Add detailed tasks to docstring

* docs: Fix typo in doc for categorical transformer.
* feat: Add option to take a column from Matrix.
I created the method `Matrix::take_column` that uses the `Matrix::take`-interface to extract a single column from a matrix. I need that feature in the implementation of  `StandardScaler`.
* feat: Add `StandardScaler`.
Authored-by: titoeb <timtoebrock@googlemail.com>

Cargo.toml

@@ -17,6 +17,7 @@ default = ["datasets"]
 ndarray-bindings = ["ndarray"]
 nalgebra-bindings = ["nalgebra"]
 datasets = []
+fp_bench = []
 
 [dependencies]
 ndarray = { version = "0.15", optional = true }
@@ -26,6 +27,7 @@ num = "0.4"
 rand = "0.8"
 rand_distr = "0.4"
 serde = { version = "1", features = ["derive"], optional = true }
+itertools = "0.10.3"
 
 [target.'cfg(target_arch = "wasm32")'.dependencies]
 getrandom = { version = "0.2", features = ["js"] }
@@ -46,3 +48,8 @@ harness = false
 name = "naive_bayes"
 harness = false
 required-features = ["ndarray-bindings", "nalgebra-bindings"]
+
+[[bench]]
+name = "fastpair"
+harness = false
+required-features = ["fp_bench"]

benches/fastpair.rs

@@ -0,0 +1,56 @@
+use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
+
+// to run this bench you have to change the declaraion in mod.rs ---> pub mod fastpair;
+use smartcore::algorithm::neighbour::fastpair::FastPair;
+use smartcore::linalg::naive::dense_matrix::*;
+use std::time::Duration;
+
+fn closest_pair_bench(n: usize, m: usize) -> () {
+    let x = DenseMatrix::<f64>::rand(n, m);
+    let fastpair = FastPair::new(&x);
+    let result = fastpair.unwrap();
+
+    result.closest_pair();
+}
+
+fn closest_pair_brute_bench(n: usize, m: usize) -> () {
+    let x = DenseMatrix::<f64>::rand(n, m);
+    let fastpair = FastPair::new(&x);
+    let result = fastpair.unwrap();
+
+    result.closest_pair_brute();
+}
+
+fn bench_fastpair(c: &mut Criterion) {
+    let mut group = c.benchmark_group("FastPair");
+
+    // with full samples size (100) the test will take too long
+    group.significance_level(0.1).sample_size(30);
+    // increase from default 5.0 secs
+    group.measurement_time(Duration::from_secs(60));
+
+    for n_samples in [100_usize, 1000_usize].iter() {
+        for n_features in [10_usize, 100_usize, 1000_usize].iter() {
+            group.bench_with_input(
+                BenchmarkId::from_parameter(format!(
+                    "fastpair --- n_samples: {}, n_features: {}",
+                    n_samples, n_features
+                )),
+                n_samples,
+                |b, _| b.iter(|| closest_pair_bench(*n_samples, *n_features)),
+            );
+            group.bench_with_input(
+                BenchmarkId::from_parameter(format!(
+                    "brute --- n_samples: {}, n_features: {}",
+                    n_samples, n_features
+                )),
+                n_samples,
+                |b, _| b.iter(|| closest_pair_brute_bench(*n_samples, *n_features)),
+            );
+        }
+    }
+    group.finish();
+}
+
+criterion_group!(benches, bench_fastpair);
+criterion_main!(benches);

src/algorithm/neighbour/bbd_tree.rs

@@ -59,7 +59,7 @@ impl<T: RealNumber> BBDTree<T> {
         tree
     }
 
-    pub(in crate) fn clustering(
+    pub(crate) fn clustering(
         &self,
         centroids: &[Vec<T>],
         sums: &mut Vec<Vec<T>>,

src/algorithm/neighbour/distances.rs

@@ -0,0 +1,48 @@
+//!
+//! Dissimilarities for vector-vector distance
+//!
+//! Representing distances as pairwise dissimilarities, so to build a
+//! graph of closest neighbours. This representation can be reused for
+//! different implementations (initially used in this library for FastPair).
+use std::cmp::{Eq, Ordering, PartialOrd};
+
+#[cfg(feature = "serde")]
+use serde::{Deserialize, Serialize};
+
+use crate::math::num::RealNumber;
+
+///
+/// The edge of the subgraph is defined by `PairwiseDistance`.
+/// The calling algorithm can store a list of distsances as
+/// a list of these structures.
+///
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone, Copy)]
+pub struct PairwiseDistance<T: RealNumber> {
+    /// index of the vector in the original `Matrix` or list
+    pub node: usize,
+
+    /// index of the closest neighbor in the original `Matrix` or same list
+    pub neighbour: Option<usize>,
+
+    /// measure of distance, according to the algorithm distance function
+    /// if the distance is None, the edge has value "infinite" or max distance
+    /// each algorithm has to match
+    pub distance: Option<T>,
+}
+
+impl<T: RealNumber> Eq for PairwiseDistance<T> {}
+
+impl<T: RealNumber> PartialEq for PairwiseDistance<T> {
+    fn eq(&self, other: &Self) -> bool {
+        self.node == other.node
+            && self.neighbour == other.neighbour
+            && self.distance == other.distance
+    }
+}
+
+impl<T: RealNumber> PartialOrd for PairwiseDistance<T> {
+    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
+        self.distance.partial_cmp(&other.distance)
+    }
+}

