4af750bec058da0cf5d4a3245a443549b9d7fd33,skopt/dummy_opt.py,,dummy_minimize,#,9

Before Change

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

    X = np.zeros((maxiter, n_params))
    y = np.zeros(maxiter)

    for i in range(maxiter):
        X[i] = lb + (ub - lb) * rng.rand(n_params)

After Change

    if not np.isscalar(init_y):
        raise ValueError(
            "The function to be optimized should return a scalar")
    y = np.asarray([init_y] + [func(X[i]) for i in range(maxiter - 1)])

    res = OptimizeResult()
    best = np.argmin(y)
    res.x = X[best]

Instances