Implement Clone, Debug, Copy for all linalg structures.

src/linalg/bidiagonal.rs

@@ -10,6 +10,7 @@ use geometry::Reflection;
 
 
 /// The bidiagonalization of a general matrix.
+#[derive(Clone, Debug)]
 pub struct Bidiagonal<N: Real, R: DimMin<C>, C: Dim>
     where DimMinimum<R, C>: DimSub<U1>,
           DefaultAllocator: Allocator<N, R, C>             +
@@ -25,6 +26,15 @@ pub struct Bidiagonal<N: Real, R: DimMin<C>, C: Dim>
     upper_diagonal: bool
 }
 
+impl<N: Real, R: DimMin<C>, C: Dim> Copy for Bidiagonal<N, R, C>
+    where DimMinimum<R, C>: DimSub<U1>,
+          DefaultAllocator: Allocator<N, R, C>             +
+                            Allocator<N, DimMinimum<R, C>> +
+                            Allocator<N, DimDiff<DimMinimum<R, C>, U1>>,
+          MatrixMN<N, R, C>: Copy,
+          VectorN<N, DimMinimum<R, C>>: Copy,
+          VectorN<N, DimDiff<DimMinimum<R, C>, U1>>: Copy { }
+
 impl<N: Real, R: DimMin<C>, C: Dim> Bidiagonal<N, R, C>
     where DimMinimum<R, C>: DimSub<U1>,
           DefaultAllocator: Allocator<N, R, C>             +

src/linalg/cholesky.rs

@@ -7,11 +7,16 @@ use allocator::Allocator;
 use dimension::{Dim, Dynamic, DimSub};
 
 /// The cholesky decomposion of a symmetric-definite-positive matrix.
+#[derive(Clone, Debug)]
 pub struct Cholesky<N: Real, D: Dim>
     where DefaultAllocator: Allocator<N, D, D> {
     chol: MatrixN<N, D>
 }
 
+impl<N: Real, D: Dim> Copy for Cholesky<N, D>
+    where DefaultAllocator: Allocator<N, D, D>,
+          MatrixN<N, D>: Copy { }
+
 impl<N: Real, D: DimSub<Dynamic>> Cholesky<N, D>
     where DefaultAllocator: Allocator<N, D, D> {
     /// Attempts to compute the sholesky decomposition of `matrix`.

src/linalg/eigen.rs

@@ -16,6 +16,7 @@ use geometry::{Reflection, UnitComplex};
 
 
 /// The eigendecomposition of a matrix with real eigenvalues.
+#[derive(Clone, Debug)]
 pub struct RealEigen<N: Real, D: Dim>
     where DefaultAllocator: Allocator<N, D, D> + 
                             Allocator<N, D> {
@@ -23,6 +24,13 @@ pub struct RealEigen<N: Real, D: Dim>
     pub eigenvalues:  VectorN<N, D>
 }
 
+
+impl<N: Real, D: Dim> Copy for RealEigen<N, D>
+    where DefaultAllocator: Allocator<N, D, D> + 
+                            Allocator<N, D>,
+          MatrixN<N, D>: Copy,
+          VectorN<N, D>: Copy { }
+
 impl<N: Real, D: Dim> RealEigen<N, D>
     where D: DimSub<U1>,                        // For Hessenberg.
           ShapeConstraint: DimEq<Dynamic, DimDiff<D, U1>>, // For Hessenberg.

src/linalg/full_piv_lu.rs

@@ -11,6 +11,7 @@ use linalg::PermutationSequence;
 
 
 /// LU decomposition with full pivoting.
+#[derive(Clone, Debug)]
 pub struct FullPivLU<N: Real, R: DimMin<C>, C: Dim>
     where DefaultAllocator: Allocator<N, R, C> +
                             Allocator<(usize, usize), DimMinimum<R, C>> {
@@ -20,6 +21,13 @@ pub struct FullPivLU<N: Real, R: DimMin<C>, C: Dim>
 }
 
 
+impl<N: Real, R: DimMin<C>, C: Dim> Copy for FullPivLU<N, R, C>
+    where DefaultAllocator: Allocator<N, R, C> +
+                            Allocator<(usize, usize), DimMinimum<R, C>>,
+          MatrixMN<N, R, C>: Copy,
+          PermutationSequence<DimMinimum<R, C>>: Copy { }
+
+
 impl<N: Real, R: DimMin<C>, C: Dim> FullPivLU<N, R, C>
     where DefaultAllocator: Allocator<N, R, C> +
                             Allocator<(usize, usize), DimMinimum<R, C>> {

src/linalg/hessenberg.rs

@@ -8,6 +8,7 @@ use constraint::{ShapeConstraint, DimEq};
 use linalg::householder;
 
