def deriveConstraints(game):
constraints = []
for p in range(2): # player
for s in range(len(game.players[p].strategies)):
for alt_s in range(len(game.players[p].strategies)):
if s == alt_s:
continue
constraint = [None for i in range(len(game.contingencies))]
for t in range(len(game.players[not p].strategies)):
if p == 0:
index = CorrEq.contingencyToIndex((s,t),len(game.players[1].strategies))
assert(constraint[index] == None)
constraint[index] = float(game._get_contingency(s,t)[p]) - float(game._get_contingency(alt_s,t)[p])
else:
index = CorrEq.contingencyToIndex((t,s),len(game.players[1].strategies))
assert(constraint[index] == None)
constraint[index] = float(game._get_contingency(t,s)[p]) - float(game._get_contingency(t,alt_s)[p])
constraint = [0 if i==None else i for i in constraint]
constraints.append(constraint)
return constraints
def testGame(file_path):
TEST_CASES = 100
game = gambit.read_game(file_path)
vertices, edges = CorrEq.findPolygon(game)
edges = CorrEq.removeExtraEdges(vertices, edges)
constraints = deriveConstraints(game)
points = CorrEq.orderVertices(edges)
if points == None:
points = vertices
for i in range(TEST_CASES):
# Pick a random probability distribution
probs = [random() for j in range(len(game.contingencies))]
# Ensure probabilities sum to 1
probs = [j / sum(probs) for j in probs]
# Determine whether the distribution is a correlated equilibrium
# It is if it satisfies the constraints in the LP
is_corr_eq = True
for j in constraints:
if sum(multiply(probs, j)) < 0:
is_corr_eq = False
break
u1, u2 = CorrEq.probsToUtilities(probs, game)
inside = pnpoly(u1, u2, points)
if is_corr_eq == True and inside == False:
raise Exception('Test failed on: ' + file_path)
print 'Test failed on: ' + file_path
def testGame(file_path):
TEST_CASES = 100
game = gambit.read_game(file_path)
vertices,edges = CorrEq.findPolygon(game)
edges = CorrEq.removeExtraEdges(vertices,edges)
constraints = deriveConstraints(game)
points = CorrEq.orderVertices(edges)
if points == None:
points = vertices
for i in range(TEST_CASES):
# Pick a random probability distribution
probs = [random() for j in range(len(game.contingencies))]
# Ensure probabilities sum to 1
probs = [j/sum(probs) for j in probs]
# Determine whether the distribution is a correlated equilibrium
# It is if it satisfies the constraints in the LP
is_corr_eq = True
for j in constraints:
if sum(multiply(probs,j)) < 0:
is_corr_eq = False
break
u1,u2 = CorrEq.probsToUtilities(probs, game)
inside = pnpoly(u1,u2,points)
if is_corr_eq == True and inside == False:
raise Exception('Test failed on: ' + file_path)
print 'Test failed on: ' + file_path
def deriveConstraints(game):
constraints = []
for p in range(2): # player
for s in range(len(game.players[p].strategies)):
for alt_s in range(len(game.players[p].strategies)):
if s == alt_s:
continue
constraint = [None for i in range(len(game.contingencies))]
for t in range(len(game.players[not p].strategies)):
if p == 0:
index = CorrEq.contingencyToIndex(
(s, t), len(game.players[1].strategies))
assert (constraint[index] == None)
constraint[index] = float(
game._get_contingency(s, t)[p]) - float(
game._get_contingency(alt_s, t)[p])
else:
index = CorrEq.contingencyToIndex(
(t, s), len(game.players[1].strategies))
assert (constraint[index] == None)
constraint[index] = float(
game._get_contingency(t, s)[p]) - float(
game._get_contingency(t, alt_s)[p])
constraint = [0 if i == None else i for i in constraint]
constraints.append(constraint)
return constraints
