def test_normalformgame_constant_payoffs():
g = NormalFormGame((2, 2))
ok_(g.is_nash((0, 0)))
ok_(g.is_nash((0, 1)))
ok_(g.is_nash((1, 0)))
ok_(g.is_nash((1, 1)))
def __init__(self, data):
if isinstance(data, NormalFormGame):
if data.N != 2:
raise ValueError('input game must be a two-player game')
self.g = data
else: # data must be array_like
payoffs = np.asarray(data)
if not (payoffs.ndim in [2, 3]):
raise ValueError(
'input data must be a square matrix or a bimatrix')
self.g = NormalFormGame(payoffs)
self.N = self.g.N # Must be 2
self.players = self.g.players
self.nums_actions = self.g.nums_actions
self.tie_breaking = 'smallest'
self.current_actions = np.zeros(self.N, dtype=int)
self.belief_sizes = tuple(self.nums_actions[1 - i]
for i in range(self.N))
# Create instance variable `current_belief` for self.players
for player, belief_size in zip(self.players, self.belief_sizes):
player.current_belief = np.empty(belief_size)
self._decreasing_gain = lambda t: 1 / (t + 1)
self.step_size = self._decreasing_gain
class TestNormalFormGame_Asym2p:
"""Test the methods of NormalFormGame with asymmetric two players"""
def setUp(self):
"""Setup a NormalFormGame instance"""
matching_pennies_bimatrix = [[(1, -1), (-1, 1)], [(-1, 1), (1, -1)]]
self.g = NormalFormGame(matching_pennies_bimatrix)
def test_getitem(self):
assert_array_equal(self.g[1, 0], [-1, 1])
def test_is_nash_against_pure(self):
ok_(not self.g.is_nash((0, 0)))
def test_is_nash_against_mixed(self):
ok_(self.g.is_nash(([1 / 2, 1 / 2], [1 / 2, 1 / 2])))
class TestNormalFormGame_Sym2p:
"""Test the methods of NormalFormGame with symmetric two players"""
def setUp(self):
"""Setup a NormalFormGame instance"""
coordination_game_matrix = [[4, 0], [3, 2]]
self.g = NormalFormGame(coordination_game_matrix)
def test_getitem(self):
assert_array_equal(self.g[0, 1], [0, 3])
def test_is_nash_pure(self):
ok_(self.g.is_nash((0, 0)))
def test_is_nash_mixed(self):
ok_(self.g.is_nash(([2 / 3, 1 / 3], [2 / 3, 1 / 3])))
def __init__(self, data):
if isinstance(data, NormalFormGame):
if data.N != 2:
raise ValueError('input game must be a two-player game')
self.g = data
else: # data must be array_like
payoffs = np.asarray(data)
if not (payoffs.ndim in [2, 3]):
raise ValueError(
'input data must be a square matrix or a bimatrix'
)
self.g = NormalFormGame(payoffs)
self.N = self.g.N # Must be 2
self.players = self.g.players
self.nums_actions = self.g.nums_actions
self.tie_breaking = 'smallest'
self.current_actions = np.zeros(self.N, dtype=int)
self.belief_sizes = tuple(
self.nums_actions[1-i] for i in range(self.N)
)
# Create instance variable `current_belief` for self.players
for player, belief_size in zip(self.players, self.belief_sizes):
player.current_belief = np.empty(belief_size)
self._decreasing_gain = lambda t: 1 / (t+1)
self.step_size = self._decreasing_gain
def test_normalformgame_setitem_1p():
g = NormalFormGame(2)
eq_(g.N, 1) # Number of players
g[0] = 10 # Set payoff 10 for action 0
eq_(g.players[0].payoff_array[0], 10)
def test_normalformgame_input_action_sizes():
g = NormalFormGame((2, 3, 4))
eq_(g.N, 3) # Number of players
assert_array_equal(g.players[0].payoff_array, np.zeros((2, 3, 4)))
assert_array_equal(g.players[1].payoff_array, np.zeros((3, 4, 2)))
assert_array_equal(g.players[2].payoff_array, np.zeros((4, 2, 3)))
class TestNormalFormGame_3p:
"""Test the methods of NormalFormGame with three players"""
def setUp(self):
"""Setup a NormalFormGame instance"""
payoffs_2opponents = [[[3, 6], [4, 2]], [[1, 0], [5, 7]]]
player = Player(payoffs_2opponents)
self.g = NormalFormGame([player for i in range(3)])
