Genetic algorithm

bin/Classess/Track.py

@@ -1,5 +1,17 @@
 class Track:
-    def __init__(self, road, distance):
+    def __init__(self, priority, road):
+        self.priority = priority
         self.road = road
-        self.distance = distance
+
+    def __eq__(self, other):
+        try:
+            return self.priority == other.priority
+        except AttributeError:
+            return NotImplemented
+
+    def __lt__(self, other):
+        try:
+            return self.priority < other.priority
+        except AttributeError:
+            return NotImplemented
 

bin/Classess/Travel.py

@@ -1,5 +1,10 @@
 import queue
-from itertools import permutations, islice, combinations
+from itertools import permutations, islice
+from math import sqrt
+import random
+
+from resources.Globals import NUMBER_OF_INDIVIDUALS_FOR_DUEL, NUMBER_OF_POINTS_PERMUTATION, PERCENT_OF_MUTATION, \
+    PERCENT_OF_OUTGOING_INDIVIDUALS
 
 
 class Travel:
@@ -9,12 +14,190 @@ class Travel:
 
 
 def genetic_algorithm(travel_map):
-    population = queue.PriorityQueue()
+    population = []
     road_map = list(travel_map.keys())
-    points_permutation = list(map(list, islice(permutations(road_map), 10)))
-    # for i in range(0, len(points_permutation)):
-    #     distance =
-    #     subject = Track()
-    # print(points_permutation)
-    # print(len(points_permutation))
+    points_permutation = list(map(list, islice(permutations(road_map), NUMBER_OF_POINTS_PERMUTATION)))
+    # Generate the first population
+    for i in range(0, len(points_permutation)):
+        road = points_permutation[i]
+        priority = adaptation_function(points_permutation[i], travel_map)
+
+        population.append((priority, road))
+
+    while len(population) < 10000:
+        parent1, parent2 = tournament_selection(population)
+
+        child = edge_recombination_crossover(parent1[1], parent2[1])
+        child_priority = adaptation_function(child, travel_map)
+
+        population.append((child_priority, child))
+
+        mutation_function(population, travel_map)
+        population.sort(key=lambda x: x[0], reverse=True)
+
+    return population[0]
+
+
+def adaptation_function(list_points, travel_map):
+    index_of_point = 0
+    distance = 0
+    while True:
+
+        if index_of_point < (-len(list_points)):
+            return round((1 / distance) * 1000000)
+
+        if index_of_point == (len(list_points) - 1):
+            x1 = travel_map.get(list_points[index_of_point])[0]
+            y1 = travel_map.get(list_points[index_of_point])[1]
+
+            x2 = travel_map.get(list_points[-len(list_points)])[0]
+            y2 = travel_map.get(list_points[-len(list_points)])[1]
+
+            index_of_point = -len(list_points) - 1
+        else:
+            x1 = travel_map.get(list_points[index_of_point])[0]
+            y1 = travel_map.get(list_points[index_of_point])[1]
+
+            x2 = travel_map.get(list_points[index_of_point + 1])[0]
+            y2 = travel_map.get(list_points[index_of_point + 1])[1]
+
+            index_of_point += 1
+
+        distance += sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+
+
+def tournament_selection(population):
+    individuals_for_duel1 = []
+    individuals_for_duel2 = []
+    population_length = len(population)
+
+    while True:
+
+        if len(individuals_for_duel1) == NUMBER_OF_INDIVIDUALS_FOR_DUEL and len(individuals_for_duel2) == NUMBER_OF_INDIVIDUALS_FOR_DUEL:
+            break
+
+        if len(individuals_for_duel1) != NUMBER_OF_INDIVIDUALS_FOR_DUEL:
+            index1 = random.randint(0, population_length - 1)
+            candidate_for_duel1 = population[index1]
+            if candidate_for_duel1 not in individuals_for_duel1:
+                individuals_for_duel1.append(candidate_for_duel1)
+
+        if len(individuals_for_duel2) != NUMBER_OF_INDIVIDUALS_FOR_DUEL:
+            index2 = random.randint(0, population_length - 1)
+            candidate_for_duel2 = population[index2]
+            if candidate_for_duel2 not in individuals_for_duel1 and candidate_for_duel2 not in individuals_for_duel2:
+                individuals_for_duel2.append(candidate_for_duel2)
+
