integrated explosion animation, reformatted roulette-GA file

algorithms/learn/genetic_algorithm/ga_roulette.py

@@ -0,0 +1,175 @@
+import numpy as np
+import random
+import operator
+import pandas as pd
+from algorithms.learn.genetic_algorithm import helpers
+
+import os
+from minefield import Minefield
+gl_minefield = None
+scores_table = None
+
+
+class Fitness:
+    def __init__(self, route):
+        self.route = route
+        self.score = 0
+        self.fitness = 0.0
+
+    def route_distance(self):
+        if self.score == 0:
+            self.score = helpers.get_score(gl_minefield, self.route, scores_table)
+
+        return self.score
+
+    def route_fitness(self):
+        if self.fitness == 0:
+            self.fitness = 1000 / float(self.route_distance())
+
+        return self.fitness
+
+
+def create_route(mine_list):
+    route = random.sample(mine_list, len(mine_list))
+    return route
+
+
+def initial_population(pop_size, mine_list):
+    population = []
+
+    for i in range(0, pop_size):
+        population.append(create_route(mine_list))
+    return population
+
+
+def rank_routes(population):
+    fitness_results = {}
+
+    for i in range(0, len(population)):
+        fitness_results[i] = Fitness(population[i]).route_fitness()
+
+    return sorted(fitness_results.items(), key=operator.itemgetter(1), reverse=True)
+
+
+def selection(pop_ranked, elite_size):
+    selection_results = []
+    df = pd.DataFrame(np.array(pop_ranked), columns=["Index", "Fitness"])
+    df['cum_sum'] = df.Fitness.cumsum()
+    df['cum_perc'] = 100 * df.cum_sum / df.Fitness.sum()
+
+    for i in range(0, elite_size):
+        selection_results.append(pop_ranked[i][0])
+    for i in range(0, len(pop_ranked) - elite_size):
+        pick = 100 * random.random()
+        for j in range(0, len(pop_ranked)):
+            if pick <= df.iat[j, 3]:
+                selection_results.append(pop_ranked[j][0])
+                break
+    return selection_results
+
+
+def mating_pool(population, selection_results):
+    matingpool = []
+    for i in range(0, len(selection_results)):
+        index = selection_results[i]
+        matingpool.append(population[index])
+    return matingpool
+
+
+def breed(parent1, parent2):
+    child_p1 = []
+
+    gene_a = int(random.random() * len(parent1))
+    gene_b = int(random.random() * len(parent1))
+
+    start_gene = min(gene_a, gene_b)
+    end_gene = max(gene_a, gene_b)
+
+    for i in range(start_gene, end_gene):
+        child_p1.append(parent1[i])
+
+    child_p2 = [item for item in parent2 if item not in child_p1]
+
+    child = child_p1 + child_p2
+    return child
+
+
+def breed_population(matingpool, elite_size):
+    children = []
+    length = len(matingpool) - elite_size
+    pool = random.sample(matingpool, len(matingpool))
+
+    for i in range(0, elite_size):
+        children.append(matingpool[i])
+
+    for i in range(0, length):
+        child = breed(pool[i], pool[len(matingpool) - i - 1])
+        children.append(child)
+    return children
+
+
+def mutate(individual, mutation_rate):
+    for swapped in range(len(individual)):
+        if random.random() < mutation_rate:
+            swap_with = int(random.random() * len(individual))
+
+            city1 = individual[swapped]
+            city2 = individual[swap_with]
+
+            individual[swapped] = city2
+            individual[swap_with] = city1
+    return individual
+
+
+def mutate_population(population, mutation_rate):
+    mutated_pop = []
+
+    for ind in range(0, len(population)):
+        mutated_ind = mutate(population[ind], mutation_rate)
+        mutated_pop.append(mutated_ind)
+    return mutated_pop
+
+
+def next_generation(scores, current_gen, elite_size, mutation_rate):
+    selection_results = selection(scores, elite_size)
+    matingpool = mating_pool(current_gen, selection_results)
+    children = breed_population(matingpool, elite_size)
+    next_gen = mutate_population(children, mutation_rate)
+    return next_gen
+
+
+def genetic_algorithm(minefield, population, pop_size, elite_size, mutation_rate, generations):
+    global gl_minefield, scores_table
+    gl_minefield = minefield
+
+    scores_table = helpers.create_scores_table(gl_minefield)
+
+    pop = initial_population(pop_size, population)
+    scores = rank_routes(pop)
+
+    print("Initial score: " + str(1000 / scores[0][1]))
+
+    for i in range(0, generations):
+        pop = next_generation(scores, pop, elite_size, mutation_rate)
+        scores = rank_routes(pop)
+
+        print(f"Generation {i} best score: {str(1000 / scores[0][1])}")
+        best_route_index = scores[0][0]
+        best_route = pop[best_route_index]
+        print(best_route)
+
+    print("Final score: " + str(1000 / scores[0][1]))
+    best_route_index = scores[0][0]
+    best_route = pop[best_route_index]
+    return best_route
+
+
+if __name__ == "__main__":
+    gl_minefield = Minefield(os.path.join("..", "..", "..", "resources", "minefields", "fourthmap.json"))
+
+    genetic_algorithm(minefield=gl_minefield,
+                      population=helpers.get_mines_coords(gl_minefield),
+                      pop_size=100,
+                      elite_size=20,
+                      mutation_rate=0.01,
+                      generations=100)

