583, 0.8294362, -0.3646043, -1.6038017, -0.9188110, -0.63760340],
+                1.19720880,
-            &[-1.02637715, 1.0747931, -0.8089055, -0.4726863, -0.2064826, -0.3325532, 0.17966051],
+                -1.8391378,
-            &[-1.45817729, -0.8942353, 0.3459245, 1.5068363, -2.0180708, -0.3696350, -1.19575563],
+                0.3019585,
-            &[-0.07318103, -0.2783787, 1.2237598, 0.1995332, 0.2545336, -0.1392502, -1.88207227],
+                -1.1165701,
-            &[0.88248425, -0.9360321, 0.1393172, 0.1393281, -0.3277873, -0.5553013, 1.63805985],
+                -1.7210814,
-            &[0.12641406, -0.8710055, -0.2712301, 0.2296515, 1.1781535, -0.2158704, -0.27529472]
+                0.4918882,
+                -0.04247433,
+            ],
+            &[
+                0.06605075,
+                1.0315583,
+                0.8294362,
+                -0.3646043,
+                -1.6038017,
+                -0.9188110,
+                -0.63760340,
+            ],
+            &[
+                -1.02637715,
+                1.0747931,
+                -0.8089055,
+                -0.4726863,
+                -0.2064826,
+                -0.3325532,
+                0.17966051,
+            ],
+            &[
+                -1.45817729,
+                -0.8942353,
+                0.3459245,
+                1.5068363,
+                -2.0180708,
+                -0.3696350,
+                -1.19575563,
+            ],
+            &[
+                -0.07318103,
+                -0.2783787,
+                1.2237598,
+                0.1995332,
+                0.2545336,
+                -0.1392502,
+                -1.88207227,
+            ],
+            &[
+                0.88248425, -0.9360321, 0.1393172, 0.1393281, -0.3277873, -0.5553013, 1.63805985,
+            ],
+            &[
+                0.12641406,
+                -0.8710055,
+                -0.2712301,
+                0.2296515,
+                1.1781535,
+                -0.2158704,
+                -0.27529472,
+            ],
         ]);
 
-        let s: Vec<f64> = vec![3.8589375, 3.4396766, 2.6487176, 2.2317399, 1.5165054, 0.8109055, 0.2706515];
+        let s: Vec<f64> = vec![
+            3.8589375, 3.4396766, 2.6487176, 2.2317399, 1.5165054, 0.8109055, 0.2706515,
+        ];
 
         let U = DenseMatrix::from_array(&[
-            &[-0.3082776, 0.77676231, 0.01330514, 0.23231424, -0.47682758, 0.13927109, 0.02640713],
+            &[
-            &[-0.4013477, -0.09112050, 0.48754440, 0.47371793, 0.40636608, 0.24600706, -0.37796295],
+                -0.3082776,
-            &[0.0599719, -0.31406586, 0.45428229, -0.08071283, -0.38432597, 0.57320261, 0.45673993],
+                0.77676231,
-            &[-0.7694214, -0.12681435, -0.05536793, -0.62189972, -0.02075522, -0.01724911, -0.03681864],
+                0.01330514,
-            &[-0.3319069, -0.17984404, -0.54466777, 0.45335157, 0.19377726, 0.12333423, 0.55003852],
+                0.23231424,
-            &[0.1259351, 0.49087824, 0.16349687, -0.32080176, 0.64828744, 0.20643772, 0.38812467],
+                -0.47682758,
-            &[0.1491884, 0.01768604, -0.47884363, -0.14108924, 0.03922507, 0.73034065, -0.43965505]
+                0.13927109,
+                0.02640713,
+            ],
+            &[
+                -0.4013477,
+                -0.09112050,
+                0.48754440,
+                0.47371793,
+                0.40636608,
+                0.24600706,
+                -0.37796295,
+            ],
+            &[
+                0.0599719,
+                -0.31406586,
+                0.45428229,
+                -0.08071283,
+                -0.38432597,
+                0.57320261,
+                0.45673993,
+            ],
+            &[
+                -0.7694214,
+                -0.12681435,
+                -0.05536793,
+                -0.62189972,
+                -0.02075522,
+                -0.01724911,
+                -0.03681864,
+            ],
+            &[
+                -0.3319069,
+                -0.17984404,
+                -0.54466777,
+                0.45335157,
+                0.19377726,
+                0.12333423,
+                0.55003852,
+            ],
+            &[
+                0.1259351,
+                0.49087824,
+                0.16349687,
+                -0.32080176,
+                0.64828744,
+                0.20643772,
+                0.38812467,
+            ],
+            &[
+                0.1491884,
+                0.01768604,
+                -0.47884363,
+                -0.14108924,
+                0.03922507,
+                0.73034065,
+                -0.43965505,
+            ],
         ]);
 
         let V = DenseMatrix::from_array(&[
-            &[-0.2122609, -0.54650056, 0.08071332, -0.43239135, -0.2925067, 0.1414550, 0.59769207],
+            &[
-            &[-0.1943605, 0.63132116, -0.54059857, -0.37089970, -0.1363031, 0.2892641, 0.17774114],
+                -0.2122609,
-            &[0.3031265, -0.06182488, 0.18579097, -0.38606409, -0.5364911, 0.2983466, -0.58642548],
+                -0.54650056,
-            &[0.1844063, 0.24425278, 0.25923756, 0.59043765, -0.4435443, 0.3959057, 0.37019098],
+                0.08071332,
-            &[-0.7164205, 0.30694911, 0.58264743, -0.07458095, -0.1142140, -0.1311972, -0.13124764],
+                -0.43239135,
-            &[-0.1103067, -0.10633600, 0.18257905, -0.03638501, 0.5722925, 0.7784398, -0.09153611],
+                -0.2925067,
-            &[-0.5156083, -0.36573746, -0.47613340, 0.41342817, -0.2659765, 0.1654796, -0.32346758]
+                0.1414550,
+                0.59769207,
+            ],
+            &[
+                -0.1943605,
+                0.63132116,
+                -0.54059857,
+                -0.37089970,
+                -0.1363031,
+                0.2892641,
+                0.17774114,
+            ],
+            &[
+                0.3031265,
+                -0.06182488,
+                0.18579097,
+                -0.38606409,
+                -0.5364911,
+                0.2983466,
+                -0.58642548,
+            ],
+            &[
+                0.1844063, 0.24425278, 0.25923756, 0.59043765, -0.4435443, 0.3959057, 0.37019098,
+            ],
+            &[
+                -0.7164205,
+                0.30694911,
+                0.58264743,
+                -0.07458095,
+                -0.1142140,
+                -0.1311972,
+                -0.13124764,
+            ],
+            &[
+                -0.1103067,
+                -0.10633600,
+                0.18257905,
+                -0.03638501,
+                0.5722925,
+                0.7784398,
+                -0.09153611,
+            ],
+            &[
+                -0.5156083,
+                -0.36573746,
+                -0.47613340,
+                0.41342817,
+                -0.2659765,
+                0.1654796,
+                -0.32346758,
+            ],
         ]);
 
         let svd = A.svd();
@@ -502,21 +656,14 @@ mod tests {
         for i in 0..s.len() {
             assert!((s[i] - svd.s[i]).abs() < 1e-4);
         }
-
     }
 
     #[test]
     fn solve() {
-
         let a = DenseMatrix::from_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
         let b = DenseMatrix::from_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]);
-            let expected_w = DenseMatrix::from_array(&[
+        let expected_w = DenseMatrix::from_array(&[&[-0.20, -1.28], &[0.87, 2.22], &[0.47, 0.66]]);
-                &[-0.20, -1.28],
-                &[0.87, 2.22],
-                &[0.47, 0.66]
-            ]);
         let w = a.svd_solve_mut(b);
         assert!(w.approximate_eq(&expected_w, 1e-2));
     }
-    
 }

src/linear/linear_regression.rs

@@ -1,34 +1,32 @@
 use std::fmt::Debug;
 
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub enum LinearRegressionSolver {
     QR,
-    SVD
+    SVD,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct LinearRegression<T: FloatExt, M: Matrix<T>> {
     coefficients: M,
     intercept: T,
-    solver: LinearRegressionSolver
+    solver: LinearRegressionSolver,
 }
 
 impl<T: FloatExt, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
     fn eq(&self, other: &Self) -> bool {
-        self.coefficients == other.coefficients &&
+        self.coefficients == other.coefficients
-        (self.intercept - other.intercept).abs() <= T::epsilon()
+            && (self.intercept - other.intercept).abs() <= T::epsilon()
     }
 }
 
 impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
-
     pub fn fit(x: &M, y: &M::RowVector, solver: LinearRegressionSolver) -> LinearRegression<T, M> {
-
         let y_m = M::from_row_vector(y.clone());
         let b = y_m.transpose();
         let (x_nrows, num_attributes) = x.shape();
@@ -42,7 +40,7 @@ impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
 
         let w = match solver {
             LinearRegressionSolver::QR => a.qr_solve_mut(b),
-            LinearRegressionSolver::SVD => a.svd_solve_mut(b)
+            LinearRegressionSolver::SVD => a.svd_solve_mut(b),
         };
 
         let wights = w.slice(0..num_attributes, 0..1);
@@ -50,7 +48,7 @@ impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
         LinearRegression {
             intercept: w.get(num_attributes, 0),
             coefficients: wights,
-            solver: solver
+            solver: solver,
         }
     }
 
@@ -60,50 +58,54 @@ impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
         y_hat.add_mut(&M::fill(nrows, 1, self.intercept));
         y_hat.transpose().to_row_vector()
     }
-
 }
 
 #[cfg(test)]
 mod tests {
     use super::*;
-    use nalgebra::{DMatrix, RowDVector};
     use crate::linalg::naive::dense_matrix::*;
+    use nalgebra::{DMatrix, RowDVector};
 
     #[test]
     fn ols_fit_predict() {
+        let x = DMatrix::from_row_slice(
+            16,
+            6,
+            &[
+                234.289, 235.6, 159.0, 107.608, 1947., 60.323, 259.426, 232.5, 145.6, 108.632,
+                1948., 61.122, 258.054, 368.2, 161.6, 109.773, 1949., 60.171, 284.599, 335.1,
+                165.0, 110.929, 1950., 61.187, 328.975, 209.9, 309.9, 112.075, 1951., 63.221,
+                346.999, 193.2, 359.4, 113.270, 1952., 63.639, 365.385, 187.0, 354.7, 115.094,
+                1953., 64.989, 363.112, 357.8, 335.0, 116.219, 1954., 63.761, 397.469, 290.4,
+                304.8, 117.388, 1955., 66.019, 419.180, 282.2, 285.7, 118.734, 1956., 67.857,
+                442.769, 293.6, 279.8, 120.445, 1957., 68.169, 444.546, 468.1, 263.7, 121.950,
+                1958., 66.513, 482.704, 381.3, 255.2, 123.366, 1959., 68.655, 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,
+            ],
+        );
 
-            let x = DMatrix::from_row_slice(16, 6, &[
+        let y: RowDVector<f64> = RowDVector::from_vec(vec![
-                234.289, 235.6, 159.0, 107.608, 1947., 60.323,
+            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,
-                259.426, 232.5, 145.6, 108.632, 1948., 61.122,
+            114.2, 115.7, 116.9,
-                258.054, 368.2, 161.6, 109.773, 1949., 60.171,
+        ]);
-                284.599, 335.1, 165.0, 110.929, 1950., 61.187,
-                328.975, 209.9, 309.9, 112.075, 1951., 63.221,
-                346.999, 193.2, 359.4, 113.270, 1952., 63.639,
-                365.385, 187.0, 354.7, 115.094, 1953., 64.989,
-                363.112, 357.8, 335.0, 116.219, 1954., 63.761,
-                397.469, 290.4, 304.8, 117.388, 1955., 66.019,
-                419.180, 282.2, 285.7, 118.734, 1956., 67.857,
-                442.769, 293.6, 279.8, 120.445, 1957., 68.169,
-                444.546, 468.1, 263.7, 121.950, 1958., 66.513,
-                482.704, 381.3, 255.2, 123.366, 1959., 68.655,
-                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]);
-        
-            let y: RowDVector<f64> = RowDVector::from_vec(vec!(83.0,  88.5,  88.2,  89.5,  96.2,  98.1,  99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9));            
 
         let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x);
 
         let y_hat_svd = LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x);
 
