feat: adds new distance measures + LU decomposition

benches/distance.rs

@@ -4,12 +4,12 @@ extern crate smartcore;
 
 use criterion::Criterion;
 use criterion::black_box;
-use smartcore::math::distance::euclidian::*;
+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(|| 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/neighbour/cover_tree.rs

@@ -44,7 +44,7 @@ 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(), D::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());
@@ -84,7 +84,7 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
     }   
 
     pub fn find(&self, p: &T, k: usize) -> Vec<usize>{
-        let mut qi_p_ds = vec!((self.root(), D::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);
@@ -115,7 +115,7 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
             let p = &self.nodes.get(p_id.index).unwrap().data;
             let mut i = 0;
             while i != s.len() {
-                let d = D::distance(p, &s[i]);
+                let d = self.distance.distance(p, &s[i]);
                 if d <= r {
                     my_near.0.push(s.remove(i));
                 } else if d > r && d <= F::two() * r{
@@ -169,7 +169,7 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
 
         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, D::distance(&n.data, &p))));
+        children.extend(q.into_iter().map(|n| (n, self.distance.distance(&n.data, &p))));
 
         children
         
@@ -219,7 +219,7 @@ impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
         let mut p_selected: Vec<&Node<T>> = Vec::new();
         for p in next_nodes {
             for q in nodes {
-                if D::distance(&p.data, &q.data) <= tree.base.powf(F::from(i).unwrap()) {
+                if tree.distance.distance(&p.data, &q.data) <= tree.base.powf(F::from(i).unwrap()) {
                     p_selected.push(*p);
                 }
             }                        
@@ -233,7 +233,7 @@ 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!(D::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()));
                 } 
             }                                    
         }        
@@ -280,7 +280,7 @@ mod tests {
     struct SimpleDistance{}
 
     impl Distance<i32, f64> for SimpleDistance {
-        fn distance(a: &i32, b: &i32) -> f64 {
+        fn distance(&self, a: &i32, b: &i32) -> f64 {
             (a - b).abs() as f64
         }
     }  

src/algorithm/neighbour/linear_search.rs

@@ -38,7 +38,7 @@ impl<T, F: FloatExt, D: Distance<T, F>> LinearKNNSearch<T, F, D> {
 
         for i in 0..self.data.len() {
 
-            let d = D::distance(&from, &self.data[i]);
+            let d = self.distance.distance(&from, &self.data[i]);
             let datum = heap.peek_mut();            
             if d < datum.distance {
                 datum.distance = d;
@@ -81,7 +81,7 @@ mod tests {
     struct SimpleDistance{}
 
     impl Distance<i32, f64> for SimpleDistance {
-        fn distance(a: &i32, b: &i32) -> f64 {
+        fn distance(&self, a: &i32, b: &i32) -> f64 {
             (a - b).abs() as f64
         }
     }    

src/linalg/evd.rs

@@ -829,9 +829,7 @@ mod tests {
             &[0.6952105,  0.43984484, -0.7036135]
         ]);
 
-        let evd = A.evd(false);   
+        let evd = A.evd(false);        
-
-        println!("{}", &evd.V.abs());
         
         assert!(eigen_vectors.abs().approximate_eq(&evd.V.abs(), 1e-4));        
         for i in 0..eigen_values.len() {

