from hmmlearn.hmm import MultinomialHMM from pattern.ge_params import GEParams from pattern.read_losses_from_csv import read_losses_from_csv, SEQUENCE_COL, RECEIVED_COL startprob_prior = np.array([0.99, 0.01]) transmat_prior = np.array([[0.95, 0.05], [0.95, 0.05]]) emissionprob_prior = np.array([[0.9, 0.1], [0.1, 0.9]]) model = MultinomialHMM(n_components=2, verbose=False, n_iter=1000, tol=1e-3) model.startprob_ = startprob_prior model.transmat_ = transmat_prior model.emissionprob_ = emissionprob_prior model.init_params = 'st' def fit_ge_params(losses: np.array) -> GEParams: model.fit(losses) return GEParams.from_hmm(model) def main(csv_path: str, max_length: int, use_received: bool = False, verbose: bool = False): out_dir, csv_name = os.path.split(csv_path) expected = None try: expected = GEParams.from_file(os.path.join(out_dir,