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)