old_contents: #!/usr/bin/env python
  # encoding: utf-8
  
  # The MIT License (MIT)
  
  # Copyright (c) 2014 CNRS
  
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  
  # The above copyright notice and this permission notice shall be included in
  # all copies or substantial portions of the Software.
  
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  # AUTHORS
  # Hervé BREDIN - http://herve.niderb.fr
  
  from gmm import SKLearnGMMClassification, SKLearnGMMUBMClassification
  
  
  class Test_GMMClassification:
  
      def setup(self):
  
          from sklearn.datasets import load_digits
          dataset = load_digits(n_class=10)
  
          X = dataset.data
          y = dataset.target
  
          self.trnX = X[::2]
          self.tstX = X[1::2]
          self.trny = y[::2]
          self.tsty = y[1::2]
  
      def test_gmm(self):
          gmm = SKLearnGMMClassification(n_components=8,
                                         calibration='pass')
          gmm.fit(self.trnX, self.trny)
          assert gmm.score(self.tstX, self.tsty) > 0.85
  
      def test_gmmubm(self):
          gmm = SKLearnGMMUBMClassification(n_components=8,
                                            calibration='pass')
          gmm.fit(self.trnX, self.trny)
          assert gmm.score(self.tstX, self.tsty) > 0.85
  
      def test_isotonic(self):
          gmm = SKLearnGMMUBMClassification(n_components=8,
                                            calibration='isotonic')
          gmm.fit(self.trnX, self.trny)
          assert gmm.score(self.tstX, self.tsty) > 0.85
  
new_contents: #!/usr/bin/env python
  # encoding: utf-8
  
  # The MIT License (MIT)
  
  # Copyright (c) 2014 CNRS
  
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  
  # The above copyright notice and this permission notice shall be included in
  # all copies or substantial portions of the Software.
  
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  # AUTHORS
  # Hervé BREDIN - http://herve.niderb.fr
  
  from gmm import SKLearnGMMClassification, SKLearnGMMUBMClassification
  
  
  class Test_GMMClassification:
  
      def setup(self):
  
          from sklearn.datasets import load_digits
          dataset = load_digits(n_class=10)
  
          X = dataset.data
          y = dataset.target
  
          self.trnX = X[::2]
          self.tstX = X[1::2]
          self.trny = y[::2]
          self.tsty = y[1::2]
  
      def test_gmm(self):
          gmm = SKLearnGMMClassification(n_components=8)
          gmm.fit(self.trnX, self.trny)
          assert gmm.score(self.tstX, self.tsty) > 0.85
  
      def test_gmmubm(self):
          gmm = SKLearnGMMUBMClassification(n_components=8)
          gmm.fit(self.trnX, self.trny)
          assert gmm.score(self.tstX, self.tsty) > 0.85
  
      def test_isotonic(self):
          gmm = SKLearnGMMUBMClassification(n_components=8,
                                            calibration='isotonic')
          gmm.fit(self.trnX, self.trny)
          assert gmm.score(self.tstX, self.tsty) > 0.85
  
subject: fix test for GMM-based classification