def findEquilibria(self, method='CMAES'):
"""
Find all equilibria, using method
:param method: of computing equilibria
:type method: str, one of Game.METHODS
:return: list of NE, if not found returns None
:rtype: list of StrategyProfile
"""
if method == 'pne':
result = self.getPNE()
if len(result) == 0:
return None
else:
return result
elif self.num_players == 2 and method == 'support_enumeration':
result = support_enumeration.computeNE(self)
self.degenerate = self.isDegenerate()
if len(result) == 0:
return None
else:
return result
elif method == 'CMAES':
result = cmaes.fmin(self.LyapunovFunction, self.sum_shape)
elif method in self.METHODS:
result = scipy.optimize.minimize(self.LyapunovFunction,
np.random.rand(self.sum_shape),
method=method, tol=1e-10,
options={"maxiter": 1e3 * self.sum_shape ** 2})
logging.info(result)
if result.success:
r = [sp.StrategyProfile(result.x, self.shape)]
return r
else:
return None
def findEquilibria(self, method='CMAES'):
"""
Find all equilibria, using method
:param method: of computing equilibria
:type method: str, one of Game.METHODS
:return: list of NE, if not found returns None
:rtype: list of StrategyProfile
"""
if method == 'pne':
result = self.getPNE()
if len(result) == 0:
return None
else:
return result
elif self.num_players == 2 and method == 'support_enumeration':
result = support_enumeration.computeNE(self)
self.degenerate = self.isDegenerate()
if len(result) == 0:
return None
else:
return result
elif method == 'CMAES':
result = cmaes.fmin(self.LyapunovFunction, self.sum_shape)
elif method in self.METHODS:
result = scipy.optimize.minimize(self.LyapunovFunction,
np.random.rand(self.sum_shape),
method=method, tol=1e-10,
options={"maxiter": 1e3 * self.sum_shape ** 2})
logging.info(result)
if result.success:
r = [sp.StrategyProfile(result.x, self.shape)]
return r
else:
return None
def findEquilibria(self, method='CMAES'):
"""
Find all equilibria, using method
@params method method from Game.METHODS to be used
@return list of NE(list of probabilities), if not found return None
"""
if method == 'pne':
result = self.getPNE()
if len(result) == 0:
return None
else:
return result
elif self.num_players == 2 and method == 'support_enumeration':
result = support_enumeration.computeNE(self)
if len(result) == 0:
return None
else:
return result
elif method == 'CMAES':
result = cmaes.fmin(self.LyapunovFunction, self.sum_shape)
elif method in self.METHODS:
result = scipy.optimize.minimize(self.LyapunovFunction,
np.random.rand(self.sum_shape),
method=method, tol=1e-10,
options={"maxiter":1e3 * self.sum_shape ** 2})
logging.info(result)
if result.success:
r = []
r.append(result.x)
return r
else:
return None
