<s> import numpy as np <EOL> from . . import kern <EOL> from . . core import GP , Param <EOL> from . . likelihoods import Gaussian <EOL> class GPLVM ( GP ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , Y , input_dim , init = '<STR_LIT>' , X = None , kernel = None , name = "<STR_LIT>" ) : <EOL> """<STR_LIT>""" <EOL> if X is None : <EOL> from . . util . initialization import initialize_latent <EOL> X , fracs = initialize_latent ( init , input_dim , Y ) <EOL> else : <EOL> fracs = np . ones ( input_dim ) <EOL> if kernel is None : <EOL> kernel = kern . RBF ( input_dim , lengthscale = fracs , ARD = input_dim > <NUM_LIT:1> ) + kern . Bias ( input_dim , np . exp ( - <NUM_LIT:2> ) ) <EOL> likelihood = Gaussian ( ) <EOL> super ( GPLVM , self ) . __init__ ( X , Y , kernel , likelihood , name = '<STR_LIT>' ) <EOL> self . X = Param ( '<STR_LIT>' , X ) <EOL> self . link_parameter ( self . X , index = <NUM_LIT:0> ) <EOL> def parameters_changed ( self ) : <EOL> super ( GPLVM , self ) . parameters_changed ( ) <EOL> self . X . gradient = self . kern . gradients_X ( self . grad_dict [ '<STR_LIT>' ] , self . X , None ) </s>