src/linalg/lu.rs

@@ -0,0 +1,254 @@
+#![allow(non_snake_case)]
+
+use std::fmt::Debug;
+use std::marker::PhantomData;
+
+use crate::math::num::FloatExt;
+use crate::linalg::BaseMatrix;
+
+#[derive(Debug, Clone)]
+pub struct LU<T: FloatExt, M: BaseMatrix<T>> {            
+    LU: M,    
+    pivot: Vec<usize>,
+    pivot_sign: i8,
+    singular: bool,
+    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;
+        for j in 0..n {
+            if LU.get(j, j) == T::zero() {
+                singular = true;
+                break;
+            }
+        }
+
+        LU {
+            LU: LU,
+            pivot: pivot,
+            pivot_sign: pivot_sign,
+            singular: singular,
+            phantom: PhantomData
+        }
+    }
+
+    pub fn L(&self) -> M {
+        let (n_rows, n_cols) = self.LU.shape();
+        let mut L = M::zeros(n_rows, n_cols);
+
+        for i in 0..n_rows {
+            for j in 0..n_cols {
+                if i > j {
+                    L.set(i, j, self.LU.get(i, j));
+                } else if i == j {
+                    L.set(i, j, T::one());
+                } else {
+                    L.set(i, j, T::zero());
+                }
+            }
+        }
+
+        L
+    }
+
+    pub fn U(&self) -> M {
+        let (n_rows, n_cols) = self.LU.shape();
+        let mut U = M::zeros(n_rows, n_cols);
+
+        for i in 0..n_rows {
+            for j in 0..n_cols {
+                if i <= j {
+                    U.set(i, j, self.LU.get(i, j));                
+                } else {
+                    U.set(i, j, T::zero());
+                }
+            }
+        }
+        
+        U
+    }
+
+    pub fn pivot(&self) -> M {
+        let (_, n) = self.LU.shape();
+        let mut piv = M::zeros(n, n);
+        
+        for i in 0..n {
+            piv.set(i, self.pivot[i], T::one());
+        }
+        
+        piv
+    }
+
+    pub fn inverse(&self) -> M {
+        let (m, n) = self.LU.shape();
+
+        if m != n {
+            panic!("Matrix is not square: {}x{}", m, n);
+        }
+
+        let mut inv = M::zeros(n, n);
+                
+        for i in 0..n {
+            inv.set(i, i, T::one());
+        }
+
+        inv = self.solve(inv);
+        return inv;
+    }
+
+    fn solve(&self, mut b: M) -> M {
+        let (m, n) = self.LU.shape();
+        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);
+        }
+
+        if self.singular {
+            panic!("Matrix is singular.");
+        }
+
+        let mut X = M::zeros(b_m, b_n);
+
+        for j in 0..b_n {
+            for i in 0..m {
+                X.set(i, j, b.get(self.pivot[i], j));
+            }
+        }
+        
+        for k in 0..n {
+            for i in k+1..n {
+                for j in 0..b_n {
+                    X.sub_element_mut(i, j, X.get(k, j) * self.LU.get(i, k));
+                }
+            }
+        }
+
+        for k in (0..n).rev() {
+            for j in 0..b_n {
+                X.div_element_mut(k, j, self.LU.get(k, k));
+            }
+
+            for i in 0..k {
+                for j in 0..b_n {
+                    X.sub_element_mut(i, j, X.get(k, j) * self.LU.get(i, k));
+                }
+            }
+        }
+        
+        for j in 0..b_n {
+            for i in 0..m {
+                b.set(i, j, X.get(i, j));
+            }
+        }
+
+        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];
+        for i in 0..m {
+            piv[i] = i;
+        }
+
+        let mut pivsign = 1;
+        let mut LUcolj = vec![T::zero(); m];
+
+        for j in 0..n {
+
+            for i in 0..m {
+                LUcolj[i] = self.get(i, j);
+            }
+
+            for i in 0..m {
+                let kmax = usize::min(i, j);
+                let mut s = T::zero();
+                for k in 0..kmax {
+                    s = s + self.get(i, k) * LUcolj[k];
+                }
+
+                LUcolj[i] = LUcolj[i] - s;
+                self.set(i, j, LUcolj[i]);
+            }
+
+            let mut p = j;
+            for i in j+1..m {
+                if LUcolj[i].abs() > LUcolj[p].abs() {
+                    p = i;
+                }
+            }
+            if p != j {
+                for k in 0..n {
+                    let t = self.get(p, k);
+                    self.set(p, k, self.get(j, k));
+                    self.set(j, k, t);
+                }
+                let k = piv[p];
+                piv[p] = piv[j];
+                piv[j] = k;
+                pivsign = -pivsign;
+            }
+            
+            if j < m && self.get(j, j) != T::zero() {
+                for i in j+1..m {
+                    self.div_element_mut(i, j, self.get(j, j));
+                }
+            }
+        }        
+
+        LU::new(self, piv, pivsign)
+
+    }
+
+    fn lu_solve_mut(self, b: Self) -> Self {
+
+        self.lu_mut().solve(b)        
+
+    } 
+}
+
+#[cfg(test)]
+mod tests {    
+    use super::*;
+    use crate::linalg::naive::dense_matrix::*; 
+
+    #[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 lu = a.lu();
+            assert!(lu.L().approximate_eq(&expected_L, 1e-4));
+            assert!(lu.U().approximate_eq(&expected_U, 1e-4));
+            assert!(lu.pivot().approximate_eq(&expected_pivot, 1e-4));            
+    }
+
+    #[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 a_inv = a.lu().inverse();
+            println!("{}", a_inv);
+            assert!(a_inv.approximate_eq(&expected, 1e-4));            
+    }
+}

src/linalg/mod.rs

@@ -2,6 +2,7 @@ pub mod naive;
 pub mod qr;
 pub mod svd;
 pub mod evd;
+pub mod lu;
 pub mod ndarray_bindings;
 pub mod nalgebra_bindings;
 
