Implement Kronecker product

src/linalg/impl_linalg.rs

@@ -14,6 +14,7 @@ use crate::dimension::offset_from_low_addr_ptr_to_logical_ptr;
 use crate::{LinalgScalar, Zip};
 
 use std::any::TypeId;
+use std::mem::MaybeUninit;
 use alloc::vec::Vec;
 
 #[cfg(feature = "blas")]
@@ -699,6 +700,40 @@ unsafe fn general_mat_vec_mul_impl<A, S1, S2>(
     }
 }
 
+
+/// Kronecker product of 2D matrices.
+///
+/// The kronecker product of a LxN matrix A and a MxR matrix B is a (L*M)x(N*R)
+/// matrix K formed by the block multiplication A_ij * B.
+pub fn kron<'a, A, S1, S2>(a: &ArrayBase<S1, Ix2>, b: &'a ArrayBase<S2, Ix2>) -> Array<A, Ix2>
+where
+    S1: Data<Elem = A>,
+    S2: Data<Elem = A>,
+    A: LinalgScalar,
+    A: std::ops::Mul<&'a ArrayBase<S2, Ix2>, Output = Array<A, Ix2>>,
+{
+    let dimar = a.shape()[0];
+    let dimac = a.shape()[1];
+    let dimbr = b.shape()[0];
+    let dimbc = b.shape()[1];
+    let mut out: Array2<MaybeUninit<A>> = Array2::uninit((
+        dimar
+            .checked_mul(dimbr)
+            .expect("Dimensions of kronecker product output array overflows usize."),
+        dimac
+            .checked_mul(dimbc)
+            .expect("Dimensions of kronecker product output array overflows usize."),
+    ));
+    Zip::from(out.exact_chunks_mut((dimbr, dimbc)))
+        .and(a)
+        .for_each(|out, &a| {
+            Zip::from(out).and(b).for_each(|out, &b| {
+                *out = MaybeUninit::new(a * b);
+            })
+        });
+    unsafe { out.assume_init() }
+}
+
 #[inline(always)]
 /// Return `true` if `A` and `B` are the same type
 fn same_type<A: 'static, B: 'static>() -> bool {

src/linalg/mod.rs

@@ -11,5 +11,6 @@
 pub use self::impl_linalg::general_mat_mul;
 pub use self::impl_linalg::general_mat_vec_mul;
 pub use self::impl_linalg::Dot;
+pub use self::impl_linalg::kron;
 
 mod impl_linalg;

tests/oper.rs

@@ -6,6 +6,7 @@
 )]
 #![cfg(feature = "std")]
 use ndarray::linalg::general_mat_mul;
+use ndarray::linalg::kron;
 use ndarray::prelude::*;
 use ndarray::{rcarr1, rcarr2};
 use ndarray::{Data, LinalgScalar};
@@ -820,3 +821,65 @@ fn vec_mat_mul() {
         }
     }
 }
+
+#[test]
+fn kron_square_f64() {
+    let a = arr2(&[[1.0, 0.0], [0.0, 1.0]]);
+    let b = arr2(&[[0.0, 1.0], [1.0, 0.0]]);
+
+    assert_eq!(
+        kron(&a, &b),
+        arr2(&[
+            [0.0, 1.0, 0.0, 0.0],
+            [1.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 1.0],
+            [0.0, 0.0, 1.0, 0.0]
+        ]),
+    );
+
+    assert_eq!(
+        kron(&b, &a),
+        arr2(&[
+            [0.0, 0.0, 1.0, 0.0],
+            [0.0, 0.0, 0.0, 1.0],
+            [1.0, 0.0, 0.0, 0.0],
+            [0.0, 1.0, 0.0, 0.0]
+        ]),
+    )
+}
+
+#[test]
+fn kron_square_i64() {
+    let a = arr2(&[[1, 0], [0, 1]]);
+    let b = arr2(&[[0, 1], [1, 0]]);
+
+    assert_eq!(
+        kron(&a, &b),
+        arr2(&[[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]]),
+    );
+
+    assert_eq!(
+        kron(&b, &a),
+        arr2(&[[0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0]]),
+    )
+}
+
+#[test]
+fn kron_i64() {
+    let a = arr2(&[[1, 0]]);
+    let b = arr2(&[[0, 1], [1, 0]]);
+    let r = arr2(&[[0, 1, 0, 0], [1, 0, 0, 0]]);
+    assert_eq!(kron(&a, &b), r);
+
+    let a = arr2(&[[1, 0], [0, 0], [0, 1]]);
+    let b = arr2(&[[0, 1], [1, 0]]);
+    let r = arr2(&[
+        [0, 1, 0, 0],
+        [1, 0, 0, 0],
+        [0, 0, 0, 0],
+        [0, 0, 0, 0],
+        [0, 0, 0, 1],
+        [0, 0, 1, 0],
+    ]);
+    assert_eq!(kron(&a, &b), r);
+}