from numpy.random import choice, randint, seed


def get_first_random_solution(m, data):
    seed(42)
    random_indexes = choice(len(data.index), size=m)
    return data.iloc[random_indexes]


def replace_worst_element(previous, data):
    solution = previous.copy()
    worst_index = previous["distance"].astype(float).idxmin()
    random_candidate = data.loc[randint(low=0, high=len(data.index))]
    solution.loc[worst_index] = random_candidate
    return solution


def get_random_solution(previous, data):
    solution = replace_worst_element(previous, data)
    while solution["distance"].sum() <= previous["distance"].sum():
        if solution.equals(previous):
            break
        solution = replace_worst_element(previous=solution, data=data)
    return solution


def explore_neighbourhood(element, data, max_iterations=100000):
    neighbourhood = []
    neighbourhood.append(element)
    for _ in range(max_iterations):
        previous_solution = neighbourhood[-1]
        neighbour = get_random_solution(previous=previous_solution, data=data)
        if neighbour.equals(previous_solution):
            break
        neighbourhood.append(neighbour)
    return neighbour


def local_search(m, data):
    first_solution = get_first_random_solution(m=m, data=data)
    best_solution = explore_neighbourhood(element=first_solution, data=data)
    return best_solution