old_contents: # Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
  # Licensed under the BSD 3-clause license (see LICENSE.txt)
  
  
  import numpy as np
  from .. import kern
  from ..core import GP, Param
  from ..likelihoods import Gaussian
  
  
  class GPLVM(GP):
      """
      Gaussian Process Latent Variable Model
  
  
      """
      def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, name="gplvm"):
  
          """
          :param Y: observed data
          :type Y: np.ndarray
          :param input_dim: latent dimensionality
          :type input_dim: int
          :param init: initialisation method for the latent space
          :type init: 'PCA'|'random'
          """
          if X is None:
              from ..util.initialization import initialize_latent
              X, fracs = initialize_latent(init, input_dim, Y)
          else:
              fracs = np.ones(input_dim)
          if kernel is None:
              kernel = kern.RBF(input_dim, lengthscale=fracs, ARD=input_dim > 1) + kern.Bias(input_dim, np.exp(-2))
  
          likelihood = Gaussian()
  
          super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')
  
          self.X = Param('latent_mean', X)
          self.link_parameter(self.X, index=0)
  
      def parameters_changed(self):
          super(GPLVM, self).parameters_changed()
          self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)
  
new_contents: # Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
  # Licensed under the BSD 3-clause license (see LICENSE.txt)
  
  
  import numpy as np
  from .. import kern
  from ..core import GP, Param
  from ..likelihoods import Gaussian
  
  
  class GPLVM(GP):
      """
      Gaussian Process Latent Variable Model
  
  
      """
      def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, name="gplvm", Y_metadata=None, normalizer=False):
  
          """
          :param Y: observed data
          :type Y: np.ndarray
          :param input_dim: latent dimensionality
          :type input_dim: int
          :param init: initialisation method for the latent space
          :type init: 'PCA'|'random'
          :param normalizer:
            normalize the outputs Y.
            If normalizer is True, we will normalize using Standardize.
            If normalizer is False (the default), no normalization will be done.
          :type normalizer: bool
          """
          if X is None:
              from ..util.initialization import initialize_latent
              X, fracs = initialize_latent(init, input_dim, Y)
          else:
              fracs = np.ones(input_dim)
          if kernel is None:
              kernel = kern.RBF(input_dim, lengthscale=fracs, ARD=input_dim > 1) + kern.Bias(input_dim, np.exp(-2))
  
          likelihood = Gaussian()
  
          super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM', Y_metadata=Y_metadata, normalizer=normalizer)
  
          self.X = Param('latent_mean', X)
          self.link_parameter(self.X, index=0)
  
      def parameters_changed(self):
          super(GPLVM, self).parameters_changed()
          self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)
  
subject: Update GPLVM class to use metadata and output normalizers.