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src/x/b/xbob.paper.tpami2013-1.0.0/xbob/paper/tpami2013/scripts/plda_example_iris.py   xbob.paper.tpami2013(Download)
 
  print("Training your PLDA machine...")
  pldabase = bob.machine.PLDABase(data.values()[0].shape[1], dim_f, dim_g)
  trainer = bob.trainer.PLDATrainer()
  trainer.train(pldabase, data.values())

src/x/b/xbob.spkrec-1.0.3/xbob/spkrec/tools/IVector.py   xbob.spkrec(Download)
 
    # train machine
    self.m_plda_base = bob.machine.PLDABase(input_dimension, self.m_config.SUBSPACE_DIMENSION_OF_F, self.m_config.SUBSPACE_DIMENSION_OF_G, self.m_config.variance_flooring)
    t.train(self.m_plda_base, training_features)
 
      self.m_pca_machine = bob.machine.LinearMachine(proj_hdf5file)
    proj_hdf5file.cd('/plda')
    self.m_plda_base = bob.machine.PLDABase(proj_hdf5file)
    self.m_plda_machine = bob.machine.PLDAMachine(self.m_plda_base)
    self.m_plda_trainer = bob.trainer.PLDATrainer()

src/b/o/bob.spear-1.1.2/spear/tools/IVector.py   bob.spear(Download)
 
    # train machine
    self.m_plda_base = bob.machine.PLDABase(input_dimension, self.m_config.SUBSPACE_DIMENSION_OF_F, self.m_config.SUBSPACE_DIMENSION_OF_G, self.m_config.variance_flooring)
    t.train(self.m_plda_base, training_features)
 
      self.m_pca_machine = bob.machine.LinearMachine(proj_hdf5file)
    proj_hdf5file.cd('/plda')
    self.m_plda_base = bob.machine.PLDABase(proj_hdf5file)
    self.m_plda_machine = bob.machine.PLDAMachine(self.m_plda_base)
    self.m_plda_trainer = bob.trainer.PLDATrainer()

src/x/b/xbob.thesis.elshafey2014-0.0.1a0/xbob/thesis/elshafey2014/utils/miris.py   xbob.thesis.elshafey2014(Download)
 
  # Initialize PLDA
  pldabase = bob.machine.PLDABase(2, 1, 1)
  pldatrainer = bob.trainer.PLDATrainer(100)
 

src/x/b/xbob.paper.tpami2013-1.0.0/xbob/paper/tpami2013/plda.py   xbob.paper.tpami2013(Download)
  T.init_sigma_method = init_s_method
  T.init_sigma_ratio = init_s_ratio
  machine = bob.machine.PLDABase(d, nf, ng) 
  T.train(machine, data)
  return machine
def load_base_model(plda_model_filename):
  if not os.path.exists(plda_model_filename):
    raise RuntimeError("Cannot find PLDA Base Model %s" % (plda_model_filename))
  return bob.machine.PLDABase(bob.io.HDF5File(plda_model_filename))
 

src/f/a/facereclib-1.2.1/facereclib/tools/PLDA.py   facereclib(Download)
 
    # train machine
    self.m_plda_base = bob.machine.PLDABase(input_dimension, self.m_subspace_dimension_of_f, self.m_subspace_dimension_of_g)
    t.train(self.m_plda_base, training_features)
 
      self.m_pca_machine = bob.machine.LinearMachine(proj_hdf5file)
    proj_hdf5file.cd('/plda')
    self.m_plda_base = bob.machine.PLDABase(proj_hdf5file)
    #self.m_plda_base = bob.machine.PLDABase(bob.io.HDF5File(projector_file))
    self.m_plda_machine = bob.machine.PLDAMachine(self.m_plda_base)

src/f/a/facereclib-HEAD/facereclib/tools/PLDA.py   facereclib(Download)
 
    # train machine
    self.m_plda_base = bob.machine.PLDABase(input_dimension, self.m_subspace_dimension_of_f, self.m_subspace_dimension_of_g)
    t.train(self.m_plda_base, training_features)
 
      self.m_pca_machine = bob.machine.LinearMachine(proj_hdf5file)
    proj_hdf5file.cd('/plda')
    self.m_plda_base = bob.machine.PLDABase(proj_hdf5file)
    #self.m_plda_base = bob.machine.PLDABase(bob.io.HDF5File(projector_file))
    self.m_plda_machine = bob.machine.PLDAMachine(self.m_plda_base)

src/x/b/xbob.thesis.elshafey2014-0.0.1a0/xbob/thesis/elshafey2014/tools/MyPLDA.py   xbob.thesis.elshafey2014(Download)
 
    # train machine
    self.m_plda_base = bob.machine.PLDABase(input_dimension, self.m_subspace_dimension_of_f, self.m_subspace_dimension_of_g)
    t.train(self.m_plda_base, training_features)
 

src/f/a/facereclib-1.2.1/facereclib/tests/test_tools.py   facereclib(Download)
    pca_machine = bob.machine.LinearMachine(test_file)
    test_file.cd('/plda')
    plda_machine = bob.machine.PLDABase(test_file)
    # TODO: compare the PCA machines
    #self.assertEqual(pca_machine, tool.m_pca_machine)

src/f/a/facereclib-HEAD/facereclib/tests/test_tools.py   facereclib(Download)
    pca_machine = bob.machine.LinearMachine(test_file)
    test_file.cd('/plda')
    plda_machine = bob.machine.PLDABase(test_file)
    # TODO: compare the PCA machines
    #self.assertEqual(pca_machine, tool.m_pca_machine)