Merge pull request #233 from rust-ndarray/least-square-lax-trait-0.12

Add LeastSquaresSvdDivideConquer_ into Lapack trait for 0.12.* branch

Author: termoshtt

src/lapack/mod.rs

@@ -44,6 +44,7 @@ pub trait Lapack:
     + Eigh_
     + Triangular_
     + Tridiagonal_
+    + LeastSquaresSvdDivideConquer_
 {
 }
 

src/least_squares.rs

@@ -152,7 +152,7 @@ where
 /// valid representation for `ArrayBase`.
 impl<E, D> LeastSquaresSvd<D, E, Ix1> for ArrayBase<D, Ix2>
 where
-    E: Scalar + Lapack + LeastSquaresSvdDivideConquer_,
+    E: Scalar + Lapack,
     D: Data<Elem = E>,
 {
     /// Solve a least squares problem of the form `Ax = rhs`
@@ -175,7 +175,7 @@ where
 /// valid representation for `ArrayBase`.
 impl<E, D> LeastSquaresSvd<D, E, Ix2> for ArrayBase<D, Ix2>
 where
-    E: Scalar + Lapack + LeastSquaresSvdDivideConquer_,
+    E: Scalar + Lapack,
     D: Data<Elem = E>,
 {
     /// Solve a least squares problem of the form `Ax = rhs`
@@ -200,7 +200,7 @@ where
 /// valid representation for `ArrayBase`.
 impl<E, D> LeastSquaresSvdInto<D, E, Ix1> for ArrayBase<D, Ix2>
 where
-    E: Scalar + Lapack + LeastSquaresSvdDivideConquer_,
+    E: Scalar + Lapack,
     D: DataMut<Elem = E>,
 {
     /// Solve a least squares problem of the form `Ax = rhs`
@@ -226,7 +226,7 @@ where
 /// valid representation for `ArrayBase`.
 impl<E, D> LeastSquaresSvdInto<D, E, Ix2> for ArrayBase<D, Ix2>
 where
-    E: Scalar + Lapack + LeastSquaresSvdDivideConquer_,
+    E: Scalar + Lapack,
     D: DataMut<Elem = E>,
 {
     /// Solve a least squares problem of the form `Ax = rhs`
@@ -252,7 +252,7 @@ where
 /// valid representation for `ArrayBase`.
 impl<E, D> LeastSquaresSvdInPlace<D, E, Ix1> for ArrayBase<D, Ix2>
 where
-    E: Scalar + Lapack + LeastSquaresSvdDivideConquer_,
+    E: Scalar + Lapack,
     D: DataMut<Elem = E>,
 {
     /// Solve a least squares problem of the form `Ax = rhs`
@@ -284,15 +284,15 @@ fn compute_least_squares_srhs<E, D1, D2>(
     rhs: &mut ArrayBase<D2, Ix1>,
 ) -> Result<LeastSquaresResult<E, Ix1>>
 where
-    E: Scalar + Lapack + LeastSquaresSvdDivideConquer_,
+    E: Scalar + Lapack,
     D1: DataMut<Elem = E>,
     D2: DataMut<Elem = E>,
 {
     let LeastSquaresOutput::<E> {
         singular_values,
         rank,
     } = unsafe {
-        <E as LeastSquaresSvdDivideConquer_>::least_squares(
+        E::least_squares(
             a.layout()?,
             a.as_allocated_mut()?,
             rhs.as_slice_memory_order_mut()
@@ -333,7 +333,7 @@ fn compute_residual_scalar<E: Scalar, D: Data<Elem = E>>(
 /// valid representation for `ArrayBase`.
 impl<E, D> LeastSquaresSvdInPlace<D, E, Ix2> for ArrayBase<D, Ix2>
 where
-    E: Scalar + Lapack + LeastSquaresSvdDivideConquer_,
+    E: Scalar + Lapack,
     D: DataMut<Elem = E>,
 {
     /// Solve a least squares problem of the form `Ax = rhs`
@@ -366,7 +366,7 @@ fn compute_least_squares_nrhs<E, D1, D2>(
     rhs: &mut ArrayBase<D2, Ix2>,
 ) -> Result<LeastSquaresResult<E, Ix2>>
 where
-    E: Scalar + Lapack + LeastSquaresSvdDivideConquer_,
+    E: Scalar + Lapack,
     D1: DataMut<Elem = E>,
     D2: DataMut<Elem = E>,
 {
@@ -376,7 +376,7 @@ where
         singular_values,
         rank,
     } = unsafe {
-        <E as LeastSquaresSvdDivideConquer_>::least_squares_nrhs(
+        E::least_squares_nrhs(
             a_layout,
             a.as_allocated_mut()?,
             rhs_layout,