-            assert!(y.iter().zip(y_hat_qr.iter()).all(|(&a, &b)| (a - b).abs() <= 5.0));
+        assert!(y
-            assert!(y.iter().zip(y_hat_svd.iter()).all(|(&a, &b)| (a - b).abs() <= 5.0));
+            .iter()
-
+            .zip(y_hat_qr.iter())
+            .all(|(&a, &b)| (a - b).abs() <= 5.0));
+        assert!(y
+            .iter()
+            .zip(y_hat_svd.iter())
+            .all(|(&a, &b)| (a - b).abs() <= 5.0));
     }
 
     #[test]
     fn ols_fit_predict_nalgebra() {
-
         let x = DenseMatrix::from_array(&[
             &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
             &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
@@ -120,17 +122,26 @@ mod tests {
             &[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
             &[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]]);
+            &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
+        ]);
 
-            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);
+        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,
+        ];
 
         let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x);
 
         let y_hat_svd = LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x);
 
-            assert!(y.iter().zip(y_hat_qr.iter()).all(|(&a, &b)| (a - b).abs() <= 5.0));
+        assert!(y
-            assert!(y.iter().zip(y_hat_svd.iter()).all(|(&a, &b)| (a - b).abs() <= 5.0));            
+            .iter()
-
+            .zip(y_hat_qr.iter())
+            .all(|(&a, &b)| (a - b).abs() <= 5.0));
+        assert!(y
+            .iter()
+            .zip(y_hat_svd.iter())
+            .all(|(&a, &b)| (a - b).abs() <= 5.0));
     }
 
     #[test]
@@ -151,13 +162,18 @@ mod tests {
             &[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
             &[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]]);
+            &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
+        ]);
 
-        let y = vec!(83.0,  88.5,  88.2,  89.5,  96.2,  98.1,  99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9);
+        let y = vec![
+            83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
+            114.2, 115.7, 116.9,
+        ];
 
         let lr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR);
 
-        let deserialized_lr: LinearRegression<f64, DenseMatrix<f64>> = serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
+        let deserialized_lr: LinearRegression<f64, DenseMatrix<f64>> =
+            serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
 
         assert_eq!(lr, deserialized_lr);
     }

src/linear/logistic_regression.rs

@@ -1,21 +1,21 @@
 use std::fmt::Debug;
 use std::marker::PhantomData;
 
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
-use crate::optimization::FunctionOrder;
+use crate::math::num::FloatExt;
+use crate::optimization::first_order::lbfgs::LBFGS;
 use crate::optimization::first_order::{FirstOrderOptimizer, OptimizerResult};
 use crate::optimization::line_search::Backtracking;
-use crate::optimization::first_order::lbfgs::LBFGS;
+use crate::optimization::FunctionOrder;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct LogisticRegression<T: FloatExt, M: Matrix<T>> {
     weights: M,
     classes: Vec<T>,
     num_attributes: usize,
-    num_classes: usize
+    num_classes: usize,
 }
 
 trait ObjectiveFunction<T: FloatExt, M: Matrix<T>> {
@@ -36,31 +36,29 @@ trait ObjectiveFunction<T: FloatExt, M: Matrix<T>> {
 struct BinaryObjectiveFunction<'a, T: FloatExt, M: Matrix<T>> {
     x: &'a M,
     y: Vec<usize>,
-    phantom: PhantomData<&'a T>
+    phantom: PhantomData<&'a T>,
 }
 
 impl<T: FloatExt, M: Matrix<T>> PartialEq for LogisticRegression<T, M> {
     fn eq(&self, other: &Self) -> bool {
-
+        if self.num_classes != other.num_classes
-        if self.num_classes != other.num_classes ||
+            || self.num_attributes != other.num_attributes
-            self.num_attributes != other.num_attributes ||
+            || self.classes.len() != other.classes.len()
-            self.classes.len() != other.classes.len() {
+        {
-            return false
+            return false;
         } else {
             for i in 0..self.classes.len() {
                 if (self.classes[i] - other.classes[i]).abs() > T::epsilon() {
-                    return false
+                    return false;
                 }
             }
 
-            return self.weights == other.weights
+            return self.weights == other.weights;
         }
-        
     }
 }
 
 impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for BinaryObjectiveFunction<'a, T, M> {
-
     fn f(&self, w_bias: &M) -> T {
         let mut f = T::zero();
         let (n, _) = self.x.shape();
@@ -74,13 +72,11 @@ impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for BinaryObjectiveF
     }
 
     fn df(&self, g: &mut M, w_bias: &M) {
-        
         g.copy_from(&M::zeros(1, g.shape().1));
 
         let (n, p) = self.x.shape();
 
         for i in 0..n {
-                       
             let wx = BinaryObjectiveFunction::partial_dot(w_bias, self.x, 0, i);
 
             let dyi = (T::from(self.y[i]).unwrap()) - wx.sigmoid();
@@ -89,27 +85,30 @@ impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for BinaryObjectiveF
             }
             g.set(0, p, g.get(0, p) - dyi);
         }
-        
     }
-
 }
 
 struct MultiClassObjectiveFunction<'a, T: FloatExt, M: Matrix<T>> {
     x: &'a M,
     y: Vec<usize>,
     k: usize,
-    phantom: PhantomData<&'a T>
+    phantom: PhantomData<&'a T>,
 }
 
-impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for MultiClassObjectiveFunction<'a, T, M> {    
+impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M>
-
+    for MultiClassObjectiveFunction<'a, T, M>
+{
     fn f(&self, w_bias: &M) -> T {
         let mut f = T::zero();
         let mut prob = M::zeros(1, self.k);
         let (n, p) = self.x.shape();
         for i in 0..n {
             for j in 0..self.k {
-                prob.set(0, j, MultiClassObjectiveFunction::partial_dot(w_bias, self.x, j * (p + 1), i));
+                prob.set(
+                    0,
+                    j,
+                    MultiClassObjectiveFunction::partial_dot(w_bias, self.x, j * (p + 1), i),
+                );
             }
             prob.softmax_mut();
             f = f - prob.get(0, self.y[i]).ln();
@@ -119,7 +118,6 @@ impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for MultiClassObject
     }
 
     fn df(&self, g: &mut M, w: &M) {
-        
         g.copy_from(&M::zeros(1, g.shape().1));
 
         let mut prob = M::zeros(1, self.k);
@@ -127,7 +125,11 @@ impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for MultiClassObject
 
         for i in 0..n {
             for j in 0..self.k {
-                prob.set(0, j, MultiClassObjectiveFunction::partial_dot(w, self.x, j * (p + 1), i));
+                prob.set(
+                    0,
+                    j,
+                    MultiClassObjectiveFunction::partial_dot(w, self.x, j * (p + 1), i),
+                );
             }
 
             prob.softmax_mut();
@@ -142,15 +144,11 @@ impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for MultiClassObject
                 g.set(0, j * (p + 1) + p, g.get(0, j * (p + 1) + p) - yi);
             }
         }
-        
     }
-
 }
 
 impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
-
     pub fn fit(x: &M, y: &M::RowVector) -> LogisticRegression<T, M> {
-
         let y_m = M::from_row_vector(y.clone());
         let (x_nrows, num_attributes) = x.shape();
         let (_, y_nrows) = y_m.shape();
@@ -171,17 +169,14 @@ impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
         }
 
         if k < 2 {
-
             panic!("Incorrect number of classes: {}", k);
-
         } else if k == 2 {
-
             let x0 = M::zeros(1, num_attributes + 1);
 
             let objective = BinaryObjectiveFunction {
                 x: x,
                 y: yi,
-                phantom: PhantomData           
+                phantom: PhantomData,
             };
 
             let result = LogisticRegression::minimize(x0, objective);
@@ -192,16 +187,14 @@ impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
                 num_attributes: num_attributes,
                 num_classes: k,
             }
-
         } else {
-
             let x0 = M::zeros(1, (num_attributes + 1) * k);
 
             let objective = MultiClassObjectiveFunction {
                 x: x,
                 y: yi,
                 k: k,
-                phantom: PhantomData
+                phantom: PhantomData,
             };
 
             let result = LogisticRegression::minimize(x0, objective);
@@ -212,11 +205,9 @@ impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
                 weights: weights,
                 classes: classes,
                 num_attributes: num_attributes,
-                num_classes: k                
+                num_classes: k,
             }
         }
-        
-        
     }
 
     pub fn predict(&self, x: &M) -> M::RowVector {
@@ -227,9 +218,12 @@ impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
             let x_and_bias = x.v_stack(&M::ones(nrows, 1));
             let y_hat: Vec<T> = x_and_bias.dot(&self.weights.transpose()).to_raw_vector();
             for i in 0..n {
-                result.set(0, i, self.classes[if y_hat[i].sigmoid() > T::half() { 1 } else { 0 }]);
+                result.set(
+                    0,
+                    i,
+                    self.classes[if y_hat[i].sigmoid() > T::half() { 1 } else { 0 }],
+                );
             }
-
         } else {
             let (nrows, _) = x.shape();
             let x_and_bias = x.v_stack(&M::ones(nrows, 1));
@@ -243,21 +237,21 @@ impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
     }
 
     pub fn coefficients(&self) -> M {
-        self.weights.slice(0..self.num_classes, 0..self.num_attributes)
+        self.weights
+            .slice(0..self.num_classes, 0..self.num_attributes)
     }
 
     pub fn intercept(&self) -> M {
-        self.weights.slice(0..self.num_classes, self.num_attributes..self.num_attributes+1)
+        self.weights.slice(
+            0..self.num_classes,
+            self.num_attributes..self.num_attributes + 1,
+        )
     }
 
     fn minimize(x0: M, objective: impl ObjectiveFunction<T, M>) -> OptimizerResult<T, M> {
-        let f = |w: &M| -> T {                
+        let f = |w: &M| -> T { objective.f(w) };
-            objective.f(w)
-        };
 
-        let df = |g: &mut M, w: &M| {
+        let df = |g: &mut M, w: &M| objective.df(g, w);
-            objective.df(g, w)
-        };
 
         let mut ls: Backtracking<T> = Default::default();
         ls.order = FunctionOrder::THIRD;
@@ -265,19 +259,17 @@ impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
 
         optimizer.optimize(&f, &df, &x0, &ls)
     }
-
 }
 
 #[cfg(test)]
 mod tests {
     use super::*;
     use crate::linalg::naive::dense_matrix::*;
-    use ndarray::{arr1, arr2, Array1};
     use crate::metrics::*;
+    use ndarray::{arr1, arr2, Array1};
 
     #[test]
     fn multiclass_objective_f() {
-
         let x = DenseMatrix::from_array(&[
             &[1., -5.],
             &[2., 5.],
@@ -293,7 +285,8 @@ mod tests {
             &[9., 5.],
             &[10., -2.],
             &[8., 2.],
-            &[ 9.,  0.]]);
+            &[9., 0.],
+        ]);
 
         let y = vec![0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 1];
 
@@ -301,24 +294,31 @@ mod tests {
             x: &x,
             y: y,
             k: 3,
-            phantom: PhantomData
+            phantom: PhantomData,
         };
 
         let mut g: DenseMatrix<f64> = DenseMatrix::zeros(1, 9);
 