src/algorithm/neighbour/fastpair.rs

@@ -0,0 +1,570 @@
+#![allow(non_snake_case)]
+use itertools::Itertools;
+///
+/// # FastPair: Data-structure for the dynamic closest-pair problem.
+///
+/// Reference:
+///  Eppstein, David: Fast hierarchical clustering and other applications of
+///  dynamic closest pairs. Journal of Experimental Algorithmics 5 (2000) 1.
+///
+/// Example:
+/// ```
+/// use smartcore::algorithm::neighbour::distances::PairwiseDistance;
+/// use smartcore::linalg::naive::dense_matrix::DenseMatrix;
+/// use smartcore::algorithm::neighbour::fastpair::FastPair;
+/// let x = DenseMatrix::<f64>::from_2d_array(&[
+///     &[5.1, 3.5, 1.4, 0.2],
+///     &[4.9, 3.0, 1.4, 0.2],
+///     &[4.7, 3.2, 1.3, 0.2],
+///     &[4.6, 3.1, 1.5, 0.2],
+///     &[5.0, 3.6, 1.4, 0.2],
+///     &[5.4, 3.9, 1.7, 0.4],
+/// ]);
+/// let fastpair = FastPair::new(&x);
+/// let closest_pair: PairwiseDistance<f64> = fastpair.unwrap().closest_pair();
+/// ```
+/// <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
+/// <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+use std::collections::HashMap;
+
+use crate::algorithm::neighbour::distances::PairwiseDistance;
+use crate::error::{Failed, FailedError};
+use crate::linalg::Matrix;
+use crate::math::distance::euclidian::Euclidian;
+use crate::math::num::RealNumber;
+
+///
+/// Inspired by Python implementation:
+/// <https://github.com/carsonfarmer/fastpair/blob/b8b4d3000ab6f795a878936667eee1b557bf353d/fastpair/base.py>
+/// MIT License (MIT) Copyright (c) 2016 Carson Farmer
+///
+/// affinity used is Euclidean so to allow linkage with single, ward, complete and average
+///
+#[derive(Debug, Clone)]
+pub struct FastPair<'a, T: RealNumber, M: Matrix<T>> {
+    /// initial matrix
+    samples: &'a M,
+    /// closest pair hashmap (connectivity matrix for closest pairs)
+    pub distances: HashMap<usize, PairwiseDistance<T>>,
+    /// conga line used to keep track of the closest pair
+    pub neighbours: Vec<usize>,
+}
+
+impl<'a, T: RealNumber, M: Matrix<T>> FastPair<'a, T, M> {
+    ///
+    /// Constructor
+    /// Instantiate and inizialise the algorithm
+    ///
+    pub fn new(m: &'a M) -> Result<Self, Failed> {
+        if m.shape().0 < 3 {
+            return Err(Failed::because(
+                FailedError::FindFailed,
+                "min number of rows should be 3",
+            ));
+        }
+
+        let mut init = Self {
+            samples: m,
+            // to be computed in init(..)
+            distances: HashMap::with_capacity(m.shape().0),
+            neighbours: Vec::with_capacity(m.shape().0 + 1),
+        };
+        init.init();
+        Ok(init)
+    }
+
+    ///
+    /// Initialise `FastPair` by passing a `Matrix`.
+    /// Build a FastPairs data-structure from a set of (new) points.
+    ///
+    fn init(&mut self) {
+        // basic measures
+        let len = self.samples.shape().0;
+        let max_index = self.samples.shape().0 - 1;
+
+        // Store all closest neighbors
+        let _distances = Box::new(HashMap::with_capacity(len));
+        let _neighbours = Box::new(Vec::with_capacity(len));
+
+        let mut distances = *_distances;
+        let mut neighbours = *_neighbours;
+
+        // fill neighbours with -1 values
+        neighbours.extend(0..len);
+
+        // init closest neighbour pairwise data
+        for index_row_i in 0..(max_index) {
+            distances.insert(
+                index_row_i,
+                PairwiseDistance {
+                    node: index_row_i,
+                    neighbour: None,
+                    distance: Some(T::max_value()),
+                },
+            );
+        }
+
+        // loop through indeces and neighbours
+        for index_row_i in 0..(len) {
+            // start looking for the neighbour in the second element
+            let mut index_closest = index_row_i + 1; // closest neighbour index
+            let mut nbd: Option<T> = distances[&index_row_i].distance; // init neighbour distance
+            for index_row_j in (index_row_i + 1)..len {
+                distances.insert(
+                    index_row_j,
+                    PairwiseDistance {
+                        node: index_row_j,
+                        neighbour: Some(index_row_i),
+                        distance: nbd,
+                    },
+                );
+
+                let d = Euclidian::squared_distance(
+                    &(self.samples.get_row_as_vec(index_row_i)),
+                    &(self.samples.get_row_as_vec(index_row_j)),
+                );
+                if d < nbd.unwrap() {
+                    // set this j-value to be the closest neighbour
+                    index_closest = index_row_j;
+                    nbd = Some(d);
+                }
+            }
+
+            // Add that edge
+            distances.entry(index_row_i).and_modify(|e| {
+                e.distance = nbd;
+                e.neighbour = Some(index_closest);
+            });
+        }
+        // No more neighbors, terminate conga line.
+        // Last person on the line has no neigbors
+        distances.get_mut(&max_index).unwrap().neighbour = Some(max_index);
+        distances.get_mut(&(len - 1)).unwrap().distance = Some(T::max_value());
+
+        // compute sparse matrix (connectivity matrix)
+        let mut sparse_matrix = M::zeros(len, len);
+        for (_, p) in distances.iter() {
+            sparse_matrix.set(p.node, p.neighbour.unwrap(), p.distance.unwrap());
+        }
+
+        self.distances = distances;
+        self.neighbours = neighbours;
+    }
+
+    ///
+    /// Find closest pair by scanning list of nearest neighbors.
+    ///
+    #[allow(dead_code)]
+    pub fn closest_pair(&self) -> PairwiseDistance<T> {
+        let mut a = self.neighbours[0]; // Start with first point
+        let mut d = self.distances[&a].distance;
+        for p in self.neighbours.iter() {
+            if self.distances[p].distance < d {
+                a = *p; // Update `a` and distance `d`
+                d = self.distances[p].distance;
+            }
+        }
+        let b = self.distances[&a].neighbour;
+        PairwiseDistance {
+            node: a,
+            neighbour: b,
+            distance: d,
+        }
+    }
+
+    ///
+    /// Brute force algorithm, used only for comparison and testing
+    ///
+    #[cfg(feature = "fp_bench")]
+    pub fn closest_pair_brute(&self) -> PairwiseDistance<T> {
+        let m = self.samples.shape().0;
+
+        let mut closest_pair = PairwiseDistance {
+            node: 0,
+            neighbour: None,
+            distance: Some(T::max_value()),
+        };
+        for pair in (0..m).combinations(2) {
+            let d = Euclidian::squared_distance(
+                &(self.samples.get_row_as_vec(pair[0])),
+                &(self.samples.get_row_as_vec(pair[1])),
+            );
+            if d < closest_pair.distance.unwrap() {
+                closest_pair.node = pair[0];
+                closest_pair.neighbour = Some(pair[1]);
+                closest_pair.distance = Some(d);
+            }
+        }
+        closest_pair
+    }
+
+    //
+    // Compute distances from input to all other points in data-structure.
+    // input is the row index of the sample matrix
+    //
+    #[allow(dead_code)]
+    fn distances_from(&self, index_row: usize) -> Vec<PairwiseDistance<T>> {
+        let mut distances = Vec::<PairwiseDistance<T>>::with_capacity(self.samples.shape().0);
+        for other in self.neighbours.iter() {
+            if index_row != *other {
+                distances.push(PairwiseDistance {
+                    node: index_row,
+                    neighbour: Some(*other),
+                    distance: Some(Euclidian::squared_distance(
+                        &(self.samples.get_row_as_vec(index_row)),
+                        &(self.samples.get_row_as_vec(*other)),
+                    )),
+                })
+            }
+        }
+        distances
+    }
+}
+
+#[cfg(test)]
+mod tests_fastpair {
+
+    use super::*;
+    use crate::linalg::naive::dense_matrix::*;
+
+    #[test]
+    fn fastpair_init() {
+        let x: DenseMatrix<f64> = DenseMatrix::rand(10, 4);
+        let _fastpair = FastPair::new(&x);
+        assert!(_fastpair.is_ok());
+
+        let fastpair = _fastpair.unwrap();
+
+        let distances = fastpair.distances;
+        let neighbours = fastpair.neighbours;
+
+        assert!(distances.len() != 0);
+        assert!(neighbours.len() != 0);
+
+        assert_eq!(10, neighbours.len());
+        assert_eq!(10, distances.len());
+    }
+
+    #[test]
+    fn dataset_has_at_least_three_points() {
+        // Create a dataset which consists of only two points:
+        // A(0.0, 0.0) and B(1.0, 1.0).
+        let dataset = DenseMatrix::<f64>::from_2d_array(&[&[0.0, 0.0], &[1.0, 1.0]]);
+
+        // We expect an error when we run `FastPair` on this dataset,
+        // becuase `FastPair` currently only works on a minimum of 3
+        // points.
+        let _fastpair = FastPair::new(&dataset);
+
+        match _fastpair {
+            Err(e) => {
+                let expected_error =
+                    Failed::because(FailedError::FindFailed, "min number of rows should be 3");
+                assert_eq!(e, expected_error)
+            }
+            _ => {
+                assert!(false);
