import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=RuntimeWarning)
from hmmlearn.hmm import GaussianHMM
import numpy as np
#samples:
X = np.array([[-1.03573482, -1.03573482], [6.62721065, 11.62721065],
[3.19196949, 8.19196949], [0.38798214, 0.38798214],
[2.56845104, 7.56845104], [5.03699793, 10.03699793],
[5.87873937, 10.87873937], [4.27000819, -1.72999181],
[4.02692237, -1.97307763], [5.7222677, 10.7222677]])
# Trainning a new model over samples:
model = GaussianHMM(n_components=3, covariance_type="diag").fit(X)
# Create a new copy of the trained model:
new_model = GaussianHMM(n_components=3, covariance_type="diag")
new_model.startprob_ = model.startprob_
new_model.transmat_ = model.transmat_
new_model.means_ = model.means_
m = model._covars_
n = model.covars_
p = model.get_params()
new_model.covars_ = model._covars_
# Predict from X:
X_N = new_model.predict(X)
print(X_N)
