0a60b2d2807cf370e984b0bd9f7c4d0edabe4267,skopt/gbrt_opt.py,,gbrt_minimize,#Any#Any#Any#Any#Any#Any#Any#,24

Before Change

    // Bounds
    num_params = len(bounds)
    lower_bounds, upper_bounds = extract_bounds(bounds)

    // Default estimator
    if base_estimator is None:

After Change

        http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html
    
    rng = check_random_state(random_state)
    space = Space(dimensions)

    // Default estimator
    if base_estimator is None:
        base_estimator = GradientBoostingQuantileRegressor(random_state=rng)

    // Record the points and function values evaluated as part of
    // the minimization
    Xi = np.zeros((maxiter, space.n_dims))
    yi = np.zeros(maxiter)

    // Initialize with random points
    if n_start == 0:
        raise ValueError("Need at least one starting point.")

    if maxiter == 0:
        raise ValueError("Need to perform at least one iteration.")

    n_start = min(n_start, maxiter)

    Xi[:n_start] = space.rvs(n_samples=n_start, random_state=rng)
    yi[:n_start] = [func(xi) for xi in Xi[:n_start]]
    i = np.argmin(yi[:n_start])
    best_y = yi[i]
    best_x = Xi[i]

    models = []

Instances