import numpy as np
import math
import random
from geneticalgorithm import geneticalgorithm as ga
from astar2 import *


class Mine():
    def __init__(self, row, column):
        self.row = row
        self.columnt = column

    



class GeneticalWithLib():

    Miasta = [[0,   130,     180,   300],
            [130, 0,       320,   350],
            [180, 320,     0,     360],
            [300, 350,     360,   0]]
    #Miasta =   [[0, 2, 6, 3, 7],
    #            [2, 0, 6, 4, 8],
    #            [6, 6, 0, 5, 8],
    #            [3, 4, 5, 0, 9],
    #            [7, 8, 8, 9, 0]]

    self.mine_points = set()


    def getCoordinates(self):
        temp_map = [list(item) for item in SweeperAgent.loadMap('genetic_maps/map1.txt')]

        a_row = 0
        a_column = 0

        for row in range(MAP_SIZE):
            for column, pos in enumerate(temp_map[row]):
                if pos == "m" :
                    a_row = row
                    a_column = column
                    location = tuple([a_row, a_column])
                    self.mine_points.add(location)

    def setDistance(self):
        pass


    def fillDistanceMatrix(self):
        pass



    def f(X):
        for i in range(len(Miasta[0])):
            if X[i] not in Miasta[i]:
                return np.sum(X)+12000
        for i in range(len(Miasta[0])):
            if i == 0:
                if (X[0]-X[len(Miasta[0])-1]) == 0:
                    return np.sum(X)+12000
            else:
                if abs(X[i]-X[i-1]) == 0:
                    return np.sum(X)+12000
                if i != len(Miasta[0])-1:
                    if abs(X[i+1]-X[i-1]) == 0:
                        return np.sum(X)+12000
        var = []
        for j in range(len(Miasta[0])):
            var.append(0)
        for i in range(len(Miasta[0])):
            var[Miasta[i].index(X[i])] += 1
        for s in var:
            if s != 1:
                return np.sum(X)+9000
        return np.sum(X)

    varbound = np.array([[130,300],
                        [130,350],
                        [180,360],
                        [300,360]])

    #varbound = np.array([[2,7],
    #                    [2,8],
    #                    [5,8],
    #                    [4,9],
    #                    [7,9]])

    algorithm_param = {'max_num_iteration': 3000,\
                    'population_size':100,\
                    'mutation_probability':0.1,\
                    'elit_ratio': 0.01,\
                    'crossover_probability': 0.5,\
                    'parents_portion': 0.3,\
                    'crossover_type':'uniform',\
                    'max_iteration_without_improv':None}

    model=ga(function=f,\
                dimension=len(Miasta[0]),\
                variable_type='int',\
                variable_boundaries=varbound,\
                algorithm_parameters=algorithm_param)

    model.run()
    #print(f([180, 350, 180, 350]))