from typing import Tuple

import numpy as np
import numpy.typing as npt
import copy

from common import plot_plan, read_data

original_perm = None

def hill_climbing(distances: npt.NDArray) -> Tuple[float, npt.NDArray]:
    size = len(distances)
    perm = np.arange(size)
    np.random.shuffle(perm)

    print(perm)
    global original_perm
    original_perm = copy.deepcopy(perm)
    current_route: float = np.sum([distances[perm[i - 1], perm[i]] for i in range(size)])
    found_improvement = True
    while found_improvement:
        found_improvement = False
        tmp_improvement: float = current_route
        improvement_index: int = -1
        for i in range(size):
            perm[i - 1], perm[i] = perm[i], perm[i - 1]
            tmp_route: float = np.sum([distances[perm[i - 1], perm[i]] for i in range(size)])
            if tmp_route < tmp_improvement:
                tmp_improvement = tmp_route
                improvement_index = i
                found_improvement = True

            perm[i - 1], perm[i] = perm[i], perm[i - 1]

        if found_improvement:
            current_route = tmp_improvement
            perm[improvement_index - 1], perm[improvement_index] = (
                perm[improvement_index],
                perm[improvement_index - 1],
            )

    print(perm)

    return (current_route, perm)


if __name__ == "__main__":
    cities, data = read_data("./european_cities.csv")
    distance, perm = hill_climbing(data[:10,:10])

    plot_plan(list(map(lambda i: cities[i], list(perm))))
    plot_plan(list(map(lambda i: cities[i], list(original_perm))))