def testUtility():
try:
sample_board = Board(RandomPlayer(), RandomPlayer())
# setting up the board as though we've been playing
sample_board.move_count = 4
sample_board.__board_state__ = [[11, 0, 0, 0, 21, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 22, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 12, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]]
sample_board.__last_queen_move__ = {
sample_board.queen_11: (0, 0),
sample_board.queen_12: (4, 5),
sample_board.queen_21: (0, 4),
sample_board.queen_22: (2, 2)
}
test = sample_board.get_legal_moves()
h = OpenMoveEvalFn()
print 'OpenMoveEvalFn Test: This board has a score of %s.' % (
h.score(sample_board))
sample_board.print_board()
except NotImplementedError:
print 'OpenMoveEvalFn Test: Not implemented'
except:
print 'OpenMoveEvalFn Test: ERROR OCCURRED'
print traceback.format_exc()
def main():
try:
sample_board = Board(RandomPlayer(), RandomPlayer())
# setting up the board as though we've been playing
sample_board.move_count = 1
sample_board.__board_state__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 'Q', 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]
]
sample_board.__last_queen_move__ = (3,3)
test = sample_board.get_legal_moves()
#h = OpenMoveEvalFn()
h = CustomEvalFn()
print 'OpenMoveEvalFn Test: This board has a score of %s.' % (h.score(sample_board))
except NotImplementedError:
print 'OpenMoveEvalFn Test: Not implemented'
except:
print 'OpenMoveEvalFn Test: ERROR OCCURRED'
print traceback.format_exc()
try:
"""Example test to make sure
your minimax works, using the
#computer_player_moves."""
# create dummy 5x5 board
p1 = CustomPlayer()
p2 = CustomPlayer(search_depth=3)
#p2 = HumanPlayer()
b = Board(p1, p2, 5, 5)
b.__board_state__ = [
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 'Q', 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]
]
b.__last_queen_move__ = (2, 2)
b.move_count = 1
output_b = b.copy()
winner, move_history, termination = b.play_isolation_name_changed()
print 'Minimax Test: Runs Successfully'
print winner
# Uncomment to see example game
#print game_as_text(winner, move_history, termination, output_b)
except NotImplementedError:
print 'Minimax Test: Not Implemented'
except:
print 'Minimax Test: ERROR OCCURRED'
print traceback.format_exc()
"""Example test you can run
to make sure your AI does better
than random."""
try:
r = CustomPlayer_1(8)
# h = RandomPlayer()
h = CustomPlayer()
#r = RandomPlayer()
game = Board(r, h, 7, 7)
output_b = game.copy()
winner, move_history, termination = game.play_isolation_name_changed()
if 'CustomPlayer' in str(winner):
print 'CustomPlayer Test: CustomPlayer Won'
else:
print 'CustomPlayer Test: CustomPlayer Lost'
# Uncomment to see game
print game_as_text(winner, move_history, termination, output_b)
except NotImplementedError:
print 'CustomPlayer Test: Not Implemented'
except:
print 'CustomPlayer Test: ERROR OCCURRED'
print traceback.format_exc()
def main():
try:
sample_board = Board(RandomPlayer(), RandomPlayer())
# setting up the board as though we've been playing
sample_board.move_count = 4
sample_board.__board_state__ = [[11, 0, 0, 0, 21, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 22, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 12, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]]
sample_board.__last_queen_move__ = {
sample_board.queen_11: (0, 0),
sample_board.queen_12: (4, 5),
sample_board.queen_21: (0, 4),
sample_board.queen_22: (2, 2)
}
test = sample_board.get_legal_moves()
h = OpenMoveEvalFn()
print 'OpenMoveEvalFn Test: This board has a score of %s.' % (
h.score(sample_board))
except NotImplementedError:
print 'OpenMoveEvalFn Test: Not implemented'
except:
print 'OpenMoveEvalFn Test: ERROR OCCURRED'
print traceback.format_exc()
try:
"""Example test to make sure
your minimax works, using the
OpenMoveEvalFunction evaluation function.
This can be used for debugging your code
with different model Board states.
