import math
import random
from model.base_model import Model

class MyBFSModel(Model):
    def __init__(self):
        super().__init__()

    def init(self, nodes):
        """
        Put your initialization here.
        """
        super().init(nodes)

    def fit(self, max_it=1000):
        """
        Put your iteration process here.
        """

        UCS_solutions = []
    
        for i in range(0, self.N):
            solution = [i]
            UCS_solutions.append(solution)
    
        # Breadth First: Set each city as starting point, then go to next city simultaneously
        for step in range(0, self.N - 1):
            # print("[step]", step)
            unvisited_list = list(range(0, self.N))
            # For each search path
            for i in range(0, self.N):
                cur_city = UCS_solutions[i][-1]
                unvisited_list = list( set(range(0, self.N)) - set(UCS_solutions[i]) )
                # print(unvisited_list)
                closest_neighbour = -1
                shortest_distance = math.inf
                for j in unvisited_list:
                    if(self.dist(cur_city, j) < shortest_distance):
                        closest_neighbour = j
                        shortest_distance = self.dist(cur_city, j)
                UCS_solutions[i].append(closest_neighbour)

        for i in range(0, self.N):
            self.fitness_list.append(self.fitness(UCS_solutions[i]))
 
        self.best_solution = UCS_solutions[ self.fitness_list.index(min(self.fitness_list)) ]
        self.fitness_list.append(self.fitness(self.best_solution))

        return self.best_solution, self.fitness_list