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from games import *
# Creating the game instances
f52 = Fig52Game()
ttt = TicTacToe()
def gen_state(to_move='X', x_positions=[], o_positions=[], h=3, v=3, k=3):
"""Given whose turn it is to move, the positions of X's on the board, the
positions of O's on the board, and, (optionally) number of rows, columns
and how many consecutive X's or O's required to win, return the corresponding
game state"""
moves = set([(x, y) for x in range(1, h + 1) for y in range(1, v + 1)]) \
- set(x_positions) - set(o_positions)
moves = list(moves)
board = {}
for pos in x_positions:
board[pos] = 'X'
for pos in o_positions:
board[pos] = 'O'
return GameState(to_move=to_move, utility=0, board=board, moves=moves)
def test_minimax_decision():
assert minimax_decision('A', f52) == 'a1'
assert minimax_decision('B', f52) == 'b1'
assert minimax_decision('C', f52) == 'c1'
assert minimax_decision('D', f52) == 'd3'
def test_alphabeta_search():
assert alphabeta_search('A', f52) == 'a1'
assert alphabeta_search('B', f52) == 'b1'
assert alphabeta_search('C', f52) == 'c1'
assert alphabeta_search('D', f52) == 'd3'
state = gen_state(to_move='X', x_positions=[(1, 1), (3, 3)],
o_positions=[(1, 2), (3, 2)])
assert alphabeta_search(state, ttt) == (2, 2)
state = gen_state(to_move='O', x_positions=[(1, 1), (3, 1), (3, 3)],
o_positions=[(1, 2), (3, 2)])
assert alphabeta_search(state, ttt) == (2, 2)
state = gen_state(to_move='O', x_positions=[(1, 1)],
o_positions=[])
assert alphabeta_search(state, ttt) == (2, 2)
state = gen_state(to_move='X', x_positions=[(1, 1), (3, 1)],
o_positions=[(2, 2), (3, 1)])
assert alphabeta_search(state, ttt) == (1, 3)
def test_random_tests():
assert Fig52Game().play_game(alphabeta_player, alphabeta_player) == 3
# The player 'X' (one who plays first) in TicTacToe never loses:
assert ttt.play_game(alphabeta_player, alphabeta_player) >= 0
# The player 'X' (one who plays first) in TicTacToe never loses:
assert ttt.play_game(alphabeta_player, random_player) >= 0