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src/x/b/xbob.example.faceverify-1.0.0/xbob/example/faceverify/tests/__init__.py   xbob.example.faceverify(Download)
    pca = train(images)
    if regenerate_references:
      pca.save(bob.io.HDF5File(self.resource('pca_projector.hdf5'), 'w'))
 
    # load PCA reference and check that it is still similar
    pca_ref = bob.machine.LinearMachine(bob.io.HDF5File(self.resource('pca_projector.hdf5')))
    ubm = train(trainset, number_of_gaussians = 2)
    if regenerate_references:
      ubm.save(bob.io.HDF5File(self.resource('dct_ubm.hdf5'), 'w'))
 
    # load PCA reference and check that it is still similar
    ubm_ref = bob.machine.GMMMachine(bob.io.HDF5File(self.resource('dct_ubm.hdf5')))
    model = enroll(enrollset, ubm, enroller)
    if regenerate_references:
      model.save(bob.io.HDF5File(self.resource('dct_model.hdf5'), 'w'))
 
    model_ref = bob.machine.GMMMachine(bob.io.HDF5File(self.resource('dct_model.hdf5')))

src/x/b/xbob.paper.example-0.2.0/xbob/paper/example/utils.py   xbob.paper.example(Download)
def save_machine(X_mean, machine, filename):
  """Saves the machine and the mean vector into an hdf5 file"""
  f = bob.io.HDF5File(filename, 'w')
  f.set('X_mean', X_mean)
  machine.save(f)
  del f
 
def load_machine(filename):
  """Loads the machine and the mean vector from an hdf5 file"""
  f = bob.io.HDF5File(filename)

src/a/n/antispoofing.lbptop-1.0.4/antispoofing/lbptop/script/lbptop_make_scores.py   antispoofing.lbptop(Download)
     #Loading the PCA machine
    if(pcaFile != ''):
      hdf5File_pca    = bob.io.HDF5File(pcaFile,openmode_string='r')
      pcaMachine      = bob.machine.LinearMachine(hdf5File_pca)
      featureVector   = pca.pcareduce(pcaMachine, featureVector);
    #Loading the machine
    if(machineType=='Linear'):
      hdf5File_linear = bob.io.HDF5File(machineFile,openmode_string='r')
      machine         = bob.machine.LinearMachine(hdf5File_linear)
      scores          = lda.get_scores(machine, featureVector)

src/a/n/antispoofing.lbptop-1.0.4/antispoofing/lbptop/script/lbptop_ldatrain.py   antispoofing.lbptop(Download)
    #### Saving the machines
    if(pca_reduction):
      hdf5File_pca = bob.io.HDF5File(os.path.join(outputDir, 'pca_machine_'+ str(energy) + '-' + models[i] +'.txt'),openmode_string='w')
      pcaMachine.save(hdf5File_pca)
      del hdf5File_pca
 
    hdf5File_lda = bob.io.HDF5File(os.path.join(outputDir, 'lda_machine_'+ str(energy) + "-" + models[i] +'.txt'),openmode_string='w')

src/a/n/antispoofing.dog-1.0.2/antispoofing/dog/script/make_scores.py   antispoofing.dog(Download)
     #Loading the PCA machine
    if(pcaFile != ''):
      hdf5File_pca    = bob.io.HDF5File(pcaFile,openmode_string='r')
      pcaMachine      = bob.machine.LinearMachine(hdf5File_pca)
      featureVector   = pca.pcareduce(pcaMachine, featureVector);
    #Loading the machine
    if(machineType=='Linear'):
      hdf5File_linear = bob.io.HDF5File(machineFile,openmode_string='r')
      machine         = bob.machine.LinearMachine(hdf5File_linear)
      scores          = lda.get_scores(machine, featureVector)

src/a/n/antispoofing.dog-1.0.2/antispoofing/dog/script/ldatrain.py   antispoofing.dog(Download)
  #### Saving the machines
  if(pca_reduction):
    hdf5File_pca = bob.io.HDF5File(os.path.join(outputDir, 'pca_machine_'+ str(energy) + '.txt'),openmode_string='w')
    pcaMachine.save(hdf5File_pca)
    del hdf5File_pca
 
  hdf5File_lda = bob.io.HDF5File(os.path.join(outputDir, 'lda_machine_'+ str(energy) +'.txt'),openmode_string='w')

src/a/n/antispoofing.competition_icb2013-1.1.0/antispoofing/competition_icb2013/script/ldatrain.py   antispoofing.competition_icb2013(Download)
    test_real = pca.pcareduce(pca_machine, test_real); test_attack = pca.pcareduce(pca_machine, test_attack)
    print "Saving PCA machine..."
    hdf5File_pca = bob.io.HDF5File(os.path.join(args.outputdir, 'pca_machine_'+ str(energy)), openmode_string='w')
    pca_machine.save(hdf5File_pca)
    del hdf5File_pca
  lda_machine.shape = (lda_machine.shape[0], 1) #only use first component!
  print "Saving LDA machine..."
  hdf5File_lda = bob.io.HDF5File(os.path.join(args.outputdir, 'lda_machine_'+ str(energy)), openmode_string='w')
  lda_machine.save(hdf5File_lda)
  del hdf5File_lda

src/a/n/antispoofing.utils-1.1.3/antispoofing/utils/ml/svmCountermeasure.py   antispoofing.utils(Download)
def writeNormalizationData(fileName,lowbound,highbound,mins,maxs):
  hdf5File = bob.io.HDF5File(fileName, openmode_string='w')
  hdf5File.append('lowbound',lowbound)
  hdf5File.append('highbound',highbound)
  hdf5File.append('mins',mins)
def readNormalizationData(fileName):
 
  #Opening HDF5 Files
  hdf5 = bob.io.HDF5File(fileName, openmode_string='r')
  lowbound  = hdf5.read('lowbound')

src/a/n/antispoofing.lbptop-1.0.4/antispoofing/lbptop/spoof/scores.py   antispoofing.lbptop(Download)
 
 
  hdf5File = bob.io.HDF5File(outputFile, openmode_string='w')
  hdf5File.set('data',data)
  del hdf5File

src/a/n/antispoofing.lbptop-1.0.4/antispoofing/lbptop/script/lbptop_svmtrain.py   antispoofing.lbptop(Download)
    #Saving the machines
    if(pca_reduction):
      hdf5File_pca = bob.io.HDF5File(os.path.join(outputDir, 'pca_machine_'+ str(energy) + '-' + models[i] +'.txt'),openmode_string='w')
      pcaMachine.save(hdf5File_pca)
      del hdf5File_pca

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