def match_list()

in neural/utils_mous.py [0:0]

• 24 lines of code
• 8 McCabe index (conditional complexity)

def match_list(A, B, on_replace='delete'):
    """Match two lists of different sizes and return corresponding indices
    Parameters
    ----------
    A: list | array, shape (n,)
        The values of the first list
    B: list | array: shape (m, )
        The values of the second list
    Returns
    -------
    A_idx : array
        The indices of the A list that match those of the B
    B_idx : array
        The indices of the B list that match those of the A
    """
    unique = np.unique(np.r_[A, B])
    label_encoder = dict((k, v) for v, k in enumerate(unique))

    def int_to_unicode(array):
        return ''.join([str(chr(label_encoder[ii])) for ii in array])

    changes = editops(int_to_unicode(A), int_to_unicode(B))
    B_sel = np.arange(len(B)).astype(float)
    A_sel = np.arange(len(A)).astype(float)
    for type, val_a, val_b in changes:
        if type == 'insert':
            B_sel[val_b] = np.nan
        elif type == 'delete':
            A_sel[val_a] = np.nan
        elif on_replace == 'delete':
            # print('delete replace')
            A_sel[val_a] = np.nan
            B_sel[val_b] = np.nan
        elif on_replace == 'keep':
            # print('keep replace')
            pass
        else:
            raise NotImplementedError
    B_sel = B_sel[np.where(~np.isnan(B_sel))]
    A_sel = A_sel[np.where(~np.isnan(A_sel))]
    assert len(B_sel) == len(A_sel)
    return A_sel.astype(int), B_sel.astype(int)