class Machine_Learning_bool: def __init__(self,values,all_data,**kwargs): self.period = kwargs.get('period', 0.005) self.start_time = values['time'] self.previous_time = values['time'] self.previous_be = values['be'] self.duration = kwargs.get('duration', float('inf')) self.previous_vel = values['av'] sys.path.insert(0, "../../Machine_Learning") from ml import ML self.ml = ML("../../Machine_Learning/ MACHINE_LEARNING_FILE ") def algo(sefl,values,all_data): time.sleep(0.01) if values['time'] - self.start_time < self.duration: torso_bool, legs_bool = values['t_move'], values['l_move'] action = self.ml.get_action([ values['be'], values['av'], torso_bool, legs_bool ]) print values['time'], values['be'], values['av'], action if (action == 0 and not legs_bool): return ["legs_extended", 0.8] elif (action == 1 and not legs_bool): return ["legs_retracted", 0.8] elif (action = 2 and not torso_bool): return ["torso_retracted"] elif (action == 3 and not torso_bool): return ["torso_extended"] elif action == 4: pass else:
class Machine_Learning(): def __init__(self,values,all_data,**kwargs): self.period = kwargs.get('period', 0.005) self.start_time = values['time'] self.previous_time = values['time'] self.previous_be = values['be'] self.duration = kwargs.get('duration', float('inf')) self.previous_vel = values['av'] sys.path.insert(0, "../../Machine_Learning") from ml import ML self.ml = ML("../../Machine_Learning/winner.pkl") def algo(self,values,all_data): time.sleep(0.01) if values['time'] - self.start_time < self.duration: action = self.ml.get_action([ values['be'], values['av'] ]) print values['time'], values['be'], values['av'], action if action == 1: return ["legs_retracted", 1.0] elif action == 0: return ["legs_extended", 1.0] elif action == 3: return "torso_extended" elif action == 2: return "torso_retracted" elif action == 4: pass else: return 'switch'
class Machine_Learning(): def __init__(self, values, all_data, **kwargs): self.period = kwargs.get('period', 0.005) self.start_time = values['time'] self.previous_time = values['time'] self.previous_be = values['be'] self.duration = kwargs.get('duration', float('inf')) self.previous_vel = values['av'] sys.path.insert(0, "../../Machine_Learning") from ml import ML self.ml = ML() def algo(self, values, all_data): if values['time'] - self.start_time < self.duration: action = self.ml.get_action([values['be'], values['av']]) if action == 0: return "legs_in" elif action == 1: return "legs_out" elif action == 2: return "torso_out" elif action == 3: return "torso_in" elif action == 4: pass print values['time'], values['be'], values['av'], action time.sleep(0.01)