algorithms/learn/genetic_algorithm/ga_tournament.py

@@ -7,20 +7,22 @@ from numpy.random import rand
 
 from algorithms.learn.genetic_algorithm import helpers
 
+
 # this is helper function for sum_distance function, it counts the taxi cab distance between 2 vectors [x,y]
-def distance(x,y):
-    temp1 = abs(x[0]-y[0])
-    temp2 = abs(x[1]-y[1])
-    vector_distance = temp1+temp2
+def distance(x, y):
+    temp1 = abs(x[0] - y[0])
+    temp2 = abs(x[1] - y[1])
+    vector_distance = temp1 + temp2
     return vector_distance
 
+
 # this is fitting function which tells how well specimen fits the environment
 # this function counts the sum of distances between vectors for a specimen
 # this was just for testing, it should be probably changed to A*
 def sum_distance(speciment):
     sum = 0
-    for i in range(0,len(speciment)-2):
-        pom = distance(speciment[i],speciment[i+1])
+    for i in range(0, len(speciment) - 2):
+        pom = distance(speciment[i], speciment[i + 1])
         sum = sum + pom
     return sum
 
@@ -30,7 +32,7 @@ def sum_distance(speciment):
 def selection(pop, scores, k=10):
     # first random selection
     selection_ix = randint(len(pop))
-    for ix in randint(0, len(pop), k- 1):
+    for ix in randint(0, len(pop), k - 1):
         # check if better (e.g. perform a tournament)
         if scores[ix] < scores[selection_ix]:
             selection_ix = ix
@@ -40,8 +42,8 @@ def selection(pop, scores, k=10):
 # crossover two parents to create two children
 # this function creates speciments for new generation
 def crossover(p1, p2, r_cross):
-    p1=list(p1)
-    p2=list(p2)
+    p1 = list(p1)
+    p2 = list(p2)
     # children are copies of parents by default
     c1, c2 = p1.copy(), p2.copy()
     # check for recombination
@@ -71,17 +73,16 @@ def mutation(speciment, r_mut):
         if rand() < r_mut:
             # flip the bit
             temp = speciment[i]
-            pom = random.randint(0,len(speciment)-1)
+            pom = random.randint(0, len(speciment) - 1)
             speciment[i] = speciment[pom]
             speciment[pom] = temp
 
 
-
 # genetic algorithm
 def genetic_algorithm(minefield, objective, n_iter, n_pop, r_cross, r_mut):
     # this is hardcoded list of coordinates of all mines (for tests only) which represents one speciment in population
     # it is then permutated to get set number of species and create population
-    speciment=helpers.get_mines_coords(minefield)
+    speciment = helpers.get_mines_coords(minefield)
     pop = [random.sample(speciment, len(speciment)) for _ in range(n_pop)]
     # permutation function returns tuples so I change them to lists
 