-        objective.df(&mut g, &DenseMatrix::vector_from_array(&[1., 2., 3., 4., 5., 6., 7., 8., 9.]));
+        objective.df(
-        objective.df(&mut g, &DenseMatrix::vector_from_array(&[1., 2., 3., 4., 5., 6., 7., 8., 9.]));
+            &mut g,
+            &DenseMatrix::vector_from_array(&[1., 2., 3., 4., 5., 6., 7., 8., 9.]),
+        );
+        objective.df(
+            &mut g,
+            &DenseMatrix::vector_from_array(&[1., 2., 3., 4., 5., 6., 7., 8., 9.]),
+        );
 
         assert!((g.get(0, 0) + 33.000068218163484).abs() < std::f64::EPSILON);
 
-        let f = objective.f(&DenseMatrix::vector_from_array(&[1., 2., 3., 4., 5., 6., 7., 8., 9.]));
+        let f = objective.f(&DenseMatrix::vector_from_array(&[
+            1., 2., 3., 4., 5., 6., 7., 8., 9.,
+        ]));
 
         assert!((f - 408.0052230582765).abs() < std::f64::EPSILON);
     }
 
     #[test]
     fn binary_objective_f() {
-
         let x = DenseMatrix::from_array(&[
             &[1., -5.],
             &[2., 5.],
@@ -334,14 +334,15 @@ mod tests {
             &[9., 5.],
             &[10., -2.],
             &[8., 2.],
-            &[ 9.,  0.]]);
+            &[9., 0.],
+        ]);
 
         let y = vec![0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1];
 
         let objective = BinaryObjectiveFunction {
             x: &x,
             y: y,
-            phantom: PhantomData          
+            phantom: PhantomData,
         };
 
         let mut g: DenseMatrix<f64> = DenseMatrix::zeros(1, 3);
@@ -360,7 +361,6 @@ mod tests {
 
     #[test]
     fn lr_fit_predict() {
-
         let x = DenseMatrix::from_array(&[
             &[1., -5.],
             &[2., 5.],
@@ -376,7 +376,8 @@ mod tests {
             &[9., 5.],
             &[10., -2.],
             &[8., 2.],
-            &[ 9.,  0.]]);
+            &[9., 0.],
+        ]);
         let y: Vec<f64> = vec![0., 0., 1., 1., 2., 1., 1., 0., 0., 2., 1., 1., 0., 0., 1.];
 
         let lr = LogisticRegression::fit(&x, &y);
@@ -389,9 +390,10 @@ mod tests {
 
         let y_hat = lr.predict(&x);
 
-        assert_eq!(y_hat, vec![0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]);
+        assert_eq!(
-        
+            y_hat,
-
+            vec![0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        );
     }
 
     #[test]
@@ -411,12 +413,14 @@ mod tests {
             &[9., 5.],
             &[10., -2.],
             &[8., 2.],
-            &[ 9.,  0.]]);
+            &[9., 0.],
+        ]);
         let y: Vec<f64> = vec![0., 0., 1., 1., 2., 1., 1., 0., 0., 2., 1., 1., 0., 0., 1.];
 
         let lr = LogisticRegression::fit(&x, &y);
 
-        let deserialized_lr: LogisticRegression<f64, DenseMatrix<f64>> = serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
+        let deserialized_lr: LogisticRegression<f64, DenseMatrix<f64>> =
+            serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
 
         assert_eq!(lr, deserialized_lr);
     }
@@ -443,17 +447,22 @@ mod tests {
             [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]]);
+            [5.2, 2.7, 3.9, 1.4],
-        let y: Array1<f64> = arr1(&[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]);        
+        ]);
+        let y: Array1<f64> = arr1(&[
+            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);
 
         let y_hat = lr.predict(&x);
 
-        let error: f64 = y.into_iter().zip(y_hat.into_iter()).map(|(&a, &b)| (a - b).abs()).sum();
+        let error: f64 = y
+            .into_iter()
+            .zip(y_hat.into_iter())
+            .map(|(&a, &b)| (a - b).abs())
+            .sum();
 
         assert!(error <= 1.0);
-        
     }
-
 }

src/math/distance/euclidian.rs

@@ -1,12 +1,11 @@
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
 use crate::math::num::FloatExt;
 
 use super::Distance;
 
 #[derive(Serialize, Deserialize, Debug)]
-pub struct Euclidian {    
+pub struct Euclidian {}
-}
 
 impl Euclidian {
     pub fn squared_distance<T: FloatExt>(x: &Vec<T>, y: &Vec<T>) -> T {
@@ -21,18 +20,14 @@ impl Euclidian {
 
         sum
     }
-    
 }
 
 impl<T: FloatExt> Distance<Vec<T>, T> for Euclidian {
-
     fn distance(&self, x: &Vec<T>, y: &Vec<T>) -> T {
         Euclidian::squared_distance(x, y).sqrt()
     }
-
 }
 
-
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -46,5 +41,4 @@ mod tests {
 
         assert!((l2 - 5.19615242).abs() < 1e-8);
     }
-
 }

src/math/distance/hamming.rs

@@ -1,15 +1,13 @@
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
 use crate::math::num::FloatExt;
 
 use super::Distance;
 
 #[derive(Serialize, Deserialize, Debug)]
-pub struct Hamming {
+pub struct Hamming {}
-}
 
 impl<T: PartialEq, F: FloatExt> Distance<Vec<T>, F> for Hamming {
-
     fn distance(&self, x: &Vec<T>, y: &Vec<T>) -> F {
         if x.len() != y.len() {
             panic!("Input vector sizes are different");
@@ -24,10 +22,8 @@ impl<T: PartialEq, F: FloatExt> Distance<Vec<T>, F> for Hamming {
 
         F::from_i64(dist).unwrap() / F::from_usize(x.len()).unwrap()
     }
-
 }
 
-
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -41,5 +37,4 @@ mod tests {
 
         assert!((h - 0.42857142).abs() < 1e-8);
     }
-    
 }

src/math/distance/mahalanobis.rs

@@ -2,7 +2,7 @@
 
 use std::marker::PhantomData;
 
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
 use crate::math::num::FloatExt;
 
@@ -13,7 +13,7 @@ use crate::linalg::Matrix;
 pub struct Mahalanobis<T: FloatExt, M: Matrix<T>> {
     pub sigma: M,
     pub sigmaInv: M,
-    t: PhantomData<T>
+    t: PhantomData<T>,
 }
 
 impl<T: FloatExt, M: Matrix<T>> Mahalanobis<T, M> {
@@ -23,7 +23,7 @@ impl<T: FloatExt, M: Matrix<T>> Mahalanobis<T, M> {
         Mahalanobis {
             sigma: sigma,
             sigmaInv: sigmaInv,
-            t: PhantomData
+            t: PhantomData,
         }
     }
 
@@ -33,21 +33,30 @@ impl<T: FloatExt, M: Matrix<T>> Mahalanobis<T, M> {
         Mahalanobis {
             sigma: sigma,
             sigmaInv: sigmaInv,
-            t: PhantomData
+            t: PhantomData,
         }
     }
 }
 
 impl<T: FloatExt, M: Matrix<T>> Distance<Vec<T>, T> for Mahalanobis<T, M> {
-    
     fn distance(&self, x: &Vec<T>, y: &Vec<T>) -> T {
         let (nrows, ncols) = self.sigma.shape();
         if x.len() != nrows {
-            panic!("Array x[{}] has different dimension with Sigma[{}][{}].", x.len(), nrows, ncols);
+            panic!(
+                "Array x[{}] has different dimension with Sigma[{}][{}].",
+                x.len(),
+                nrows,
+                ncols
+            );
         }
 
         if y.len() != nrows {
-            panic!("Array y[{}] has different dimension with Sigma[{}][{}].", y.len(), nrows, ncols);
+            panic!(
+                "Array y[{}] has different dimension with Sigma[{}][{}].",
+                y.len(),
+                nrows,
+                ncols
+            );
         }
 
         println!("{}", self.sigmaInv);
@@ -68,10 +77,8 @@ impl<T: FloatExt, M: Matrix<T>> Distance<Vec<T>, T> for Mahalanobis<T, M> {
 
         s.sqrt()
     }
-
 }
 
-
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -84,7 +91,8 @@ mod tests {
             &[66., 570., 33.],
             &[68., 590., 37.],
             &[69., 660., 46.],
-            &[ 73., 600.,  55.]]); 
+            &[73., 600., 55.],
+        ]);
 
         let a = data.column_mean();
         let b = vec![66., 640., 44.];
@@ -93,5 +101,4 @@ mod tests {
 
         println!("{}", mahalanobis.distance(&a, &b));
     }
-
 }

src/math/distance/manhattan.rs

@@ -1,15 +1,13 @@
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
 use crate::math::num::FloatExt;
 
 use super::Distance;
 
 #[derive(Serialize, Deserialize, Debug)]
-pub struct Manhattan {
+pub struct Manhattan {}
-}
 
 impl<T: FloatExt> Distance<Vec<T>, T> for Manhattan {
-
     fn distance(&self, x: &Vec<T>, y: &Vec<T>) -> T {
         if x.len() != y.len() {
             panic!("Input vector sizes are different");
@@ -22,10 +20,8 @@ impl<T: FloatExt> Distance<Vec<T>, T> for Manhattan {
 
         dist
     }
-
 }
 
-
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -39,5 +35,4 @@ mod tests {
 
         assert!((l1 - 9.0).abs() < 1e-8);
     }
-
 }

src/math/distance/minkowski.rs

@@ -1,4 +1,4 @@
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
 use crate::math::num::FloatExt;
 
@@ -6,11 +6,10 @@ use super::Distance;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct Minkowski<T: FloatExt> {
-    pub p: T
+    pub p: T,
 }
 
 impl<T: FloatExt> Distance<Vec<T>, T> for Minkowski<T> {
-
     fn distance(&self, x: &Vec<T>, y: &Vec<T>) -> T {
         if x.len() != y.len() {
             panic!("Input vector sizes are different");
@@ -27,10 +26,8 @@ impl<T: FloatExt> Distance<Vec<T>, T> for Minkowski<T> {
 
         dist.powf(T::one() / self.p)
     }
-
 }
 
-
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -57,7 +54,4 @@ mod tests {
 
         let _: f64 = Minkowski { p: 0.0 }.distance(&a, &b);
     }
-
-
-
 }

src/math/distance/mod.rs

@@ -1,8 +1,8 @@
 pub mod euclidian;
-pub mod minkowski;
-pub mod manhattan;
 pub mod hamming;
 pub mod mahalanobis;
+pub mod manhattan;
+pub mod minkowski;
 
 use crate::math::num::FloatExt;
 
@@ -10,8 +10,7 @@ pub trait Distance<T, F: FloatExt>{
     fn distance(&self, a: &T, b: &T) -> F;
 }
 
