@domain_constructor(loss_target=(-2.5))
def gauss_wave2():
 rng = np.random.RandomState(123)
 var = 0.1
 x = hp.uniform('x', (-20), 20)
 amp = hp.uniform('amp', 0, 1)
 t = (scope.normal(0, var, rng=rng) + (2 * scope.exp((- (old_div(x, 5.0) ** 2)))))
 return {'loss': (- hp.choice('hf', [t, (t + (scope.sin(x) * amp))])), 'loss_variance': var, 'status': base.STATUS_OK}

@ domain_constructor ( loss_target = ( - 2.5 ) ) def gauss_wave2 ( ) : rng = np . random . RandomState ( 123 ) var = 0.1 x = hp . uniform ( 'x' , ( - 20 ) , 20 ) amp = hp . uniform ( 'amp' , 0 , 1 ) t = ( scope . normal ( 0 , var , rng = rng ) + ( 2 * scope . exp ( ( - ( old_div ( x , 5.0 ) ** 2 ) ) ) ) ) return { 'loss' : ( - hp . choice ( 'hf' , [ t , ( t + ( scope . sin ( x ) * amp ) ) ] ) ) , 'loss_variance' : var , 'status' : base . STATUS_OK }
variant of the gausswave problem in which noise is added to the score function .