GenericAI_Sweeper/genetic.py

import random

class Genetic():
    pass






    def calc_cost(self, mines_list):
        self.mined_tile_count = len(mines_list)
        total_cost = 0
        i = 0
        while i < (self.mined_tile_count - 1):
            total_cost = total_cost + self.manhattan_cost_counter(mines_list, i)
            i = i + 1
        
        return total_cost

    def manhattan_cost_counter(self, mines_list, i):

        
        x1, y1 = mines_list[i][0], mines_list[i][1]
        x2, y2 = mines_list[i+1][0], mines_list[i+1][1]


        return abs(x2 - x1) + abs(y2 - y1)

    def population_initialization(self, mines_list, i):
        first_mine = mines_list[0]
        x = len(mines_list)-1
        shuffled_coordinates = []
        while x > 0:
            shuffled_coordinates.append(mines_list[x])
            x = x - 1
        
        mine_chromosome = []
        while i > 0:
            child = random.sample(shuffled_coordinates, len(shuffled_coordinates))
            child.insert(0, first_mine)
            #child.insert(x, first_mine)
            mine_chromosome.append(child)
            i = i - 1
        return mine_chromosome

    def fitness_function(self, generations):
        total_cost = 0
        chromosome_count = 0
        chromosome_costs = []
        y = 0
        #problem - wyjście poza liste
        if len(generations) == 0:
            return

        first_cost = self.calc_cost(generations[y])
        cheapest_chromosome = generations[y]
        lowest_cost = first_cost
        highest_cost = first_cost
        for i in generations:
            chromosome_costs.append(self.calc_cost(i))
            total_cost = total_cost + self.calc_cost(i)
            chromosome_count = chromosome_count + 1
            if self.calc_cost(i) < lowest_cost:
                lowest_cost = self.calc_cost(i)
                cheapest_chromosome = i
            if self.calc_cost(i) > highest_cost:
                highest_cost = self.calc_cost(i)
        
        average_fitness = total_cost/chromosome_count
        chromosome_fitness = []
        total_chromosome_fitness = 0
        l = 0
        for i in chromosome_costs:
            chromosome_fitness.append(round(((average_fitness/chromosome_costs[l])*10), 2))
            total_chromosome_fitness += round(((average_fitness/chromosome_costs[l])*10),2)
            l = l + 1

        return(chromosome_fitness, lowest_cost, highest_cost, average_fitness, cheapest_chromosome)


    def fitness_population_selection(self, first_generation, chromosome_fitness):
        x = len(chromosome_fitness)
        roulette_table = []
        average_population = []
        i = 0
        interval = 0
        while x > 0:
            interval = interval + chromosome_fitness[i]
            roulette_table.append(round(interval, 2))
            x = x - 1
            i = i + 1
        x = len(chromosome_fitness)/2
        max = roulette_table[i - 1]
        while x > 0:
            i = 0
            n = random.uniform(0, max)
            while n > roulette_table[i]:
                i = i + 1
            average_population.append(first_generation[i])
            x = x - 1
        return average_population

    def crossover(self, average_population):
        post_crossover_population = []
        x = len(average_population) - 1
        if x <= 0:
            print("no enough chromosomes to crossover")
            return
        while x > 0:
            parent_1 = average_population[x]
            parent_2 = average_population[x-1]
            child_1 = []
            child_2 = []
            crossover_decision = random.randint(1, 100)
            if (crossover_decision < 11) and (parent_1 != parent_2):
                crossover_point = random.randint(1, (len(average_population[x])-3))
                l = 0 
                k = crossover_point
                while k >= 0:
                    child_1.append(parent_1[l])
                    l = l + 1
                    k = k - 1
                k = crossover_point
                while k < (len(parent_1) - 1):
                    for i in parent_2:
                        if i not in child_1:
                            child_1.append(i)
                        k = k + 1
                
                l = 0
                k = crossover_point
                while k >= 0:
                    child_2.append(parent_2[l])
                    l = l + 1
                    k = k - 1
                k = crossover_point
                while k >= 0:
                    child_2.append(parent_2[l])
                    l = l + 1
                    k = k - 1
                k = crossover_point
                while k < (len(parent_1) - 1):
                    for i in parent_1:
                        if i not in child_2:
                            child_2.append(i)
                        k = k + 1
                #child_1.append(0)
                #child_2.append(0)
            else:
                child_1 = parent_1
                child_2 = parent_2
            post_crossover_population.append(child_1)
            post_crossover_population.append(child_2)
            x = x - 1