+            }
+        }
+    }
+
+    #[test]
+    fn one_dimensional_dataset_minimal() {
+        let dataset = DenseMatrix::<f64>::from_2d_array(&[&[0.0], &[2.0], &[9.0]]);
+
+        let result = FastPair::new(&dataset);
+        assert!(result.is_ok());
+
+        let fastpair = result.unwrap();
+        let closest_pair = fastpair.closest_pair();
+        let expected_closest_pair = PairwiseDistance {
+            node: 0,
+            neighbour: Some(1),
+            distance: Some(4.0),
+        };
+        assert_eq!(closest_pair, expected_closest_pair);
+
+        let closest_pair_brute = fastpair.closest_pair_brute();
+        assert_eq!(closest_pair_brute, expected_closest_pair);
+    }
+
+    #[test]
+    fn one_dimensional_dataset_2() {
+        let dataset = DenseMatrix::<f64>::from_2d_array(&[&[27.0], &[0.0], &[9.0], &[2.0]]);
+
+        let result = FastPair::new(&dataset);
+        assert!(result.is_ok());
+
+        let fastpair = result.unwrap();
+        let closest_pair = fastpair.closest_pair();
+        let expected_closest_pair = PairwiseDistance {
+            node: 1,
+            neighbour: Some(3),
+            distance: Some(4.0),
+        };
+        assert_eq!(closest_pair, fastpair.closest_pair_brute());
+        assert_eq!(closest_pair, expected_closest_pair);
+    }
+
+    #[test]
+    fn fastpair_new() {
+        // compute
+        let x = DenseMatrix::<f64>::from_2d_array(&[
+            &[5.1, 3.5, 1.4, 0.2],
+            &[4.9, 3.0, 1.4, 0.2],
+            &[4.7, 3.2, 1.3, 0.2],
+            &[4.6, 3.1, 1.5, 0.2],
+            &[5.0, 3.6, 1.4, 0.2],
+            &[5.4, 3.9, 1.7, 0.4],
+            &[4.6, 3.4, 1.4, 0.3],
+            &[5.0, 3.4, 1.5, 0.2],
+            &[4.4, 2.9, 1.4, 0.2],
+            &[4.9, 3.1, 1.5, 0.1],
+            &[7.0, 3.2, 4.7, 1.4],
+            &[6.4, 3.2, 4.5, 1.5],
+            &[6.9, 3.1, 4.9, 1.5],
+            &[5.5, 2.3, 4.0, 1.3],
+            &[6.5, 2.8, 4.6, 1.5],
+        ]);
+        let fastpair = FastPair::new(&x);
+        assert!(fastpair.is_ok());
+
+        // unwrap results
+        let result = fastpair.unwrap();
+
+        // list of minimal pairwise dissimilarities
+        let dissimilarities = vec![
+            (
+                1,
+                PairwiseDistance {
+                    node: 1,
+                    neighbour: Some(9),
+                    distance: Some(0.030000000000000037),
+                },
+            ),
+            (
+                10,
+                PairwiseDistance {
+                    node: 10,
+                    neighbour: Some(12),
+                    distance: Some(0.07000000000000003),
+                },
+            ),
+            (
+                11,
+                PairwiseDistance {
+                    node: 11,
+                    neighbour: Some(14),
+                    distance: Some(0.18000000000000013),
+                },
+            ),
+            (
+                12,
+                PairwiseDistance {
+                    node: 12,
+                    neighbour: Some(14),
+                    distance: Some(0.34000000000000086),
+                },
+            ),
+            (
+                13,
+                PairwiseDistance {
+                    node: 13,
+                    neighbour: Some(14),
+                    distance: Some(1.6499999999999997),
+                },
+            ),
+            (
+                14,
+                PairwiseDistance {
+                    node: 14,
+                    neighbour: Some(14),
+                    distance: Some(f64::MAX),
+                },
+            ),
+            (
+                6,
+                PairwiseDistance {
+                    node: 6,
+                    neighbour: Some(7),
+                    distance: Some(0.18000000000000027),
+                },
+            ),
+            (
+                0,
+                PairwiseDistance {
+                    node: 0,
+                    neighbour: Some(4),
+                    distance: Some(0.01999999999999995),
+                },
+            ),
+            (
+                8,
+                PairwiseDistance {
+                    node: 8,
+                    neighbour: Some(9),
+                    distance: Some(0.3100000000000001),
+                },
+            ),
+            (
+                2,
+                PairwiseDistance {
+                    node: 2,
+                    neighbour: Some(3),
+                    distance: Some(0.0600000000000001),
+                },
+            ),
+            (
+                3,
+                PairwiseDistance {
+                    node: 3,
+                    neighbour: Some(8),
+                    distance: Some(0.08999999999999982),
+                },
+            ),
+            (
+                7,
+                PairwiseDistance {
+                    node: 7,
+                    neighbour: Some(9),
+                    distance: Some(0.10999999999999982),
+                },
+            ),
+            (
+                9,
+                PairwiseDistance {
+                    node: 9,
+                    neighbour: Some(13),
+                    distance: Some(8.69),
+                },
+            ),
+            (
+                4,
+                PairwiseDistance {
+                    node: 4,
+                    neighbour: Some(7),
+                    distance: Some(0.050000000000000086),
+                },
+            ),
+            (
+                5,
+                PairwiseDistance {
+                    node: 5,
+                    neighbour: Some(7),
+                    distance: Some(0.4900000000000002),
+                },
+            ),
+        ];
+
+        let expected: HashMap<_, _> = dissimilarities.into_iter().collect();
+
+        for i in 0..(x.shape().0 - 1) {
+            let input_node = result.samples.get_row_as_vec(i);
+            let input_neighbour: usize = expected.get(&i).unwrap().neighbour.unwrap();
+            let distance = Euclidian::squared_distance(
+                &input_node,
+                &result.samples.get_row_as_vec(input_neighbour),
+            );
+
+            assert_eq!(i, expected.get(&i).unwrap().node);
+            assert_eq!(
+                input_neighbour,
+                expected.get(&i).unwrap().neighbour.unwrap()
+            );
+            assert_eq!(distance, expected.get(&i).unwrap().distance.unwrap());
+        }
+    }
+
+    #[test]
+    fn fastpair_closest_pair() {
+        let x = DenseMatrix::<f64>::from_2d_array(&[
+            &[5.1, 3.5, 1.4, 0.2],
+            &[4.9, 3.0, 1.4, 0.2],
+            &[4.7, 3.2, 1.3, 0.2],
+            &[4.6, 3.1, 1.5, 0.2],
+            &[5.0, 3.6, 1.4, 0.2],
+            &[5.4, 3.9, 1.7, 0.4],
+            &[4.6, 3.4, 1.4, 0.3],
+            &[5.0, 3.4, 1.5, 0.2],
+            &[4.4, 2.9, 1.4, 0.2],
+            &[4.9, 3.1, 1.5, 0.1],
+            &[7.0, 3.2, 4.7, 1.4],
+            &[6.4, 3.2, 4.5, 1.5],
+            &[6.9, 3.1, 4.9, 1.5],
+            &[5.5, 2.3, 4.0, 1.3],
+            &[6.5, 2.8, 4.6, 1.5],
+        ]);
+        // compute
+        let fastpair = FastPair::new(&x);
+        assert!(fastpair.is_ok());
+
+        let dissimilarity = fastpair.unwrap().closest_pair();
+        let closest = PairwiseDistance {
+            node: 0,
+            neighbour: Some(4),
+            distance: Some(0.01999999999999995),
+        };
+
+        assert_eq!(closest, dissimilarity);
+    }
+
+    #[test]
+    fn fastpair_closest_pair_random_matrix() {
+        let x = DenseMatrix::<f64>::rand(200, 25);
+        // compute
+        let fastpair = FastPair::new(&x);
+        assert!(fastpair.is_ok());
+
+        let result = fastpair.unwrap();
+
+        let dissimilarity1 = result.closest_pair();
+        let dissimilarity2 = result.closest_pair_brute();
+
+        assert_eq!(dissimilarity1, dissimilarity2);
+    }
+
+    #[test]
+    fn fastpair_distances() {
+        let x = DenseMatrix::<f64>::from_2d_array(&[
+            &[5.1, 3.5, 1.4, 0.2],
+            &[4.9, 3.0, 1.4, 0.2],
+            &[4.7, 3.2, 1.3, 0.2],
+            &[4.6, 3.1, 1.5, 0.2],
+            &[5.0, 3.6, 1.4, 0.2],
+            &[5.4, 3.9, 1.7, 0.4],
+            &[4.6, 3.4, 1.4, 0.3],
+            &[5.0, 3.4, 1.5, 0.2],
+            &[4.4, 2.9, 1.4, 0.2],
+            &[4.9, 3.1, 1.5, 0.1],
+            &[7.0, 3.2, 4.7, 1.4],
+            &[6.4, 3.2, 4.5, 1.5],
+            &[6.9, 3.1, 4.9, 1.5],
+            &[5.5, 2.3, 4.0, 1.3],
+            &[6.5, 2.8, 4.6, 1.5],
+        ]);
+        // compute
+        let fastpair = FastPair::new(&x);
+        assert!(fastpair.is_ok());
+
+        let dissimilarities = fastpair.unwrap().distances_from(0);
+
+        let mut min_dissimilarity = PairwiseDistance {
+            node: 0,
+            neighbour: None,
+            distance: Some(f64::MAX),
+        };
+        for p in dissimilarities.iter() {
+            if p.distance.unwrap() < min_dissimilarity.distance.unwrap() {
+                min_dissimilarity = p.clone()
+            }
+        }
+
+        let closest = PairwiseDistance {
+            node: 0,
+            neighbour: Some(4),
+            distance: Some(0.01999999999999995),
+        };
+
+        assert_eq!(closest, min_dissimilarity);
+    }
+}

