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Implement Clone, Debug, Copy for all linalg structures.

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This commit is contained in:
Sébastien Crozet 2017-08-13 19:53:00 +02:00 committed by Sébastien Crozet
parent b84c7e10df
commit 70bb2cbe46
12 changed files with 93 additions and 4 deletions
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14
src/linalg/bidiagonal.rs
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@ -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> +
@ -17,14 +18,23 @@ pub struct Bidiagonal<N: Real, R: DimMin<C>, C: Dim>
Allocator<N, DimDiff<DimMinimum<R, C>, U1>> {
// FIXME: perhaps we should pack the axises into different vectors so that axises for `v_t` are
// contiguous. This prevents some useless copies.
uv: MatrixMN<N, R, C>,
uv: MatrixMN<N, R, C>,
/// The diagonal elements of the decomposed matrix.
pub diagonal: VectorN<N, DimMinimum<R, C>>,
pub diagonal: VectorN<N, DimMinimum<R, C>>,
/// The off-diagonal elements of the decomposed matrix.
pub off_diagonal: VectorN<N, DimDiff<DimMinimum<R, C>, U1>>,
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> +

5
src/linalg/cholesky.rs
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@ -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`.

8
src/linalg/eigen.rs
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@ -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.

8
src/linalg/full_piv_lu.rs
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@ -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>> {

7
src/linalg/hessenberg.rs
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@ -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> +

7
src/linalg/lu.rs
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@ -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.

7
src/linalg/permutation_sequence.rs
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@ -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>{
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> {

8
src/linalg/qr.rs
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@ -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> +

7
src/linalg/schur.rs
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@ -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.

12
src/linalg/svd.rs
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@ -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> +

7
src/linalg/symmetric_eigen.rs
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@ -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> {

7
src/linalg/symmetric_tridiagonal.rs
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@ -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>> {