Struct LeastSquaresResult 

pub struct LeastSquaresResult<E, I>
where
    E: Scalar,
    I: Dimension,
{
    pub singular_values: ArrayBase<OwnedRepr<<E as Scalar>::Real>, Dim<[usize; 1]>>,
    pub solution: ArrayBase<OwnedRepr<E>, I>,
    pub rank: i32,
    pub residual_sum_of_squares: Option<ArrayBase<OwnedRepr<<E as Scalar>::Real>, <I as Dimension>::Smaller>>,
}

Result of a LeastSquares computation

Takes two type parameters, E, the element type of the matrix (one of f32, f64, c32 or c64) and I, the dimension of b in the equation Ax = b (one of Ix1 or Ix2). If I is Ix1, the right-hand-side (RHS) is a n x 1 column vector and the solution is a m x 1 column vector. If I is Ix2, the RHS is a n x k matrix (which can be seen as solving Ax = b k times for different b) and the solution is a m x k matrix.

Fields

singular_values: ArrayBase<OwnedRepr<<E as Scalar>::Real>, Dim<[usize; 1]>>

The singular values of the matrix A in Ax = b

solution: ArrayBase<OwnedRepr<E>, I>

The solution vector or matrix x which is the best solution to Ax = b, i.e. minimizing the 2-norm ||b - Ax||

rank: i32

The rank of the matrix A in Ax = b

residual_sum_of_squares: Option<ArrayBase<OwnedRepr<<E as Scalar>::Real>, <I as Dimension>::Smaller>>

If n < m and rank(A) == n, the sum of squares If b is a (m x 1) vector, this is a 0-dimensional array (single value) If b is a (m x k) matrix, this is a (k x 1) column vector

Trait Implementations

impl<E, I> Clone for LeastSquaresResult<E, I>

where E: Clone + Scalar, I: Clone + Dimension, <E as Scalar>::Real: Clone, <I as Dimension>::Smaller: Clone,

fn clone(&self) -> LeastSquaresResult<E, I>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<E, I> Debug for LeastSquaresResult<E, I>

where E: Debug + Scalar, I: Debug + Dimension, <E as Scalar>::Real: Debug, <I as Dimension>::Smaller: Debug,

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.