src/algorithm/neighbour/mod.rs

@@ -41,6 +41,10 @@ use serde::{Deserialize, Serialize};
 pub(crate) mod bbd_tree;
 /// tree data structure for fast nearest neighbor search
 pub mod cover_tree;
+/// dissimilarities for vector-vector distance. Linkage algorithms used in fastpair
+pub mod distances;
+/// fastpair closest neighbour algorithm
+pub mod fastpair;
 /// very simple algorithm that sequentially checks each element of the list until a match is found or the whole list has been searched.
 pub mod linear_search;
 

src/algorithm/sort/heap_select.rs

@@ -12,7 +12,7 @@ pub struct HeapSelection<T: PartialOrd + Debug> {
     heap: Vec<T>,
 }
 
-impl<'a, T: PartialOrd + Debug> HeapSelection<T> {
+impl<T: PartialOrd + Debug> HeapSelection<T> {
     pub fn with_capacity(k: usize) -> HeapSelection<T> {
         HeapSelection {
             k,

src/cluster/hierarchical.rs

@@ -0,0 +1,63 @@
+/// # Hierarchical clustering
+/// 
+/// Implement hierarchical clustering methods:
+/// * Agglomerative clustering (current)
+/// * Bisecting K-Means (future)
+/// * Fastcluster (future)
+/// 
+
+/* 
+class AgglomerativeClustering():
+    """
+    Parameters
+    ----------
+    n_clusters : int or None, default=2
+        The number of clusters to find. It must be ``None`` if
+        ``distance_threshold`` is not ``None``.
+    affinity : str or callable, default='euclidean'
+        If linkage is "ward", only "euclidean" is accepted.
+    linkage : {'ward',}, default='ward'
+        Which linkage criterion to use. The linkage criterion determines which
+        distance to use between sets of observation. The algorithm will merge
+        the pairs of cluster that minimize this criterion.
+        - 'ward' minimizes the variance of the clusters being merged.
+    compute_distances : bool, default=False
+        Computes distances between clusters even if `distance_threshold` is not
+        used. This can be used to make dendrogram visualization, but introduces
+        a computational and memory overhead.
+    """
+
+    def fit(X):
+        # compute tree
+        # <https://github.com/scikit-learn/scikit-learn/blob/02ebf9e68fe1fc7687d9e1047b9e465ae0fd945e/sklearn/cluster/_agglomerative.py#L172>
+        parents, childern = ward_tree(X, ....)
+        # compute clusters
+        # <https://github.com/scikit-learn/scikit-learn/blob/70c495250fea7fa3c8c1a4631e6ddcddc9f22451/sklearn/cluster/_hierarchical_fast.pyx#L98>
+        labels = _hierarchical.hc_get_heads(parents)
+        # assign cluster numbers
+        self.labels_ = np.searchsorted(np.unique(labels), labels)
+
+*/
+
+// implement ward tree
+// use scipy.cluster.hierarchy.ward
+// <https://github.com/scipy/scipy/blob/main/scipy/cluster/hierarchy.py#L738>
+// use linkage
+// <https://github.com/scipy/scipy/blob/main/scipy/cluster/hierarchy.py#L837>
+// use nn_chain
+// <https://github.com/scipy/scipy/blob/main/scipy/cluster/_hierarchy.pyx#L906>
+
+// implement hc_get_heads
+
+
+mod tests {
+    // >>> from sklearn.cluster import AgglomerativeClustering
+    // >>> import numpy as np
+    // >>> X = np.array([[1, 2], [1, 4], [1, 0],
+    // ...               [4, 2], [4, 4], [4, 0]])
+    // >>> clustering = AgglomerativeClustering().fit(X)
+    // >>> clustering
+    // AgglomerativeClustering()
+    // >>> clustering.labels_
+    // array([1, 1, 1, 0, 0, 0])
+}

src/ensemble/random_forest_classifier.rs

@@ -55,8 +55,7 @@ use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::{Failed, FailedError};
-use crate::linalg::naive::dense_matrix::DenseMatrix;
-use crate::linalg::{BaseMatrix, Matrix};
+use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 use crate::tree::decision_tree_classifier::{
     which_max, DecisionTreeClassifier, DecisionTreeClassifierParameters, SplitCriterion,
@@ -317,37 +316,6 @@ impl<T: RealNumber> RandomForestClassifier<T> {
         which_max(&result)
     }
 
-    /// Predict the per-class probabilties for each observation.
-    /// The probability is calculated as the fraction of trees that predicted a given class
-    pub fn predict_probs<M: Matrix<T>>(&self, x: &M) -> Result<DenseMatrix<f64>, Failed> {
-        let mut result = DenseMatrix::<f64>::zeros(x.shape().0, self.classes.len());
-
-        let (n, _) = x.shape();
-
-        for i in 0..n {
-            let row_probs = self.predict_probs_for_row(x, i);
-
-            for (j, item) in row_probs.iter().enumerate() {
-                result.set(i, j, *item);
-            }
-        }
-
-        Ok(result)
-    }
-
-    fn predict_probs_for_row<M: Matrix<T>>(&self, x: &M, row: usize) -> Vec<f64> {
-        let mut result = vec![0; self.classes.len()];
-
-        for tree in self.trees.iter() {
-            result[tree.predict_for_row(x, row)] += 1;
-        }
-
-        result
-            .iter()
-            .map(|n| *n as f64 / self.trees.len() as f64)
-            .collect()
-    }
-
     fn sample_with_replacement(y: &[usize], num_classes: usize, rng: &mut impl Rng) -> Vec<usize> {
         let class_weight = vec![1.; num_classes];
         let nrows = y.len();
@@ -373,7 +341,7 @@ impl<T: RealNumber> RandomForestClassifier<T> {
 }
 
 #[cfg(test)]
-mod tests_prob {
+mod tests {
     use super::*;
     use crate::linalg::naive::dense_matrix::DenseMatrix;
     use crate::metrics::*;
@@ -514,71 +482,4 @@ mod tests_prob {
 
         assert_eq!(forest, deserialized_forest);
     }
-
-    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
-    #[test]
-    fn fit_predict_probabilities() {
-        let x = DenseMatrix::<f64>::from_2d_array(&[
-            &[5.1, 3.5, 1.4, 0.2],
-            &[4.9, 3.0, 1.4, 0.2],
-            &[4.7, 3.2, 1.3, 0.2],
-            &[4.6, 3.1, 1.5, 0.2],
-            &[5.0, 3.6, 1.4, 0.2],
-            &[5.4, 3.9, 1.7, 0.4],
-            &[4.6, 3.4, 1.4, 0.3],
-            &[5.0, 3.4, 1.5, 0.2],
-            &[4.4, 2.9, 1.4, 0.2],
-            &[4.9, 3.1, 1.5, 0.1],
-            &[7.0, 3.2, 4.7, 1.4],
-            &[6.4, 3.2, 4.5, 1.5],
-            &[6.9, 3.1, 4.9, 1.5],
-            &[5.5, 2.3, 4.0, 1.3],
-            &[6.5, 2.8, 4.6, 1.5],
-            &[5.7, 2.8, 4.5, 1.3],
-            &[6.3, 3.3, 4.7, 1.6],
-            &[4.9, 2.4, 3.3, 1.0],
-            &[6.6, 2.9, 4.6, 1.3],
-            &[5.2, 2.7, 3.9, 1.4],
-        ]);
-        let 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 {
-                criterion: SplitCriterion::Gini,
-                max_depth: None,
-                min_samples_leaf: 1,
-                min_samples_split: 2,
-                n_trees: 100,
-                m: Option::None,
-                keep_samples: false,
-                seed: 87,
-            },
-        )
-        .unwrap();
-
-        println!("{:?}", classifier.classes);
-
-        let results = classifier.predict_probs(&x).unwrap();
-        println!("{:?}", x.shape());
-        println!("{:?}", results);
-        println!("{:?}", results.shape());
-
-        assert_eq!(
-            results,
-            DenseMatrix::<f64>::from_array(
-                20,
-                2,
-                &[
-                    1.0, 0.78, 0.95, 0.82, 1.0, 0.92, 0.99, 0.96, 0.36, 0.33, 0.02, 0.02, 0.0, 0.0,
-                    0.0, 0.0, 0.03, 0.05, 0.0, 0.02, 0.0, 0.22, 0.05, 0.18, 0.0, 0.08, 0.01, 0.04,
-                    0.64, 0.67, 0.98, 0.98, 1.0, 1.0, 1.0, 1.0, 0.97, 0.95, 1.0, 0.98
-                ]
-            )
-        );
-        assert!(false);
-    }
 }

