content: import unittest
  import sys
  
  import numpy as np
  
  from sklearn.mixture import DPGMM, VBGMM
  from sklearn.mixture.dpgmm import log_normalize
  from sklearn.datasets import make_blobs
  from sklearn.utils.testing import assert_array_less, assert_equal
  from sklearn.mixture.tests.test_gmm import GMMTester
  from sklearn.externals.six.moves import cStringIO as StringIO
  
  np.seterr(all='warn')
  
  
  def test_class_weights():
      # check that the class weights are updated
      # simple 3 cluster dataset
      X, y = make_blobs(random_state=1)
      for Model in [DPGMM, VBGMM]:
          dpgmm = Model(n_components=10, random_state=1, alpha=20, n_iter=50)
          dpgmm.fit(X)
          # get indices of components that are used:
          indices = np.unique(dpgmm.predict(X))
          active = np.zeros(10, dtype=np.bool)
          active[indices] = True
          # used components are important
          assert_array_less(.1, dpgmm.weights_[active])
          # others are not
          assert_array_less(dpgmm.weights_[~active], .05)
  
  
  def test_verbose_boolean():
      # checks that the output for the verbose output is the same
      # for the flag values '1' and 'True'
      # simple 3 cluster dataset
      X, y = make_blobs(random_state=1)
      for Model in [DPGMM, VBGMM]:
          dpgmm_bool = Model(n_components=10, random_state=1, alpha=20,
                             n_iter=50, verbose=True)
          dpgmm_int = Model(n_components=10, random_state=1, alpha=20,
                            n_iter=50, verbose=1)
  
          old_stdout = sys.stdout
          sys.stdout = StringIO()
          try:
              # generate output with the boolean flag
              dpgmm_bool.fit(X)
              verbose_output = sys.stdout
              verbose_output.seek(0)
              bool_output = verbose_output.readline()
              # generate output with the int flag
              dpgmm_int.fit(X)
              verbose_output = sys.stdout
              verbose_output.seek(0)
              int_output = verbose_output.readline()
              assert_equal(bool_output, int_output)
          finally:
              sys.stdout = old_stdout
  
  
  def test_verbose_first_level():
      # simple 3 cluster dataset
      X, y = make_blobs(random_state=1)
      for Model in [DPGMM, VBGMM]:
          dpgmm = Model(n_components=10, random_state=1, alpha=20, n_iter=50,
                        verbose=1)
  
          old_stdout = sys.stdout
          sys.stdout = StringIO()
          try:
              dpgmm.fit(X)
          finally:
              sys.stdout = old_stdout
  
  
  def test_verbose_second_level():
      # simple 3 cluster dataset
      X, y = make_blobs(random_state=1)
      for Model in [DPGMM, VBGMM]:
          dpgmm = Model(n_components=10, random_state=1, alpha=20, n_iter=50,
                        verbose=2)
  
          old_stdout = sys.stdout
          sys.stdout = StringIO()
          try:
              dpgmm.fit(X)
          finally:
              sys.stdout = old_stdout
  
  
  def test_log_normalize():
      v = np.array([0.1, 0.8, 0.01, 0.09])
      a = np.log(2 * v)
      assert np.allclose(v, log_normalize(a), rtol=0.01)
  
  
  def do_model(self, **kwds):
      return VBGMM(verbose=False, **kwds)
  
  
  class DPGMMTester(GMMTester):
      model = DPGMM
      do_test_eval = False
  
      def score(self, g, train_obs):
          _, z = g.score_samples(train_obs)
          return g.lower_bound(train_obs, z)
  
  
  class TestDPGMMWithSphericalCovars(unittest.TestCase, DPGMMTester):
      covariance_type = 'spherical'
      setUp = GMMTester._setUp
  
  
  class TestDPGMMWithDiagCovars(unittest.TestCase, DPGMMTester):
      covariance_type = 'diag'
      setUp = GMMTester._setUp
  
  
  class TestDPGMMWithTiedCovars(unittest.TestCase, DPGMMTester):
      covariance_type = 'tied'
      setUp = GMMTester._setUp
  
  
  class TestDPGMMWithFullCovars(unittest.TestCase, DPGMMTester):
      covariance_type = 'full'
      setUp = GMMTester._setUp
  
  
  class VBGMMTester(GMMTester):
      model = do_model
      do_test_eval = False
  
      def score(self, g, train_obs):
          _, z = g.score_samples(train_obs)
          return g.lower_bound(train_obs, z)
  
  
  class TestVBGMMWithSphericalCovars(unittest.TestCase, VBGMMTester):
      covariance_type = 'spherical'
      setUp = GMMTester._setUp
  
  
  class TestVBGMMWithDiagCovars(unittest.TestCase, VBGMMTester):
      covariance_type = 'diag'
      setUp = GMMTester._setUp
  
  
  class TestVBGMMWithTiedCovars(unittest.TestCase, VBGMMTester):
      covariance_type = 'tied'
      setUp = GMMTester._setUp
  
  
  class TestVBGMMWithFullCovars(unittest.TestCase, VBGMMTester):
      covariance_type = 'full'
      setUp = GMMTester._setUp
  