(self, objectivefx, n_iter=20)
return super(BayesianOptimizer, self).optimize(lambda x: self._evaluate_objectives(x, fxs), n_iter=n_iter)
Run Bayesian optimization for a number of iterations.
        
        Before the loop is initiated, first all points retrieved by :meth:`~.optim.Optimizer.get_initial` are evaluated
        on the objective and black-box constraints. These points are then added to the acquisition function 
        by calling :meth:`~.acquisition.Acquisition.set_data` (and hence, the underlying models). 
        
        Each iteration a new data point is selected for evaluation by optimizing an acquisition function. This point
        updates the models.
        
        :param objectivefx: (list of) expensive black-box objective and constraint functions. For evaluation, the 
            responses of all the expensive functions are aggregated column wise.
            Unlike the typical :class:`~.optim.Optimizer` interface, these functions should not return gradients. 
        :param n_iter: number of iterations to run
        :return: OptimizeResult object
Run Bayesian optimization for a number of iterations.
        
        Before the loop is initiated, first all points retrieved by :meth:`~.optim.Optimizer.get_initial` are evaluated
        on the objective and black-box constraints. These points are then added to the acquisition function 
        by calling :meth:`~.acquisition.Acquisition.set_data` (and hence, the underlying models). 
        
        Each iteration a new data point is selected for evaluation by optimizing an acquisition function. This point
        updates the models.
        
        :param objectivefx: (list of) expensive black-box objective and constraint functions. For evaluation, the 
            responses of all the expensive functions are aggregated column wise.
            Unlike the typical :class:`~.optim.Optimizer` interface, these functions should not return gradients. 
        :param n_iter: number of iterations to run
        :return: OptimizeResult object
Run Bayesian optimization for a number of iterations . Before the loop is initiated first all points retrieved by : meth : ~ . optim . Optimizer . get_initial are evaluated on the objective and black - box constraints . These points are then added to the acquisition function by calling : meth : ~ . acquisition . Acquisition . set_data ( and hence the underlying models ) . Each iteration a new data point is selected for evaluation by optimizing an acquisition function . This point updates the models . : param objectivefx : ( list of ) expensive black - box objective and constraint functions . For evaluation the responses of all the expensive functions are aggregated column wise . Unlike the typical : class : ~ . optim . Optimizer interface these functions should not return gradients . : param n_iter : number of iterations to run : return : OptimizeResult object
def optimize(self, objectivefx, n_iter=20):
        """
        Run Bayesian optimization for a number of iterations.
        
        Before the loop is initiated, first all points retrieved by :meth:`~.optim.Optimizer.get_initial` are evaluated
        on the objective and black-box constraints. These points are then added to the acquisition function 
        by calling :meth:`~.acquisition.Acquisition.set_data` (and hence, the underlying models). 
        
        Each iteration a new data point is selected for evaluation by optimizing an acquisition function. This point
        updates the models.
        
        :param objectivefx: (list of) expensive black-box objective and constraint functions. For evaluation, the 
            responses of all the expensive functions are aggregated column wise.
            Unlike the typical :class:`~.optim.Optimizer` interface, these functions should not return gradients. 
        :param n_iter: number of iterations to run
        :return: OptimizeResult object
        """
        fxs = np.atleast_1d(objectivefx)
        return super(BayesianOptimizer, self).optimize(lambda x: self._evaluate_objectives(x, fxs), n_iter=n_iter)
def optimize ( self , objectivefx , n_iter = 20 ) : fxs = np . atleast_1d ( objectivefx ) return super ( BayesianOptimizer , self ) . optimize ( lambda x : self . _evaluate_objectives ( x , fxs ) , n_iter = n_iter )