def continue_sampling(self, matrix):
if matrix.profile_dict == {}:
return True
game = matrix.toGame()
decision = False
equilibria = []
all_eq = []
for old_eq in self.old_equilibria:
new_eq = Nash.replicator_dynamics(game, old_eq, self.iters, self.converge_threshold)
decision = decision or linalg.norm(new_eq-old_eq, 2) > self.compare_threshold
distances = map(lambda e: linalg.norm(e-new_eq, 2), equilibria)
if Regret.regret(game, new_eq) <= self.regret_threshold and \
all([d >= self.dist_threshold for d in distances]):
equilibria.append(new_eq)
all_eq.append(new_eq)
for m in game.biasedMixtures() + [game.uniformMixture()] + \
[game.randomMixture() for __ in range(self.random_restarts)]:
eq = Nash.replicator_dynamics(game, m, self.iters, self.converge_threshold)
distances = map(lambda e: linalg.norm(e-eq,2), equilibria)
if Regret.regret(game, eq) <= self.regret_threshold and \
all([d >= self.dist_threshold for d in distances]):
equilibria.append(eq)
decision = True
all_eq.append(eq)
if len(equilibria) == 0:
decision = True
self.old_equilibria = [min(all_eq, key=lambda e: Regret.regret(game, e))]
else:
self.old_equilibria = equilibria
return decision
def continue_sampling(self, matrix):
if matrix.profile_dict == {}:
return True
game = matrix.toGame()
decision = True
for m in game.biasedMixtures() + [game.uniformMixture()] + \
[game.randomMixture() for __ in range(self.random_restarts)]:
eq = Nash.replicator_dynamics(game, m, self.iters, self.converge_threshold)
confidence_interval = self.ci_calculator.one_sided_interval(matrix, eq, self.alpha)
if confidence_interval < self.delta:
self.eq.append(eq)
decision = False
return decision
