def parse_args():
parser = io_parser()
parser.add_argument("-base", type=str, default="", help="Base game to " + \
"be used for regret calculations. If unspecified, in-game " + \
"regrets are calculated.")
parser.add_argument("-r", metavar="REGRET", type=float, default=1e-3, \
help="Max allowed regret for approximate Nash equilibria.")
parser.add_argument("-d", metavar="DISTANCE", type=float, default=1e-3, \
help="L2-distance threshold to consider equilibria distinct.")
parser.add_argument("-c", metavar="CONVERGENCE", type=float, default=1e-8, \
help="Replicator dynamics convergence thrshold.")
parser.add_argument("-i", metavar="ITERATIONS", type=int, default=10000, \
help="Max replicator dynamics iterations.")
parser.add_argument("-s", metavar="SUPPORT", type=float, default=1e-3, \
help="Min probability for a strategy to be considered in support.")
parser.add_argument("--pure", action="store_true", help="compute " + \
"pure-strategy Nash equilibria")
parser.add_argument("--mixed", action="store_true", help="compute " + \
"mixed-strategy Nash equilibria")
args = parser.parse_args()
games = readGame(args.input)
if not isinstance(games, list):
games = [games]
try:
base_game = readGame(args.b)
except:
base_game = None
return games, base_game, args
def __init__(self, name, full=False):
TestbedObject.__init__(self, name, "games", "name", {"full":full})
self.game = readGame(self.json)
game.players[r] - 1)
full_profile[r][s] += 1
else:
full_profile[r] = FullGameProfile(reduced_profile[\
r], game.players[r])
profiles.append(Profile(full_profile))
return profiles
from GameIO import readGame, toJSON, io_parser
def parse_args():
parser = io_parser()
parser.add_argument("type", choices=["DPR", "HR", "TR"], help="Type " + \
"of reduction to perform.")
parser.add_argument("players", type=int, default=[], nargs="*", help= \
"Number of players in each reduced-game role.")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
game = readGame(args.input)
players = dict(zip(game.roles, args.players))
if args.type == "DPR":
print toJSON(DeviationPreservingReduction(game, players))
elif args.type == "HR":
print toJSON(HierarchicalReduction(game, players))
elif args.type == "TR":
print toJSON(TwinsReduction(game))
maximal=False
if new_sg in subgames or new_sg in maximal_subgames:
continue
if any([IsSubgame(new_sg, g) for g in \
subgames.union(maximal_subgames)]):
continue
subgames.add(new_sg)
if maximal:
sg = Subgame(full_game, sg.strategies)
if len(sg) > 0:
maximal_subgames.add(sg)
return sorted(maximal_subgames, key=len)
from GameIO import readGame, toJSON, io_parser
from json import dumps
def parse_args():
parser = io_parser()
parser.add_argument("-known", type=str, default="[]", help= \
"File with known complete subgames. Improves runtime.")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
game = readGame(args.input)
subgames = readGame(args.known)
print dumps(toJSON(*Cliques(game, subgames)))
