class WildPlay:
def __init__(self, AI=("random", "random"), AI_iteration=(0, 0)):
self.game = None
self.turn = 0
self.AI = AI
self.clf = None
self.AI_iteration = AI_iteration
def initialize(self):
if self.AI[0] == "sklearn" or self.AI[1] == "sklearn":
print("importing training_set_X")
from training_set_X_10000_1_test import X as training_set_X
from training_set_Y_10000_1_test import Y as training_set_Y
self.clf = svm.SVR(kernel='linear')
print("fitting sklearn")
self.clf.fit(training_set_X, training_set_Y)
def reset_game(self):
self.game = MagicCardGame()
self.game.initialize()
self.turn = 0
def get_current_choice(self):
if self.AI[self.game.get_current_player()] == "random":
## random choice
available_set = ()
if len(self.game.available_attackers()) != 0:
available_set += (2, )
elif len(self.game.available_play_cards()) != 0:
available_set += (1, )
else:
available_set += (3, )
inp = available_set[0]
if inp == 1:
card_id = random.choice(self.game.available_play_cards())
available_set += (card_id, )
target = None
if cards[card_id][3] is not None:
target = random.choice(
self.game.available_play_card_targets(card_id))
available_set += (target, )
elif inp == 2:
attacker_position = random.choice(
self.game.available_attackers())
target_position = random.choice(
self.game.available_attacking_targets(attacker_position))
available_set += (
attacker_position,
target_position,
)
elif inp == 3:
available_set += (None, None)
else:
print("Invalid option!")
return available_set, None
elif self.AI[self.game.get_current_player()] == "minimax":
## minimax choice
minimax = MinimaxWithSampling(self.game)
current_choice = minimax.search()
win_rate = minimax.max_utility
return current_choice, win_rate
elif self.AI_iteration[self.game.get_current_player()] > 0:
## sklearn AI choice
return SklearnAI(self.game, self.clf).get_best_choice()
else:
raise Exception("Invalid AI!")
def play_game(self, verbose=False):
if verbose:
self.game.show_state()
while not self.game.is_end():
self.turn += 1
self.game.start_turn()
if self.game.is_end():
break
inp = 0
while inp != 3:
## input("Enter any key to continue: ")
if verbose:
self.game.show_state()
self.game.show_available_play_cards()
self.game.show_available_attackers()
current_choice, win_rate = self.get_current_choice()
inp = current_choice[0]
if verbose:
print("Action:", inp)
if inp == 1:
card_id = current_choice[1]
if verbose:
print("Playing card:", cards[card_id][0])
target = current_choice[2]
if verbose:
print("Target =", target)
self.game.play_card(card_id, target=target)
if self.game.is_end():
break
elif inp == 2:
attacker_position = current_choice[1]
if verbose:
print("Attacker: ", attacker_position)
self.game.show_available_attacking_targets(
attacker_position)
target_position = current_choice[2]
if verbose:
print("Attacked target:", target_position)
self.game.attack(attacker_position, target_position)
if self.game.is_end():
break
elif inp == 3:
break
else:
print("Invalid option!")