src/linalg/evd.rs

@@ -25,6 +25,19 @@
 //! let eigenvectors: DenseMatrix<f64> = evd.V;
 //! let eigenvalues: Vec<f64> = evd.d;
 //! ```
+//! ```
+//! use smartcore::linalg::naive::dense_matrix::*;
+//! use smartcore::linalg::evd::*;
+//!
+//! let A = DenseMatrix::from_2d_array(&[
+//!     &[-5.0, 2.0],
+//!     &[-7.0, 4.0],
+//! ]);
+//!
+//! let evd = A.evd(false).unwrap();
+//! let eigenvectors: DenseMatrix<f64> = evd.V;
+//! let eigenvalues: Vec<f64> = evd.d;
+//! ```
 //!
 //! ## References:
 //! * ["Numerical Recipes: The Art of Scientific Computing",  Press W.H., Teukolsky S.A., Vetterling W.T, Flannery B.P, 3rd ed., Section 11 Eigensystems](http://numerical.recipes/)
@@ -799,10 +812,10 @@ fn sort<T: RealNumber, M: BaseMatrix<T>>(d: &mut [T], e: &mut [T], V: &mut M) {
             }
             i -= 1;
         }
-        d[i as usize + 1] = real;
-        e[i as usize + 1] = img;
+        d[(i + 1) as usize] = real;
+        e[(i + 1) as usize] = img;
         for (k, temp_k) in temp.iter().enumerate().take(n) {
-            V.set(k, i as usize + 1, *temp_k);
+            V.set(k, (i + 1) as usize, *temp_k);
         }
     }
 }

src/linalg/mod.rs

@@ -651,6 +651,10 @@ pub trait BaseMatrix<T: RealNumber>: Clone + Debug {
 
         result
     }
+    /// Take an individual column from the matrix.
+    fn take_column(&self, column_index: usize) -> Self {
+        self.take(&[column_index], 1)
+    }
 }
 
 /// Generic matrix with additional mixins like various factorization methods.
@@ -761,4 +765,21 @@ mod tests {
         assert_eq!(m.take(&vec!(1, 1, 3), 0), expected_0);
         assert_eq!(m.take(&vec!(1, 0), 1), expected_1);
     }
+
+    #[test]
+    fn take_second_column_from_matrix() {
+        let four_columns: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
+            &[0.0, 1.0, 2.0, 3.0],
+            &[0.0, 1.0, 2.0, 3.0],
+            &[0.0, 1.0, 2.0, 3.0],
+            &[0.0, 1.0, 2.0, 3.0],
+        ]);
+
+        let second_column = four_columns.take_column(1);
+        assert_eq!(
+            second_column,
+            DenseMatrix::from_2d_array(&[&[1.0], &[1.0], &[1.0], &[1.0]]),
+            "The second column was not extracted correctly"
+        );
+    }
 }

src/linear/lasso_optimizer.rs

@@ -211,9 +211,7 @@ impl<T: RealNumber, M: Matrix<T>> InteriorPointOptimizer<T, M> {
     }
 }
 
-impl<'a, T: RealNumber, M: Matrix<T>> BiconjugateGradientSolver<T, M>
-    for InteriorPointOptimizer<T, M>
-{
+impl<T: RealNumber, M: Matrix<T>> BiconjugateGradientSolver<T, M> for InteriorPointOptimizer<T, M> {
     fn solve_preconditioner(&self, a: &M, b: &M, x: &mut M) {
         let (_, p) = a.shape();
 

src/optimization/mod.rs

@@ -5,7 +5,7 @@ pub type F<'a, T, X> = dyn for<'b> Fn(&'b X) -> T + 'a;
 pub type DF<'a, X> = dyn for<'b> Fn(&'b mut X, &'b X) + 'a;
 
 #[allow(clippy::upper_case_acronyms)]
-#[derive(Debug, PartialEq)]
+#[derive(Debug, PartialEq, Eq)]
 pub enum FunctionOrder {
     SECOND,
     THIRD,

src/preprocessing/mod.rs

@@ -1,5 +1,7 @@
-/// Transform a data matrix by replaceing all categorical variables with their one-hot vector equivalents
+/// Transform a data matrix by replacing all categorical variables with their one-hot vector equivalents
 pub mod categorical;
 mod data_traits;
+/// Preprocess numerical matrices.
+pub mod numerical;
 /// Encode a series (column, array) of categorical variables as one-hot vectors
 pub mod series_encoder;