@@ -13,6 +14,7 @@ use crate::math::num::FloatExt;
 use svd::SVDDecomposableMatrix;
 use evd::EVDDecomposableMatrix;
 use qr::QRDecomposableMatrix;
+use lu::LUDecomposableMatrix;
 
 pub trait BaseMatrix<T: FloatExt>: Clone + Debug {  
 
@@ -172,11 +174,13 @@ pub trait BaseMatrix<T: FloatExt>: Clone + Debug {
 
     fn argmax(&self) -> Vec<usize>; 
     
-    fn unique(&self) -> Vec<T>;    
+    fn unique(&self) -> Vec<T>;   
+    
+    fn cov(&self) -> Self;
 
 }
 
-pub trait Matrix<T: FloatExt>: BaseMatrix<T> + SVDDecomposableMatrix<T> + EVDDecomposableMatrix<T> + QRDecomposableMatrix<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{

src/linalg/naive/dense_matrix.rs

@@ -13,6 +13,7 @@ pub use crate::linalg::BaseMatrix;
 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;
 
 #[derive(Debug, Clone)]
@@ -188,6 +189,8 @@ impl<T: FloatExt> EVDDecomposableMatrix<T> for DenseMatrix<T> {}
 
 impl<T: FloatExt> QRDecomposableMatrix<T> for DenseMatrix<T> {}
 
+impl<T: FloatExt> LUDecomposableMatrix<T> for DenseMatrix<T> {}
+
 impl<T: FloatExt> Matrix<T> for DenseMatrix<T> {}
 
 impl<T: FloatExt> PartialEq for DenseMatrix<T> {
@@ -679,6 +682,34 @@ impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {
         result
     }
 
+    fn cov(&self) -> Self {
+
+        let (m, n) = self.shape();
+
+        let mu = self.column_mean();        
+
+        let mut cov = Self::zeros(n, n);
+
+        for k in 0..m {
+            for i in 0..n {
+                for j in 0..=i {
+                    cov.add_element_mut(i, j, (self.get(k, i) -  mu[i]) * (self.get(k, j) - mu[j]));
+                }
+            }
+        }
+
+        let m_t = T::from(m - 1).unwrap();
+
+        for i in 0..n {
+            for j in 0..=i {
+                cov.div_element_mut(i, j, m_t); 
+                cov.set(j, i, cov.get(i, j));
+            }
+        }
+
+        cov
+    }
+
 }
 
 #[cfg(test)]
@@ -887,4 +918,11 @@ mod tests {
             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 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

@@ -9,6 +9,7 @@ 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;
 
 impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> BaseMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>>
 {
@@ -318,6 +319,10 @@ impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum
         result
     }
 
+    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>> {}
@@ -326,6 +331,8 @@ impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum
 
 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> SmartCoreMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>> {}
 
 #[cfg(test)]

src/linalg/ndarray_bindings.rs

@@ -14,6 +14,7 @@ use crate::linalg::Matrix;
 use crate::linalg::svd::SVDDecomposableMatrix;
 use crate::linalg::evd::EVDDecomposableMatrix;
 use crate::linalg::qr::QRDecomposableMatrix;
+use crate::linalg::lu::LUDecomposableMatrix;
 
 
 impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> BaseMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
@@ -286,6 +287,10 @@ impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign
         result
     }
 
+    fn cov(&self) -> Self {
+        panic!("Not implemented");
+    }
+
 }
 
 impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> SVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
@@ -294,6 +299,8 @@ impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign
 
 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> Matrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
 
 #[cfg(test)]

src/math/distance/euclidian.rs

@@ -21,17 +21,13 @@ impl Euclidian {
     
         sum
     }
-
-    pub fn distance<T: FloatExt>(x: &Vec<T>, y: &Vec<T>) -> T {    
-        Euclidian::squared_distance(x, y).sqrt()
-    }
     