if self.game.is_end():
break
self.game.end_turn()
if verbose:
self.game.show_state()
self.game.end_game()
print(" run_turns = %d, Winner is player %s!" %
(self.turn, self.game.get_winner()))
class SklearnIterator:
def __init__(self, training_size, AI="minimax", AI_iteration=0):
self.game = None
self.X = None # X is the state
self.Y = None # Y is the play choice
self.AI = AI
self.clf = None
self.AI_iteration = AI_iteration
self.training_size = training_size
self.training_count = [0]
def initialize(self):
if self.AI == "sklearn":
print("importing training_set_X")
from training_set_X_5000_0_test import X as training_set_X
from training_set_Y_5000_0_test import Y as training_set_Y
self.clf = svm.SVR(kernel='linear')
print("fitting sklearn")
self.clf.fit(training_set_X, training_set_Y)
self.X = open(
"C:\\Users\\Zhi Li\\Desktop\\CIS 667 Introduction to AI\\Project\\codes\\gaming part\\20181122 game with coin\\training_data\\training_set_X_%d_%d_test.py"
% (training_size, AI_iteration), "w+")
self.Y = open(
"C:\\Users\\Zhi Li\\Desktop\\CIS 667 Introduction to AI\\Project\\codes\\gaming part\\20181122 game with coin\\training_data\\training_set_Y_%d_%d_test.py"
% (training_size, AI_iteration), "w+")
self.X.write("X = [\n")
self.Y.write("Y = [\n")
def reset_game(self):
self.game = MagicCardGame()
self.game.initialize()
def get_current_choice(self):
if randint(0, 9) < 8:
## random choice
available_set = ()
if len(self.game.available_attackers()) != 0:
available_set += (2, )
elif len(self.game.available_play_cards()) != 0:
available_set += (1, )
else:
available_set += (3, )
inp = available_set[0]
if inp == 1:
card_id = random.choice(self.game.available_play_cards())
available_set += (card_id, )
target = None
if cards[card_id][3] is not None:
target = random.choice(
self.game.available_play_card_targets(card_id))
available_set += (target, )
elif inp == 2:
attacker_position = random.choice(
self.game.available_attackers())
target_position = random.choice(
self.game.available_attacking_targets(attacker_position))
available_set += (
attacker_position,
target_position,
)
elif inp == 3:
available_set += (None, None)
else:
print("Invalid option!")
return available_set, None
elif self.AI == "minimax":
## minimax choice
minimax = MinimaxWithSampling(self.game,
self.X,
self.Y,
training_count=self.training_count)
current_choice = minimax.search()
win_rate = minimax.max_utility
return current_choice, win_rate
elif self.AI_iteration > 0:
## sklearn AI choice
return SklearnAI(
self.game,
self.clf,
self.X,
self.Y,
training_count=self.training_count).get_best_choice()
else:
raise Exception("Invalid AI!")
def play_game(self, verbose=False):
if verbose:
self.game.show_state()
while not self.game.is_end():
self.game.start_turn()
if self.game.is_end():
break
inp = 0
while inp != 3:
## input("Enter any key to continue: ")
if verbose:
self.game.show_state()
self.game.show_available_play_cards()
self.game.show_available_attackers()
current_choice, win_rate = self.get_current_choice()
inp = current_choice[0]
if verbose:
print("Action:", inp)
if inp == 1:
card_id = current_choice[1]
if verbose:
print("Playing card:", cards[card_id][0])
target = current_choice[2]
if verbose:
print("Target =", target)
self.game.play_card(card_id, target=target)
if self.game.is_end():
break
elif inp == 2:
attacker_position = current_choice[1]
if verbose:
print("Attacker: ", attacker_position)
self.game.show_available_attacking_targets(
attacker_position)
target_position = current_choice[2]
if verbose:
print("Attacked target:", target_position)
self.game.attack(attacker_position, target_position)
if self.game.is_end():
break
elif inp == 3:
break
else:
print("Invalid option!")
if self.game.is_end():
break
self.game.end_turn()
if verbose:
self.game.show_state()
self.game.end_game()
print("Winner is player %s!" % self.game.get_winner())
print("training_count = %d" % self.training_count[0])
return self.training_count[0]
def end_game(self):
self.X.write("\n]")
self.Y.write("\n]")
self.X.close()
self.Y.close()
class SklearnIterator:
def __init__(self, AI = "minimax", AI_iteration = 0):
self.game = None
self.X = None # X is the state
self.Y = None # Y is the play choice
self.AI = AI
self.clf = None
self.AI_iteration = AI_iteration
def initialize(self):
if self.AI == "sklearn":
print("importing training_set_X")
from training_set_X0 import X as training_set_X
from training_set_Y0 import Y as training_set_Y
self.clf = svm.SVR(kernel = 'linear')
print("fitting sklearn")
self.clf.fit(training_set_X, training_set_Y)
self.X = open("C:\\Users\\Zhi Li\\Desktop\\CIS 667 Introduction to AI\\Project\\codes\\gaming part\\20181122 game with coin\\training_data\\training_set_X 50 %d.py" % AI_iteration, "w+")
self.Y = open("C:\\Users\\Zhi Li\\Desktop\\CIS 667 Introduction to AI\\Project\\codes\\gaming part\\20181122 game with coin\\training_data\\training_set_Y 50 %d.py" % AI_iteration, "w+")
self.X.write("X = [\n")
self.Y.write("Y = [\n")
def reset_game(self):
self.game = MagicCardGame()
self.game.initialize()
def get_current_choice(self):
lst = [0] * (Constants.AVAILABLE_CARDS + Constants.PLAYER_MINION_UPPERBOUND * 4 + 3 + 1)
## hand0(134), target0(14), battlefield0(7),battlefield_target0(7) hero0,h1,h1, end_turn
if self.AI == "minimax":
minimax = MinimaxWithSampling(self.game, self.X, self.Y)
current_choice = minimax.search()
win_rate = minimax.max_utility
return current_choice, win_rate
elif self.AI_iteration > 0:
return SklearnAI(self.game, self.clf, self.X, self.Y).get_best_choice()
else:
raise Exception ("Invalid AI!")