src/preprocessing/numerical.rs

@@ -0,0 +1,404 @@
+//! # Standard-Scaling For [RealNumber](../../math/num/trait.RealNumber.html) Matricies
+//! Transform a data [Matrix](../../linalg/trait.BaseMatrix.html) by removing the mean and scaling to unit variance.
+//!
+//! ### Usage Example
+//! ```
+//! use smartcore::api::{Transformer, UnsupervisedEstimator};
+//! use smartcore::linalg::naive::dense_matrix::DenseMatrix;
+//! use smartcore::preprocessing::numerical;
+//! let data = DenseMatrix::from_2d_vec(&vec![
+//!     vec![0.0, 0.0],
+//!     vec![0.0, 0.0],
+//!     vec![1.0, 1.0],
+//!     vec![1.0, 1.0],
+//! ]);
+//!
+//! let standard_scaler =
+//! numerical::StandardScaler::fit(&data, numerical::StandardScalerParameters::default())
+//!    .unwrap();
+//! let transformed_data = standard_scaler.transform(&data).unwrap();
+//! assert_eq!(
+//!     transformed_data,
+//!     DenseMatrix::from_2d_vec(&vec![
+//!         vec![-1.0, -1.0],
+//!         vec![-1.0, -1.0],
+//!         vec![1.0, 1.0],
+//!         vec![1.0, 1.0],
+//!     ])
+//! );
+//! ```
+use crate::api::{Transformer, UnsupervisedEstimator};
+use crate::error::{Failed, FailedError};
+use crate::linalg::Matrix;
+use crate::math::num::RealNumber;
+
+/// Configure Behaviour of `StandardScaler`.
+#[derive(Clone, Debug, Copy, Eq, PartialEq)]
+pub struct StandardScalerParameters {
+    /// Optionaly adjust mean to be zero.
+    with_mean: bool,
+    /// Optionally adjust standard-deviation to be one.
+    with_std: bool,
+}
+impl Default for StandardScalerParameters {
+    fn default() -> Self {
+        StandardScalerParameters {
+            with_mean: true,
+            with_std: true,
+        }
+    }
+}
+
+/// With the `StandardScaler` data can be adjusted so
+/// that every column has a mean of zero and a standard
+/// deviation of one. This can improve model training for
+/// scaling sensitive models like neural network or nearest
+/// neighbors based models.
+#[derive(Clone, Debug, Default, Eq, PartialEq)]
+pub struct StandardScaler<T: RealNumber> {
+    means: Vec<T>,
+    stds: Vec<T>,
+    parameters: StandardScalerParameters,
+}
+impl<T: RealNumber> StandardScaler<T> {
+    /// When the mean should be adjusted, the column mean
+    /// should be kept. Otherwise, replace it by zero.
+    fn adjust_column_mean(&self, mean: T) -> T {
+        if self.parameters.with_mean {
+            mean
+        } else {
+            T::zero()
+        }
+    }
+    /// When the standard-deviation should be adjusted, the column
+    /// standard-deviation should be kept. Otherwise, replace it by one.
+    fn adjust_column_std(&self, std: T) -> T {
+        if self.parameters.with_std {
+            ensure_std_valid(std)
+        } else {
+            T::one()
+        }
+    }
+}
+
+/// Make sure the standard deviation is valid. If it is
+/// negative or zero, it should replaced by the smallest
+/// positive value the type can have. That way we can savely
+/// divide the columns with the resulting scalar.
+fn ensure_std_valid<T: RealNumber>(value: T) -> T {
+    value.max(T::min_positive_value())
+}
+
+/// During `fit` the `StandardScaler` computes the column means and standard deviation.
+impl<T: RealNumber, M: Matrix<T>> UnsupervisedEstimator<M, StandardScalerParameters>
+    for StandardScaler<T>
+{
+    fn fit(x: &M, parameters: StandardScalerParameters) -> Result<Self, Failed> {
+        Ok(Self {
+            means: x.column_mean(),
+            stds: x.std(0),
+            parameters,
+        })
+    }
+}
+
+/// During `transform` the `StandardScaler` applies the summary statistics
+/// computed during `fit` to set the mean of each column to zero and the
+/// standard deviation to one.
+impl<T: RealNumber, M: Matrix<T>> Transformer<M> for StandardScaler<T> {
+    fn transform(&self, x: &M) -> Result<M, Failed> {
+        let (_, n_cols) = x.shape();
+        if n_cols != self.means.len() {
+            return Err(Failed::because(
+                FailedError::TransformFailed,
+                &format!(
+                    "Expected {} columns, but got {} columns instead.",
+                    self.means.len(),
+                    n_cols,
+                ),
+            ));
+        }
+
+        Ok(build_matrix_from_columns(
+            self.means
+                .iter()
+                .zip(self.stds.iter())
+                .enumerate()
+                .map(|(column_index, (column_mean, column_std))| {
+                    x.take_column(column_index)
+                        .sub_scalar(self.adjust_column_mean(*column_mean))
+                        .div_scalar(self.adjust_column_std(*column_std))
+                })
+                .collect(),
+        )
+        .unwrap())
+    }
+}
+
+/// From a collection of matrices, that contain columns, construct
+/// a matrix by stacking the columns horizontally.
+fn build_matrix_from_columns<T, M>(columns: Vec<M>) -> Option<M>
+where
+    T: RealNumber,
+    M: Matrix<T>,
+{
+    if let Some(output_matrix) = columns.first().cloned() {
+        return Some(
+            columns
+                .iter()
+                .skip(1)
+                .fold(output_matrix, |current_matrix, new_colum| {
+                    current_matrix.h_stack(new_colum)
+                }),
+        );
+    } else {
+        None
+    }
+}
+
+#[cfg(test)]
+mod tests {
+
+    mod helper_functionality {
+        use super::super::{build_matrix_from_columns, ensure_std_valid};
+        use crate::linalg::naive::dense_matrix::DenseMatrix;
+
+        #[test]
+        fn combine_three_columns() {
+            assert_eq!(
+                build_matrix_from_columns(vec![
+                    DenseMatrix::from_2d_vec(&vec![vec![1.0], vec![1.0], vec![1.0],]),
+                    DenseMatrix::from_2d_vec(&vec![vec![2.0], vec![2.0], vec![2.0],]),
+                    DenseMatrix::from_2d_vec(&vec![vec![3.0], vec![3.0], vec![3.0],])
+                ]),
+                Some(DenseMatrix::from_2d_vec(&vec![
+                    vec![1.0, 2.0, 3.0],
+                    vec![1.0, 2.0, 3.0],
+                    vec![1.0, 2.0, 3.0]
+                ]))
+            )
+        }
+
+        #[test]
+        fn negative_value_should_be_replace_with_minimal_positive_value() {
+            assert_eq!(ensure_std_valid(-1.0), f64::MIN_POSITIVE)
+        }
+
+        #[test]
+        fn zero_should_be_replace_with_minimal_positive_value() {
+            assert_eq!(ensure_std_valid(0.0), f64::MIN_POSITIVE)
+        }
+    }
+    mod standard_scaler {
+        use super::super::{StandardScaler, StandardScalerParameters};
+        use crate::api::{Transformer, UnsupervisedEstimator};
+        use crate::linalg::naive::dense_matrix::DenseMatrix;
+        use crate::linalg::BaseMatrix;
+
+        #[test]
+        fn dont_adjust_mean_if_used() {
+            assert_eq!(
+                (StandardScaler {
+                    means: vec![],
+                    stds: vec![],
+                    parameters: StandardScalerParameters {
+                        with_mean: true,
+                        with_std: true
+                    }
+                })
+                .adjust_column_mean(1.0),
+                1.0
+            )
