import math
import random
from model.base_model import Model

class MyDPDModel(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 = []

        # Depth First: Set one city as starting point, iterate to the end, then select next city as starting point.
        MSTs = []
        for i in range(0, self.N):
            MSTs.append([-1] * self.N)
        for i in range(0, self.N):
            solution = []
            solution.append(i)
            unvisited_list = list(range(0, self.N))
            cur_city = i
            # print("[starting]", i)
            for steps in range(self.N - 1):
                # print(unvisited_list)
                unvisited_list.remove(cur_city)
                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):
                        solution.append(j)
                        cur_city = j
                        break
            # print(solution)
            MST_solutions.append(solution)
            self.fitness_list.append(self.fitness(solution))

        self.best_solution = MST_solutions[ self.fitness_list.index(min(self.fitness_list)) ]

        return self.best_solution, self.fitness_list