 }
 
 impl<T: FloatExt> Distance<Vec<T>, T> for Euclidian {
 
-    fn distance(x: &Vec<T>, y: &Vec<T>) -> T {    
+    fn distance(&self, x: &Vec<T>, y: &Vec<T>) -> T {    
-        Self::distance(x, y)
+        Euclidian::squared_distance(x, y).sqrt()
     } 
 
 }
@@ -46,9 +42,9 @@ mod tests {
         let a = vec![1., 2., 3.];
         let b = vec![4., 5., 6.];             
         
-        let d_arr: f64 = Euclidian::distance(&a, &b);        
+        let l2: f64 = Euclidian{}.distance(&a, &b);        
 
-        assert!((d_arr - 5.19615242).abs() < 1e-8);        
+        assert!((l2 - 5.19615242).abs() < 1e-8);        
     }    
 
 }

src/math/distance/hamming.rs

@@ -0,0 +1,45 @@
+use serde::{Serialize, Deserialize};
+
+use crate::math::num::FloatExt;
+
+use super::Distance;
+
+#[derive(Serialize, Deserialize, Debug)]
+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");
+        }
+    
+        let mut dist = 0;
+        for i in 0..x.len() {
+            if x[i] != y[i]{
+                dist += 1;
+            }
+        }        
+    
+        F::from_i64(dist).unwrap() / F::from_usize(x.len()).unwrap()
+    } 
+
+}
+
+
+#[cfg(test)]
+mod tests {
+    use super::*;    
+
+    #[test]
+    fn minkowski_distance() {
+        let a = vec![1, 0, 0, 1, 0, 0, 1];
+        let b = vec![1, 1, 0, 0, 1, 0, 1];             
+        
+        let h: f64 = Hamming{}.distance(&a, &b);        
+        
+        assert!((h - 0.42857142).abs() < 1e-8);
+    }  
+    
+}

src/math/distance/mahalanobis.rs

@@ -0,0 +1,97 @@
+#![allow(non_snake_case)]
+
+use std::marker::PhantomData;
+
+use serde::{Serialize, Deserialize};
+
+use crate::math::num::FloatExt;
+
+use super::Distance;
+use crate::linalg::Matrix;
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct Mahalanobis<T: FloatExt, M: Matrix<T>> {
+    pub sigma: M,
+    pub sigmaInv: M,
+    t: PhantomData<T>
+}
+
+impl<T: FloatExt, M: Matrix<T>> Mahalanobis<T, M> {
+    pub fn new(data: &M) -> Mahalanobis<T, M> {
+        let sigma = data.cov();
+        let sigmaInv = sigma.lu().inverse();
+        Mahalanobis {
+            sigma: sigma,
+            sigmaInv: sigmaInv,
+            t: PhantomData
+        }
+    }
+
+    pub fn new_from_covariance(cov: &M) -> Mahalanobis<T, M> {
+        let sigma = cov.clone();
+        let sigmaInv = sigma.lu().inverse();
+        Mahalanobis {
+            sigma: sigma,
+            sigmaInv: sigmaInv,
+            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);
+        }
+
+        if y.len() != nrows {
+            panic!("Array y[{}] has different dimension with Sigma[{}][{}].", y.len(), nrows, ncols);
+        }
+
+        println!("{}", self.sigmaInv);
+
+        let n = x.len();
+        let mut z = vec![T::zero(); n];
+        for i in 0..n {
+            z[i] = x[i] - y[i];
+        }        
+
+        // np.dot(np.dot((a-b),VI),(a-b).T)
+        let mut s = T::zero();
+        for j in 0..n {
+            for i in 0..n {
+                s = s + self.sigmaInv.get(i, j) * z[i] * z[j];
+            }
+        }
+
+        s.sqrt()
+    } 
+
+}
+
+
+#[cfg(test)]
+mod tests {
+    use super::*; 
+    use crate::linalg::naive::dense_matrix::*;
+
+    #[test]
+    fn mahalanobis_distance() {
+        let data = DenseMatrix::from_array(&[
+            &[ 64., 580.,  29.],
+            &[ 66., 570.,  33.],
+            &[ 68., 590.,  37.],
+            &[ 69., 660.,  46.],
+            &[ 73., 600.,  55.]]); 
+            
+        let a = data.column_mean();
+        let b = vec![66., 640., 44.];
+        
+        let mahalanobis = Mahalanobis::new(&data);        
+
+        println!("{}", mahalanobis.distance(&a, &b));
+    }
+
+}