-pub struct Distances{    
+pub struct Distances {}
-}
 
 impl Distances {
     pub fn euclidian() -> euclidian::Euclidian {

src/math/num.rs

@@ -1,9 +1,8 @@
-use std::fmt::{Debug, Display};
 use num_traits::{Float, FromPrimitive};
 use rand::prelude::*;
+use std::fmt::{Debug, Display};
 
 pub trait FloatExt: Float + FromPrimitive + Debug + Display + Copy {
-
     fn copysign(self, sign: Self) -> Self;
 
     fn ln_1pe(self) -> Self;
@@ -15,7 +14,6 @@ pub trait FloatExt: Float + FromPrimitive + Debug + Display + Copy {
     fn two() -> Self;
 
     fn half() -> Self;
-
 }
 
 impl FloatExt for f64 {
@@ -29,19 +27,16 @@ impl FloatExt for f64 {
         } else {
             return self.exp().ln_1p();
         }
-
     }
 
     fn sigmoid(self) -> f64 {
-        
         if self < -40. {
             return 0.;
         } else if self > 40. {
             return 1.;
         } else {
-            return 1. / (1. + f64::exp(-self))
+            return 1. / (1. + f64::exp(-self));
         }
-        
     }
 
     fn rand() -> f64 {
@@ -69,19 +64,16 @@ impl FloatExt for f32 {
         } else {
             return self.exp().ln_1p();
         }
-
     }
 
     fn sigmoid(self) -> f32 {
-        
         if self < -40. {
             return 0.;
         } else if self > 40. {
             return 1.;
         } else {
-            return 1. / (1. + f32::exp(-self))
+            return 1. / (1. + f32::exp(-self));
         }
-        
     }
 
     fn rand() -> f32 {

src/metrics/accuracy.rs

@@ -1,7 +1,7 @@
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
 use crate::linalg::BaseVector;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct Accuracy {}
@@ -9,7 +9,11 @@ pub struct Accuracy{}
 impl Accuracy {
     pub fn get_score<T: FloatExt, V: BaseVector<T>>(&self, y_true: &V, y_prod: &V) -> T {
         if y_true.len() != y_prod.len() {
-            panic!("The vector sizes don't match: {} != {}", y_true.len(), y_prod.len());
+            panic!(
+                "The vector sizes don't match: {} != {}",
+                y_true.len(),
+                y_prod.len()
+            );
         }
 
         let n = y_true.len();
@@ -23,7 +27,6 @@ impl Accuracy {
 
         T::from_i64(positive).unwrap() / T::from_usize(n).unwrap()
     }
-    
 }
 
 #[cfg(test)]
@@ -41,5 +44,4 @@ mod tests {
         assert!((score1 - 0.5).abs() < 1e-8);
         assert!((score2 - 1.0).abs() < 1e-8);
     }
-
 }

src/metrics/mod.rs

@@ -1,9 +1,9 @@
 pub mod accuracy;
-pub mod recall;
 pub mod precision;
+pub mod recall;
 
-use crate::math::num::FloatExt;
 use crate::linalg::BaseVector;
+use crate::math::num::FloatExt;
 
 pub struct ClassificationMetrics {}
 

src/metrics/precision.rs

@@ -1,7 +1,7 @@
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
 use crate::linalg::BaseVector;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct Precision {}
@@ -9,7 +9,11 @@ pub struct Precision{}
 impl Precision {
     pub fn get_score<T: FloatExt, V: BaseVector<T>>(&self, y_true: &V, y_prod: &V) -> T {
         if y_true.len() != y_prod.len() {
-            panic!("The vector sizes don't match: {} != {}", y_true.len(), y_prod.len());
+            panic!(
+                "The vector sizes don't match: {} != {}",
+                y_true.len(),
+                y_prod.len()
+            );
         }
 
         let mut tp = 0;
@@ -17,11 +21,17 @@ impl Precision {
         let n = y_true.len();
         for i in 0..n {
             if y_true.get(i) != T::zero() && y_true.get(i) != T::one() {
-                panic!("Precision can only be applied to binary classification: {}", y_true.get(i));
+                panic!(
+                    "Precision can only be applied to binary classification: {}",
+                    y_true.get(i)
+                );
             }
 
             if y_prod.get(i) != T::zero() && y_prod.get(i) != T::one() {
-                panic!("Precision can only be applied to binary classification: {}", y_prod.get(i));
+                panic!(
+                    "Precision can only be applied to binary classification: {}",
+                    y_prod.get(i)
+                );
             }
 
             if y_prod.get(i) == T::one() {
@@ -35,7 +45,6 @@ impl Precision {
 
         T::from_i64(tp).unwrap() / T::from_i64(p).unwrap()
     }
-    
 }
 
 #[cfg(test)]
@@ -53,5 +62,4 @@ mod tests {
         assert!((score1 - 0.5).abs() < 1e-8);
         assert!((score2 - 1.0).abs() < 1e-8);
     }
-
 }

src/metrics/recall.rs

@@ -1,7 +1,7 @@
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
 use crate::linalg::BaseVector;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct Recall {}
@@ -9,7 +9,11 @@ pub struct Recall{}
 impl Recall {
     pub fn get_score<T: FloatExt, V: BaseVector<T>>(&self, y_true: &V, y_prod: &V) -> T {
         if y_true.len() != y_prod.len() {
-            panic!("The vector sizes don't match: {} != {}", y_true.len(), y_prod.len());
+            panic!(
+                "The vector sizes don't match: {} != {}",
+                y_true.len(),
+                y_prod.len()
+            );
         }
 
         let mut tp = 0;
@@ -17,11 +21,17 @@ impl Recall {
         let n = y_true.len();
         for i in 0..n {
             if y_true.get(i) != T::zero() && y_true.get(i) != T::one() {
-                panic!("Recall can only be applied to binary classification: {}", y_true.get(i));
+                panic!(
+                    "Recall can only be applied to binary classification: {}",
+                    y_true.get(i)
+                );
             }
 
             if y_prod.get(i) != T::zero() && y_prod.get(i) != T::one() {
-                panic!("Recall can only be applied to binary classification: {}", y_prod.get(i));
+                panic!(
+                    "Recall can only be applied to binary classification: {}",
+                    y_prod.get(i)
+                );
             }
 
             if y_true.get(i) == T::one() {
@@ -35,7 +45,6 @@ impl Recall {
 
         T::from_i64(tp).unwrap() / T::from_i64(p).unwrap()
     }
-    
 }
 
 #[cfg(test)]
@@ -53,5 +62,4 @@ mod tests {
         assert!((score1 - 0.5).abs() < 1e-8);
         assert!((score2 - 1.0).abs() < 1e-8);
     }
-
 }

src/neighbors/knn.rs

@@ -1,66 +1,70 @@
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
-use crate::math::distance::Distance;
-use crate::linalg::{Matrix, row_iter};
-use crate::algorithm::neighbour::linear_search::LinearKNNSearch;
 use crate::algorithm::neighbour::cover_tree::CoverTree;
+use crate::algorithm::neighbour::linear_search::LinearKNNSearch;
+use crate::linalg::{row_iter, Matrix};
+use crate::math::distance::Distance;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct KNNClassifier<T: FloatExt, D: Distance<Vec<T>, T>> {
     classes: Vec<T>,
     y: Vec<usize>,
     knn_algorithm: KNNAlgorithmV<T, D>,
-    k: usize
+    k: usize,
 }
 
 pub enum KNNAlgorithmName {
     LinearSearch,
-    CoverTree
+    CoverTree,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
 pub enum KNNAlgorithmV<T: FloatExt, D: Distance<Vec<T>, T>> {
     LinearSearch(LinearKNNSearch<Vec<T>, T, D>),
-    CoverTree(CoverTree<Vec<T>, T, D>)
+    CoverTree(CoverTree<Vec<T>, T, D>),
 }
 
 impl KNNAlgorithmName {
-
+    fn fit<T: FloatExt, D: Distance<Vec<T>, T>>(
-    fn fit<T: FloatExt, D: Distance<Vec<T>, T>>(&self, data: Vec<Vec<T>>, distance: D) -> KNNAlgorithmV<T, D> {
+        &self,
+        data: Vec<Vec<T>>,
+        distance: D,
+    ) -> KNNAlgorithmV<T, D> {
         match *self {
-            KNNAlgorithmName::LinearSearch => KNNAlgorithmV::LinearSearch(LinearKNNSearch::new(data, distance)),
+            KNNAlgorithmName::LinearSearch => {
+                KNNAlgorithmV::LinearSearch(LinearKNNSearch::new(data, distance))
+            }
             KNNAlgorithmName::CoverTree => KNNAlgorithmV::CoverTree(CoverTree::new(data, distance)),
         }
     }
-
 }
 
 impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNAlgorithmV<T, D> {
     fn find(&self, from: &Vec<T>, k: usize) -> Vec<usize> {
         match *self {
             KNNAlgorithmV::LinearSearch(ref linear) => linear.find(from, k),
-            KNNAlgorithmV::CoverTree(ref cover) => cover.find(from, k)
+            KNNAlgorithmV::CoverTree(ref cover) => cover.find(from, k),
         }
     }
 }
 
-
 impl<T: FloatExt, D: Distance<Vec<T>, T>> PartialEq for KNNClassifier<T, D> {
     fn eq(&self, other: &Self) -> bool {
-        if self.classes.len() != other.classes.len() ||
+        if self.classes.len() != other.classes.len()
-            self.k != other.k ||
+            || self.k != other.k
-            self.y.len() != other.y.len() {
+            || self.y.len() != other.y.len()
-                return false
+        {
+            return false;
         } else {
             for i in 0..self.classes.len() {
                 if (self.classes[i] - other.classes[i]).abs() > T::epsilon() {
-                    return false
+                    return false;
                 }
             }
             for i in 0..self.y.len() {
                 if self.y[i] != other.y[i] {
-                    return false
+                    return false;
                 }
             }
             true
@@ -69,9 +73,13 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> PartialEq for KNNClassifier<T, D> {
 }
 
 impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
-
+    pub fn fit<M: Matrix<T>>(
-    pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, k: usize, distance: D, algorithm: KNNAlgorithmName) -> KNNClassifier<T, D> {
+        x: &M,
-
+        y: &M::RowVector,
+        k: usize,
+        distance: D,
+        algorithm: KNNAlgorithmName,
+    ) -> KNNClassifier<T, D> {
         let y_m = M::from_row_vector(y.clone());
 
         let (_, y_n) = y_m.shape();
@@ -87,24 +95,35 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
             yi[i] = classes.iter().position(|c| yc == *c).unwrap();
         }
 
-        assert!(x_n == y_n, format!("Size of x should equal size of y; |x|=[{}], |y|=[{}]", x_n, y_n));
+        assert!(
+            x_n == y_n,
+            format!(
+                "Size of x should equal size of y; |x|=[{}], |y|=[{}]",
+                x_n, y_n
+            )
+        );
 
         assert!(k > 1, format!("k should be > 1, k=[{}]", k));
 
-        KNNClassifier{classes:classes, y: yi, k: k, knn_algorithm: algorithm.fit(data, distance)}
+        KNNClassifier {
-
+            classes: classes,
+            y: yi,
+            k: k,
+            knn_algorithm: algorithm.fit(data, distance),
+        }
     }
 
     pub fn predict<M: Matrix<T>>(&self, x: &M) -> M::RowVector {
         let mut result = M::zeros(1, x.shape().0);
 
-        row_iter(x).enumerate().for_each(|(i, x)| result.set(0, i, self.classes[self.predict_for_row(x)]));
+        row_iter(x)
+            .enumerate()
+            .for_each(|(i, x)| result.set(0, i, self.classes[self.predict_for_row(x)]));
 
         result.to_row_vector()
     }
 
     fn predict_for_row(&self, x: Vec<T>) -> usize {
-
         let idxs = self.knn_algorithm.find(&x, self.k);
         let mut c = vec![0; self.classes.len()];
         let mut max_c = 0;
@@ -118,27 +137,26 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
         }
 
         max_i
-
     }
-    
 }
 
 #[cfg(test)]
 mod tests {
     use super::*;
-    use crate::math::distance::Distances;
     use crate::linalg::naive::dense_matrix::DenseMatrix;
+    use crate::math::distance::Distances;
 
     #[test]
     fn knn_fit_predict() {
-        let x = DenseMatrix::from_array(&[
+        let x = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
-            &[1., 2.],
-            &[3., 4.],
-            &[5., 6.], 
-            &[7., 8.], 
-            &[9., 10.]]); 
         let y = vec![2., 2., 2., 3., 3.];
-        let knn = KNNClassifier::fit(&x, &y, 3, Distances::euclidian(), KNNAlgorithmName::LinearSearch);        
+        let knn = KNNClassifier::fit(
+            &x,
+            &y,
+            3,
+            Distances::euclidian(),
+            KNNAlgorithmName::LinearSearch,
+        );
         let r = knn.predict(&x);
         assert_eq!(5, Vec::len(&r));
         assert_eq!(y.to_vec(), r);
@@ -146,19 +164,19 @@ mod tests {
 
     #[test]
     fn serde() {
-        let x = DenseMatrix::from_array(&[
+        let x = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
-            &[1., 2.],
-            &[3., 4.],
-            &[5., 6.], 
-            &[7., 8.], 
-            &[9., 10.]]); 
         let y = vec![2., 2., 2., 3., 3.];
 