def play_game(self, verbose = False):
if verbose:
self.game.show_state()
while not self.game.is_end():
self.game.start_turn()
if self.game.is_end():
break
inp = 0
while inp != 3:
## input("Enter any key to continue: ")
if verbose:
self.game.show_state()
self.game.show_available_play_cards()
self.game.show_available_attackers()
current_choice, win_rate = self.get_current_choice()
inp = current_choice[0]
if verbose:
print("Action:", inp)
if inp == 1:
card_id = current_choice[1]
if verbose:
print("Playing card:", cards[card_id][0])
if cards[card_id][3] is not None:
if verbose:
self.game.show_available_play_card_targets(card_id)
target = current_choice[2]
else:
target = None
if verbose:
print("Target =", target)
self.game.play_card(card_id, target = target)
if self.game.is_end():
break
elif inp == 2:
attacker_position = current_choice[1]
if verbose:
print("Attacker: ", attacker_position)
self.game.show_available_attacking_targets(attacker_position)
target_position = current_choice[2]
if verbose:
print("Attacked target:", target_position)
self.game.attack(attacker_position, target_position)
if self.game.is_end():
break
elif inp == 3:
break
else:
print("Invalid option!")
if self.game.is_end():
break
self.game.end_turn()
if verbose:
self.game.show_state()
self.game.end_game()
print("Winner is player %s!" % self.game.get_winner())
def end_game(self):
self.X.write("\n]")
self.Y.write("\n]")
self.X.close()
self.Y.close()
class PlayMinimaxSamplingWithRandom:
def __init__(self):
self.game = MagicCardGame()
def play(self, AI_player=0, verbose=False):
self.game.initialize()
if verbose:
self.game.show_state()
r = 0
while not self.game.is_end():
r += 1
self.game.start_turn()
if self.game.is_end():
break
inp = -10
while inp != 3:
if verbose:
## input("Enter any key to continue: ")
self.game.show_state()
self.game.show_available_play_cards()
self.game.show_available_attackers()
available_set = ()
if len(self.game.available_play_cards()) != 0:
available_set += (1, )
if len(self.game.available_attackers()) != 0:
available_set += (2, )
available_set += (3, )
minimax_result = MinimaxWithSampling(self.game).search()
inp = minimax_result[0]
if verbose:
print("Action:", inp)
if inp == 1:
card_id = minimax_result[1]
if verbose:
print("Playing card:", cards[card_id][0])
if cards[card_id][3] is not None:
if verbose:
self.game.show_available_play_card_targets(card_id)
target = minimax_result[2]
else:
target = None
if verbose:
print("Target =", target)
self.game.play_card(card_id, target=target)
if self.game.is_end():
break
elif inp == 2:
attacker_position = minimax_result[1]
if verbose:
print("Attacker: ", attacker_position)
self.game.show_available_attacking_targets(
attacker_position)
target_position = minimax_result[2]
if verbose:
print("Attacked target:", target_position)
self.game.attack(attacker_position, target_position)
if self.game.is_end():
break
elif inp == 3:
break
else:
print("Invalid option!")