def test_getitem(self):
assert_array_equal(self.g[0, 0, 1], [6, 4, 1])
def test_is_nash_pure(self):
ok_(self.g.is_nash((0, 0, 0)))
ok_(not self.g.is_nash((0, 0, 1)))
def test_is_nash_mixed(self):
p = (1 + np.sqrt(65)) / 16
ok_(self.g.is_nash(([1 - p, p], [1 - p, p], [1 - p, p])))
def test_normalformgame_setitem():
g = NormalFormGame((2, 2))
g[0, 0] = (0, 10)
g[0, 1] = (0, 10)
g[1, 0] = (3, 5)
g[1, 1] = (-2, 0)
assert_array_equal(g.players[0].payoff_array, [[0, 0], [3, -2]])
assert_array_equal(g.players[1].payoff_array, [[10, 5], [10, 0]])
class TestNormalFormGame_1p:
"""Test for degenerate NormalFormGame with a single player"""
def setUp(self):
"""Setup a NormalFormGame instance"""
data = [[0], [1], [1]]
self.g = NormalFormGame(data)
def test_construction(self):
"""Degenerate game: construction"""
ok_(self.g.N == 1)
assert_array_equal(self.g.players[0].payoff_array, [0, 1, 1])
def test_getitem(self):
"""Degenerate game: __getitem__"""
eq_(self.g[0], 0)
def test_is_nash_pure(self):
"""Degenerate game: is_nash with pure action"""
ok_(self.g.is_nash((1, )))
ok_(not self.g.is_nash((0, )))
def test_is_nash_mixed(self):
"""Degenerate game: is_nash with mixed action"""
ok_(self.g.is_nash(([0, 1 / 2, 1 / 2], )))
def test_normalformgame_invalid_input_payoff_profiles():
g = NormalFormGame(np.zeros((2, 2, 1)))
def test_normalformgame_invalid_input_nosquare_matrix():
g = NormalFormGame(np.zeros((2, 3)))
def test_normalformgame_invalid_input_players_num_inconsistent():
p0 = Player(np.zeros((2, 2, 2)))
p1 = Player(np.zeros((2, 2, 2)))
g = NormalFormGame([p0, p1])
class FictitiousPlay(object):
"""
Fictitious play with two players
Parameters
----------
data : array_like(float) or NormalFormGame
Attributes
----------
g : NormalFormGame
players : list(Player) # tuple
nums_actions : tuple(int)
current_beliefs : tuple(ndarray(float, ndim=1))
"""
def __init__(self, data):
if isinstance(data, NormalFormGame):
if data.N != 2:
raise ValueError('input game must be a two-player game')
self.g = data
else: # data must be array_like
payoffs = np.asarray(data)
if not (payoffs.ndim in [2, 3]):
raise ValueError(
'input data must be a square matrix or a bimatrix')
self.g = NormalFormGame(payoffs)
self.N = self.g.N # Must be 2
self.players = self.g.players
self.nums_actions = self.g.nums_actions
self.tie_breaking = 'smallest'
self.current_actions = np.zeros(self.N, dtype=int)
self.belief_sizes = tuple(self.nums_actions[1 - i]
for i in range(self.N))
# Create instance variable `current_belief` for self.players
for player, belief_size in zip(self.players, self.belief_sizes):
player.current_belief = np.empty(belief_size)
self._decreasing_gain = lambda t: 1 / (t + 1)
self.step_size = self._decreasing_gain
def __repr__(self):
msg = "Fictitious play for "
g_repr = self.g.__repr__()
msg += g_repr
return msg
def __str__(self):
return self.__repr__()
def set_init_actions(self, init_actions=None):
if init_actions is None:
init_actions = np.zeros(self.N, dtype=int)
for i, n in enumerate(self.nums_actions):
init_actions[i] = np.random.randint(n)
self.current_actions[:] = init_actions
# Initialize current_belief for each player
for i, player in enumerate(self.players):
player.current_belief[:] = \
pure2mixed(self.belief_sizes[i], init_actions[1-i])
@property
def current_beliefs(self):
return tuple(player.current_belief for player in self.players)
def play(self):
for i, player in enumerate(self.players):
self.current_actions[i] = \
player.best_response(player.current_belief,
tie_breaking=self.tie_breaking)
def update_beliefs(self, step_size):
for i, player in enumerate(self.players):
