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,