        return post_crossover_population

    def mutation(self, post_crossover_popualtion):
        k = len(post_crossover_popualtion) - 1
        while k >= 0:
            mutation_decision = random.randint(0, 100)
            if mutation_decision < 3:
                mutated_chromosome = post_crossover_popualtion[k]
                post_crossover_popualtion.remove(mutated_chromosome)
                l = len(mutated_chromosome) - 1

                x = random.randint(1, l)
                y = random.randint(1, l)
                while x == y:
                    y = random.randint(1, l)
                replacement = mutated_chromosome[x]
                mutated_chromosome[y] = replacement

                post_crossover_popualtion.insert(k, mutated_chromosome)
        
            k = k - 1
        post_mutation_population = post_crossover_popualtion

        return post_mutation_population

    def optimize(self, post_mutation_population, mine_list):
        post_optimization_population = post_mutation_population
        i = len(post_mutation_population)
        l = 1
        while l < i:
            k = 1
            while k >= 0:
                if post_mutation_population[l] == post_mutation_population[k - 1]:
                    post_optimization_population.remove(post_mutation_population[k-1])
                    x = len(mine_list) - 2
                    shuffled_coordinates = []
                    while x > 0:
                        shuffled_coordinates.append(mine_list[x])
                        x = x - 1
                    x = len(mine_list) - 1
                    new_chromosome = random.sample(shuffled_coordinates, len(shuffled_coordinates))
                    new_chromosome.insert(0, mine_list[0])
                    new_chromosome.insert(x, mine_list[0])
                    post_optimization_population.append(new_chromosome)
                
                k = k - 1
            l = l + 1
        return post_optimization_population

    def genetic_algorythm(self, coordinates):
        self.full_cost = self.calc_cost(coordinates)
        self.first_generation = self.population_initialization(coordinates,10)
        self.fitness, self.lowest_cost, self.highest_cost, self.average_fitness_of_first_genetation, self.cheapest_individual = self.fitness_function(self.first_generation)
        self.average_population = self.fitness_population_selection(self.first_generation, self.fitness)
        self.population_after_crossover = self.crossover(self.average_population)


        if self.population_after_crossover == None:
            print("Finished in " + str(self.which_generation) + "generations")
            return

        self.population_after_mutation = self.mutation(self.population_after_crossover)
        self.population_after_optimization = self.optimize(self.population_after_mutation, coordinates)
        self.max_cost = self.highest_cost
        self.min_cost = self.lowest_cost
        self.cheapest_route = self.cheapest_individual
        i = 2
        self.which_generation = 1
        while i < 41:
            print(" ")
            print("***********")
            print("Generation " + str(i))
            print("***********")
            print(" ")

            if self.fitness_function(self.population_after_optimization) == None:
                return

            self.fitness, self.lowest_cost, self.highest_cost, self.average_fitness_of_first_genetation, self.cheapest_individual = self.fitness_function(self.population_after_optimization)
            if self.highest_cost > self.max_cost:
                self.max_cost = self.highest_cost
            if self.lowest_cost < self.min_cost:
                self.min_cost = self.lowest_cost
                self.cheapest_route = self.cheapest_individual
                self.which_generation = i
                print("New lowest cost: " + str(self.min_cost))
                print("New cheapest route: " + str(self.cheapest_route))
            
            self.average_population = self.fitness_population_selection(self.population_after_mutation, self.fitness)

            self.population_after_crossover = self.crossover(self.average_population)

            if self.population_after_crossover == None:
                print("Finished")
                return

            self.population_after_mutation = self.mutation(self.population_after_crossover)
            self.population_after_optimization = self.optimize(self.population_after_mutation, coordinates)
            i = i + 1
            if(self.min_cost)/(self.average_fitness_of_first_genetation) < (0.7):
                print("Finished after " + str(i)+ " generations")
                break
            print("Average fitness of first generation: " + str(self.average_fitness_of_first_genetation))
            print("Relation of efficiency growth: " + str((self.min_cost)/(self.average_fitness_of_first_genetation)))
            print("Lowest cost is " + str(self.min_cost))
            print("Cheapest route is: " + str(self.cheapest_route))
    
    def run(self):
        Mines = [[1,1],
                 [2,4],
                 [3,1],
                 [4,7],
                 [5,3],
                 [7,6]]
        
        self.genetic_algorythm(Mines)