Trait ndarray_linalg::least_squares::LeastSquaresSvdInto

pub trait LeastSquaresSvdInto<D, E, I> where
    D: Data<Elem = E>,
    E: Scalar + Lapack,
    I: Dimension, {
    fn least_squares_into(
        self,
        rhs: ArrayBase<D, I>
    ) -> Result<LeastSquaresResult<E, I>>;
}

Solve least squares for owned matrices

Required Methods

fn least_squares_into(
    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, D1, D2> LeastSquaresSvdInto<D2, E, Dim<[usize; 1]>> for ArrayBase<D1, Ix2> where
    E: Scalar + Lapack,
    D1: DataMut<Elem = E>,
    D2: 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<D2, 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, D1, D2> LeastSquaresSvdInto<D2, E, Dim<[usize; 2]>> for ArrayBase<D1, Ix2> where
    E: Scalar + Lapack,
    D1: DataMut<Elem = E>,
    D2: 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. D1, D2 can be any valid representation for ArrayBase (over E).

fn least_squares_into(
    self,
    rhs: ArrayBase<D2, 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.

Implementors