 /// The Hessenberg decomposition of a general matrix.
+#[derive(Clone, Debug)]
 pub struct Hessenberg<N: Real, D: DimSub<U1>>
     where DefaultAllocator: Allocator<N, D, D> +
                             Allocator<N, DimDiff<D, U1>> {
@@ -16,6 +17,12 @@ pub struct Hessenberg<N: Real, D: DimSub<U1>>
     subdiag: VectorN<N, DimDiff<D, U1>>
 }
 
+impl<N: Real, D: DimSub<U1>> Copy for Hessenberg<N, D>
+    where DefaultAllocator: Allocator<N, D, D> +
+                            Allocator<N, DimDiff<D, U1>>,
+          MatrixN<N, D>: Copy,
+          VectorN<N, DimDiff<D, U1>>: Copy { }
+
 impl<N: Real, D: DimSub<U1>> Hessenberg<N, D>
     where DefaultAllocator: Allocator<N, D, D> +
                             Allocator<N, D>    +

src/linalg/lu.rs

@@ -11,6 +11,7 @@ use linalg::PermutationSequence;
 
 
 /// LU decomposition with partial (row) pivoting.
+#[derive(Clone, Debug)]
 pub struct LU<N: Real, R: DimMin<C>, C: Dim>
     where DefaultAllocator: Allocator<N, R, C> +
                             Allocator<(usize, usize), DimMinimum<R, C>> {
@@ -18,6 +19,12 @@ pub struct LU<N: Real, R: DimMin<C>, C: Dim>
     p:  PermutationSequence<DimMinimum<R, C>>
 }
 
+impl<N: Real, R: DimMin<C>, C: Dim> Copy for LU<N, R, C>
+    where DefaultAllocator: Allocator<N, R, C> +
+                            Allocator<(usize, usize), DimMinimum<R, C>>,
+          MatrixMN<N, R, C>: Copy,
+          PermutationSequence<DimMinimum<R, C>>: Copy { }
+
 /// Performs a LU decomposition to overwrite `out` with the inverse of `matrix`.
 ///
 /// If `matrix` is not invertible, `false` is returned and `out` may contain invalid data.

src/linalg/permutation_sequence.rs

@@ -8,12 +8,17 @@ use allocator::Allocator;
 
 
 /// A sequence of permutations.
+#[derive(Clone, Debug)]
 pub struct PermutationSequence<D: Dim>
     where DefaultAllocator: Allocator<(usize, usize), D> {
     len:  usize,
     ipiv: VectorN<(usize, usize), D>
 }
 
+impl<D: Dim> Copy for PermutationSequence<D>
+    where DefaultAllocator: Allocator<(usize, usize), D>,
+          VectorN<(usize, usize), D>: Copy { }
+
 impl<D: Dim> PermutationSequence<D>
     where DefaultAllocator: Allocator<(usize, usize), D> {
 

src/linalg/qr.rs

@@ -10,6 +10,7 @@ use geometry::Reflection;
 
 
 /// The QR decomposition of a general matrix.
+#[derive(Clone, Debug)]
 pub struct QR<N: Real, R: DimMin<C>, C: Dim>
     where DefaultAllocator: Allocator<N, R, C> +
                             Allocator<N, DimMinimum<R, C>> {
@@ -17,6 +18,13 @@ pub struct QR<N: Real, R: DimMin<C>, C: Dim>
     diag: VectorN<N, DimMinimum<R, C>>,
 }
 
+
+impl<N: Real, R: DimMin<C>, C: Dim> Copy for QR<N, R, C>
+    where DefaultAllocator: Allocator<N, R, C> +
+                            Allocator<N, DimMinimum<R, C>>,
+          MatrixMN<N, R, C>: Copy,
+          VectorN<N, DimMinimum<R, C>>: Copy { }
+
 impl<N: Real, R: DimMin<C>, C: Dim> QR<N, R, C>
     where DefaultAllocator: Allocator<N, R, C> +
                             Allocator<N, R>    +

src/linalg/schur.rs

@@ -15,6 +15,7 @@ use geometry::{Reflection, UnitComplex};
 
