feat: adds new distance measures + LU decomposition

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));
+    }
+
+}