def estimate_incumbent(self, startpoints):
        x_opt = np.zeros([len(startpoints), self.X_lower.shape[0]])
        fval = np.zeros([len(startpoints)])
        for i, startpoint in enumerate(startpoints):
            if self.method == "scipy":
                if self.with_gradients:
                    res = optimize.fmin_l_bfgs_b(self.f, startpoint, self.df,
                                bounds=list(zip(self.X_lower, self.X_upper)))
                    x_opt[i] = res[0]
                    fval[i] = res[1]
                else:
                    res = optimize.minimize(self.f, startpoint,
                                bounds=list(zip(self.X_lower, self.X_upper)),
                                method="L-BFGS-B")
                    x_opt[i] = res["x"]
                    fval[i] = res["fun"]
            elif self.method == 'cmaes':
                res = cma.fmin(self.f, startpoint, 0.6,
                              options={"bounds": [self.X_lower, self.X_upper]})
                x_opt[i] = res[0]
                fval[i] = res[1]

        # Return the point with the lowest function value
        best = np.argmin(fval)
        return x_opt[best, np.newaxis, :], fval[best, np.newaxis, np.newaxis]
Test whether argument path is an executable file.
dataset/ETHPy150Open automl/RoBO/robo/incumbent/posterior_optimization.py/PosteriorMeanOptimization.estimate_incumbent--crossover with--dataset/ETHPy150Open deanhiller/databus/webapp/play1.3.x/python/Lib/CGIHTTPServer.py/CGIHTTPRequestHandler.is_executable