code: def test_fit_with_priors(
      self, implementation, init_params="mc", params="stmc", n_iter=5
  ):
      startprob_prior = 10 * self.startprob + 2.0
      transmat_prior = 10 * self.transmat + 2.0
      means_prior = self.means
      means_weight = 2.0
      covars_weight = 2.0
      if self.covariance_type in ("full", "tied"):
          covars_weight += self.n_features
      covars_prior = self.covars
      h = hmm.GaussianHMM(
          self.n_components,
          self.covariance_type,
          implementation=implementation,
      )
      h.startprob_ = self.startprob
      h.startprob_prior = startprob_prior
      h.transmat_ = normalized(
          self.transmat + np.diag(self.prng.rand(self.n_components)), 1
      )
      h.transmat_prior = transmat_prior
      h.means_ = 20 * self.means
      h.means_prior = means_prior
      h.means_weight = means_weight
      h.covars_ = self.covars
      h.covars_prior = covars_prior
      h.covars_weight = covars_weight
  
      lengths = [200] * 10
      X, _state_sequence = h.sample(
          sum(lengths), random_state=self.prng
      )
  
      # Re-initialize the parameters and check that we can converge to
      # the original parameter values.
      h_learn = hmm.GaussianHMM(
          self.n_components,
          self.covariance_type,
          init_params=init_params,
          params=params,
          implementation=implementation,
      )
      # don't use random parameters for testing
      init = 1.0 / h_learn.n_components
      h_learn.startprob_ = np.full(h_learn.n_components, init)
      h_learn.transmat_ = np.full(
          (h_learn.n_components, h_learn.n_components), init
      )
  
      h_learn.n_iter = 0
      h_learn.fit(X, lengths=lengths)
  
      assert_log_likelihood_increasing(h_learn, X, lengths, n_iter)
  
      # Make sure we've converged to the right parameters.
      # In general, to account for state switching,
      # compare sorted values.
      # a) means
      assert_allclose(
          sorted(h.means_.ravel().tolist()),
          sorted(h_learn.means_.ravel().tolist()),
          0.01,
      )
      # b) covars are hard to estimate precisely from a relatively small
      #    sample, thus the large threshold
  
      # account for how we store the covars_compressed
      orig = np.broadcast_to(h._covars_, h_learn._covars_.shape)
      assert_allclose(
          sorted(orig.ravel().tolist()),
          sorted(h_learn._covars_.ravel().tolist()),
          10,
      )
  
imports: import numpy
  from numpy.testing import assert_allclose
  import pytest
  from sklearn.utils import check_random_state
  from ..None import hmm
  from .None import assert_log_likelihood_increasing, make_covar_matrix, normalized
label_desc: Use image node_id 12 for calling the GaussianHMMTestMixin obj's underlying member method code with example usage: obj.test_fit_with_priors(implementation, init_params, params, n_iter) without return types