-        let knn = KNNClassifier::fit(&x, &y, 3, Distances::euclidian(), KNNAlgorithmName::CoverTree);        
+        let knn = KNNClassifier::fit(
+            &x,
+            &y,
+            3,
+            Distances::euclidian(),
+            KNNAlgorithmName::CoverTree,
+        );
 
         let deserialized_knn = bincode::deserialize(&bincode::serialize(&knn).unwrap()).unwrap();
 
         assert_eq!(knn, deserialized_knn);
-        
     }
 }

src/optimization/first_order/gradient_descent.rs

@@ -1,15 +1,15 @@
 use std::default::Default;
 
-use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
-use crate::optimization::{F, DF};
+use crate::math::num::FloatExt;
-use crate::optimization::line_search::LineSearchMethod;
 use crate::optimization::first_order::{FirstOrderOptimizer, OptimizerResult};
+use crate::optimization::line_search::LineSearchMethod;
+use crate::optimization::{DF, F};
 
 pub struct GradientDescent<T: FloatExt> {
     pub max_iter: usize,
     pub g_rtol: T,
-    pub g_atol: T
+    pub g_atol: T,
 }
 
 impl<T: FloatExt> Default for GradientDescent<T> {
@@ -17,16 +17,19 @@ impl<T: FloatExt> Default for GradientDescent<T> {
         GradientDescent {
             max_iter: 10000,
             g_rtol: T::epsilon().sqrt(),
-            g_atol: T::epsilon()
+            g_atol: T::epsilon(),
         }
     }
 }
 
-impl<T: FloatExt> FirstOrderOptimizer<T> for GradientDescent<T>
+impl<T: FloatExt> FirstOrderOptimizer<T> for GradientDescent<T> {
-{
+    fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(
-
+        &self,
-    fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(&self, f: &'a F<T, X>, df: &'a DF<X>, x0: &X, ls: &'a LS) -> OptimizerResult<T, X> {        
+        f: &'a F<T, X>,
-
+        df: &'a DF<X>,
+        x0: &X,
+        ls: &'a LS,
+    ) -> OptimizerResult<T, X> {
         let mut x = x0.clone();
         let mut fx = f(&x);
 
@@ -73,7 +76,7 @@ impl<T: FloatExt> FirstOrderOptimizer<T> for GradientDescent<T>
         OptimizerResult {
             x: x,
             f_x: f_x,
-            iterations: iter
+            iterations: iter,
         }
     }
 }
@@ -87,14 +90,18 @@ mod tests {
 
     #[test]
     fn gradient_descent() {
-
         let x0 = DenseMatrix::vector_from_array(&[-1., 1.]);
         let f = |x: &DenseMatrix<f64>| {
             (1.0 - x.get(0, 0)).powf(2.) + 100.0 * (x.get(0, 1) - x.get(0, 0).powf(2.)).powf(2.)
         };
 
         let df = |g: &mut DenseMatrix<f64>, x: &DenseMatrix<f64>| {
-            g.set(0, 0, -2. * (1. - x.get(0, 0)) - 400. * (x.get(0, 1) - x.get(0, 0).powf(2.)) * x.get(0, 0));
+            g.set(
+                0,
+                0,
+                -2. * (1. - x.get(0, 0))
+                    - 400. * (x.get(0, 1) - x.get(0, 0).powf(2.)) * x.get(0, 0),
+            );
             g.set(0, 1, 200. * (x.get(0, 1) - x.get(0, 0).powf(2.)));
         };
 
@@ -107,7 +114,5 @@ mod tests {
         assert!((result.f_x - 0.0).abs() < 1e-5);
         assert!((result.x.get(0, 0) - 1.0).abs() < 1e-2);
         assert!((result.x.get(0, 1) - 1.0).abs() < 1e-2);
-
     }
-
 }

src/optimization/first_order/lbfgs.rs

@@ -1,11 +1,11 @@
 use std::default::Default;
 use std::fmt::Debug;
 
-use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
-use crate::optimization::{F, DF};
+use crate::math::num::FloatExt;
-use crate::optimization::line_search::LineSearchMethod;
 use crate::optimization::first_order::{FirstOrderOptimizer, OptimizerResult};
+use crate::optimization::line_search::LineSearchMethod;
+use crate::optimization::{DF, F};
 
 pub struct LBFGS<T: FloatExt> {
     pub max_iter: usize,
@@ -16,7 +16,7 @@ pub struct LBFGS<T: FloatExt> {
     pub f_abstol: T,
     pub f_reltol: T,
     pub successive_f_tol: usize,
-    pub m: usize
+    pub m: usize,
 }
 
 impl<T: FloatExt> Default for LBFGS<T> {
@@ -30,15 +30,13 @@ impl<T: FloatExt> Default for LBFGS<T> {
             f_abstol: T::zero(),
             f_reltol: T::zero(),
             successive_f_tol: 1,
-            m: 10
+            m: 10,
         }
     }
 }
 
 impl<T: FloatExt> LBFGS<T> {
-
     fn two_loops<X: Matrix<T>>(&self, state: &mut LBFGSState<T, X>) {
-
         let lower = state.iteration.max(self.m) - self.m;
         let upper = state.iteration;
 
@@ -49,7 +47,9 @@ impl<T: FloatExt> LBFGS<T> {
             let dgi = &state.dg_history[i];
             let dxi = &state.dx_history[i];
             state.twoloop_alpha[i] = state.rho[i] * dxi.vector_dot(&state.twoloop_q);
-            state.twoloop_q.sub_mut(&dgi.mul_scalar(state.twoloop_alpha[i]));
+            state
+                .twoloop_q
+                .sub_mut(&dgi.mul_scalar(state.twoloop_alpha[i]));
         }
 
         if state.iteration > 0 {
@@ -67,11 +67,12 @@ impl<T: FloatExt> LBFGS<T> {
             let dgi = &state.dg_history[i];
             let dxi = &state.dx_history[i];
             let beta = state.rho[i] * dgi.vector_dot(&state.s);
-            state.s.add_mut(&dxi.mul_scalar(state.twoloop_alpha[i] - beta));                             
+            state
+                .s
+                .add_mut(&dxi.mul_scalar(state.twoloop_alpha[i] - beta));
         }
 
         state.s.mul_scalar_mut(-T::one());
-         
     }
 
     fn init_state<X: Matrix<T>>(&self, x: &X) -> LBFGSState<T, X> {
@@ -93,11 +94,17 @@ impl<T: FloatExt> LBFGS<T> {
             iteration: 0,
             counter_f_tol: 0,
             s: x.clone(),
-            alpha: T::one()
+            alpha: T::one(),
         }
     }
 
-    fn update_state<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(&self, f: &'a F<T, X>, df: &'a DF<X>, ls: &'a LS, state: &mut LBFGSState<T, X>) {        
+    fn update_state<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(
+        &self,
+        f: &'a F<T, X>,
+        df: &'a DF<X>,
+        ls: &'a LS,
+        state: &mut LBFGSState<T, X>,
+    ) {
         self.two_loops(state);
 
         df(&mut state.x_df_prev, &state.x);
@@ -127,7 +134,6 @@ impl<T: FloatExt> LBFGS<T> {
         state.x.add_mut(&state.dx);
         state.x_f = f(&state.x);
         df(&mut state.x_df, &state.x);
-
     }
 
     fn assess_convergence<X: Matrix<T>>(&self, state: &mut LBFGSState<T, X>) -> bool {
@@ -186,13 +192,17 @@ struct LBFGSState<T: FloatExt, X: Matrix<T>> {
     iteration: usize,
     counter_f_tol: usize,
     s: X,
-    alpha: T
+    alpha: T,
 }
 
 impl<T: FloatExt> FirstOrderOptimizer<T> for LBFGS<T> {
-
+    fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(
-    fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(&self, f: &F<T, X>, df: &'a DF<X>, x0: &X, ls: &'a LS) -> OptimizerResult<T, X> {     
+        &self,
-        
+        f: &F<T, X>,
+        df: &'a DF<X>,
+        x0: &X,
+        ls: &'a LS,
+    ) -> OptimizerResult<T, X> {
         let mut state = self.init_state(x0);
 
         df(&mut state.x_df, &x0);
@@ -202,7 +212,6 @@ impl<T: FloatExt> FirstOrderOptimizer<T> for LBFGS<T> {
         let stopped = false;
 
         while !converged && !stopped && state.iteration < self.max_iter {
-            
             self.update_state(f, df, ls, &mut state);
 
             converged = self.assess_convergence(&mut state);
@@ -212,17 +221,14 @@ impl<T: FloatExt> FirstOrderOptimizer<T> for LBFGS<T> {
             }
 
             state.iteration += 1;
-
         }
 
         OptimizerResult {
             x: state.x,
             f_x: state.x_f,
-            iterations: state.iteration
+            iterations: state.iteration,
         }
-        
     }
-
 }
 
 #[cfg(test)]
@@ -240,7 +246,12 @@ mod tests {
         };
 
         let df = |g: &mut DenseMatrix<f64>, x: &DenseMatrix<f64>| {
-            g.set(0, 0, -2. * (1. - x.get(0, 0)) - 400. * (x.get(0, 1) - x.get(0, 0).powf(2.)) * x.get(0, 0));
+            g.set(
+                0,
+                0,
+                -2. * (1. - x.get(0, 0))
+                    - 400. * (x.get(0, 1) - x.get(0, 0).powf(2.)) * x.get(0, 0),
+            );
             g.set(0, 1, 200. * (x.get(0, 1) - x.get(0, 0).powf(2.)));
         };
         let mut ls: Backtracking<f64> = Default::default();

src/optimization/first_order/mod.rs

@@ -1,22 +1,27 @@
-pub mod lbfgs;
 pub mod gradient_descent;
+pub mod lbfgs;
 
 use std::clone::Clone;
 use std::fmt::Debug;
 
-use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
+use crate::math::num::FloatExt;
 use crate::optimization::line_search::LineSearchMethod;
-use crate::optimization::{F, DF};
+use crate::optimization::{DF, F};
 
 pub trait FirstOrderOptimizer<T: FloatExt> {
-    fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(&self, f: &F<T, X>, df: &'a DF<X>, x0: &X, ls: &'a LS) -> OptimizerResult<T, X>;    
+    fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(
+        &self,
+        f: &F<T, X>,
+        df: &'a DF<X>,
+        x0: &X,
+        ls: &'a LS,
+    ) -> OptimizerResult<T, X>;
 }
 
 #[derive(Debug, Clone)]
-pub struct OptimizerResult<T: FloatExt, X: Matrix<T>> 
+pub struct OptimizerResult<T: FloatExt, X: Matrix<T>> {
-{
     pub x: X,
     pub f_x: T,
-    pub iterations: usize
+    pub iterations: usize,
 }

src/optimization/line_search.rs

@@ -1,14 +1,21 @@
-use num_traits::Float;
 use crate::optimization::FunctionOrder;
+use num_traits::Float;
 
 pub trait LineSearchMethod<T: Float> {
-    fn search<'a>(&self, f: &(dyn Fn(T) -> T), df: &(dyn Fn(T) -> T), alpha: T, f0: T, df0: T) -> LineSearchResult<T>;
+    fn search<'a>(
+        &self,
+        f: &(dyn Fn(T) -> T),
+        df: &(dyn Fn(T) -> T),
+        alpha: T,
+        f0: T,
+        df0: T,
+    ) -> LineSearchResult<T>;
 }
 
