<s> """<STR_LIT>""" <EOL> from kameleon_mcmc . distribution . Distribution import Distribution <EOL> from numpy . ma . core import shape , zeros , exp <EOL> from shogun . Features import BinaryLabels , RealFeatures <EOL> from shogun . Classifier import LaplacianInferenceMethod , LogitLikelihood , ZeroMean <EOL> from shogun . Kernel import GaussianARDKernel <EOL> num_threads = <NUM_LIT:1> <EOL> print "<STR_LIT>" % num_threads <EOL> ZeroMean ( ) . parallel . set_num_threads ( num_threads ) <EOL> class PseudoMarginalHyperparameterDistribution ( Distribution ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , X , y , n_importance , prior , ridge = None ) : <EOL> Distribution . __init__ ( self , dimension = shape ( X ) [ <NUM_LIT:1> ] ) <EOL> self . n_importance = n_importance <EOL> self . prior = prior <EOL> self . ridge = ridge <EOL> self . X = X <EOL> self . y = y <EOL> def log_pdf ( self , thetas ) : <EOL> assert ( len ( shape ( thetas ) ) == <NUM_LIT:2> ) <EOL> assert ( shape ( thetas ) [ <NUM_LIT:1> ] == self . dimension ) <EOL> result = zeros ( len ( thetas ) ) <EOL> for i in range ( len ( thetas ) ) : <EOL> labels = BinaryLabels ( self . y ) <EOL> feats_train = RealFeatures ( self . X . T ) <EOL> kernel = GaussianARDKernel ( <NUM_LIT:10> , <NUM_LIT:1> ) <EOL> kernel . set_weights ( exp ( thetas [ i ] ) ) <EOL> mean = ZeroMean ( ) <EOL> likelihood = LogitLikelihood ( ) <EOL> inference = LaplacianInferenceMethod ( kernel , feats_train , mean , labels , likelihood ) <EOL> inference . set_scale ( exp ( <NUM_LIT:0> ) ) <EOL> if self . ridge is not None : <EOL> log_ml_estimate = inference . get_marginal_likelihood_estimate ( self . n_importance , self . ridge ) <EOL> else : <EOL> log_ml_estimate = inference . get_marginal_likelihood_estimate ( self . n_importance ) <EOL> log_prior = self . prior . log_pdf ( thetas [ i ] . reshape ( <NUM_LIT:1> , len ( thetas [ i ] ) ) ) <EOL> result [ i ] = log_ml_estimate + log_prior <EOL> return result </s>
