<s> '''<STR_LIT>''' <EOL> import GPy <EOL> import numpy as np <EOL> from robo . task . rembo import REMBO <EOL> from robo . task . synthetic_functions . branin import Branin <EOL> from robo . models . gpy_model import GPyModel <EOL> from robo . maximizers . cmaes import CMAES <EOL> from robo . solver . bayesian_optimization import BayesianOptimization <EOL> from robo . acquisition . ei import EI <EOL> class BraninInBillionDims ( REMBO ) : <EOL> def __init__ ( self ) : <EOL> self . b = Branin ( ) <EOL> X_lower = np . concatenate ( ( self . b . X_lower , np . zeros ( [ <NUM_LIT> ] ) ) ) <EOL> X_upper = np . concatenate ( ( self . b . X_upper , np . ones ( [ <NUM_LIT> ] ) ) ) <EOL> super ( BraninInBillionDims , self ) . __init__ ( X_lower , X_upper , d = <NUM_LIT:2> ) <EOL> def objective_function ( self , x ) : <EOL> return self . b . objective_function ( x [ : , : <NUM_LIT:2> ] ) <EOL> task = BraninInBillionDims ( ) <EOL> kernel = GPy . kern . Matern52 ( input_dim = task . n_dims ) <EOL> model = GPyModel ( kernel , optimize = True , num_restarts = <NUM_LIT:10> ) <EOL> acquisition_func = EI ( model , task . X_lower , task . X_upper ) <EOL> maximizer = CMAES ( acquisition_func , task . X_lower , task . X_upper ) <EOL> bo = BayesianOptimization ( acquisition_func = acquisition_func , <EOL> model = model , <EOL> maximize_func = maximizer , <EOL> task = task ) <EOL> bo . run ( <NUM_LIT> ) </s>
