old_contents: # Copyright (c) 2012, James Hensman
  # Licensed under the BSD 3-clause license (see LICENSE.txt)
  
  import numpy as np
  from ..core import SVIGP
  from .. import likelihoods
  from .. import kern
  
  class SVIGPRegression(SVIGP):
      """
      Gaussian Process model for regression
  
      This is a thin wrapper around the SVIGP class, with a set of sensible defalts
  
      :param X: input observations
      :param Y: observed values
      :param kernel: a GPy kernel, defaults to rbf+white
      :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
      :type normalize_X: False|True
      :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
      :type normalize_Y: False|True
      :rtype: model object
  
      .. Note:: Multiple independent outputs are allowed using columns of Y
  
      """
  
      def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, q_u=None, batchsize=10):
          # kern defaults to rbf (plus white for stability)
          if kernel is None:
              kernel = kern.rbf(X.shape[1], variance=1., lengthscale=4.) + kern.white(X.shape[1], 1e-3)
  
          # Z defaults to a subset of the data
          if Z is None:
              i = np.random.permutation(X.shape[0])[:num_inducing]
              Z = X[i].copy()
          else:
              assert Z.shape[1] == X.shape[1]
  
          # likelihood defaults to Gaussian
          likelihood = likelihoods.Gaussian(Y, normalize=False)
  
          SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
          self.load_batch()
  
      def getstate(self):
          return GPBase.getstate(self)
  
  
      def setstate(self, state):
          return GPBase.setstate(self, state)
  
  
new_contents: # Copyright (c) 2012, James Hensman
  # Licensed under the BSD 3-clause license (see LICENSE.txt)
  
  import numpy as np
  from ..core import SVIGP
  from .. import likelihoods
  from .. import kern
  
  class SVIGPRegression(SVIGP):
      """
      Gaussian Process model for regression
  
      This is a thin wrapper around the SVIGP class, with a set of sensible defalts
  
      :param X: input observations
      :param Y: observed values
      :param kernel: a GPy kernel, defaults to rbf+white
      :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
      :type normalize_X: False|True
      :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
      :type normalize_Y: False|True
      :rtype: model object
  
      .. Note:: Multiple independent outputs are allowed using columns of Y
  
      """
  
      def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, q_u=None, batchsize=10, normalize_Y=False):
          # kern defaults to rbf (plus white for stability)
          if kernel is None:
              kernel = kern.rbf(X.shape[1], variance=1., lengthscale=4.) + kern.white(X.shape[1], 1e-3)
  
          # Z defaults to a subset of the data
          if Z is None:
              i = np.random.permutation(X.shape[0])[:num_inducing]
              Z = X[i].copy()
          else:
              assert Z.shape[1] == X.shape[1]
  
          # likelihood defaults to Gaussian
          likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
  
          SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
          self.load_batch()
  
      def getstate(self):
          return GPBase.getstate(self)
  
  
      def setstate(self, state):
          return GPBase.setstate(self, state)
  
  
subject: Normalize Y given as an argument to constructor