old_contents: # Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
  # Licensed under the BSD 3-clause license (see LICENSE.txt)
  
  from GPy.core.model import Model
  from .parameterization import Param, Parameterized
  from . import parameterization
  
  from .gp import GP
  from .svgp import SVGP
  from .sparse_gp import SparseGP
  from .gp_grid import GpGrid
  from .mapping import *
  
  
  #===========================================================================
  # Handle priors, this needs to be
  # cleaned up at some point
  #===========================================================================
  def randomize(self, rand_gen=None, *args, **kwargs):
      """
      Randomize the model.
      Make this draw from the prior if one exists, else draw from given random generator
  
      :param rand_gen: np random number generator which takes args and kwargs
      :param flaot loc: loc parameter for random number generator
      :param float scale: scale parameter for random number generator
      :param args, kwargs: will be passed through to random number generator
      """
      if rand_gen is None:
          rand_gen = np.random.normal
      # first take care of all parameters (from N(0,1))
      x = rand_gen(size=self._size_transformed(), *args, **kwargs)
      updates = self.update_model()
      self.update_model(False) # Switch off the updates
      self.optimizer_array = x  # makes sure all of the tied parameters get the same init (since there's only one prior object...)
      # now draw from prior where possible
      x = self.param_array.copy()
      [np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.items() if not p is None]
      unfixlist = np.ones((self.size,),dtype=np.bool)
      from paramz.transformations import __fixed__
      unfixlist[self.constraints[__fixed__]] = False
      self.param_array.flat[unfixlist] = x.view(np.ndarray).ravel()[unfixlist]
      self.update_model(updates)
      
  Model.randomize = randomize
  Param.randomize = randomize
  Parameterized.randomize = randomize
  
new_contents: # Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
  # Licensed under the BSD 3-clause license (see LICENSE.txt)
  
  """
  This module does...
  
  """
  
  from GPy.core.model import Model
  from .parameterization import Param, Parameterized
  from . import parameterization
  
  from .gp import GP
  from .svgp import SVGP
  from .sparse_gp import SparseGP
  from .gp_grid import GpGrid
  from .mapping import *
  
  
  #===========================================================================
  # Handle priors, this needs to be
  # cleaned up at some point
  #===========================================================================
  def randomize(self, rand_gen=None, *args, **kwargs):
      """
      Randomize the model.
      Make this draw from the prior if one exists, else draw from given random generator
  
      :param rand_gen: np random number generator which takes args and kwargs
      :param flaot loc: loc parameter for random number generator
      :param float scale: scale parameter for random number generator
      :param args, kwargs: will be passed through to random number generator
      """
      if rand_gen is None:
          rand_gen = np.random.normal
      # first take care of all parameters (from N(0,1))
      x = rand_gen(size=self._size_transformed(), *args, **kwargs)
      updates = self.update_model()
      self.update_model(False) # Switch off the updates
      self.optimizer_array = x  # makes sure all of the tied parameters get the same init (since there's only one prior object...)
      # now draw from prior where possible
      x = self.param_array.copy()
      [np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.items() if not p is None]
      unfixlist = np.ones((self.size,),dtype=np.bool)
      from paramz.transformations import __fixed__
      unfixlist[self.constraints[__fixed__]] = False
      self.param_array.flat[unfixlist] = x.view(np.ndarray).ravel()[unfixlist]
      self.update_model(updates)
      
  Model.randomize = randomize
  Param.randomize = randomize
  Parameterized.randomize = randomize
  
subject: add placeholder