if self.game.is_end():
break
self.game.end_turn()
if verbose:
self.game.show_state()
self.game.end_game()
print("round = %s" % r, end=" ")
return self.game.get_winner()
class RandomSamplingPlay:
def __init__(self, verbose=False):
self.game = MagicCardGame()
self.verbose = verbose
self.winner = -10000
def initialize(self):
self.game.initialize()
def set_state(self, new_game):
self.game = new_game
def get_winner(self):
return self.winner
def start_game(self):
if self.verbose:
self.game.show_state()
while not self.game.is_end():
self.game.start_turn()
if self.game.is_end():
break
inp = 0
while inp != 3:
## input("Enter any key to continue: ")
if self.verbose:
self.game.show_state()
self.game.show_available_play_cards()
self.game.show_available_attackers()
available_set = ()
if len(self.game.available_play_cards()) != 0:
available_set += (1, )
if len(self.game.available_attackers()) != 0:
available_set += (2, )
available_set += (3, )
inp = available_set[0]
if self.verbose:
print("Action:", inp)
if inp == 1:
card_id = random.choice(self.game.available_play_cards())
if self.verbose:
print("Playing card:", cards[card_id][0])
if cards[card_id][3] is not None:
if self.verbose:
self.game.show_available_play_card_targets(card_id)
target = random.choice(
self.game.available_play_card_targets(card_id))
else:
target = None
if self.verbose:
print("Target =", target)
self.game.play_card(card_id, target=target)
if self.game.is_end():
break
elif inp == 2:
attacker_position = random.choice(
self.game.available_attackers())
if self.verbose:
print("Attacker: ", attacker_position)
self.game.show_available_attacking_targets(
attacker_position)
target_position = random.choice(
self.game.available_attacking_targets(
attacker_position))
if self.verbose:
print("Attacked target:", target_position)
self.game.attack(attacker_position, target_position)
if self.game.is_end():
break
elif inp == 3:
break
else:
print("Invalid option!")
if self.game.is_end():
break
self.game.end_turn()
if self.verbose:
self.game.show_state()
self.game.end_game()
self.winner = self.game.get_winner()
class TrainingDataGenerator:
def __init__(self, AI="minimax"):
self.game = None
self.X = None # X is the state
self.Y = None # Y is the play choice
self.AI = AI
self.clf = None
def initialize(self):
if self.AI == "sklearn":
self.clf = svm.SVR(kernel='linear')
print("fitting sklearn")
self.clf.fit(training_set_X, training_set_Y)
self.X = open(
"C:\\Users\\Zhi Li\\Desktop\\CIS 667 Introduction to AI\\Project\\codes\\gaming part\\20181122 game with coin\\training_data\\training_set_X150.py",
"w+")
self.Y = open(
"C:\\Users\\Zhi Li\\Desktop\\CIS 667 Introduction to AI\\Project\\codes\\gaming part\\20181122 game with coin\\training_data\\training_set_Y150.py",
"w+")
self.X.write("X = [\n")
self.Y.write("Y = [\n")
def reset_game(self):
self.game = MagicCardGame()
self.game.initialize()
def get_random_choice(self):
lst = [0] * (Constants.AVAILABLE_CARDS +
Constants.PLAYER_MINION_UPPERBOUND * 4 + 3 + 1)
## hand0(134), target0(14), battlefield0(7),battlefield_target0(7) hero0,h1,h1, end_turn
new_game_state, random_choice = RandomChoiceGenerator(
self.game).get_random_play()
if random_choice[0] == 1:
lst[random_choice[1] - 1] = 1
if random_choice[2] is not None:
if random_choice[2] < 0:
if random_choice[2] == Constants.PLAYER_NICKNAME[0]:
lst[Constants.AVAILABLE_CARDS +
Constants.PLAYER_MINION_UPPERBOUND * 2 + 1 - 1] = 1
else:
lst[Constants.AVAILABLE_CARDS +
Constants.PLAYER_MINION_UPPERBOUND * 2 + 2 - 1] = 1
elif random_choice[
