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src/x/b/xbob.example.faceverify-1.0.0/xbob/example/faceverify/dct_ubm.py   xbob.example.faceverify(Download)
  kmeans_trainer.train(kmeans, training_set)
 
  [variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(training_set)
  means = kmeans.means
 

src/x/b/xbob.thesis.elshafey2014-0.0.1a0/xbob/thesis/elshafey2014/tools/ParaUBMGMM.py   xbob.thesis.elshafey2014(Download)
      gmm_machine = bob.machine.GMMMachine(self.m_tool.m_gaussians, data.shape[1])
 
      [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data)
 
      # Initializes the GMM

src/x/b/xbob.spkrec-1.0.3/xbob/spkrec/script/ParallelUBMGMM.py   xbob.spkrec(Download)
      gmm_machine = bob.machine.GMMMachine(self.m_tool.m_gaussians, data.shape[1])
 
      [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data)
 
      # Initializes the GMM

src/b/o/bob.spear-1.1.2/spear/script/ParallelUBMGMM.py   bob.spear(Download)
      gmm_machine = bob.machine.GMMMachine(self.m_tool.m_gaussians, data.shape[1])
 
      [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data)
 
      # Initializes the GMM

src/f/a/facereclib-1.2.1/facereclib/tools/ParallelUBMGMM.py   facereclib(Download)
      gmm_machine = bob.machine.GMMMachine(self.m_tool.m_gaussians, data.shape[1])
 
      [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data)
 
      # Initializes the GMM

src/f/a/facereclib-HEAD/facereclib/tools/ParallelUBMGMM.py   facereclib(Download)
      gmm_machine = bob.machine.GMMMachine(self.m_tool.m_gaussians, data.shape[1])
 
      [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data)
 
      # Initializes the GMM

src/x/b/xbob.spkrec-1.0.3/xbob/spkrec/preprocessing/Energy.py   xbob.spkrec(Download)
 
 
    [variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(normalized_energy)
    means = kmeans.means
    if numpy.isnan(means[0]) or numpy.isnan(means[1]):

src/b/o/bob.spear-1.1.2/spear/preprocessing/Energy.py   bob.spear(Download)
 
 
    [variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(normalized_energy)
    means = kmeans.means
    if numpy.isnan(means[0]) or numpy.isnan(means[1]):

src/f/a/facereclib-1.2.1/facereclib/tools/UBMGMM.py   facereclib(Download)
    kmeans_trainer.train(kmeans, normalized_array)
 
    [variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(normalized_array)
    means = kmeans.means
 

src/f/a/facereclib-HEAD/facereclib/tools/UBMGMM.py   facereclib(Download)
    kmeans_trainer.train(kmeans, normalized_array)
 
    [variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(normalized_array)
    means = kmeans.means
 

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