+    winner_of_duel1 = max(individuals_for_duel1, key=lambda x: x[0])
+    winner_of_duel2 = max(individuals_for_duel2, key=lambda x: x[0])
+
+    return winner_of_duel1, winner_of_duel2
+
+def edge_recombination_crossover(parent1, parent2):
+    dict_of_neighbors = generate_dict_of_neighbors(parent1, parent2)
+
+    gen_index = random.randint(0, len(parent1) - 1)
+    gen = parent1[gen_index]
+    child = []
+    while True:
+
+        child.append(gen)
+
+        if len(child) == len(parent1):
+            return child
+
+        for key in dict_of_neighbors.keys():
+            if gen in dict_of_neighbors[key]:
+                dict_of_neighbors[key].remove(gen)
+
+        if not dict_of_neighbors[gen]:
+            while True:
+                # new_gen = random.randint(parent1[0], parent1[-1])
+                new_gen_index = random.randint(0, len(parent1) - 1)
+                new_gen = parent1[new_gen_index]
+                if new_gen not in child:
+                    break
+        else:
+            new_gen = dict_of_neighbors[gen][0]
+            best_neighbor = len(dict_of_neighbors[new_gen])
+            for neighbor in dict_of_neighbors[gen][1:]:
+                possible_best_neighbor = len(dict_of_neighbors[neighbor])
+                if possible_best_neighbor <= best_neighbor:
+                    best_neighbor = possible_best_neighbor
+                    new_gen = neighbor
+        gen = new_gen
+
+
+def generate_dict_of_neighbors(parent1, parent2):
+    dict_of_neighbors = {}
+    for i in range(0, len(parent1)):
+        list_of_neighbors = []
+        element = parent1[i]
+        if i == 0:
+            left_neighbor1 = parent1[-1]
+            right_neighbor1 = parent1[i + 1]
+        elif i == (len(parent1) - 1):
+            left_neighbor1 = parent1[i - 1]
+            right_neighbor1 = parent1[0]
+        else:
+            left_neighbor1 = parent1[i - 1]
+            right_neighbor1 = parent1[i + 1]
+
+        list_of_neighbors.extend([left_neighbor1, right_neighbor1])
+
+        index = parent2.index(element)
+        if index == 0:
+            left_neighbor2 = parent2[-1]
+            right_neighbor2 = parent2[index + 1]
+        elif index == (len(parent2) - 1):
+            left_neighbor2 = parent2[index - 1]
+            right_neighbor2 = parent2[0]
+        else:
+            left_neighbor2 = parent2[index - 1]
+            right_neighbor2 = parent2[index + 1]
+
+        if left_neighbor2 not in list_of_neighbors:
+            list_of_neighbors.append(left_neighbor2)
+        if right_neighbor2 not in list_of_neighbors:
+            list_of_neighbors.append(right_neighbor2)
+
+        dict_of_neighbors[element] = list_of_neighbors
+
+    return dict_of_neighbors
+
+
+def mutation_function(population, travel_map):
+    mutation_percentage = random.random()
+    if mutation_percentage <= PERCENT_OF_MUTATION:
+        count_individual_for_mutation = round(len(population) * mutation_percentage)
+        mutants = set()
+        for i in range(0, count_individual_for_mutation):
+            while True:
+                individual_for_mutation = random.randint(0, len(population) - 1)
+                if individual_for_mutation not in mutants:
+                    mutants.add(individual_for_mutation)
+                    candidate_mutant = population[individual_for_mutation]
+                    while True:
+                        chromosome1 = random.randint(0, len(candidate_mutant[1]) - 1)
+                        chromosome2 = random.randint(0, len(candidate_mutant[1]) - 1)
+                        if chromosome1 != chromosome2:
+                            candidate_mutant[1][chromosome1], candidate_mutant[1][chromosome2] = candidate_mutant[1][chromosome2], candidate_mutant[1][chromosome1]
+
+                        candidate_mutant_priority = adaptation_function(candidate_mutant[1], travel_map)
+                        mutant = (candidate_mutant_priority, candidate_mutant[1])
+
+                        if mutant not in population:
+                            population[individual_for_mutation] = mutant
+
+                        break
+                    break
+
+
+
+
+
+
+
+
+
 

bin/main/main.py

@@ -56,14 +56,11 @@ def Fill(bool):
 
