Function svd

Singular value decomposition of real matrices.

This currently computes a thin SVD via symmetric eigendecomposition. It supports fullMatrices=false; fullMatrices=true is accepted only for square inputs where thin and full output shapes match.

svd(
    a: ArrayLike,
    opts: { computeUv?: boolean; fullMatrices?: boolean } & {
        computeUv: false;
    },
): Array
Parameters
- a: ArrayLike
- opts: { computeUv?: boolean; fullMatrices?: boolean } & { computeUv: false }
Returns Array
- Defined in library/numpy-linalg.ts:98
svd(
a: ArrayLike,
opts?: { computeUv?: boolean; fullMatrices?: boolean },
): [Array, Array, Array]
Parameters
- a: ArrayLike
- Optionalopts: { computeUv?: boolean; fullMatrices?: boolean }
Returns [Array, Array, Array]
- Defined in library/numpy-linalg.ts:99

Function svd

Parameters

Returns Array

Parameters

Returns [Array, Array, Array]