+        }
+        #[test]
+        fn replace_mean_with_zero_if_not_used() {
+            assert_eq!(
+                (StandardScaler {
+                    means: vec![],
+                    stds: vec![],
+                    parameters: StandardScalerParameters {
+                        with_mean: false,
+                        with_std: true
+                    }
+                })
+                .adjust_column_mean(1.0),
+                0.0
+            )
+        }
+        #[test]
+        fn dont_adjust_std_if_used() {
+            assert_eq!(
+                (StandardScaler {
+                    means: vec![],
+                    stds: vec![],
+                    parameters: StandardScalerParameters {
+                        with_mean: true,
+                        with_std: true
+                    }
+                })
+                .adjust_column_std(10.0),
+                10.0
+            )
+        }
+        #[test]
+        fn replace_std_with_one_if_not_used() {
+            assert_eq!(
+                (StandardScaler {
+                    means: vec![],
+                    stds: vec![],
+                    parameters: StandardScalerParameters {
+                        with_mean: true,
+                        with_std: false
+                    }
+                })
+                .adjust_column_std(10.0),
+                1.0
+            )
+        }
+
+        /// Helper function to apply fit as well as transform at the same time.
+        fn fit_transform_with_default_standard_scaler(
+            values_to_be_transformed: &DenseMatrix<f64>,
+        ) -> DenseMatrix<f64> {
+            StandardScaler::fit(
+                values_to_be_transformed,
+                StandardScalerParameters::default(),
+            )
+            .unwrap()
+            .transform(values_to_be_transformed)
+            .unwrap()
+        }
+
+        /// Fit transform with random generated values, expected values taken from
+        /// sklearn.
+        #[test]
+        fn fit_transform_random_values() {
+            let transformed_values =
+                fit_transform_with_default_standard_scaler(&DenseMatrix::from_2d_array(&[
+                    &[0.1004222429, 0.2194113576, 0.9310663354, 0.3313593793],
+                    &[0.2045493861, 0.1683865411, 0.5071506765, 0.7257355264],
+                    &[0.5708488802, 0.1846414616, 0.9590802982, 0.5591871046],
+                    &[0.8387612750, 0.5754861361, 0.5537109852, 0.1077646442],
+                ]));
+            println!("{}", transformed_values);
+            assert!(transformed_values.approximate_eq(
+                &DenseMatrix::from_2d_array(&[
+                    &[-1.1154020653, -0.4031985330, 0.9284605204, -0.4271473866],
+                    &[-0.7615464283, -0.7076698384, -1.1075452562, 1.2632979631],
+                    &[0.4832504303, -0.6106747444, 1.0630075435, 0.5494084257],
+                    &[1.3936980634, 1.7215431158, -0.8839228078, -1.3855590021],
+                ]),
+                1.0
+            ))
+        }
+
+        /// Test `fit` and `transform` for a column with zero variance.
+        #[test]
+        fn fit_transform_with_zero_variance() {
+            assert_eq!(
+                fit_transform_with_default_standard_scaler(&DenseMatrix::from_2d_array(&[
+                    &[1.0],
+                    &[1.0],
+                    &[1.0],
+                    &[1.0]
+                ])),
+                DenseMatrix::from_2d_array(&[&[0.0], &[0.0], &[0.0], &[0.0]]),
+                "When scaling values with zero variance, zero is expected as return value"
+            )
+        }
+
+        /// Test `fit` for columns with nice summary statistics.
+        #[test]
+        fn fit_for_simple_values() {
+            assert_eq!(
+                StandardScaler::fit(
+                    &DenseMatrix::from_2d_array(&[
+                        &[1.0, 1.0, 1.0],
+                        &[1.0, 2.0, 5.0],
+                        &[1.0, 1.0, 1.0],
+                        &[1.0, 2.0, 5.0]
+                    ]),
+                    StandardScalerParameters::default(),
+                ),
+                Ok(StandardScaler {
+                    means: vec![1.0, 1.5, 3.0],
+                    stds: vec![0.0, 0.5, 2.0],
+                    parameters: StandardScalerParameters {
+                        with_mean: true,
+                        with_std: true
+                    }
+                })
+            )
+        }
+        /// Test `fit` for random generated values.
+        #[test]
+        fn fit_for_random_values() {
+            let fitted_scaler = StandardScaler::fit(
+                &DenseMatrix::from_2d_array(&[
+                    &[0.1004222429, 0.2194113576, 0.9310663354, 0.3313593793],
+                    &[0.2045493861, 0.1683865411, 0.5071506765, 0.7257355264],
+                    &[0.5708488802, 0.1846414616, 0.9590802982, 0.5591871046],
+                    &[0.8387612750, 0.5754861361, 0.5537109852, 0.1077646442],
+                ]),
+                StandardScalerParameters::default(),
+            )
+            .unwrap();
+
+            assert_eq!(
+                fitted_scaler.means,
+                vec![0.42864544605, 0.2869813741, 0.737752073825, 0.431011663625],
+            );
+
+            assert!(
+                &DenseMatrix::from_2d_vec(&vec![fitted_scaler.stds]).approximate_eq(
+                    &DenseMatrix::from_2d_array(&[&[
+                        0.29426447500954,
+                        0.16758497615485,
+                        0.20820945786863,
+                        0.23329718831165
+                    ],]),
+                    0.00000000000001
+                )
+            )
+        }
+
+        /// If `with_std` is set to `false` the values should not be
+        /// adjusted to have a std of one.
+        #[test]
+        fn transform_without_std() {
+            let standard_scaler = StandardScaler {
+                means: vec![1.0, 3.0],
+                stds: vec![1.0, 2.0],
+                parameters: StandardScalerParameters {
+                    with_mean: true,
+                    with_std: false,
+                },
+            };
+
+            assert_eq!(
+                standard_scaler.transform(&DenseMatrix::from_2d_array(&[&[0.0, 2.0], &[2.0, 4.0]])),
+                Ok(DenseMatrix::from_2d_array(&[&[-1.0, -1.0], &[1.0, 1.0]]))
+            )
+        }
+
+        /// If `with_mean` is set to `false` the values should not be adjusted
+        /// to have a mean of zero.
+        #[test]
+        fn transform_without_mean() {
+            let standard_scaler = StandardScaler {
+                means: vec![1.0, 2.0],
+                stds: vec![2.0, 3.0],
+                parameters: StandardScalerParameters {
+                    with_mean: false,
+                    with_std: true,
+                },
+            };
+
+            assert_eq!(
+                standard_scaler
+                    .transform(&DenseMatrix::from_2d_array(&[&[0.0, 9.0], &[4.0, 12.0]])),
+                Ok(DenseMatrix::from_2d_array(&[&[0.0, 3.0], &[2.0, 4.0]]))
+            )
+        }
+    }
+}

