def main():
parser = IO.io_parser('Create Data for Memory Experiments')
parser.add_argument('players', type=int, help='number of players')
parser.add_argument('strategies', type=int, help='number of strategies')
IO.sys.argv = IO.sys.argv[:3] + ["-input", None] + IO.sys.argv[3:]
args = parser.parse_args()
game = RandomGames.uniform_symmetric_game(args.players, args.strategies, 0, 100).to_asymmetric_game()
open(args.output + ".nfg", 'w').write(IO.to_NFG_asym(game))
def construct_game(input):
options = input.get('options', {})
if input['type'] == 'local_effect':
game = RandomGames.local_effect_game(**options)
RandomGames.rescale_payoffs(game)
elif input['type'] == 'congestion':
game = RandomGames.congestion_game(**options)
RandomGames.rescale_payoffs(game)
elif input['type'] == 'uniform':
game = RandomGames.uniform_symmetric_game(**options)
RandomGames.rescale_payoffs(game)
elif input['type'] == 'file':
game = Reductions.deviation_preserving_reduction(GameIO.read(input['file']), {'All': 6})
return game
def bootstrap_experiment(base_game_func, noise_model, statistic=regret, \
num_games=1000, stdevs=[1.,10.,100.], sample_sizes=[5,10,20, \
50,100,200,500], equilibrium_search=mixed_nash, bootstrap_args=[]):
results = [{s:{} for s in stdevs} for i in range(num_games)]
for i in range(num_games):
base_game = base_game_func()
RG.rescale_payoffs(base_game, 0, 100)
for stdev in stdevs:
sample_game = RG.add_noise(base_game, noise_model, stdev, \
sample_sizes[-1])
for sample_size in sample_sizes:
subsample_game = subsample(sample_game, sample_size)
equilibria = equilibrium_search(subsample_game)
results[i][stdev][sample_size] = [ \
{ \
"profile" : eq,
"statistic" : statistic(base_game, eq),
"bootstrap" : bootstrap(subsample_game, eq, statistic, \
*bootstrap_args)
} for eq in equilibria]
return results