# x[i] = x[i] + step_size * (a[1-i] - x[i])
# = (1-step_size) * x[i] + step_size * a[1-i]
# where x[i] = player's current_belief,
# a[1-i] = opponent's current_action.
player.current_belief *= 1 - step_size
player.current_belief[self.current_actions[1 - i]] += step_size
def simulate(self, ts_length, init_actions=None):
beliefs_sequence = np.empty((ts_length, sum(self.nums_actions)))
beliefs_iter = self.simulate_iter(ts_length, init_actions)
for t, beliefs in enumerate(beliefs_iter):
(beliefs_sequence[t, :self.belief_sizes[0]],
beliefs_sequence[t, self.belief_sizes[0]:]) = beliefs
return (beliefs_sequence[:, :self.belief_sizes[0]],
beliefs_sequence[:, self.belief_sizes[0]:])
def simulate_iter(self, ts_length, init_actions=None):
self.set_init_actions(init_actions)
for t in range(ts_length):
yield self.current_beliefs
self.play()
self.update_beliefs(self.step_size(t + 1))
def replicate(self, T, num_reps, init_actions=None):
"""
Returns
-------
out : tuple(ndarray(float, ndim=2))
"""
out = np.empty((num_reps, sum(self.nums_actions)))
for j in range(num_reps):
beliefs_iter = self.simulate_iter(T + 1, init_actions)
for beliefs in beliefs_iter:
x = beliefs
out[j, :self.belief_sizes[0]], out[j, self.belief_sizes[0]:] = x
return out[:, :self.belief_sizes[0]], out[:, self.belief_sizes[0]:]
def test_normalformgame_input_action_sizes_1p():
g = NormalFormGame(2)
eq_(g.N, 1) # Number of players
assert_array_equal(g.players[0].payoff_array, np.zeros(2))
def setUp(self):
"""Setup a NormalFormGame instance"""
data = [[0], [1], [1]]
self.g = NormalFormGame(data)
def setUp(self):
'''Setup a FictitiousPlay instance'''
payoff_bimatrix = np.zeros((2, 3, 2)) # 2 x 3 game
g = NormalFormGame(payoff_bimatrix)
self.fp = FictitiousPlay(g)
def setUp(self):
"""Setup a NormalFormGame instance"""
matching_pennies_bimatrix = [[(1, -1), (-1, 1)], [(-1, 1), (1, -1)]]
self.g = NormalFormGame(matching_pennies_bimatrix)
def setUp(self):
"""Setup a NormalFormGame instance"""
payoffs_2opponents = [[[3, 6], [4, 2]], [[1, 0], [5, 7]]]
player = Player(payoffs_2opponents)
self.g = NormalFormGame([player for i in range(3)])
def setUp(self):
"""Setup a NormalFormGame instance"""
coordination_game_matrix = [[4, 0], [3, 2]]
self.g = NormalFormGame(coordination_game_matrix)
class FictitiousPlay(object):
"""
Fictitious play with two players
Parameters
----------
data : array_like(float) or NormalFormGame
Attributes
----------
g : NormalFormGame
players : list(Player) # tuple
nums_actions : tuple(int)
current_beliefs : tuple(ndarray(float, ndim=1))
"""
def __init__(self, data):
if isinstance(data, NormalFormGame):
if data.N != 2:
raise ValueError('input game must be a two-player game')
self.g = data
else: # data must be array_like
payoffs = np.asarray(data)
if not (payoffs.ndim in [2, 3]):
raise ValueError(
'input data must be a square matrix or a bimatrix'
)
self.g = NormalFormGame(payoffs)
self.N = self.g.N # Must be 2
self.players = self.g.players
self.nums_actions = self.g.nums_actions
self.tie_breaking = 'smallest'
self.current_actions = np.zeros(self.N, dtype=int)
self.belief_sizes = tuple(
self.nums_actions[1-i] for i in range(self.N)
)
# Create instance variable `current_belief` for self.players
for player, belief_size in zip(self.players, self.belief_sizes):
player.current_belief = np.empty(belief_size)
self._decreasing_gain = lambda t: 1 / (t+1)
self.step_size = self._decreasing_gain
def __repr__(self):
msg = "Fictitious play for "
g_repr = self.g.__repr__()
msg += g_repr
return msg
def __str__(self):
return self.__repr__()
def set_init_actions(self, init_actions=None):
if init_actions is None:
init_actions = np.zeros(self.N, dtype=int)
for i, n in enumerate(self.nums_actions):
init_actions[i] = np.random.randint(n)
self.current_actions[:] = init_actions
# Initialize current_belief for each player
for i, player in enumerate(self.players):
player.current_belief[:] = \
pure2mixed(self.belief_sizes[i], init_actions[1-i])
@property
def current_beliefs(self):
return tuple(player.current_belief for player in self.players)
def play(self):
for i, player in enumerate(self.players):
self.current_actions[i] = \
player.best_response(player.current_belief,
tie_breaking=self.tie_breaking)
def update_beliefs(self, step_size):
for i, player in enumerate(self.players):
# x[i] = x[i] + step_size * (a[1-i] - x[i])
# = (1-step_size) * x[i] + step_size * a[1-i]
# where x[i] = player's current_belief,
# a[1-i] = opponent's current_action.
player.current_belief *= 1 - step_size
player.current_belief[self.current_actions[1-i]] += step_size
def simulate(self, ts_length, init_actions=None):
beliefs_sequence = np.empty((ts_length, sum(self.nums_actions)))
beliefs_iter = self.simulate_iter(ts_length, init_actions)
for t, beliefs in enumerate(beliefs_iter):
(beliefs_sequence[t, :self.belief_sizes[0]],
beliefs_sequence[t, self.belief_sizes[0]:]) = beliefs
return (beliefs_sequence[:, :self.belief_sizes[0]],
beliefs_sequence[:, self.belief_sizes[0]:])
def simulate_iter(self, ts_length, init_actions=None):
self.set_init_actions(init_actions)
for t in range(ts_length):
yield self.current_beliefs
self.play()
self.update_beliefs(self.step_size(t+1))
def replicate(self, T, num_reps, init_actions=None):
"""
Returns
-------
out : tuple(ndarray(float, ndim=2))
"""
out = np.empty((num_reps, sum(self.nums_actions)))
for j in range(num_reps):
beliefs_iter = self.simulate_iter(T+1, init_actions)
for beliefs in beliefs_iter:
x = beliefs
out[j, :self.belief_sizes[0]], out[j, self.belief_sizes[0]:] = x
return out[:, :self.belief_sizes[0]], out[:, self.belief_sizes[0]:]