前提准备

  • pip install sklearn

  • pip install hmmlearn

代码

  1. import numpy as np
  2. from hmmlearn import hmm
  4. ##############################################################
  5. # Prepare parameters for a 4-components HMM
  6. # Initial population probability
  7. startprob = np.array([0.6, 0.3, 0.1, 0.0])
  8. # The transition matrix, note that there are no transitions possible
  9. # between component 1 and 3
  10. transmat = np.array([[0.7, 0.2, 0.0, 0.1],
  11.                      [0.3, 0.5, 0.2, 0.0],
  12.                      [0.0, 0.3, 0.5, 0.2],
  13.                      [0.2, 0.0, 0.2, 0.6]])
  14. # The means of each component
  15. means = np.array([[0.0,  0.0],
  16.                   [0.0, 11.0],
  17.                   [9.0, 10.0],
  18.                   [11.0, -1.0]])
  19. # The covariance of each component
  20. covars = .5 * np.tile(np.identity(2), (4, 1, 1))
  22. # Build an HMM instance and set parameters
  23. model = hmm.GaussianHMM(n_components=4, covariance_type="full")
  25. # Instead of fitting it from the data, we directly set the estimated
  26. # parameters, the means and covariance of the components
  27. model.startprob_ = startprob
  28. model.transmat_ = transmat
  29. model.means_ = means
  30. model.covars_ = covars
  31. ###############################################################
  33. # Generate samples
  34. X, Z = model.sample(500)
  35. print(Z)