src/math/distance/manhattan.rs

@@ -0,0 +1,43 @@
+use serde::{Serialize, Deserialize};
+
+use crate::math::num::FloatExt;
+
+use super::Distance;
+
+#[derive(Serialize, Deserialize, Debug)]
+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");
+        }
+    
+        let mut dist = T::zero();
+        for i in 0..x.len() {            
+            dist = dist + (x[i] - y[i]).abs();            
+        }        
+    
+        dist
+    } 
+
+}
+
+
+#[cfg(test)]
+mod tests {
+    use super::*;    
+
+    #[test]
+    fn manhattan_distance() {
+        let a = vec![1., 2., 3.];
+        let b = vec![4., 5., 6.];             
+        
+        let l1: f64 = Manhattan{}.distance(&a, &b);             
+
+        assert!((l1 - 9.0).abs() < 1e-8);        
+    } 
+
+}

src/math/distance/minkowski.rs

@@ -0,0 +1,63 @@
+use serde::{Serialize, Deserialize};
+
+use crate::math::num::FloatExt;
+
+use super::Distance;
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct Minkowski<T: FloatExt> {
+    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");
+        }
+        if self.p < T::one() {
+            panic!("p must be at least 1");
+        }
+    
+        let mut dist = T::zero();
+        for i in 0..x.len() {
+            let d = (x[i] - y[i]).abs();
+            dist = dist + d.powf(self.p);            
+        }        
+    
+        dist.powf(T::one()/self.p)
+    } 
+
+}
+
+
+#[cfg(test)]
+mod tests {
+    use super::*;    
+
+    #[test]
+    fn minkowski_distance() {
+        let a = vec![1., 2., 3.];
+        let b = vec![4., 5., 6.];             
+        
+        let l1: f64 = Minkowski{p: 1.0}.distance(&a, &b);
+        let l2: f64 = Minkowski{p: 2.0}.distance(&a, &b);        
+        let l3: f64 = Minkowski{p: 3.0}.distance(&a, &b);        
+
+        assert!((l1 - 9.0).abs() < 1e-8);
+        assert!((l2 - 5.19615242).abs() < 1e-8);                
+        assert!((l3 - 4.32674871).abs() < 1e-8);
+    }  
+    
+    #[test]
+    #[should_panic(expected = "p must be at least 1")]
+    fn minkowski_distance_negative_p() {
+        let a = vec![1., 2., 3.];
+        let b = vec![4., 5., 6.]; 
+        
+        let _: f64 = Minkowski{p: 0.0}.distance(&a, &b);
+    }
+
+
+
+}

src/math/distance/mod.rs

@@ -1,9 +1,13 @@
 pub mod euclidian;
+pub mod minkowski;
+pub mod manhattan;
+pub mod hamming;
+pub mod mahalanobis;
 
 use crate::math::num::FloatExt;
 
 pub trait Distance<T, F: FloatExt>{
-    fn distance(a: &T, b: &T) -> F;
+    fn distance(&self, a: &T, b: &T) -> F;
 }
 
 pub struct Distances{    
@@ -13,4 +17,16 @@ impl Distances {
     pub fn euclidian() -> euclidian::Euclidian{
         euclidian::Euclidian {}
     }
+
+    pub fn minkowski<T: FloatExt>(p: T) -> minkowski::Minkowski<T>{
+        minkowski::Minkowski {p: p}
+    }
+
+    pub fn manhattan() -> manhattan::Manhattan{
+        manhattan::Manhattan {}
+    }
+
+    pub fn hamming() -> hamming::Hamming{
+        hamming::Hamming {}
+    }
 }

src/optimization/first_order/gradient_descent.rs

@@ -102,9 +102,7 @@ mod tests {
         ls.order = FunctionOrder::THIRD;
         let optimizer: GradientDescent<f64> = Default::default();
         
-        let result = optimizer.optimize(&f, &df, &x0, &ls);
+        let result = optimizer.optimize(&f, &df, &x0, &ls);        
-
-        println!("{:?}", result);
         
         assert!((result.f_x - 0.0).abs() < 1e-5);
         assert!((result.x.get(0, 0) - 1.0).abs() < 1e-2);