def run_explor(self):
"""環境の状態をセット"""
self.env.set_state()
"""MatchSet[M]を生成"""
self.match_set = XCSMatchSet(self.pop, self.env, self.actual_time)
"""MatchSet[M]に基いて,prediction array[PA]を生成"""
self.generate_prediction_array()
"""prediction array[PA]に基づいて行動選択"""
self.select_action()
"""選択した行動に基いて,ActionSet[A]を生成する."""
self.action_set = XCSActionSet(self.match_set, self.action, self.env,
self.actual_time)
"""行動を取る. 強化学習が働く"""
self.action_set.do_action()
"""ActionSet[A]内に対してパラメータ更新"""
self.action_set.update_action_set()
"""ActionSet[A]内でルールの包摂をする"""
self.action_set.do_action_set_subsumption(self.pop)
"""ActionSet[A]に対してGAを回す."""
self.run_GA()
if len(self.pop.cls) > conf.N:
self.pop.delete_from_population()
self.actual_time += 1.0
['40%', '50%', '60%', '70%', '80%', '90%', '100%', ''])
ax.grid()
filenamepng = "performance.png"
plt.savefig(filenamepng, dpi=150)
filenameeps = "performance.eps"
plt.savefig(filenameeps)
plt.show()
if __name__ == '__main__':
"""
print("main start")
xcs = XCSProgram()
print("initialized XCSProgram")
xcs.run_experiments()
"""
xcs = XCSProgram()
xcs.pop = XCSClassifierSet(xcs.env, 0.0)
xcs.init()
xcs.env.set_state()
xcs.match_set = XCSMatchSet(pop, xcs.env, 0.0)
xcs.generate_prediction_array()
xcs.match_set = XCSMatchSet(xcs.pop, xcs.env, 0.0)
xcs.generate_prediction_array()
xcs.select_action()
xcs.action_set = XCSActionSet(xcs.match_set, xcs.action, xcs.env, 0.0)
actset = xcs.action_set.get_cls()
actset = xcs.action_set.get_cls()
actset[0].get_cond()
