#NEWLINE# def __init__( self , X , Y , kernel , likelihood , Y_metadata = None ) : #NEWLINE# #INDENT# num_data = Y . shape [ 0 ] #NEWLINE# self . alpha = Param ( 'alpha' , np . zeros ( ( num_data , 1 ) ) ) #NEWLINE# self . beta = Param ( 'beta' , np . ones ( num_data ) ) #NEWLINE# inf = VarGauss ( self . alpha , self . beta ) #NEWLINE# super ( GPVariationalGaussianApproximation , self ) . __init__ ( X , Y , kernel , likelihood , name = 'VarGP' , inference_method = inf , Y_metadata = Y_metadata ) #NEWLINE# self . link_parameter ( self . alpha ) #NEWLINE# self . link_parameter ( self . beta )