def __init__(self): self.root= Tkinter.Tk() self.vis= Visual(self.root) self.relief= Relief() self.figure= None self.stand_latter= False self.root.after_idle(self.tick) self.root.bind('<KeyPress>', self.press_key) self.root.mainloop()
def main(): #Initialize pygame.init() screen = pygame.display.set_mode((WIDTH, HEIGHT)) pygame.display.set_caption(WINDOW_TITLE) icon = utils.load_image(ICON, (0,0,0)) pygame.display.set_icon(icon) #Introduction images = [utils.load_image(image) for image in INTRO_IMAGES] visual = Visual(screen, images, INTRO_TIMES) music.play_music(INTROMUSIC, 1) visual.loop() music.stop_music() #Shooter opcion opcion = menu while opcion is not exit: change = opcion(screen).loop() if change: opcion = change opcion() #Exit
class Game: def __init__(self): self.root= Tkinter.Tk() self.vis= Visual(self.root) self.relief= Relief() self.figure= None self.stand_latter= False self.root.after_idle(self.tick) self.root.bind('<KeyPress>', self.press_key) self.root.mainloop() def tick(self): self.root.after(300, self.tick) if not self.figure: self.figure= Figure() if self.relief.have_collision(self.figure.get_all()): print 'generate collision with relief' self.root.quit() self.figure.down_move() if self.try_stand_figure(): self.figure= None if self.relief.overload(): print 'You Fail' self.root.quit() self.redraw() def redraw(self): self.vis.reset() self.vis.draw(self.relief.get_all(), 'navajo white') if self.figure: self.vis.draw(self.figure.get_all(), 'alice blue') def move_figure(self, method): method() if self.relief.have_collision(self.figure.get_all()): self.figure.rollback() else: self.redraw() self.stand_latter= True def press_key(self, event): if not self.figure: return inp= event.char.upper() if inp == 'D': self.move_figure(self.figure.right_move) elif inp == 'A': self.move_figure(self.figure.left_move) elif inp == 'S': self.move_figure(self.figure.down_move) elif inp == 'E' or inp == ' ': self.move_figure(self.figure.right_turn) elif inp == 'Q': self.move_figure(self.figure.left_turn) elif inp == 'W': while not self.relief.have_collision(self.figure.get_all()): self.figure.down_move() self.figure.rollback() self.redraw() def try_stand_figure(self): result= False if self.relief.have_collision(self.figure.get_all()): self.figure.rollback() if self.stand_latter: pass else: self.relief.extend(self.figure.get_all()) self.relief.remove_filled_lines() result= True self.stand_latter= False return result
def main(): config = RunnerConfig({'local_store_directory':''}) config.read('config.ini') if len(sys.argv) >= 2: project = None iterations = iterations_from_file(config, sys.argv[1]) else: project, iterations = iterations_from_server(config) vis = Visual() vis.start() vis.draw_project_info(project) vis.draw_iteration_boxes(len(iterations)) for i, it in enumerate(iterations): #pprint(it['stories']) #pprint(it['number']) vis.draw_iteration_number(it['number'], i) vis.draw_iteration_dates(it['start'], it['finish'], i) vis.draw_stories_for_iteration(it['stories'], i) raw_input()
time.sleep(0.001) q = th.Thread(target=learn, args=(lr_steps, learning_rate)) # q=Process(target=learn, args=(lr_steps,learning_rate)) # q=ThreadWithReturnValue(target=learn, args=(lr_steps,learning_rate)) q.daemon = True q.start() processes.append(q) coord.join(processes) RL.store() if args.test: RL.test_set() # no exploration noise cross = crossroads_map() visual = Visual() obs = [] Q1_set = [] Q2_set = [] Q3_set = [] Q4_set = [] for xx in x: for yy in y: lab = str(xx) + str(yy) obs = np.concatenate( (obs, cross[lab].car_nums, cross[lab].light_state), axis=None) RL.restore() for steps in range(1000): for xx in x: for yy in y: lab = str(xx) + str(yy)
# on_hit = False # next_step=0 # def hit_me(): # global on_hit # global next_step # if on_hit == False: # on_hit = True # next_step=0 # else: # on_hit = False # next_step=1 # b = tk.Button(window, text='Next Step', width=15, # height=2, command=hit_me) # b.pack() visual=Visual() cross1=crossing(light_state=0,q_states=[0,0,0,1]) cross2=crossing(light_state=0,q_states=[0,0,1,0]) obs=np.concatenate((cross1.car_nums, cross1.light_state, cross2.car_nums, cross2.light_state),axis=None) for steps in range(20000): visual.visual_before(cross1, cross2) action=RL.choose_action(obs) if action == 0: action1 =0 action2 =0 elif action ==1: action1 =1 action2 =0
def __init__(self, screen, father, level, total_points): self.screen = screen self.father = father self.level = level self.background = utils.load_image(levels[level]['background']) #parameters self.energy_leap = levels[level]['energy_leap'] self.mold_density = levels[level]['mold_density'] self.mold_velocity = levels[level]['mold_velocity'] self.max_time = levels[level]['max_time'] #menu control self.options = [("Yes", self.father), ("No", None)] self.exit = False #Create the game clock self.clock = pygame.time.Clock() self.mm = MoldsManager(self.mold_density, self.mold_velocity) self.bm = BottleManager(*levels[self.level]['bottle_density']) self.bm.mm = self.mm self.total_points = total_points self.pointsCounter = Points(self.total_points) self.levelIndicator = LevelIndicator(self.level) self.mm.level = self self.tics = 0 self.snow_slim = pygame.sprite.Group() self.snow_fat = pygame.sprite.Group() for x in range(75): sprite = Wheather(1, 2) self.snow_slim.add(sprite) for x in range(75): sprite = Wheather(3, 5) self.snow_fat.add(sprite) self.energy_bar = EnergyBar(self.energy_leap) self.bm.energy_bar = self.energy_bar self.mm.energy_bar = self.energy_bar self.level_time = LevelTime(self.max_time) self.gadgets = pygame.sprite.RenderUpdates() self.gadgets.add(self.pointsCounter) self.gadgets.add(self.energy_bar) self.gadgets.add(self.level_time) self.gadgets.add(self.levelIndicator) self.hero = Hero() self.bm.hero = self.hero self.mm.hero = self.hero self.explotion = Explotion() self.control_down = -1 self.control_up = -1 self.control_left = -1 self.control_right = -1 self.control_tiempo = 5 self.next_scream = random.randrange(400, 500) #Show level image Visual(self.screen, [utils.load_image(levels[self.level]['img'])], [2], None).loop()
def main(): svm_params = dict(svm_type=cv2.SVM_C_SVC, kernel_type=cv2.SVM_LINEAR, degree=None, gamma=5.383, C=1) # Creates visual platrorm vis = Visual() # User input ################################################################ ans = '1' # raw_input("Would like to create model (1) or to view outputs (2) : ") if ans == '1': print "Reading data set : " raw_img, raw_lbl = MyMnist.read() print "Done \n" lbl_img = zip(raw_lbl, raw_img) lbl_img = shuffle(lbl_img) raw_lbl = np.asarray([t[0] for t in lbl_img]) raw_img = np.asarray([t[1] for t in lbl_img]).reshape( (raw_lbl.size, config.height * config.width)) print "Now lets start training : \n" # User input ################################################################ is_hog = 'y' # raw_input("Would you like to use Hog (y/n) : ") if (is_hog == 'y'): hog_scale_w = int(raw_input("What width would you like to use : ")) hog_scale_h = int( raw_input("What height would you like to use : ")) hog_degrees = int( raw_input("What amount of degrees would you like to use : ")) hog_data = np.zeros([ raw_lbl.size, config.height * config.width / hog_scale_h / hog_scale_w * hog_degrees ], 'float32') for i in range(0, raw_lbl.size): hog_data[i] = Hog.getimghog( raw_img[i].reshape((config.height, config.width)), [[1, 0, -1], [2, 0, -1], [1, 0, -1]], [[1, 2, 1], [0, 0, 0], [-1, -2, -1]], hog_scale_h, hog_scale_w, hog_degrees) raw_img = hog_data # User input ################################################################ out = 2 # int(raw_input("Would you like to use Linear (1) or Kernel (2) :")) svm_params['C'] = float(raw_input("Please enter C parameter : ")) if out == 1: svm_params['kernel_type'] = cv2.SVM_LINEAR else: svm_params['kernel_type'] = cv2.SVM_POLY svm_params['degree'] = int( 2) # float(raw_input("Please enter SVM degree parameter : ")) svm_params['gamma'] = float(raw_input("Please enter gamma : ")) name = "HOG " + str(hog_scale_h) + "x" + str(hog_scale_w) + " d" + str( hog_degrees) + " C" + str(svm_params['C']) + " G" + str( svm_params['gamma'] ) #raw_input("How would you like to name your model : ") step = int(round(raw_lbl.size * 0.1)) - 1 test_lbls = list() pred_lbls = list() for i in range(0, 10): print "Start " + str(i) + " training ", train_img = np.concatenate((raw_img[:i, :], raw_img[i + step:, :])) train_lbl = np.append(raw_lbl[:i], raw_lbl[i + step:]) test_img = raw_img[i:i + step, :] test_lbl = raw_lbl[i:i + step] test_lbls.append(test_lbl) svm = cv2.SVM() svm.train(train_img, train_lbl, params=svm_params) print "=> Predicting", pred_lbls.append(svm.predict_all(test_img)) print "=> Done" s = SvmOutput(name, svm_params, svm, np.array(test_lbls).ravel(), np.array(pred_lbls).ravel()) s.save() s.showdata() else: ans = raw_input( "Would you like to view a specific model (1) \nor the roc curve of a few models (2) :" ) models_dir = os.walk(config.default_path).next()[1] if ans == '1': print "Available models : " for i in range(1, len(models_dir) + 1): print "\t(" + str(i) + ") " + models_dir[i - 1] ans = int(raw_input("Which model would you like to view : ")) model = SvmOutput(name=models_dir[ans - 1], readfile=True) model.showdata() else: roc_models = list() ans = 'y' while (ans == 'y') & (len(models_dir) > 0): print "Available models : " for i in range(1, len(models_dir) + 1): print "\t(" + str(i) + ") " + models_dir[i - 1] model_add = int( raw_input("Which model would you like to view : ")) roc_models.append( SvmOutput(name=models_dir[model_add - 1], readfile=True)) ans = raw_input( "Would you like to choose another model (y/n) : ") models_dir.pop(model_add - 1) vis.showROC(roc_models)