code: def test_fit(X):
      d = [Exponential([2.1, 0.3, 1.1]), Exponential([1.5, 3.1, 2.2])]
      model = GeneralMixtureModel(d, max_iter=1)
      model.fit(X)
  
      assert_array_almost_equal(model._w_sum, [0.0, 0.0])
      assert_array_almost_equal(model.priors, [0.44044, 0.55956])
      assert_array_almost_equal(
          model._log_priors, numpy.log([0.44044, 0.55956])
      )
  
      d = [Exponential([2.1, 0.3, 1.1]), Exponential([1.5, 3.1, 2.2])]
      model = GeneralMixtureModel(d, max_iter=5)
      model.fit(X)
  
      assert_array_almost_equal(model._w_sum, [0.0, 0.0])
      assert_array_almost_equal(model.priors, [0.378347, 0.621653])
      assert_array_almost_equal(
          model._log_priors, numpy.log([0.378347, 0.621653])
      )
  
imports: import os
  import numpy
  import torch
  import pytest
  from pomegranate.gmm import GeneralMixtureModel
  from pomegranate.distributions import Exponential
  from .distributions._utils import _test_initialization_raises_one_parameter
  from .distributions._utils import _test_initialization
  from .distributions._utils import _test_predictions
  from .distributions._utils import _test_efd_from_summaries
  from .distributions._utils import _test_raises
  from nose.tools import assert_raises
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 32 for calling a global function with example usage: test_fit(X) without return types