@@ -116,7 +117,6 @@ def genetic_algorithm(minefield, objective, n_iter, n_pop, r_cross, r_mut):
 
 
 if __name__ == "__main__":
-
     # define the total iterations
     n_iter = 100
     # bits
@@ -131,11 +131,10 @@ if __name__ == "__main__":
     # create new minefield instance
     import os
     from minefield import Minefield
+
     minefield = Minefield(os.path.join("..", "..", "..", "resources", "minefields", "fourthmap.json"))
 
     # perform the genetic algorithm search
     best, score = genetic_algorithm(minefield, helpers.get_score, n_iter, n_pop, r_cross, r_mut)
     print('Done!')
     print('f(%s) = %f' % (best, score))
-
-

algorithms/learn/genetic_algorithm/helpers.py

@@ -87,7 +87,7 @@ def get_score(minefield, speciment, table=None):
 
             mine.active = False
 
-            minefield.next_turn(n_turns=cost)
+            minefield.next_turn(n_turns=cost, mode="ga")
             score += cost
 
         score += 200 * minefield.explosions

algorithms/learn/genetic_algorithm/new_ga.py

@@ -1,174 +0,0 @@
-import time
-import numpy as np, random, operator, pandas as pd
-from algorithms.learn.genetic_algorithm import helpers
-
-import os
-from minefield import Minefield
-gl_minefield = None
-scores_table = None
-
-
-class Fitness:
-    def __init__(self, route):
-        self.route = route
-        self.distance = 0
-        self.fitness = 0.0
-
-    def routeDistance(self):
-        if self.distance == 0:
-            self.distance = helpers.get_score(gl_minefield, self.route, scores_table)
-
-        return self.distance
-
-    def routeFitness(self):
-        if self.fitness == 0:
-            self.fitness = 1000 / float(self.routeDistance())
-
-        return self.fitness
-
-
-def createRoute(cityList):
-    route = random.sample(cityList, len(cityList))
-    return route
-
-
-def initialPopulation(popSize, cityList):
-    population = []
-
-    for i in range(0, popSize):
-        population.append(createRoute(cityList))
-    return population
-
-
-def rankRoutes(population):
-    fitnessResults = {}
-
-    for i in range(0, len(population)):
-        fitnessResults[i] = Fitness(population[i]).routeFitness()
-
-    return sorted(fitnessResults.items(), key=operator.itemgetter(1), reverse=True)
-
-
-def selection(popRanked, eliteSize):
-    selectionResults = []
-    df = pd.DataFrame(np.array(popRanked), columns=["Index", "Fitness"])
-    df['cum_sum'] = df.Fitness.cumsum()
-    df['cum_perc'] = 100 * df.cum_sum / df.Fitness.sum()
-
-    for i in range(0, eliteSize):
-        selectionResults.append(popRanked[i][0])
-    for i in range(0, len(popRanked) - eliteSize):
-        pick = 100 * random.random()
-        for i in range(0, len(popRanked)):
-            if pick <= df.iat[i, 3]:
-                selectionResults.append(popRanked[i][0])
-                break
-    return selectionResults
-
-def matingPool(population, selectionResults):
-    matingpool = []
-    for i in range(0, len(selectionResults)):
-        index = selectionResults[i]
-        matingpool.append(population[index])
-    return matingpool
-
-
-def breed(parent1, parent2):
-    child = []
-    childP1 = []
-    childP2 = []
-
-    geneA = int(random.random() * len(parent1))
-    geneB = int(random.random() * len(parent1))
-
-    startGene = min(geneA, geneB)
-    endGene = max(geneA, geneB)
-
-    for i in range(startGene, endGene):
-        childP1.append(parent1[i])
-
-    childP2 = [item for item in parent2 if item not in childP1]
-
-    child = childP1 + childP2
-    return child
-
-
-def breedPopulation(matingpool, eliteSize):
-    children = []
-    length = len(matingpool) - eliteSize
-    pool = random.sample(matingpool, len(matingpool))
-
-    for i in range(0, eliteSize):
-        children.append(matingpool[i])
-
-    for i in range(0, length):
-        child = breed(pool[i], pool[len(matingpool) - i - 1])
-        children.append(child)
-    return children
-
-
-def mutate(individual, mutationRate):
-    for swapped in range(len(individual)):
-        if (random.random() < mutationRate):
-            swapWith = int(random.random() * len(individual))
-
-            city1 = individual[swapped]
-            city2 = individual[swapWith]
-
-            individual[swapped] = city2
-            individual[swapWith] = city1
-    return individual
-
-
-def mutatePopulation(population, mutationRate):
-    mutatedPop = []
-
-    for ind in range(0, len(population)):
-        mutatedInd = mutate(population[ind], mutationRate)
-        mutatedPop.append(mutatedInd)
-    return mutatedPop
-
-
-def nextGeneration(scores, currentGen, eliteSize, mutationRate):
-    selectionResults = selection(scores, eliteSize)
-    matingpool = matingPool(currentGen, selectionResults)
-    children = breedPopulation(matingpool, eliteSize)
-    nextGeneration = mutatePopulation(children, mutationRate)
-    return nextGeneration
-
-
-def genetic_algorithm(minefield, population, popSize, eliteSize, mutationRate, generations):
-    global gl_minefield, scores_table
-    gl_minefield = minefield
-
-    scores_table = helpers.create_scores_table(gl_minefield)
-
-    pop = initialPopulation(popSize, population)
-    scores = rankRoutes(pop)
-
-    print("Initial score: " + str(1000 / scores[0][1]))
-
-    for i in range(0, generations):
-        pop = nextGeneration(scores, pop, eliteSize, mutationRate)
-        scores = rankRoutes(pop)
-
-        print(f"Generation {i} best score: {str(1000 / scores[0][1])}")
-        bestRouteIndex = scores[0][0]
-        bestRoute = pop[bestRouteIndex]
-        print(bestRoute)
-
-    print("Final score: " + str(1000 / scores[0][1]))
-    bestRouteIndex = scores[0][0]
-    bestRoute = pop[bestRouteIndex]
-    return bestRoute
-
-
-if __name__ == "__main__":
-    gl_minefield = Minefield(os.path.join("..", "..", "..", "resources", "minefields", "fourthmap.json"))
-
-    genetic_algorithm(minefield=gl_minefield,
-                      population=helpers.get_mines_coords(gl_minefield),
-                      popSize=100,
-                      eliteSize=20,
-                      mutationRate=0.01,
-                      generations=100)