         travel.points_coord.append(field.small_field_canvas.coords(field.canvas_small_images[0]))
         travel.points_coord.extend(field.mines_coord)
-        print(travel.points_coord)
 
         for i in range(0, len(travel.points_coord)):
             travel.points_map[i + 1] = travel.points_coord[i]
-        # print(travel.points_map)
-        # key = list(travel.points_map.keys())
-        # print(key)
-        tr.genetic_algorithm(travel.points_map)
+
+        print(travel.points_map)
 
 
         for i in range(0, len(field.canvas_small_images)):
@@ -202,71 +199,128 @@ def create_action_list(states, index):
     create_action_list(states, states.index(state_parent))
 
 
-def MouseClickEvent(event):
+def MouseClickEvent(track):
     global fringe
     global explored
     global action_list
 
-    start_position = field.small_field_canvas.coords(player.image_canvas_id)
-    end_position = []
+    print("The best individual is: {} {}".format(track[1], track[0]))
+    for point in range(0, len(track[1]) + 1):
+        start_position = field.small_field_canvas.coords(player.image_canvas_id)
+        if point == len(track[1]):
+            end_position = travel.points_map[1]
+        else:
+            end_position = travel.points_map[track[1][point]]
 
-    # print("Pierwsza pozycja: {} {}".format(start_position[0], start_position[1]))
+        node = nd.Node()
+        if len(fringe) == 0:
+            node.state.coord = start_position
+            node.state.direction = "east"
+        else:
+            states = []
+            for k in range(0, len(fringe)):
+                new_state = fringe[k].state.coord
+                states.append(new_state)
+            start_node = fringe[-1]
 
-    for i in range(0, len(field.canvas_small_images)):
-        img_coords = field.small_field_canvas.coords(field.canvas_small_images[i])
-        if (img_coords[0] <= event.x and event.x <= img_coords[0] + IMAGE_SIZE) and (img_coords[1] <= event.y and event.y <= img_coords[1] + IMAGE_SIZE):
-            end_position = img_coords
-            print("Color cost: ", field.cell_expense[i])
+            node.state.coord = start_node.state.coord
+            node.state.direction = start_node.state.direction
 
-    # if len(end_position) == 2:
-    #     print("Koncowa pozycja: {} {}".format(end_position[0], end_position[1]))
+        fringe.clear()
+        explored.clear()
+        action_list.clear()
+        fringe = nd.graph_search_A(fringe, explored, node.state, end_position)
+        # fringe = nd.graph_search(fringe, explored, node.state, end_position)
 
-    node = nd.Node()
-    if len(fringe) == 0:
-        node.state.coord = start_position
-        node.state.direction = "east"
-    else:
         states = []
-        for k in range(0, len(fringe)):
-            new_state = fringe[k].state.coord
+        goal_all = []
+        for i in range(0, len(fringe)):
+            new_state = [fringe[i].state.coord, fringe[i].state.direction]
             states.append(new_state)
-        start_node = fringe[-1]
+            if end_position[0] == fringe[i].state.coord[0] and end_position[1] == fringe[i].state.coord[1]:
+                goal_all.append(fringe[i])
 
-        node.state.coord = start_node.state.coord
-        node.state.direction = start_node.state.direction
-        
-    fringe.clear()
-    explored.clear()
-    action_list.clear()
-    fringe = nd.graph_search_A(fringe, explored, node.state, end_position)
-    # fringe = nd.graph_search(fringe, explored, node.state, end_position)
+        elem_min = goal_all[0]
+        for i in range(1, len(goal_all)):
+            if elem_min.priority > goal_all[i].priority:
+                elem_min = goal_all[i]
+        index = fringe.index(elem_min)
+        fringe = fringe[:index + 1]
 
-    states = []
-    goal_all = []
-    for i in range(0, len(fringe)):
-        new_state = [fringe[i].state.coord, fringe[i].state.direction]
-        states.append(new_state)
-        if end_position[0] == fringe[i].state.coord[0] and end_position[1] == fringe[i].state.coord[1]:
-            goal_all.append(fringe[i])
+        create_action_list(states, -1)
 
