Function cost_normalization

ot/utils.py:450–492

r"""Apply normalization to the loss matrix Parameters ---------- C : ndarray, shape (n1, n2) The cost matrix to normalize. norm : str Type of normalization from 'median', 'max', 'log', 'loglog'. Any other value do not normalize. Returns ------- C

(C, norm=None, return_value=False, value=None)

def cost_normalization(C, norm=None, return_value=False, value=None):
    r"""Apply normalization to the loss matrix

    Parameters
    ----------
    C : ndarray, shape (n1, n2)
        The cost matrix to normalize.
    norm : str
        Type of normalization from 'median', 'max', 'log', 'loglog'. Any
        other value do not normalize.

    Returns
    -------
    C : ndarray, shape (`n1`, `n2`)
        The input cost matrix normalized according to given norm.
    """

    nx = get_backend(C)

    if norm is None:
        pass
    elif norm == "median":
        if value is None:
            value = nx.median(C)
        C /= value
    elif norm == "max":
        if value is None:
            value = nx.max(C)
        C /= float(value)
    elif norm == "log":
        C = nx.log(1 + C)
    elif norm == "loglog":
        C = nx.log(1 + nx.log(1 + C))
    else:
        raise ValueError(
            "Norm %s is not a valid option.\n"
            "Valid options are:\n"
            "median, max, log, loglog" % norm
        )
    if return_value:
        return C, value
    else:
        return C


def dots(*args):