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)
