Trait ndarray_linalg::least_squares::LeastSquaresSvdInto [−][src]
Solve least squares for owned matrices
Required methods
fn least_squares_into(
self,
rhs: ArrayBase<D, I>
) -> Result<LeastSquaresResult<E, I>>
[src]
self,
rhs: ArrayBase<D, I>
) -> Result<LeastSquaresResult<E, I>>
Solve a least squares problem of the form Ax = rhs
by calling A.least_squares(rhs)
, consuming both A
and rhs
. This uses the memory location of A
and
rhs
, which avoids some extra memory allocations.
A
and rhs
must have the same layout, i.e. they must
be both either row- or column-major format, otherwise a
IncompatibleShape
error is raised.
Implementations on Foreign Types
impl<E, D> LeastSquaresSvdInto<D, E, Dim<[usize; 1]>> for ArrayBase<D, Ix2> where
E: Scalar + Lapack,
D: DataMut<Elem = E>,
[src]
E: Scalar + Lapack,
D: DataMut<Elem = E>,
Solve least squares for owned values and a single column vector as a right-hand side. The matrix and the RHS vector are consumed.
E
is one of f32
, f64
, c32
, c64
. D
can be any
valid representation for ArrayBase
.
fn least_squares_into(
self,
rhs: ArrayBase<D, Ix1>
) -> Result<LeastSquaresResult<E, Ix1>>
[src]
self,
rhs: ArrayBase<D, Ix1>
) -> Result<LeastSquaresResult<E, Ix1>>
Solve a least squares problem of the form Ax = rhs
by calling A.least_squares(rhs)
, where rhs
is a
single column vector. A
and rhs
are consumed.
A
and rhs
must have the same layout, i.e. they must
be both either row- or column-major format, otherwise a
IncompatibleShape
error is raised.
impl<E, D> LeastSquaresSvdInto<D, E, Dim<[usize; 2]>> for ArrayBase<D, Ix2> where
E: Scalar + Lapack,
D: DataMut<Elem = E>,
[src]
E: Scalar + Lapack,
D: DataMut<Elem = E>,
Solve least squares for owned values and a matrix as a right-hand side. The matrix and the RHS matrix are consumed.
E
is one of f32
, f64
, c32
, c64
. D
can be any
valid representation for ArrayBase
.
fn least_squares_into(
self,
rhs: ArrayBase<D, Ix2>
) -> Result<LeastSquaresResult<E, Ix2>>
[src]
self,
rhs: ArrayBase<D, Ix2>
) -> Result<LeastSquaresResult<E, Ix2>>
Solve a least squares problem of the form Ax = rhs
by calling A.least_squares(rhs)
, where rhs
is a
matrix. A
and rhs
are consumed.
A
and rhs
must have the same layout, i.e. they must
be both either row- or column-major format, otherwise a
IncompatibleShape
error is raised.