 #[derive(Debug, Clone)]
 pub struct LineSearchResult<T: Float> {
     pub alpha: T,
-    pub f_x: T
+    pub f_x: T,
 }
 
 pub struct Backtracking<T: Float> {
@@ -17,7 +24,7 @@ pub struct Backtracking<T: Float> {
     pub max_infinity_iterations: usize,
     pub phi: T,
     pub plo: T,
-    pub order: FunctionOrder
+    pub order: FunctionOrder,
 }
 
 impl<T: Float> Default for Backtracking<T> {
@@ -28,15 +35,20 @@ impl<T: Float> Default for Backtracking<T> {
             max_infinity_iterations: (-T::epsilon().log2()).to_usize().unwrap(),
             phi: T::from(0.5).unwrap(),
             plo: T::from(0.1).unwrap(),
-            order: FunctionOrder::SECOND
+            order: FunctionOrder::SECOND,
         }
     }
 }
 
 impl<T: Float> LineSearchMethod<T> for Backtracking<T> {
-    
+    fn search<'a>(
-    fn search<'a>(&self, f: &(dyn Fn(T) -> T), _: &(dyn Fn(T) -> T), alpha: T, f0: T, df0: T) -> LineSearchResult<T> {  
+        &self,
-        
+        f: &(dyn Fn(T) -> T),
+        _: &(dyn Fn(T) -> T),
+        alpha: T,
+        f0: T,
+        df0: T,
+    ) -> LineSearchResult<T> {
         let two = T::from(2.).unwrap();
         let three = T::from(3.).unwrap();
 
@@ -62,14 +74,15 @@ impl<T: Float> LineSearchMethod<T> for Backtracking<T> {
             let a_tmp;
 
             if self.order == FunctionOrder::SECOND || iteration == 0 {
-
                 a_tmp = -(df0 * a2.powf(two)) / (two * (fx1 - f0 - df0 * a2))
-                
             } else {
-
                 let div = T::one() / (a1.powf(two) * a2.powf(two) * (a2 - a1));
-                let a = (a1.powf(two) * (fx1 - f0 - df0*a2) - a2.powf(two)*(fx0 - f0 - df0*a1))*div;
+                let a = (a1.powf(two) * (fx1 - f0 - df0 * a2)
-                let b = (-a1.powf(three) * (fx1 - f0 - df0*a2) + a2.powf(three)*(fx0 - f0 - df0*a1))*div;                
+                    - a2.powf(two) * (fx0 - f0 - df0 * a1))
+                    * div;
+                let b = (-a1.powf(three) * (fx1 - f0 - df0 * a2)
+                    + a2.powf(three) * (fx0 - f0 - df0 * a1))
+                    * div;
 
                 if (a - T::zero()).powf(two).sqrt() <= T::epsilon() {
                     a_tmp = df0 / (two * b);
@@ -90,9 +103,8 @@ impl<T: Float> LineSearchMethod<T> for Backtracking<T> {
 
         LineSearchResult {
             alpha: a2,
-            f_x: fx1
+            f_x: fx1,
         }
-
     }
 }
 
@@ -102,14 +114,9 @@ mod tests {
 
     #[test]
     fn backtracking() {
+        let f = |x: f64| -> f64 { x.powf(2.) + x };
 
-            let f = |x: f64| -> f64 {                
+        let df = |x: f64| -> f64 { 2. * x + 1. };
-                x.powf(2.) + x
-            };
-
-            let df = |x: f64| -> f64 {                                         
-                2. * x + 1.
-            };
 
         let ls: Backtracking<f64> = Default::default();
 

src/optimization/mod.rs

@@ -8,5 +8,5 @@ pub type DF<'a, X> = dyn for<'b> Fn(&'b mut X, &'b X) + 'a;
 pub enum FunctionOrder {
     FIRST,
     SECOND,
-    THIRD
+    THIRD,
 }

src/tree/decision_tree_classifier.rs

@@ -1,20 +1,20 @@
+use std::collections::LinkedList;
 use std::default::Default;
 use std::fmt::Debug;
 use std::marker::PhantomData;
-use std::collections::LinkedList;
 
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
-use crate::linalg::Matrix;
 use crate::algorithm::sort::quick_sort::QuickArgSort;
+use crate::linalg::Matrix;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct DecisionTreeClassifierParameters {
     pub criterion: SplitCriterion,
     pub max_depth: Option<u16>,
     pub min_samples_leaf: usize,
-    pub min_samples_split: usize
+    pub min_samples_split: usize,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
@@ -23,14 +23,14 @@ pub struct DecisionTreeClassifier<T: FloatExt> {
     parameters: DecisionTreeClassifierParameters,
     num_classes: usize,
     classes: Vec<T>,
-    depth: u16
+    depth: u16,
 }
 
 #[derive(Serialize, Deserialize, Debug, Clone)]
 pub enum SplitCriterion {
     Gini,
     Entropy,
-    ClassificationError
+    ClassificationError,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
@@ -46,36 +46,37 @@ pub struct Node<T: FloatExt> {
 
 impl<T: FloatExt> PartialEq for DecisionTreeClassifier<T> {
     fn eq(&self, other: &Self) -> bool {
-        if self.depth != other.depth ||
+        if self.depth != other.depth
-        self.num_classes != other.num_classes ||
+            || self.num_classes != other.num_classes
-        self.nodes.len() != other.nodes.len(){
+            || self.nodes.len() != other.nodes.len()
-            return false
+        {
+            return false;
         } else {
             for i in 0..self.classes.len() {
                 if (self.classes[i] - other.classes[i]).abs() > T::epsilon() {
-                    return false
+                    return false;
                 }
             }
             for i in 0..self.nodes.len() {
                 if self.nodes[i] != other.nodes[i] {
-                    return false
+                    return false;
                 }
             }
-            return true
+            return true;
         }
     }
 }
 
 impl<T: FloatExt> PartialEq for Node<T> {
     fn eq(&self, other: &Self) -> bool {
-        self.output == other.output && 
+        self.output == other.output
-        self.split_feature == other.split_feature &&
+            && self.split_feature == other.split_feature
-        match (self.split_value, other.split_value) {
+            && match (self.split_value, other.split_value) {
                 (Some(a), Some(b)) => (a - b).abs() < T::epsilon(),
                 (None, None) => true,
                 _ => false,
-        } &&
+            }
-        match (self.split_score, other.split_score) {
+            && match (self.split_score, other.split_score) {
                 (Some(a), Some(b)) => (a - b).abs() < T::epsilon(),
                 (None, None) => true,
                 _ => false,
@@ -89,7 +90,7 @@ impl Default for DecisionTreeClassifierParameters {
             criterion: SplitCriterion::Gini,
             max_depth: None,
             min_samples_leaf: 1,
-            min_samples_split: 2
+            min_samples_split: 2,
         }
     }
 }
@@ -103,7 +104,7 @@ impl<T: FloatExt> Node<T> {
             split_value: Option::None,
             split_score: Option::None,
             true_child: Option::None,
-            false_child: Option::None            
+            false_child: Option::None,
         }
     }
 }
@@ -117,7 +118,7 @@ struct NodeVisitor<'a, T: FloatExt, M: Matrix<T>> {
     true_child_output: usize,
     false_child_output: usize,
     level: u16,
-    phantom: PhantomData<&'a T>
+    phantom: PhantomData<&'a T>,
 }
 
 fn impurity<T: FloatExt>(criterion: &SplitCriterion, count: &Vec<usize>, n: usize) -> T {
@@ -156,8 +157,14 @@ fn impurity<T: FloatExt>(criterion: &SplitCriterion, count: &Vec<usize>, n: usiz
 }
 
 impl<'a, T: FloatExt, M: Matrix<T>> NodeVisitor<'a, T, M> {
-
+    fn new(
-    fn new(node_id: usize, samples: Vec<usize>, order: &'a Vec<Vec<usize>>, x: &'a M, y: &'a Vec<usize>, level: u16) -> Self {
+        node_id: usize,
+        samples: Vec<usize>,
+        order: &'a Vec<Vec<usize>>,
+        x: &'a M,
+        y: &'a Vec<usize>,
+        level: u16,
+    ) -> Self {
         NodeVisitor {
             x: x,
             y: y,
@@ -167,10 +174,9 @@ impl<'a, T: FloatExt, M: Matrix<T>> NodeVisitor<'a, T, M> {
             true_child_output: 0,
             false_child_output: 0,
             level: level,
-            phantom: PhantomData
+            phantom: PhantomData,
         }
     }
-
 }
 
 pub(in crate) fn which_max(x: &Vec<usize>) -> usize {
@@ -188,14 +194,23 @@ pub(in crate) fn which_max(x: &Vec<usize>) -> usize {
 }
 
 impl<T: FloatExt> DecisionTreeClassifier<T> {
-
+    pub fn fit<M: Matrix<T>>(
-    pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, parameters: DecisionTreeClassifierParameters) -> DecisionTreeClassifier<T> {
+        x: &M,
+        y: &M::RowVector,
+        parameters: DecisionTreeClassifierParameters,
+    ) -> DecisionTreeClassifier<T> {
         let (x_nrows, num_attributes) = x.shape();
         let samples = vec![1; x_nrows];
         DecisionTreeClassifier::fit_weak_learner(x, y, samples, num_attributes, parameters)
     }
 
-    pub fn fit_weak_learner<M: Matrix<T>>(x: &M, y: &M::RowVector, samples: Vec<usize>, mtry: usize, parameters: DecisionTreeClassifierParameters) -> DecisionTreeClassifier<T> {
+    pub fn fit_weak_learner<M: Matrix<T>>(
+        x: &M,
+        y: &M::RowVector,
+        samples: Vec<usize>,
+        mtry: usize,
+        parameters: DecisionTreeClassifierParameters,
+    ) -> DecisionTreeClassifier<T> {
         let y_m = M::from_row_vector(y.clone());
         let (_, y_ncols) = y_m.shape();
         let (_, num_attributes) = x.shape();
@@ -232,7 +247,7 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
             parameters: parameters,
             num_classes: k,
             classes: classes,
-            depth: 0        
+            depth: 0,
         };
 
         let mut visitor = NodeVisitor::<T, M>::new(0, samples, &order, &x, &yi, 1);
@@ -245,8 +260,8 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
 
         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,),
+                Some(node) => tree.split(node, mtry, &mut visitor_queue),
-                None => break
+                None => break,
             };
         }
 
@@ -284,17 +299,19 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
                             queue.push_back(node.false_child.unwrap());
                         }
                     }
-                },
+                }
-                None => break
+                None => break,
             };
         }
 
-        return result
+        return result;
-        
     }
 
-    fn find_best_cutoff<M: Matrix<T>>(&mut self, visitor: &mut NodeVisitor<T, M>, mtry: usize) -> bool {
+    fn find_best_cutoff<M: Matrix<T>>(
-
+        &mut self,
+        visitor: &mut NodeVisitor<T, M>,
+        mtry: usize,
+    ) -> bool {
         let (n_rows, n_attr) = visitor.x.shape();
 
         let mut label = Option::None;
@@ -336,15 +353,28 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
         }
 
         for j in 0..mtry {
-            self.find_best_split(visitor, n, &count, &mut false_count, parent_impurity, variables[j]);            
+            self.find_best_split(
+                visitor,
+                n,
+                &count,
+                &mut false_count,
+                parent_impurity,
+                variables[j],
+            );
         }
 
         self.nodes[visitor.node].split_score != Option::None
-
     }
 
-    fn find_best_split<M: Matrix<T>>(&mut self, visitor: &mut NodeVisitor<T, M>, n: usize, count: &Vec<usize>, false_count: &mut Vec<usize>, parent_impurity: T, j: usize){
+    fn find_best_split<M: Matrix<T>>(
-
+        &mut self,
+        visitor: &mut NodeVisitor<T, M>,
+        n: usize,
+        count: &Vec<usize>,
+        false_count: &mut Vec<usize>,
+        parent_impurity: T,
+        j: usize,
+    ) {
         let mut true_count = vec![0; self.num_classes];
         let mut prevx = T::nan();
         let mut prevy = 0;
@@ -374,11 +404,18 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
 
                 let true_label = which_max(&true_count);
                 let false_label = which_max(false_count);
-                let gain = parent_impurity - T::from(tc).unwrap() / T::from(n).unwrap() * impurity(&self.parameters.criterion, &true_count, tc) - T::from(fc).unwrap() / T::from(n).unwrap() * impurity(&self.parameters.criterion, &false_count, fc);
+                let gain = parent_impurity
+                    - T::from(tc).unwrap() / T::from(n).unwrap()
+                        * impurity(&self.parameters.criterion, &true_count, tc)
+                    - T::from(fc).unwrap() / T::from(n).unwrap()
+                        * impurity(&self.parameters.criterion, &false_count, fc);
 
