#!/usr/bin/env python
import traceback
from isolation import Board, game_as_text
from test_players import RandomPlayer, HumanPlayer, Player
import platform
import random
if platform.system() != ‘Windows’:
import resource
from time import time, sleep
def correctOpenEvalFn(yourOpenEvalFn):
print()
try:
sample_board = Board(RandomPlayer(), RandomPlayer())
# setting up the board as though we’ve been playing
board_state = [
[“11″, ” “, ” “, “22”, ” “, ” “, ” “],
[” “, ” “, ” “, ” “, ” “, ” “, ” “],
[” “, ” “, ” “, ” “, ” “, ” “, ” “],
[“12″, ” “, ” “, “21”, ” “, ” “, ” “],
[” “, ” “, ” “, ” “, ” “, ” “, ” “],
[” “, ” “, ” “, ” “, ” “, ” “, ” “],
[“13″, ” “, ” “, ” “, ” “, ” “, “23”]
]
sample_board.set_state(board_state, True)
#test = sample_board.get_legal_moves()
h = yourOpenEvalFn()
print(‘OpenMoveEvalFn Test: This board has a score of %s.’ % (h.score(sample_board, sample_board.get_active_player())))
except NotImplementedError:
print(‘OpenMoveEvalFn Test: Not implemented’)
except:
print(‘OpenMoveEvalFn Test: ERROR OCCURRED’)
print(traceback.format_exc())
print()
def beatRandom(yourAgent):
“””Example test you can run
to make sure your AI does better
than random.”””
print(“”)
try:
r = RandomPlayer()
p = yourAgent()
game = Board(r, p, 7, 7)
output_b = game.copy()
# assign a random move to each player before playing
for idx in range(2):
moves = game.get_active_moves()
random.shuffle(moves)
move = moves[0]
game, _, _ = game.forecast_move(move)
winner, move_history, termination = game.play_isolation(time_limit=6000, print_moves=True)
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()
def algorithmTest(yourAgent, algorithm, algorithm_name):
“””Example test to make sure
your algorithm 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 5×5 board
print(f”Running the {algorithm_name} test”)
print()
try:
def time_left(): # For these testing purposes, let’s ignore timeouts
return 10000
player = yourAgent() # using as a dummy player to create a board
sample_board = Board(player, RandomPlayer())
# setting up the board as though we’ve been playing
board_state = [
[“X”, “X”, “12”, ” “, “13”, “X”, ” “],
[“X”, “X”, ” “, ” “, ” “, ” “, “X”],
[“X”, ” “, “11”, ” “, ” “, ” “, “X”],
[“X”, ” “, ” “, “X”, ” “, “X”, ” “],
[” “, ” “, “22”, ” “, ” “, ” “, ” “],
[” “, ” “, ” “, ” “, “21”, ” “, “X”],
[“X”, “23”, ” “, ” “, “X”, ” “, “X”]
]
sample_board.set_state(board_state, p1_turn=True)
test_pass = True
expected_depth_scores = [(1, -16), (2, -16), (3, -7), (4, 1)]
for depth, exp_score in expected_depth_scores:
move, score = algorithm(player, sample_board, time_left, depth=depth, my_turn=True)
print(score)
if exp_score != score:
print(f”{algorithm_name} failed for depth: “, depth)
test_pass = False
else:
print(f”{algorithm_name} passed for depth: “, depth)
if test_pass:
player = yourAgent()
sample_board = Board(RandomPlayer(), player)
# setting up the board as though we’ve been playing
board_state = [
[“X”, “X”, “22”, ” “, ” “, “X”, ” “],
[” “, “”, ” “, ” “, ” “, ” “, “X”],
[” “, “11”, ” “, ” “, ” “, ” “, ” “],
[” “, “X”, ” “, “X”, ” “, ” “, ” “],
[“12″, ” “, ” “, “21”, ” “, “13”, “X”],
[” “, ” “, ” “, ” “, ” “, “X”, ” “],
[“X”, “23”, ” “, ” “, “”, “X”, “X”]
]
sample_board.set_state(board_state, p1_turn=False)
test_pass = True
expected_depth_scores = [(1, 6), (2, 5), (3, 5), (4, 2)]
for depth, exp_score in expected_depth_scores:
move, score = algorithm(player, sample_board, time_left, depth=depth, my_turn=True)
print(score)
if exp_score != score:
print(f”{algorithm_name} failed for depth: “, depth)
test_pass = False
else:
print(f”{algorithm_name} passed for depth: “, depth)
if test_pass:
print(f”{algorithm_name} Test: Runs Successfully!”)
else:
print(f”{algorithm_name} Test: Failed”)
except NotImplementedError:
print(f'{algorithm_name} Test: Not implemented’)
except:
print(f'{algorithm_name} Test: ERROR OCCURRED’)
print(traceback.format_exc())