97070a67bafcf2ecf4ecde78f3b8d5617feb3ebd,scipy/sparse/linalg/matfuncs.py,,_ell,#Any#Any#,628

Before Change

    // can be computed exactly and efficiently.
    // Explicitly make a column vector so that this works when A is a
    // numpy matrix (as opposed to ndarray or sparse matrix).
    v = np.ones((A.shape[0], 1), dtype=float)
    M = abs(A).T
    for i in range(p):
        v = M.dot(v)
    A_abs_one_norm = max(v)

    // Treat zero norm as a special case.
    if not A_abs_one_norm:
        return 0

After Change

        A value related to a bound.

    
    if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
        raise ValueError("expected A to be like a square matrix")

    p = 2*m + 1

Instances