-                if self.nodes[visitor.node].split_score == Option::None || gain > self.nodes[visitor.node].split_score.unwrap() {                    
+                if self.nodes[visitor.node].split_score == Option::None
+                    || gain > self.nodes[visitor.node].split_score.unwrap()
+                {
                     self.nodes[visitor.node].split_feature = j;
-                    self.nodes[visitor.node].split_value = Option::Some((visitor.x.get(*i, j) + prevx) / T::two());
+                    self.nodes[visitor.node].split_value =
+                        Option::Some((visitor.x.get(*i, j) + prevx) / T::two());
                     self.nodes[visitor.node].split_score = Option::Some(gain);
                     visitor.true_child_output = true_label;
                     visitor.false_child_output = false_label;
@@ -389,10 +426,14 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
                 true_count[visitor.y[*i]] += visitor.samples[*i];
             }
         }
-
     }
 
-    fn split<'a, M: Matrix<T>>(&mut self, mut visitor: NodeVisitor<'a, T, M>, mtry: usize, visitor_queue: &mut LinkedList<NodeVisitor<'a, T, M>>) -> bool {
+    fn split<'a, M: Matrix<T>>(
+        &mut self,
+        mut visitor: NodeVisitor<'a, T, M>,
+        mtry: usize,
+        visitor_queue: &mut LinkedList<NodeVisitor<'a, T, M>>,
+    ) -> bool {
         let (n, _) = visitor.x.shape();
         let mut tc = 0;
         let mut fc = 0;
@@ -400,7 +441,9 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
 
         for i in 0..n {
             if visitor.samples[i] > 0 {
-                if visitor.x.get(i, self.nodes[visitor.node].split_feature) <= self.nodes[visitor.node].split_value.unwrap_or(T::nan()) {
+                if visitor.x.get(i, self.nodes[visitor.node].split_feature)
+                    <= self.nodes[visitor.node].split_value.unwrap_or(T::nan())
+                {
                     true_samples[i] = visitor.samples[i];
                     tc += true_samples[i];
                     visitor.samples[i] = 0;
@@ -418,22 +461,38 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
         }
 
         let true_child_idx = self.nodes.len();
-        self.nodes.push(Node::new(true_child_idx, visitor.true_child_output));
+        self.nodes
+            .push(Node::new(true_child_idx, visitor.true_child_output));
         let false_child_idx = self.nodes.len();
-        self.nodes.push(Node::new(false_child_idx, visitor.false_child_output));
+        self.nodes
+            .push(Node::new(false_child_idx, visitor.false_child_output));
 
         self.nodes[visitor.node].true_child = Some(true_child_idx);
         self.nodes[visitor.node].false_child = Some(false_child_idx);
 
         self.depth = u16::max(self.depth, visitor.level + 1);
 
-        let mut true_visitor = NodeVisitor::<T, M>::new(true_child_idx, true_samples, visitor.order, visitor.x, visitor.y, visitor.level + 1);            
+        let mut true_visitor = NodeVisitor::<T, M>::new(
+            true_child_idx,
+            true_samples,
+            visitor.order,
+            visitor.x,
+            visitor.y,
+            visitor.level + 1,
+        );
 
         if self.find_best_cutoff(&mut true_visitor, mtry) {
             visitor_queue.push_back(true_visitor);
         }
 
-        let mut false_visitor = NodeVisitor::<T, M>::new(false_child_idx, visitor.samples, visitor.order, visitor.x, visitor.y, visitor.level + 1);
+        let mut false_visitor = NodeVisitor::<T, M>::new(
+            false_child_idx,
+            visitor.samples,
+            visitor.order,
+            visitor.x,
+            visitor.y,
+            visitor.level + 1,
+        );
 
         if self.find_best_cutoff(&mut false_visitor, mtry) {
             visitor_queue.push_back(false_visitor);
@@ -441,7 +500,6 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
 
         true
     }
-
 }
 
 #[cfg(test)]
@@ -451,14 +509,22 @@ mod tests {
 
     #[test]
     fn gini_impurity() {
-        assert!((impurity::<f64>(&SplitCriterion::Gini, &vec![7, 3], 10) - 0.42).abs() < std::f64::EPSILON);
+        assert!(
-        assert!((impurity::<f64>(&SplitCriterion::Entropy, &vec![7, 3], 10) - 0.8812908992306927).abs() < std::f64::EPSILON);
+            (impurity::<f64>(&SplitCriterion::Gini, &vec![7, 3], 10) - 0.42).abs()
-        assert!((impurity::<f64>(&SplitCriterion::ClassificationError, &vec![7, 3], 10) - 0.3).abs() < std::f64::EPSILON);
+                < std::f64::EPSILON
+        );
+        assert!(
+            (impurity::<f64>(&SplitCriterion::Entropy, &vec![7, 3], 10) - 0.8812908992306927).abs()
+                < std::f64::EPSILON
+        );
+        assert!(
+            (impurity::<f64>(&SplitCriterion::ClassificationError, &vec![7, 3], 10) - 0.3).abs()
+                < std::f64::EPSILON
+        );
     }
 
     #[test]
     fn fit_predict_iris() {
-
         let x = DenseMatrix::from_array(&[
             &[5.1, 3.5, 1.4, 0.2],
             &[4.9, 3.0, 1.4, 0.2],
@@ -479,18 +545,35 @@ mod tests {
             &[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]]);
+            &[5.2, 2.7, 3.9, 1.4],
-        let y = vec![0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.];
+        ]);
+        let y = vec![
+            0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+        ];
 
-        assert_eq!(y, DecisionTreeClassifier::fit(&x, &y, Default::default()).predict(&x));
+        assert_eq!(
-        
+            y,
-        assert_eq!(3, DecisionTreeClassifier::fit(&x, &y, DecisionTreeClassifierParameters{criterion: SplitCriterion::Entropy, max_depth: Some(3), min_samples_leaf: 1, min_samples_split: 2}).depth);        
+            DecisionTreeClassifier::fit(&x, &y, Default::default()).predict(&x)
+        );
 
+        assert_eq!(
+            3,
+            DecisionTreeClassifier::fit(
+                &x,
+                &y,
+                DecisionTreeClassifierParameters {
+                    criterion: SplitCriterion::Entropy,
+                    max_depth: Some(3),
+                    min_samples_leaf: 1,
+                    min_samples_split: 2
+                }
+            )
+            .depth
+        );
     }
 
     #[test]
     fn fit_predict_baloons() {
-
         let x = DenseMatrix::from_array(&[
             &[1., 1., 1., 0.],
             &[1., 1., 1., 0.],
@@ -511,11 +594,16 @@ mod tests {
             &[0., 0., 1., 0.],
             &[0., 0., 1., 1.],
             &[0., 0., 0., 0.],
-            &[0.,0.,0.,1.]]);
+            &[0., 0., 0., 1.],
-        let y = vec![1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0.];
+        ]);
-
+        let y = vec![
-        assert_eq!(y, DecisionTreeClassifier::fit(&x, &y, Default::default()).predict(&x));
+            1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0.,
+        ];
 
+        assert_eq!(
+            y,
+            DecisionTreeClassifier::fit(&x, &y, Default::default()).predict(&x)
+        );
     }
 
     #[test]
@@ -540,14 +628,17 @@ mod tests {
             &[0., 0., 1., 0.],
             &[0., 0., 1., 1.],
             &[0., 0., 0., 0.],
-            &[0.,0.,0.,1.]]);
+            &[0., 0., 0., 1.],
-        let y = vec![1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0.];           
+        ]);
+        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());
 
-        let deserialized_tree: DecisionTreeClassifier<f64> = bincode::deserialize(&bincode::serialize(&tree).unwrap()).unwrap();
+        let deserialized_tree: DecisionTreeClassifier<f64> =
+            bincode::deserialize(&bincode::serialize(&tree).unwrap()).unwrap();
 
         assert_eq!(tree, deserialized_tree);
-        
     }
 }

src/tree/decision_tree_regressor.rs

@@ -1,25 +1,25 @@
+use std::collections::LinkedList;
 use std::default::Default;
 use std::fmt::Debug;
-use std::collections::LinkedList;
 
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
-use crate::linalg::Matrix;
 use crate::algorithm::sort::quick_sort::QuickArgSort;
+use crate::linalg::Matrix;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct DecisionTreeRegressorParameters {
     pub max_depth: Option<u16>,
     pub min_samples_leaf: usize,
-    pub min_samples_split: usize
+    pub min_samples_split: usize,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
 pub struct DecisionTreeRegressor<T: FloatExt> {
     nodes: Vec<Node<T>>,
     parameters: DecisionTreeRegressorParameters,
-    depth: u16
+    depth: u16,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
@@ -33,13 +33,12 @@ pub struct Node<T: FloatExt> {
     false_child: Option<usize>,
 }
 
-
 impl Default for DecisionTreeRegressorParameters {
     fn default() -> Self {
         DecisionTreeRegressorParameters {
             max_depth: None,
             min_samples_leaf: 1,
-            min_samples_split: 2
+            min_samples_split: 2,
         }
     }
 }
@@ -53,21 +52,21 @@ impl<T: FloatExt> Node<T> {
             split_value: Option::None,
             split_score: Option::None,
             true_child: Option::None,
-            false_child: Option::None            
+            false_child: Option::None,
         }
     }
 }
 
 impl<T: FloatExt> PartialEq for Node<T> {
     fn eq(&self, other: &Self) -> bool {
-        (self.output - other.output).abs() < T::epsilon() && 
+        (self.output - other.output).abs() < T::epsilon()
-        self.split_feature == other.split_feature && 
+            && self.split_feature == other.split_feature
-        match (self.split_value, other.split_value) {
+            && match (self.split_value, other.split_value) {
                 (Some(a), Some(b)) => (a - b).abs() < T::epsilon(),
                 (None, None) => true,
                 _ => false,
-        } &&
+            }
-        match (self.split_score, other.split_score) {
+            && match (self.split_score, other.split_score) {
                 (Some(a), Some(b)) => (a - b).abs() < T::epsilon(),
                 (None, None) => true,
                 _ => false,
@@ -78,14 +77,14 @@ impl<T: FloatExt> PartialEq for Node<T> {
 impl<T: FloatExt> PartialEq for DecisionTreeRegressor<T> {
     fn eq(&self, other: &Self) -> bool {
         if self.depth != other.depth || self.nodes.len() != other.nodes.len() {
-            return false
+            return false;
         } else {
             for i in 0..self.nodes.len() {
                 if self.nodes[i] != other.nodes[i] {
-                    return false
+                    return false;
                 }
             }
-            return true
+            return true;
         }
     }
 }
@@ -98,12 +97,18 @@ struct NodeVisitor<'a, T: FloatExt, M: Matrix<T>> {
     order: &'a Vec<Vec<usize>>,
     true_child_output: T,
     false_child_output: T,
-    level: u16
+    level: u16,
 }
 
 impl<'a, T: FloatExt, M: Matrix<T>> NodeVisitor<'a, T, M> {
-
+    fn new(
-    fn new(node_id: usize, samples: Vec<usize>, order: &'a Vec<Vec<usize>>, x: &'a M, y: &'a M, level: u16) -> Self {
+        node_id: usize,
+        samples: Vec<usize>,
+        order: &'a Vec<Vec<usize>>,
+        x: &'a M,
+        y: &'a M,
+        level: u16,
+    ) -> Self {
         NodeVisitor {
             x: x,
             y: y,
@@ -112,21 +117,29 @@ impl<'a, T: FloatExt, M: Matrix<T>> NodeVisitor<'a, T, M> {
             order: order,
             true_child_output: T::zero(),
             false_child_output: T::zero(),
-            level: level
+            level: level,
         }
     }
-
 }
 
 impl<T: FloatExt> DecisionTreeRegressor<T> {
-
+    pub fn fit<M: Matrix<T>>(
-    pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, parameters: DecisionTreeRegressorParameters) -> DecisionTreeRegressor<T> {
+        x: &M,
+        y: &M::RowVector,
+        parameters: DecisionTreeRegressorParameters,
+    ) -> DecisionTreeRegressor<T> {
         let (x_nrows, num_attributes) = x.shape();
         let samples = vec![1; x_nrows];
         DecisionTreeRegressor::fit_weak_learner(x, y, samples, num_attributes, parameters)
     }
 
