old_contents: # Copyright (c) 2015 the GPy Austhors (see AUTHORS.txt)
  # Licensed under the BSD 3-clause license (see LICENSE.txt)
  
  from .bayesian_gplvm import BayesianGPLVM
  
  class DPBayesianGPLVM(BayesianGPLVM):
      """
      Bayesian Gaussian Process Latent Variable Model with Descriminative prior
      """
      def __init__(self, Y, input_dim, X_prior, X=None, X_variance=None, init='PCA', num_inducing=10,
                   Z=None, kernel=None, inference_method=None, likelihood=None,
                   name='bayesian gplvm', mpi_comm=None, normalizer=None,
                   missing_data=False, stochastic=False, batchsize=1):
          super(DPBayesianGPLVM,self).__init__(Y=Y, input_dim=input_dim, X=X, X_variance=X_variance,
                                               init=init, num_inducing=num_inducing, Z=Z, kernel=kernel,
                                               inference_method=inference_method, likelihood=likelihood,
                                               mpi_comm=mpi_comm, normalizer=normalizer,
                                               missing_data=missing_data, stochastic=stochastic,
                                               batchsize=batchsize, name='dp bayesian gplvm')
          self.X.mean.set_prior(X_prior)
          self.link_parameter(X_prior)
  
new_contents: # Copyright (c) 2015 the GPy Authors (see AUTHORS.txt)
  # Licensed under the BSD 3-clause license (see LICENSE.txt)
  
  from .bayesian_gplvm import BayesianGPLVM
  
  class DPBayesianGPLVM(BayesianGPLVM):
      """
      Bayesian Gaussian Process Latent Variable Model with Descriminative prior
      """
      def __init__(self, Y, input_dim, X_prior, X=None, X_variance=None, init='PCA', num_inducing=10,
                   Z=None, kernel=None, inference_method=None, likelihood=None,
                   name='bayesian gplvm', mpi_comm=None, normalizer=None,
                   missing_data=False, stochastic=False, batchsize=1):
          super(DPBayesianGPLVM,self).__init__(Y=Y, input_dim=input_dim, X=X, X_variance=X_variance,
                                               init=init, num_inducing=num_inducing, Z=Z, kernel=kernel,
                                               inference_method=inference_method, likelihood=likelihood,
                                               mpi_comm=mpi_comm, normalizer=normalizer,
                                               missing_data=missing_data, stochastic=stochastic,
                                               batchsize=batchsize, name='dp bayesian gplvm')
          self.X.mean.set_prior(X_prior)
          self.link_parameter(X_prior)
  
subject: Fix typo in the header