import math
import random
from model.base_model import Model

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

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

    def getMST(self, node):
        MST = []
        distances = []
        for i in range(0, self.N):
            if i != node:
                MST.append(i)
                distances.append(self.dist(node, i))
        return [x for _,x in sorted(zip(distances, MST))]

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

        MST_solutions = []
    
        for i in range(0, self.N):
            solution = [i]
            MST_solutions.append(solution)
    
        MSTs = []
        for i in range(0, self.N):
            MSTs.append([-1] * self.N)

        # 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 = MST_solutions[i][-1]
                unvisited_list = list( set(range(0, self.N)) - set(MST_solutions[i]) )

                if MSTs[cur_city][0] == -1:
                    MST = self.getMST(cur_city)
                    MSTs[cur_city] = MST

                for j in MSTs[cur_city]:
                    if(j in unvisited_list):
                        MST_solutions[i].append(j)
                        break

        for i in range(0, self.N):
            self.fitness_list.append(self.fitness(MST_solutions[i]))
 
        self.best_solution = MST_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