-    pub fn fit_weak_learner<M: Matrix<T>>(x: &M, y: &M::RowVector, samples: Vec<usize>, mtry: usize, parameters: DecisionTreeRegressorParameters) -> DecisionTreeRegressor<T> {
+    pub fn fit_weak_learner<M: Matrix<T>>(
+        x: &M,
+        y: &M::RowVector,
+        samples: Vec<usize>,
+        mtry: usize,
+        parameters: DecisionTreeRegressorParameters,
+    ) -> DecisionTreeRegressor<T> {
         let y_m = M::from_row_vector(y.clone());
 
         let (_, y_ncols) = y_m.shape();
@@ -157,7 +170,7 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
         let mut tree = DecisionTreeRegressor {
             nodes: nodes,
             parameters: parameters,
-            depth: 0        
+            depth: 0,
         };
 
         let mut visitor = NodeVisitor::<T, M>::new(0, samples, &order, &x, &y_m, 1);
@@ -171,7 +184,7 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
         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),
-                None => break
+                None => break,
             };
         }
 
@@ -209,17 +222,19 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
                             queue.push_back(node.false_child.unwrap());
                         }
                     }
-                },
+                }
-                None => break
+                None => break,
             };
         }
 
-        return result
+        return result;
-        
     }
 
-    fn find_best_cutoff<M: Matrix<T>>(&mut self, visitor: &mut NodeVisitor<T, M>, mtry: usize) -> bool {
+    fn find_best_cutoff<M: Matrix<T>>(
-
+        &mut self,
+        visitor: &mut NodeVisitor<T, M>,
+        mtry: usize,
+    ) -> bool {
         let (_, n_attr) = visitor.x.shape();
 
         let n: usize = visitor.samples.iter().sum();
@@ -235,18 +250,24 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
             variables[i] = i;
         }
 
-        let parent_gain = T::from(n).unwrap() * self.nodes[visitor.node].output * self.nodes[visitor.node].output;
+        let parent_gain =
+            T::from(n).unwrap() * self.nodes[visitor.node].output * self.nodes[visitor.node].output;
 
         for j in 0..mtry {
             self.find_best_split(visitor, n, sum, parent_gain, variables[j]);
         }
 
         self.nodes[visitor.node].split_score != Option::None
-
     }
 
-    fn find_best_split<M: Matrix<T>>(&mut self, visitor: &mut NodeVisitor<T, M>, n: usize, sum: T, parent_gain: T, j: usize){
+    fn find_best_split<M: Matrix<T>>(
-
+        &mut self,
+        visitor: &mut NodeVisitor<T, M>,
+        n: usize,
+        sum: T,
+        parent_gain: T,
+        j: usize,
+    ) {
         let mut true_sum = T::zero();
         let mut true_count = 0;
         let mut prevx = T::nan();
@@ -256,27 +277,36 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
                 if prevx.is_nan() || visitor.x.get(*i, j) == prevx {
                     prevx = visitor.x.get(*i, j);
                     true_count += visitor.samples[*i];
-                    true_sum = true_sum + T::from(visitor.samples[*i]).unwrap() * visitor.y.get(0, *i);
+                    true_sum =
+                        true_sum + T::from(visitor.samples[*i]).unwrap() * visitor.y.get(0, *i);
                     continue;
                 }
 
                 let false_count = n - true_count;
 
-                if true_count < self.parameters.min_samples_leaf || false_count < self.parameters.min_samples_leaf {
+                if true_count < self.parameters.min_samples_leaf
+                    || false_count < self.parameters.min_samples_leaf
+                {
                     prevx = visitor.x.get(*i, j);
                     true_count += visitor.samples[*i];
-                    true_sum = true_sum + T::from(visitor.samples[*i]).unwrap() * visitor.y.get(0, *i);
+                    true_sum =
+                        true_sum + T::from(visitor.samples[*i]).unwrap() * visitor.y.get(0, *i);
                     continue;
                 }
 
                 let true_mean = true_sum / T::from(true_count).unwrap();
                 let false_mean = (sum - true_sum) / T::from(false_count).unwrap();
 
-                let gain = (T::from(true_count).unwrap() * true_mean * true_mean + T::from(false_count).unwrap() * false_mean * false_mean) - parent_gain;
+                let gain = (T::from(true_count).unwrap() * true_mean * true_mean
+                    + T::from(false_count).unwrap() * false_mean * false_mean)
+                    - parent_gain;
 
-                if self.nodes[visitor.node].split_score == Option::None || gain > self.nodes[visitor.node].split_score.unwrap() {                    
+                if self.nodes[visitor.node].split_score == Option::None
+                    || gain > self.nodes[visitor.node].split_score.unwrap()
+                {
                     self.nodes[visitor.node].split_feature = j;
-                    self.nodes[visitor.node].split_value = Option::Some((visitor.x.get(*i, j) + prevx) / T::two());
+                    self.nodes[visitor.node].split_value =
+                        Option::Some((visitor.x.get(*i, j) + prevx) / T::two());
                     self.nodes[visitor.node].split_score = Option::Some(gain);
                     visitor.true_child_output = true_mean;
                     visitor.false_child_output = false_mean;
@@ -287,10 +317,14 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
                 true_count += visitor.samples[*i];
             }
         }
-
     }
 
-    fn split<'a, M: Matrix<T>>(&mut self, mut visitor: NodeVisitor<'a, T, M>, mtry: usize, visitor_queue: &mut LinkedList<NodeVisitor<'a, T, M>>) -> bool {
+    fn split<'a, M: Matrix<T>>(
+        &mut self,
+        mut visitor: NodeVisitor<'a, T, M>,
+        mtry: usize,
+        visitor_queue: &mut LinkedList<NodeVisitor<'a, T, M>>,
+    ) -> bool {
         let (n, _) = visitor.x.shape();
         let mut tc = 0;
         let mut fc = 0;
@@ -298,7 +332,9 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
 
         for i in 0..n {
             if visitor.samples[i] > 0 {
-                if visitor.x.get(i, self.nodes[visitor.node].split_feature) <= self.nodes[visitor.node].split_value.unwrap_or(T::nan()) {
+                if visitor.x.get(i, self.nodes[visitor.node].split_feature)
+                    <= self.nodes[visitor.node].split_value.unwrap_or(T::nan())
+                {
                     true_samples[i] = visitor.samples[i];
                     tc += true_samples[i];
                     visitor.samples[i] = 0;
@@ -316,22 +352,38 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
         }
 
         let true_child_idx = self.nodes.len();
-        self.nodes.push(Node::new(true_child_idx, visitor.true_child_output));
+        self.nodes
+            .push(Node::new(true_child_idx, visitor.true_child_output));
         let false_child_idx = self.nodes.len();
-        self.nodes.push(Node::new(false_child_idx, visitor.false_child_output));
+        self.nodes
+            .push(Node::new(false_child_idx, visitor.false_child_output));
 
         self.nodes[visitor.node].true_child = Some(true_child_idx);
         self.nodes[visitor.node].false_child = Some(false_child_idx);
 
         self.depth = u16::max(self.depth, visitor.level + 1);
 
-        let mut true_visitor = NodeVisitor::<T, M>::new(true_child_idx, true_samples, visitor.order, visitor.x, visitor.y, visitor.level + 1);            
+        let mut true_visitor = NodeVisitor::<T, M>::new(
+            true_child_idx,
+            true_samples,
+            visitor.order,
+            visitor.x,
+            visitor.y,
+            visitor.level + 1,
+        );
 
         if self.find_best_cutoff(&mut true_visitor, mtry) {
             visitor_queue.push_back(true_visitor);
         }
 
-        let mut false_visitor = NodeVisitor::<T, M>::new(false_child_idx, visitor.samples, visitor.order, visitor.x, visitor.y, visitor.level + 1);
+        let mut false_visitor = NodeVisitor::<T, M>::new(
+            false_child_idx,
+            visitor.samples,
+            visitor.order,
+            visitor.x,
+            visitor.y,
+            visitor.level + 1,
+        );
 
         if self.find_best_cutoff(&mut false_visitor, mtry) {
             visitor_queue.push_back(false_visitor);
@@ -339,7 +391,6 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
 
         true
     }
-
 }
 
 #[cfg(test)]
@@ -349,7 +400,6 @@ mod tests {
 
     #[test]
     fn fit_longley() {
-
         let x = DenseMatrix::from_array(&[
             &[234.289, 235.6, 159., 107.608, 1947., 60.323],
             &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
@@ -366,8 +416,12 @@ mod tests {
             &[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
             &[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]]);
+            &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        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];        
+        ]);
+        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,
+        ];
 
         let y_hat = DecisionTreeRegressor::fit(&x, &y, Default::default()).predict(&x);
 
@@ -375,20 +429,43 @@ mod tests {
             assert!((y_hat[i] - y[i]).abs() < 0.1);
         }
 
-        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];
+        let expected_y = vec![
-        let y_hat = DecisionTreeRegressor::fit(&x, &y, DecisionTreeRegressorParameters{max_depth: Option::None, min_samples_leaf: 2, min_samples_split: 6}).predict(&x); 
+            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,
+        ];
+        let y_hat = DecisionTreeRegressor::fit(
+            &x,
+            &y,
+            DecisionTreeRegressorParameters {
+                max_depth: Option::None,
+                min_samples_leaf: 2,
+                min_samples_split: 6,
+            },
+        )
+        .predict(&x);
 
         for i in 0..y_hat.len() {
             assert!((y_hat[i] - expected_y[i]).abs() < 0.1);
         }
 
-        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];
+        let expected_y = vec![
-        let y_hat = DecisionTreeRegressor::fit(&x, &y, DecisionTreeRegressorParameters{max_depth: Option::None, min_samples_leaf: 1, min_samples_split: 3}).predict(&x); 
+            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,
+        ];
+        let y_hat = DecisionTreeRegressor::fit(
+            &x,
+            &y,
+            DecisionTreeRegressorParameters {
+                max_depth: Option::None,
+                min_samples_leaf: 1,
+                min_samples_split: 3,
+            },
+        )
+        .predict(&x);
 
         for i in 0..y_hat.len() {
             assert!((y_hat[i] - expected_y[i]).abs() < 0.1);
         }
-            
     }
 
     #[test]
@@ -409,15 +486,18 @@ mod tests {
             &[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
             &[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]]);
+            &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
-        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];           
+        ]);
+        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,
+        ];
 
         let tree = DecisionTreeRegressor::fit(&x, &y, Default::default());
 
-        let deserialized_tree: DecisionTreeRegressor<f64> = bincode::deserialize(&bincode::serialize(&tree).unwrap()).unwrap();
+        let deserialized_tree: DecisionTreeRegressor<f64> =
+            bincode::deserialize(&bincode::serialize(&tree).unwrap()).unwrap();
 
         assert_eq!(tree, deserialized_tree);
-        
     }
-
 }

src/tree/mod.rs

@@ -1,2 +1,2 @@
-pub mod decision_tree_regressor;
 pub mod decision_tree_classifier;
+pub mod decision_tree_regressor;