bayes_scores.py 文件源码

def K2(bn, data, ed=None):
    """
    K2 is bayesian posterior probability of structure given the data,
    where N'ijk = 1.
    """
    counts_dict = mle_fast(bn, data, counts=True, np=True)
    a_ijk = []
    k2 = 1
    for rv, value in counts_dict.items():
        nijk = value['cpt']
        nijk_prime = 1
        k2 *= gamma(nijk+nijk_prime)/gamma(nijk_prime)
        nij_prime = nijk_prime*(len(cpt)/bn.card(rv))
        nij = np.mean(nijk.reshape(-1, bn.card(rv)), axis=1) # sum along parents
        k2 *= gamma(nij_prime) / gamma(nij+nij_prime)

    return k2