 
 /// Real RealSchur decomposition of a square matrix.
+#[derive(Clone, Debug)]
 pub struct RealSchur<N: Real, D: Dim>
     where DefaultAllocator: Allocator<N, D, D> + 
                             Allocator<N, D> {
@@ -22,6 +23,12 @@ pub struct RealSchur<N: Real, D: Dim>
     t: MatrixN<N, D>
 }
 
+
+impl<N: Real, D: Dim> Copy for RealSchur<N, D>
+    where DefaultAllocator: Allocator<N, D, D> + 
+                            Allocator<N, D>,
+          MatrixN<N, D>: Copy { }
+
 impl<N: Real, D: Dim> RealSchur<N, D>
     where D: DimSub<U1>,                        // For Hessenberg.
           ShapeConstraint: DimEq<Dynamic, DimDiff<D, U1>>, // For Hessenberg.

src/linalg/svd.rs

@@ -16,7 +16,7 @@ use geometry::UnitComplex;
 
 
 /// The Singular Value Decomposition of a real matrix.
-#[derive(Clone)]
+#[derive(Clone, Debug)]
 pub struct SVD<N: Real, R: DimMin<C>, C: Dim>
     where DefaultAllocator: Allocator<N, R, C>                +
                             Allocator<N, DimMinimum<R, C>, C> +
@@ -30,6 +30,16 @@ pub struct SVD<N: Real, R: DimMin<C>, C: Dim>
     pub singular_values: VectorN<N, DimMinimum<R, C>>,
 }
 
+
+impl<N: Real, R: DimMin<C>, C: Dim> SVD<N, R, C>
+    where DefaultAllocator: Allocator<N, R, C>                +
+                            Allocator<N, DimMinimum<R, C>, C> +
+                            Allocator<N, R, DimMinimum<R, C>> +
+                            Allocator<N, DimMinimum<R, C>>,
+          MatrixMN<N, R, DimMinimum<R, C>>: Copy,
+          MatrixMN<N, DimMinimum<R, C>, C>: Copy,
+          VectorN<N, DimMinimum<R, C>>:     Copy { }
+
 impl<N: Real, R: DimMin<C>, C: Dim> SVD<N, R, C>
     where DimMinimum<R, C>: DimSub<U1>, // for Bidiagonal.
           DefaultAllocator: Allocator<N, R, C>                +

src/linalg/symmetric_eigen.rs

@@ -13,6 +13,7 @@ use geometry::UnitComplex;
 
 
 /// The eigendecomposition of a symmetric matrix.
+#[derive(Clone, Debug)]
 pub struct SymmetricEigen<N: Real, D: Dim>
     where DefaultAllocator: Allocator<N, D, D> +
                             Allocator<N, D> {
@@ -23,6 +24,12 @@ pub struct SymmetricEigen<N: Real, D: Dim>
     pub eigenvalues: VectorN<N, D>
 }
 
+impl<N: Real, D: Dim> SymmetricEigen<N, D>
+    where DefaultAllocator: Allocator<N, D, D> +
+                            Allocator<N, D>,
+          MatrixN<N, D>: Copy,
+          VectorN<N, D>: Copy { }
+
 impl<N: Real, D: Dim> SymmetricEigen<N, D>
     where DefaultAllocator: Allocator<N, D, D> +
                             Allocator<N, D> {

src/linalg/symmetric_tridiagonal.rs

@@ -8,6 +8,7 @@ use linalg::householder;
 
 
 /// The tridiagonalization of a symmetric matrix.
+#[derive(Clone, Debug)]
 pub struct SymmetricTridiagonal<N: Real, D: DimSub<U1>>
     where DefaultAllocator: Allocator<N, D, D> +
                             Allocator<N, DimDiff<D, U1>> {
@@ -15,6 +16,12 @@ pub struct SymmetricTridiagonal<N: Real, D: DimSub<U1>>
     off_diagonal: VectorN<N, DimDiff<D, U1>>
 }
 
+impl<N: Real, D: DimSub<U1>> SymmetricTridiagonal<N, D>
+    where DefaultAllocator: Allocator<N, D, D> +
+                            Allocator<N, DimDiff<D, U1>>,
+          MatrixN<N, D>: Copy,
+          VectorN<N, DimDiff<D, U1>>: Copy { }
+
 impl<N: Real, D: DimSub<U1>> SymmetricTridiagonal<N, D>
     where DefaultAllocator: Allocator<N, D, D> +
                             Allocator<N, DimDiff<D, U1>> {