gromov.py 文件源码

def update_kl_loss(p, lambdas, T, Cs):
    """
    Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration


    Parameters
    ----------
    p  : ndarray, shape (N,)
         weights in the targeted barycenter
    lambdas : list of the S spaces' weights
    T : list of S np.ndarray(ns,N)
        the S Ts couplings calculated at each iteration
    Cs : list of S ndarray, shape(ns,ns)
         Metric cost matrices

    Returns
    ----------
    C : ndarray, shape (ns,ns)
        updated C matrix
    """
    tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s])
                  for s in range(len(T))])
    ppt = np.outer(p, p)

    return np.exp(np.divide(tmpsum, ppt))