-    elem_min = goal_all[0]
-    for i in range(1, len(goal_all)):
-        if elem_min.priority > goal_all[i].priority:
-            elem_min = goal_all[i]
-    index = fringe.index(elem_min)
-    fringe = fringe[:index + 1]
+        # for i in range(0, len(fringe)):
+        #     print('Node{} = State: {} {}, Parent: {} {} {}, Action: {}'.format(i + 1, fringe[i].state.coord, fringe[i].state.direction, fringe[i].parent[0], fringe[i].parent[1], fringe[i].parent[2], fringe[i].action))
 
-    create_action_list(states, -1)
+        # print(action_list)
 
+        # Start moving
+        AutoMove()
+        DrawFlag()
+
+        time.sleep(SLEEP_AFTER_CHECK_MINE)
+
+
+    # start_position = field.small_field_canvas.coords(player.image_canvas_id)
+    # end_position = []
+    #
+    # # print("Pierwsza pozycja: {} {}".format(start_position[0], start_position[1]))
+    #
+    # for i in range(0, len(field.canvas_small_images)):
+    #     img_coords = field.small_field_canvas.coords(field.canvas_small_images[i])
+    #     if (img_coords[0] <= event.x and event.x <= img_coords[0] + IMAGE_SIZE) and (img_coords[1] <= event.y and event.y <= img_coords[1] + IMAGE_SIZE):
+    #         end_position = img_coords
+    #         print("Color cost: ", field.cell_expense[i])
+    #
+    # # if len(end_position) == 2:
+    # #     print("Koncowa pozycja: {} {}".format(end_position[0], end_position[1]))
+    #
+    # node = nd.Node()
+    # if len(fringe) == 0:
+    #     node.state.coord = start_position
+    #     node.state.direction = "east"
+    # else:
+    #     states = []
+    #     for k in range(0, len(fringe)):
+    #         new_state = fringe[k].state.coord
+    #         states.append(new_state)
+    #     start_node = fringe[-1]
+    #
+    #     node.state.coord = start_node.state.coord
+    #     node.state.direction = start_node.state.direction
+    #
+    # fringe.clear()
+    # explored.clear()
+    # action_list.clear()
+    # fringe = nd.graph_search_A(fringe, explored, node.state, end_position)
+    # # fringe = nd.graph_search(fringe, explored, node.state, end_position)
+    #
+    # states = []
+    # goal_all = []
     # for i in range(0, len(fringe)):
-    #     print('Node{} = State: {} {}, Parent: {} {} {}, Action: {}'.format(i + 1, fringe[i].state.coord, fringe[i].state.direction, fringe[i].parent[0], fringe[i].parent[1], fringe[i].parent[2], fringe[i].action))
-
-    print(action_list)
-
-
-
-    # Start moving
-    AutoMove()
+    #     new_state = [fringe[i].state.coord, fringe[i].state.direction]
+    #     states.append(new_state)
+    #     if end_position[0] == fringe[i].state.coord[0] and end_position[1] == fringe[i].state.coord[1]:
+    #         goal_all.append(fringe[i])
+    #
+    # elem_min = goal_all[0]
+    # for i in range(1, len(goal_all)):
+    #     if elem_min.priority > goal_all[i].priority:
+    #         elem_min = goal_all[i]
+    # index = fringe.index(elem_min)
+    # fringe = fringe[:index + 1]
+    #
+    # create_action_list(states, -1)
+    #
+    # # for i in range(0, len(fringe)):
+    # #     print('Node{} = State: {} {}, Parent: {} {} {}, Action: {}'.format(i + 1, fringe[i].state.coord, fringe[i].state.direction, fringe[i].parent[0], fringe[i].parent[1], fringe[i].parent[2], fringe[i].action))
+    #
+    # print(action_list)
+    #
+    #
+    #
+    # # Start moving
+    # AutoMove()
 
 
 def PutMines(mines_array):
@@ -341,19 +395,19 @@ def DrawFlag():
     field.small_field_canvas.create_image(player.current_x, player.current_y, anchor=NW, image=field.flag_img)
 
