from utils import phi
import numpy as np


def another_random_cube(objective, n_trials, n_dim, with_count, seed=None):
    prng = np.random.default_rng(seed)
    fopt = None
    xopt = None
    for i in range(n_trials):
        x = prng.uniform(size=n_dim)
        fx = objective(x)
        if fopt is None or xopt is None or fx < fopt:
            fopt = fx
            xopt = x
    return (fopt, xopt, n_trials) if with_count else (fopt, xopt)


def quasirandom_cube(objective, n_trials, n_dim, with_count):
    # http://extremelearning.com.au/unreasonable-effectiveness-of-quasirandom-sequences/
    magic = phi(n_dim)
    alpha = np.zeros(n_dim)
    for i in range(n_dim):
        alpha[i] = pow(1 / magic, i + 1) % 1
    xs = np.zeros((n_trials, n_dim))
    xs[0, :] = 0.5  # first point is always dead center
    for i in range(1, n_trials):
        xs[i] = (xs[i - 1] + alpha) % 1

    best_so_far = None
    for i, x in zip(range(n_trials), xs):
        fx = objective(x)
        if best_so_far is None or fx < best_so_far[0]:
            best_so_far = (fx, x)
    fopt, xopt = best_so_far
    return (fopt, xopt, n_trials) if with_count else (fopt, xopt)