How to use the hmmlearn.hmm.PoissonHMM function in hmmlearn

# compute scores_hmm (log likelihoods) of validation set:
        scores[validation] = hmm.score(PBEs_test)

        for nn in range(n_shuffles):
            # shuffle data:
            bst_test_shuffled = shuffle_func(PBEs_test)

            # score validation set with shuffled-data HMM
            shuffled[validation, nn] = hmm.score(bst_test_shuffled)

    quality = zmap(scores.mean(), shuffled.mean(axis=0))

    return quality, scores, shuffled

class PoissonHMM(PHMM):
    """Nelpy extension of PoissonHMM: Hidden Markov Model with
    independent Poisson emissions.

    Parameters
    ----------
    n_components : int
        Number of states.

    startprob_prior : array, shape (n_components, )
        Initial state occupation prior distribution.

    transmat_prior : array, shape (n_components, n_components)
        Matrix of prior transition probabilities between states.

    algorithm : string, one of the :data:`base.DECODER_ALGORITHMS`
        Decoder algorithm.