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()