norms.py 文件源码

def matrix_rank(M, tol=None):
    """Return matrix rank of array using SVD method

    Args:
        M (cupy.ndarray): Input array. Its `ndim` must be less than or equal to
            2.
        tol (None or float): Threshold of singular value of `M`.
            When `tol` is `None`, and `eps` is the epsilon value for datatype
            of `M`, then `tol` is set to `S.max() * max(M.shape) * eps`,
            where `S` is the singular value of `M`.
            It obeys :func:`numpy.linalg.matrix_rank`.

    Returns:
        cupy.ndarray: Rank of `M`.

    .. seealso:: :func:`numpy.linalg.matrix_rank`
    """
    if M.ndim < 2:
        return (M != 0).any().astype('l')
    S = decomposition.svd(M, compute_uv=False)
    if tol is None:
        tol = (S.max(axis=-1, keepdims=True) * max(M.shape[-2:]) *
               numpy.finfo(S.dtype).eps)
    return (S > tol).sum(axis=-1)