Especially important to check alphabeta
pruning"""
# create dummy 5x5 board
p1 = RandomPlayer()
p2 = HumanPlayer()
b = Board(p1, p2, 5, 5)
b.__board_state__ = [[0, 0, 0, 0, 0], [0, 0, 0, 22, 0],
[0, 0, 0, 11, 0], [0, 0, 0, 21, 12],
[0, 0, 0, 0, 0]]
b.__last_queen_move__["queen11"] = (2, 3)
b.__last_queen_move__["queen12"] = (3, 4)
b.__last_queen_move__["queen21"] = (3, 3)
b.__last_queen_move__["queen22"] = (1, 3)
b.move_count = 4
output_b = b.copy()
legal_moves = b.get_legal_moves()
winner, move_history, termination = b.play_isolation()
print 'Minimax Test: Runs Successfully'
# Uncomment to see example game
print game_as_text(winner, move_history, termination, output_b)
except NotImplementedError:
print 'Minimax Test: Not Implemented'
except:
print 'Minimax Test: ERROR OCCURRED'
print traceback.format_exc()
"""Example test you can run
to make sure your AI does better
than random."""
try:
r = RandomPlayer()
h = CustomPlayer()
game = Board(r, h, 7, 7)
output_b = game.copy()
winner, move_history, termination = game.play_isolation()
print game_as_text(winner, move_history, termination, output_b)
if 'CustomPlayer' in str(winner):
print 'CustomPlayer Test: CustomPlayer Won'
else:
print 'CustomPlayer Test: CustomPlayer Lost'
# Uncomment to see game
# print game_as_text(winner, move_history, termination, output_b)
except NotImplementedError:
print 'CustomPlayer Test: Not Implemented'
except:
print 'CustomPlayer Test: ERROR OCCURRED'
print traceback.format_exc()
def main():
print ""
try:
sample_board = Board(RandomPlayer(), RandomPlayer())
# setting up the board as though we've been playing
sample_board.move_count = 2
sample_board.__board_state__ = [
["Q1", " ", " ", " ", " ", " ", " "],
[ " ", " ", " ", " ", " ", " ", " "],
[ " ", " ", " ", " ", " ", " ", " "],
[ " ", " ", " ","Q2", " ", " ", " "],
[ " ", " ", " ", " ", " ", " ", " "],
[ " ", " ", " ", " ", " ", " ", " "],
[ " ", " ", " ", " ", " ", " ", " "]
]
sample_board.__last_queen_move__ = {sample_board.__queen_1__: (0, 0, False), \
sample_board.__queen_2__: (3, 3, False)}
test = sample_board.get_legal_moves()
h = OpenMoveEvalFn()
print 'OpenMoveEvalFn Test: This board has a score of %s.' % (h.score(sample_board))
except NotImplementedError:
print 'OpenMoveEvalFn Test: Not implemented'
except:
print 'OpenMoveEvalFn Test: ERROR OCCURRED'
print traceback.format_exc()
print ""
"""
try:
#Example test to make sure
#your minimax works, using the
#OpenMoveEvalFunction evaluation function.
#This can be used for debugging your code
#with different model Board states.
#Especially important to check alphabeta
#pruning
# create dummy 5x5 board
b = Board(RandomPlayer(), HumanPlayer(), 5, 5)
b.__board_state__ = [
[" ", " " , " ", " ", " "],
[" ", " ", " ", " ", " "],
[" ", " ", " ","Q1", " "],
[" ", " ", " ","Q2", " "],
[" ", " " , " ", " ", " "]
]
b.__last_queen_move__[b.__queen_1__] = (2, 3, False)
b.__last_queen_move__[b.__queen_2__] = (3, 3, False)
b.move_count = 2
output_b = b.copy()
legal_moves=b.get_legal_moves()
winner, move_history, termination = b.play_isolation(
time_limit=100000, print_moves=True)
print 'Minimax Test: Runs Successfully'
# Uncomment to see example game
#insert in reverse order
#initial_turn = [(2, 3, False), (3, 3, False)]
#move_history.insert(0, initial_turn)
#print game_as_text(winner, move_history, termination, output_b)
except NotImplementedError:
print 'Minimax Test: Not Implemented'
except:
print 'Minimax Test: ERROR OCCURRED'
print traceback.format_exc()
"""
"""Example test you can run
to make sure your AI does better
than random."""
win = 0
loss = 0
count = 0
for i in range(1, 51):
print ""
try:
r = RandomPlayer()
#r = CustomPlayer(search_depth=10)
h = CustomPlayer(search_depth=12)
game = Board(h, r, 7, 7)
output_b = game.copy()
winner, move_history, termination = game.play_isolation(time_limit=1000, print_moves=True)
if winner == game.__queen_1__:
win += 1.
else:
loss += 1.
count += 1
print "\n(",winner,") has won. Reason: ", termination
# Uncomment to see game
# print game_as_text(winner, move_history, termination, output_b)
except NotImplementedError:
print 'CustomPlayer Test: Not Implemented'
except:
print 'CustomPlayer Test: ERROR OCCURRED'
print traceback.format_exc()
print("total " + str(count) + " games", "Q1 win ratio: " + str(win/(win + loss)))