assets/display_assets.py

@@ -74,6 +74,9 @@ def blit_graphics(minefield):
                     seconds = tile.mine.timer % 60
                     display_time_mine(tile.position, minutes, "0" + str(seconds))
 
+                if tile.mine.blacked:
+                    const.SCREEN.blit(const.MINE_INACTIVE, tile_screen_coords)
+
     # changed sapper's movement from jumping to continuous movement (moved everything to Agent's class)
     # # sapper
     # display_sapper(

assets/explosion.py

@@ -3,12 +3,15 @@ import pygame
 import os
 
 from project_constants import V_TILE_SIZE, DIR_ASSETS, SCREEN
+from objects.mine_models.mine import Mine
 import assets.display_assets
 
 
 class Explosion(pygame.sprite.Sprite):
 
-    def __init__(self, coords: (int, int)):
+    def __init__(self, coords: (int, int) = None, mine: Mine = None):
+        if coords is None:
+            coords = mine.position
 
         pygame.sprite.Sprite.__init__(self)
 
@@ -28,6 +31,7 @@ class Explosion(pygame.sprite.Sprite):
             assets.display_assets.calculate_screen_position(coords),
             (V_TILE_SIZE, V_TILE_SIZE)
         )
+        self.mine = mine
 
     def update(self, *args, **kwargs):
 
@@ -39,6 +43,10 @@ class Explosion(pygame.sprite.Sprite):
             if self.frame == len(self.explosion_animation):
                 self.kill()
 