2] < self.game.state.battlefield.card0_length:
lst[Constants.AVAILABLE_CARDS + random_choice[2]] = 1
elif random_choice[2] < len(
self.game.state.battlefield.card_states):
lst[Constants.AVAILABLE_CARDS +
Constants.PLAYER_MINION_UPPERBOUND +
random_choice[2]] = 1
else:
raise Exception("Wrong target! target = %s" %
random_choice[2])
elif random_choice[0] == 2:
if self.game.get_current_player() == 0:
lst[Constants.AVAILABLE_CARDS +
Constants.PLAYER_MINION_UPPERBOUND * 2 + 2 +
random_choice[1]] = 1
if random_choice[2] < 0:
lst[Constants.AVAILABLE_CARDS +
Constants.PLAYER_MINION_UPPERBOUND * 4 + 3 - 1] = 1
else:
target = random_choice[
2] - self.game.state.battlefield.card0_length
lst[Constants.AVAILABLE_CARDS +
Constants.PLAYER_MINION_UPPERBOUND * 3 + 2 +
target] = 1
elif self.game.get_current_player() == 1:
attacker = random_choice[
2] - self.game.state.battlefield.card0_length
lst[Constants.AVAILABLE_CARDS +
Constants.PLAYER_MINION_UPPERBOUND * 2 + 2 + attacker] = 1
if random_choice[2] < 0:
lst[Constants.AVAILABLE_CARDS +
Constants.PLAYER_MINION_UPPERBOUND * 4 + 3 - 1] = 1
else:
lst[Constants.AVAILABLE_CARDS +
Constants.PLAYER_MINION_UPPERBOUND * 3 + 2 +
random_choice[2]] = 1
else:
raise Exception("Error!")
elif random_choice[0] == 3:
lst[-1] = 1
else:
raise Exception("Wrong input type! type = %s" % random_choice[0])
if self.AI == "minimax":
return lst, RandomSamplingFromState(new_game_state).do_sampling()
elif self.AI == "sklearn":
return lst, -1
else:
raise Exception("Invalid AI!")
def play_game(self, verbose=False):
if verbose:
self.game.show_state()
while not self.game.is_end():
self.game.start_turn()
if self.game.is_end():
break
inp = 0
while inp != 3:
## input("Enter any key to continue: ")
if verbose:
self.game.show_state()
self.game.show_available_play_cards()
self.game.show_available_attackers()
state_lst = list(self.game.get_state_tuple())
choice_list, win_rate = self.get_random_choice()
training_X = state_lst + choice_list
self.X.write(str(training_X))
self.X.write(",\n")
if self.AI == "sklearn":
win_rate = self.clf.predict([training_X])
self.Y.write(str(win_rate))
self.Y.write(",\n")
available_set = ()
if len(self.game.available_play_cards()) != 0:
available_set += (1, )
if len(self.game.available_attackers()) != 0:
available_set += (2, )
available_set += (3, )
inp = available_set[0]
if verbose:
print("Action:", inp)
if inp == 1:
card_id = random.choice(self.game.available_play_cards())
if verbose:
print("Playing card:", cards[card_id][0])
if cards[card_id][3] is not None:
if verbose:
self.game.show_available_play_card_targets(card_id)
target = random.choice(
self.game.available_play_card_targets(card_id))
else:
target = None
if verbose:
print("Target =", target)
self.game.play_card(card_id, target=target)
if self.game.is_end():
break
elif inp == 2:
attacker_position = random.choice(
self.game.available_attackers())
if verbose:
print("Attacker: ", attacker_position)
self.game.show_available_attacking_targets(
attacker_position)
target_position = random.choice(
self.game.available_attacking_targets(
attacker_position))
if verbose:
print("Attacked target:", target_position)
self.game.attack(attacker_position, target_position)
if self.game.is_end():
break
elif inp == 3:
break
else:
print("Invalid option!")
if self.game.is_end():
break
self.game.end_turn()
if verbose:
self.game.show_state()
self.game.end_game()
print("Winner is player %s!" % self.game.get_winner())
def end_game(self):
self.X.write("\n]")
self.Y.write("\n]")
self.X.close()
self.Y.close()