def setUp(self):
# From von Stengel 2007 in Algorithmic Game Theory
bimatrix = [[(3, 3), (3, 2)],
[(2, 2), (5, 6)],
[(0, 3), (6, 1)]]
g = NormalFormGame(bimatrix)
# Original best reponse polytope for player 0
vertices_P = np.array([
[0, 0, 0], # 0
[1/3, 0, 0], # a
[2/7, 1/14, 0], # b
[0, 1/6, 0], # c
[0, 1/8, 1/4], # d
[0, 0, 1/3] # e
])
labelings_P = np.array([
[0, 1, 2], # 0
[1, 2, 3], # a
[2, 3, 4], # b
[0, 2, 4], # c
[0, 3, 4], # d
[0, 1, 3] # e
])
# Sort rows lexicographically
K = labelings_P.shape[1]
ind = np.lexsort([labelings_P[:, K-k-1] for k in range(K)])
self.labelings_P = labelings_P[ind]
self.vertices_P = vertices_P[ind]
# Translated best reponse polytope for player 0
self.brp0 = _BestResponsePolytope(g.players[1], idx=0)
def setup(self):
# From von Stengel 2007 in Algorithmic Game Theory
bimatrix = [[(3, 3), (3, 2)], [(2, 2), (5, 6)], [(0, 3), (6, 1)]]
g = NormalFormGame(bimatrix)
# Original best response polytope for player 0
vertices_P = np.array([
[0, 0, 0], # 0
[1 / 3, 0, 0], # a
[2 / 7, 1 / 14, 0], # b
[0, 1 / 6, 0], # c
[0, 1 / 8, 1 / 4], # d
[0, 0, 1 / 3] # e
])
labelings_P = np.array([
[0, 1, 2], # 0
[1, 2, 3], # a
[2, 3, 4], # b
[0, 2, 4], # c
[0, 3, 4], # d
[0, 1, 3] # e
])
# Sort rows lexicographically
K = labelings_P.shape[1]
ind = np.lexsort([labelings_P[:, K - k - 1] for k in range(K)])
self.labelings_P = labelings_P[ind]
self.vertices_P = vertices_P[ind]
# Translated best response polytope for player 0
self.brp0 = _BestResponsePolytope(g.players[1], idx=0)
def test_best_response_polytope_invalid_player_instance():
bimatrix = [[(3, 3), (3, 2)],
[(2, 2), (5, 6)],
[(0, 3), (6, 1)]]
g = NormalFormGame(bimatrix)
_BestResponsePolytope(g)
def test_best_response_polytope_invalid_player_instance():
bimatrix = [[(3, 3), (3, 2)], [(2, 2), (5, 6)], [(0, 3), (6, 1)]]
g = NormalFormGame(bimatrix)
_BestResponsePolytope(g)