#NEWLINE# def __init__( self , Y , input_dim , X = None , X_variance = None , Gamma = None , init = 'PCA' , num_inducing = 10 , Z = None , kernel = None , inference_method = None , likelihood = None , name = 'Spike_and_Slab GPLVM' , group_spike = False , IBP = False , SLVM = False , alpha = 2.0 , beta = 2.0 , connM = None , tau = None , mpi_comm = None , pi = None , learnPi = False , normalizer = False , sharedX = False , variational_prior = None , **kwargs ) : #NEWLINE# #INDENT# self . group_spike = group_spike #NEWLINE# self . init = init #NEWLINE# self . sharedX = sharedX #NEWLINE# if ( X == None ) : #NEWLINE# #INDENT# from .. util.initialization import initialize_latent #NEWLINE# ( X , fracs ) = initialize_latent ( init , input_dim , Y ) #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# fracs = np . ones ( input_dim ) #NEWLINE# #UNINDENT# if ( X_variance is None ) : #NEWLINE# #INDENT# X_variance = np . random . uniform ( 0 , 0.1 , X . shape ) #NEWLINE# #UNINDENT# if ( Gamma is None ) : #NEWLINE# #INDENT# gamma = np . empty_like ( X ) #NEWLINE# gamma [ : ] = ( 0.5 + ( 0.1 * np . random . randn ( X . shape [ 0 ] , input_dim ) ) ) #NEWLINE# gamma [ ( gamma > ( 1.0 - 1e-09 ) ) ] = ( 1.0 - 1e-09 ) #NEWLINE# gamma [ ( gamma < 1e-09 ) ] = 1e-09 #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# gamma = Gamma . copy ( ) #NEWLINE# #UNINDENT# if ( Z is None ) : #NEWLINE# #INDENT# Z = np . random . permutation ( X . copy ( ) ) [ : num_inducing ] #NEWLINE# #UNINDENT# assert ( Z . shape [ 1 ] == X . shape [ 1 ] ) #NEWLINE# if ( likelihood is None ) : #NEWLINE# #INDENT# likelihood = Gaussian ( ) #NEWLINE# #UNINDENT# if ( kernel is None ) : #NEWLINE# #INDENT# kernel = kern . RBF ( input_dim , lengthscale = fracs , ARD = True ) #NEWLINE# #UNINDENT# if kernel . useGPU : #NEWLINE# #INDENT# kernel . psicomp = PSICOMP_SSRBF_GPU ( ) #NEWLINE# #UNINDENT# if ( inference_method is None ) : #NEWLINE# #INDENT# inference_method = VarDTC_minibatch ( mpi_comm = mpi_comm ) #NEWLINE# #UNINDENT# if ( pi is None ) : #NEWLINE# #INDENT# pi = np . empty ( input_dim ) #NEWLINE# pi [ : ] = 0.5 #NEWLINE# #UNINDENT# if IBP : #NEWLINE# #INDENT# self . variational_prior = ( IBPPrior ( input_dim = input_dim , alpha = alpha ) if ( variational_prior is None ) else variational_prior ) #NEWLINE# X = IBPPosterior ( X , X_variance , gamma , tau = tau , sharedX = sharedX ) #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# if SLVM : #NEWLINE# #INDENT# self . variational_prior = ( SLVMPrior ( input_dim = input_dim , alpha = alpha , beta = beta , Z = connM ) if ( variational_prior is None ) else variational_prior ) #NEWLINE# X = SLVMPosterior ( X , X_variance , gamma , tau = tau ) #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# self . variational_prior = ( SpikeAndSlabPrior ( pi = pi , learnPi = learnPi , group_spike = group_spike ) if ( variational_prior is None ) else variational_prior ) #NEWLINE# X = SpikeAndSlabPosterior ( X , X_variance , gamma , group_spike = group_spike , sharedX = sharedX ) #NEWLINE# #UNINDENT# #UNINDENT# super ( SSGPLVM , self ) . __init__ ( X , Y , Z , kernel , likelihood , variational_prior = self . variational_prior , inference_method = inference_method , name = name , mpi_comm = mpi_comm , normalizer = normalizer , ** kwargs ) #NEWLINE# self . link_parameter ( self . X , index = 0 )