src/svm/svr.rs

@@ -242,7 +242,7 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SVR<T, M, K> {
         Ok(y_hat)
     }
 
-    pub(in crate) fn predict_for_row(&self, x: M::RowVector) -> T {
+    pub(crate) fn predict_for_row(&self, x: M::RowVector) -> T {
         let mut f = self.b;
 
         for i in 0..self.instances.len() {

src/tree/decision_tree_classifier.rs

@@ -285,7 +285,7 @@ impl<'a, T: RealNumber, M: Matrix<T>> NodeVisitor<'a, T, M> {
     }
 }
 
-pub(in crate) fn which_max(x: &[usize]) -> usize {
+pub(crate) fn which_max(x: &[usize]) -> usize {
     let mut m = x[0];
     let mut which = 0;
 
@@ -421,7 +421,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
         Ok(result.to_row_vector())
     }
 
-    pub(in crate) fn predict_for_row<M: Matrix<T>>(&self, x: &M, row: usize) -> usize {
+    pub(crate) fn predict_for_row<M: Matrix<T>>(&self, x: &M, row: usize) -> usize {
         let mut result = 0;
         let mut queue: LinkedList<usize> = LinkedList::new();
 

src/tree/decision_tree_regressor.rs

@@ -321,7 +321,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
         Ok(result.to_row_vector())
     }
 
-    pub(in crate) fn predict_for_row<M: Matrix<T>>(&self, x: &M, row: usize) -> T {
+    pub(crate) fn predict_for_row<M: Matrix<T>>(&self, x: &M, row: usize) -> T {
         let mut result = T::zero();
         let mut queue: LinkedList<usize> = LinkedList::new();