BayesianOptimizer_get_next.py

    def _acquisition_function(self, x):
        return -self._get_expected_improvement(x)
        
    def _get_next_probable_point(self):
            min_ei = float(sys.maxsize)
            x_optimal = None 
            
            # Trial with an array of random data points
            
            for x_start in (np.random.random((self.batch_size,self.x_init.shape[1])) * self.scale):
                response = minimize(fun=self._acquisition_function, x0=x_start, method='L-BFGS-B')
                if response.fun[0] < min_ei:
                    min_ei = response.fun[0]
                    x_optimal = response.x
            
            return x_optimal, min_ei