def generate_training_sequences(self):
        sampler = hmmlearn.hmm.GMMHMM(
            n_components=4,
            n_mix=3,
            covariance_type="full",
            init_params=""
        )

        sampler.startprob_ = [.25, .25, .25, .25]
        sampler.transmat_ = [
            [.1, .3, .3, .3],
            [.3, .1, .3, .3],
            [.3, .3, .1, .3],
            [.3, .3, .3, .1],
        ]
        sampler.weights_ = [
            [.2, .2, .6],
            [.6, .2, .2],
            [.2, .6, .2],
            [.1, .1, .8],
        ]
        sampler.means_ = np.asarray([
            [[-10], [-12], [-9]],
            [[-5], [-4], [-3]],
            [[-1.5], [0], [1.5]],
            [[5], [7], [9]],
        ])

        sampler.covars_ = np.asarray([
            [[[.125]], [[.125]], [[.125]]],
            [[[.125]], [[.125]], [[.125]]],
            [[[.125]], [[.125]], [[.125]]],
            [[[.125]], [[.125]], [[.125]]],
        ])

        n_sequences = 10
        length = 5_000
        sequences = []
        for i in range(n_sequences):
            sequences.append(sampler.sample(5000)[0])
        return np.concatenate(sequences), [length] * n_sequences