Merge branch 'normalize'

Author: termoshtt

src/util.rs

@@ -4,10 +4,23 @@ use std::iter::Sum;
 use ndarray::*;
 use num_traits::Float;
 use super::vector::*;
+use std::ops::Div;
+
+/// construct matrix from diag
+pub fn from_diag<A>(d: &[A]) -> Array2<A>
+    where A: LinalgScalar
+{
+    let n = d.len();
+    let mut e = Array::zeros((n, n));
+    for i in 0..n {
+        e[(i, i)] = d[i];
+    }
+    e
+}
 
 /// stack vectors into matrix horizontally
 pub fn hstack<A, S>(xs: &[ArrayBase<S, Ix1>]) -> Result<Array<A, Ix2>, ShapeError>
-    where A: NdFloat,
+    where A: LinalgScalar,
           S: Data<Elem = A>
 {
     let views: Vec<_> = xs.iter()
@@ -21,7 +34,7 @@ pub fn hstack<A, S>(xs: &[ArrayBase<S, Ix1>]) -> Result<Array<A, Ix2>, ShapeErro
 
 /// stack vectors into matrix vertically
 pub fn vstack<A, S>(xs: &[ArrayBase<S, Ix1>]) -> Result<Array<A, Ix2>, ShapeError>
-    where A: NdFloat,
+    where A: LinalgScalar,
           S: Data<Elem = A>
 {
     let views: Vec<_> = xs.iter()
@@ -33,6 +46,26 @@ pub fn vstack<A, S>(xs: &[ArrayBase<S, Ix1>]) -> Result<Array<A, Ix2>, ShapeErro
     stack(Axis(0), &views)
 }
 
+pub enum NormalizeAxis {
+    Row = 0,
+    Column = 1,
+}
+
+/// normalize in L2 norm
+pub fn normalize<A, S, T>(mut m: ArrayBase<S, Ix2>, axis: NormalizeAxis) -> (ArrayBase<S, Ix2>, Vec<T>)
+    where A: LinalgScalar + NormedField<Output = T> + Div<T, Output = A>,
+          S: DataMut<Elem = A>,
+          T: Float + Sum
+{
+    let mut ms = Vec::new();
+    for mut v in m.axis_iter_mut(Axis(axis as usize)) {
+        let n = v.norm();
+        ms.push(n);
+        v.map_inplace(|x| *x = *x / n)
+    }
+    (m, ms)
+}
+
 /// check two arrays are close in maximum norm
 pub fn all_close_max<A, Tol, S1, S2, D>(test: &ArrayBase<S1, D>,
                                         truth: &ArrayBase<S2, D>,

tests/normalize.rs

@@ -0,0 +1,15 @@
+include!("header.rs");
+
+#[test]
+fn n_columns() {
+    let a = random_owned(3, 2, true);
+    let (n, v) = normalize(a.clone(), NormalizeAxis::Column);
+    all_close_l2(&n.dot(&from_diag(&v)), &a, 1e-7).unwrap();
+}
+
+#[test]
+fn n_rows() {
+    let a = random_owned(3, 2, true);
+    let (n, v) = normalize(a.clone(), NormalizeAxis::Row);
+    all_close_l2(&from_diag(&v).dot(&n), &a, 1e-7).unwrap();
+}