 
-def IsItMine():
-    visited = 0     # 0 - not mine; 1 - on this mine for the first time; 2 - already been on this mine
-
-    # Checks if the player is on the mine
-    for i in field.mines_coord:
-        if i[0] == player.current_x and i[1] == player.current_y:
-            visited = 1
-            # Checks if the player has already been on this mine
-            for y in field.visited_mines:
-                if y[0] == player.current_x and y[1] == player.current_y:
-                    visited = 2
-        if visited == 1:
-            DrawFlag()
+# def IsItMine():
+#     visited = 0     # 0 - not mine; 1 - on this mine for the first time; 2 - already been on this mine
+#
+#     # Checks if the player is on the mine
+#     for i in field.mines_coord:
+#         if i[0] == player.current_x and i[1] == player.current_y:
+#             visited = 1
+#             # Checks if the player has already been on this mine
+#             for y in field.visited_mines:
+#                 if y[0] == player.current_x and y[1] == player.current_y:
+#                     visited = 2
+#         if visited == 1:
+#             DrawFlag()
 
 
 def AutoMove():
@@ -363,7 +417,7 @@ def AutoMove():
         # Move once
         Action(action)
         # Check if player on mine and if yes, draw flag
-        IsItMine()
+        # IsItMine()
         # Update main window
         field.win.update()
 
@@ -376,13 +430,13 @@ def DrawRectangle():
     color = None
     # Chose color for rectangle
     for i in range(len(field.cell_expense)):
-        if field.cell_expense[i] == 10:
+        if field.cell_expense[i] == standard_cell_cost:
             color = "None"
-        elif field.cell_expense[i] == 20:
+        elif field.cell_expense[i] == sand_cell_cost:
             color = "yellow"
-        elif field.cell_expense[i] == 40:
+        elif field.cell_expense[i] == water_cell_cost:
             color = "dodger blue"
-        elif field.cell_expense[i] == 5:
+        elif field.cell_expense[i] == swamp_cell_cost:
             color = "green4"
         if color != "None":
             field.small_field_canvas.create_rectangle(x, y, x + IMAGE_SIZE + 2, y + IMAGE_SIZE + 2, width=2, outline=color)
@@ -415,8 +469,15 @@ def CostingOfCells():
 
 def click_button():
     btn.destroy()
-    label = Label(field.win, text='Prepod lox\nPrepod lox\nPrepod lox\nPrepod lox\nPrepod lox\nPrepod lox\n', fg='black')
+    label = Label(field.win, text="Wait... AI conquers the world!", fg='black')
     label.place(x=50, y=570)
+    field.win.update()
+    track = tr.genetic_algorithm(travel.points_map)
+
+    track[1].remove(1)
+    label.config(text=track[1])
+    field.win.update()
+    MouseClickEvent(track)
 
 
 def main():
@@ -433,7 +494,7 @@ def main():
                  foreground="#ccc",  # цвет текста
                  padx="20",  # отступ от границ до содержимого по горизонтали
                  pady="8",  # отступ от границ до содержимого по вертикали
-                 font="16",  # высота шрифта
+                 font="24",  # высота шрифта
                  command=click_button
                  )
 
@@ -478,7 +539,7 @@ def main():
     # Rectangle()
     # Binding keyboard press to function
     # field.win.bind("<Key>", Action)
-    field.small_field_canvas.bind("<Button-1>", MouseClickEvent)
+    # field.small_field_canvas.bind("<Button-1>", MouseClickEvent)
     # Starting mainloop for window
     field.win.mainloop()
 

resources/Globals.py

@@ -9,11 +9,11 @@ WINDOW_Y = 950
 # Size of small image
 IMAGE_SIZE = 50
 
-MIN_AMOUNT_OF_MINES = 0
+MIN_AMOUNT_OF_MINES = 6
 MAX_AMOUNT_OF_MINES = 11
 AMOUNT_OF_MINES = random.randint(MIN_AMOUNT_OF_MINES, MAX_AMOUNT_OF_MINES)
 
-DELAY_TIME = 0.5
+DELAY_TIME = 0.2
 
 STEP = IMAGE_SIZE + 5
 
@@ -26,7 +26,13 @@ amount_of_water_cells = 10
 water_cell_cost = 40
 
 amount_of_swamp_cells = 10
-swamp_cell_cost = 5
+swamp_cell_cost = 80
 
 x_start = 5
 y_start = 5
+
+NUMBER_OF_INDIVIDUALS_FOR_DUEL = 4
+NUMBER_OF_POINTS_PERMUTATION = 10
+PERCENT_OF_MUTATION = 0.01
+PERCENT_OF_OUTGOING_INDIVIDUALS = 0.03
+SLEEP_AFTER_CHECK_MINE = 1