+            elif self.frame == len(self.explosion_animation) - 1:
+                if self.mine is not None:
+                    self.mine.blacked = True
+
             else:
                 self.image = self.explosion_animation[self.frame]
 

game.py

@@ -5,7 +5,7 @@ import project_constants as const
 from assets.display_assets import blit_graphics
 from algorithms.search import a_star
 
-from algorithms.learn.genetic_algorithm import new_ga, helpers
+from algorithms.learn.genetic_algorithm import ga_roulette, helpers
 
 from minefield import Minefield
 
@@ -150,12 +150,12 @@ class Game:
         genetics_minefield = Minefield(const.MAP_RANDOM_10x10)
 
         sequence = \
-            new_ga.genetic_algorithm(minefield=genetics_minefield,
-                                     population=helpers.get_mines_coords(self.minefield),
-                                     popSize=100,
-                                     eliteSize=20,
-                                     mutationRate=0.01,
-                                     generations=generations)
+            ga_roulette.genetic_algorithm(minefield=genetics_minefield,
+                                          population=helpers.get_mines_coords(self.minefield),
+                                          pop_size=100,
+                                          elite_size=20,
+                                          mutation_rate=0.01,
+                                          generations=generations)
 
         self.genetic_sequence = sequence
         self.genetics_done = True

main.py

@@ -57,12 +57,6 @@ def main():
             # checking if game should stop running
             running = not is_quit_button_pressed(events)
 
-            # TODO : added for testing, remove after moving the explosion line
-            keys = pygame.key.get_pressed()
-            if keys[pygame.K_SPACE]:
-                # TODO : move this line to where explosion is called
-                const.EXPLOSIONS.add(Explosion((2, 3)))
-
             # drawing minefield and agent instances
             game.draw_minefield()
 
@@ -187,7 +181,7 @@ def main():
                     if not game.agent.defuse_a_mine(game.get_mine(game.goal)):
                         print("BOOOOOOM\n\n")
                         game.explosion(game.get_mine(game.goal))
-                        # is_game_over = True
+                        const.EXPLOSIONS.add(Explosion(mine=game.get_mine(game.goal)))
 
                     else:
                         print("guess you will live a little longer...\n\n")

minefield.py

@@ -1,4 +1,5 @@
 import project_constants as const
+from assets.explosion import Explosion
 from objects import tile as tl, agent as ag
 from objects.mine_models.time_mine import TimeMine
 import json_generator as jg
@@ -39,7 +40,7 @@ class Minefield:
 
         self.time_mines = self._get_time_mines()
 
-    def next_turn(self, n_turns=1):
+    def next_turn(self, n_turns=1, mode="main"):
         self.turn += n_turns
         self.points += n_turns
 
@@ -47,11 +48,13 @@ class Minefield:
             mine.timer = max(0, mine.starting_time - int(self.turn))
 
             if mine.timer == 0 and mine.active:
-                # TODO: BOOM
                 self.explosions += 1
                 self.points += const.EXPLOSION_PENALTY
                 mine.active = False
 
+                if mode == "main":
+                    const.EXPLOSIONS.add(Explosion(mine=mine))
+
     def get_active_mines(self):
         mines = list()
 

objects/mine_models/mine.py

@@ -17,11 +17,13 @@ class Mine(ABC):
         self.position = position
         self.wire = None
         self.active = active
+        self.blacked = False
 
     @abstractmethod
     def disarm(self, wire):
         if wire == self.wire:
             self.active = False
+            self.blacked = True
             return True
 
         else:

project_constants.py

@@ -161,6 +161,12 @@ HIGHLIGHT = pygame.transform.scale(
 )
 HIGHLIGHT.set_alpha(100)
 
+MINE_INACTIVE = pygame.transform.scale(
+    pygame.image.load(os.path.join(DIR_ASSETS, "old_tiles/tile_black.png")),
+    (V_TILE_SIZE, V_TILE_SIZE)
+)
+MINE_INACTIVE.set_alpha(160)
+
 
 # ============== #
 # ==== MAPS ==== #