def generate_games(num): # pylint: disable=too-many-branches """Produce num random games per type""" np.random.seed(0) with open(path.join(_DIR, 'example_games', 'hard_nash.json')) as fil: yield 'hard', gamereader.load(fil).normalize() with open(path.join(_DIR, 'example_games', '2x2x2.nfg')) as fil: yield 'gambit', gamereader.load(fil).normalize() for _ in range(num): yield 'random', gamegen.game(*random_small()).normalize() for _ in range(num): strats = np.random.randint(2, 5, np.random.randint(2, 4)) yield 'covariant', gamegen.covariant_game(strats).normalize() for _ in range(num): strats = np.random.randint(2, 5, 2) yield 'zero sum', gamegen.two_player_zero_sum_game(strats).normalize() for _ in range(num): yield 'prisoners', gamegen.prisoners_dilemma().normalize() for _ in range(num): yield 'chicken', gamegen.sym_2p2s_game(0, 3, 1, 2).normalize() for _ in range(num): prob = np.random.random() yield 'mix', gamegen.sym_2p2s_known_eq(prob).normalize() for _ in range(num): strats = np.random.randint(2, 4) plays = np.random.randint(2, 4) yield 'polymatrix', gamegen.polymatrix_game(plays, strats).normalize() for _ in range(num): wins = np.random.random(3) + .5 loss = -np.random.random(3) - .5 yield 'roshambo', gamegen.rock_paper_scissors(wins, loss).normalize() yield 'shapley easy', gamegen.rock_paper_scissors(win=2).normalize() yield 'shapley normal', gamegen.rock_paper_scissors(win=1).normalize() yield 'shapley hard', gamegen.rock_paper_scissors(win=0.5).normalize() for _ in range(num): yield 'normagg small', gamegen.normal_aggfn(*random_agg_small()) for _ in range(num): yield 'polyagg small', gamegen.poly_aggfn(*random_agg_small()) for _ in range(num): yield 'sineagg small', gamegen.sine_aggfn(*random_agg_small()) for _ in range(num): facs = np.random.randint(2, 6) req = np.random.randint(1, facs) players = np.random.randint(2, 11) yield 'congestion', gamegen.congestion(players, facs, req) for _ in range(num): strats = np.random.randint(2, 6) players = np.random.randint(2, 11) yield 'local effect', gamegen.local_effect(players, strats) for _ in range(num): yield 'normagg large', gamegen.normal_aggfn(*random_agg_large()) for _ in range(num): yield 'polyagg large', gamegen.poly_aggfn(*random_agg_large()) for _ in range(num): yield 'sineagg large', gamegen.sine_aggfn(*random_agg_large()) for _ in range(num): agg = gamegen.sine_aggfn(*random_agg_small()) with warnings.catch_warnings(): warnings.simplefilter('ignore', UserWarning) yield 'rbf', learning.rbfgame_train(agg)
def test_pure_prisoners_dilemma(_): game = gamegen.prisoners_dilemma() eqa = nash.pure_nash(game) assert eqa.shape[0] == 1, "didn't find exactly one equilibria in pd" expected = [0, 2] assert np.all(expected == eqa), \ "didn't find pd equilibrium"
def test_prisonzers_dilemma(_): game = gamegen.prisoners_dilemma() assert game.is_complete(), "didn't generate a full game" assert game.is_symmetric(), \ "didn't generate a symmetric game" assert np.all(2 == game.num_players), \ "didn't generate correct number of strategies" assert np.all(2 == game.num_strategies), \ "didn't generate correct number of strategies"
def test_pure_prisoners_dilemma(): """Test prisoners dilemma""" game = gamegen.prisoners_dilemma() eqa = nash.pure_equilibria(game) assert eqa.shape[0] == 1, "didn't find exactly one equilibria in pd" expected = [0, 2] assert np.all(expected == eqa), \ "didn't find pd equilibrium"
def test_prisonzers_dilemma(_): """Test prisoners dilemma""" game = gamegen.prisoners_dilemma() assert game.is_complete(), "didn't generate a full game" assert game.is_symmetric(), \ "didn't generate a symmetric game" assert np.all(game.num_role_players == 2), \ "didn't generate correct number of strategies" assert np.all(game.num_role_strats == 2), \ "didn't generate correct number of strategies"
def test_mixed_prisoners_dilemma(methods): game = gamegen.prisoners_dilemma() eqa = nash.mixed_nash(game, dist_thresh=1e-3, processes=1, **methods) assert eqa.shape[0] >= 1, \ "didn't find at least one equilibria in pd {}".format(eqa) assert all(regret.mixture_regret(game, eqm) < 1e-3 for eqm in eqa), \ "returned equilibria with high regret" expected = [0, 1] assert np.isclose(eqa, expected, atol=1e-3, rtol=1e-3).all(1).any(), \ "didn't find pd equilibrium {}".format(eqa)