246339ce01898c92ce9e143895c5def9c46dcb78,skopt/gp_opt.py,,gp_minimize,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,26

Before Change

    rng = check_random_state(random_state)

    // Bounds
    n_params = len(bounds)
    lb, ub = extract_bounds(bounds)

    // Default GP
    if base_estimator is None:

After Change

            best = np.inf

            for j in range(n_restarts_optimizer):
                x0 = space.transform(space.rvs(n_samples=1,
                                               random_state=rng))[0]

                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    x, a, _ = fmin_l_bfgs_b(
                        _acquisition, x0,
                        args=(gp, np.min(yi), acq, xi, kappa),
                        bounds=space.transformed_bounds,
                        approx_grad=True, maxiter=10)

                if a < best:
                    next_x, best = x, a

        next_x = space.inverse_transform(next_x.reshape((1, -1)))[0]
        print(next_x)
        next_y = func(next_x)
        Xi = np.vstack((Xi, next_x))
        yi.append(next_y)

Instances