class ClassifierRegressionController(Controller):
NAME = "Classifier Regression Controller"
def __init__(self, pendulum_length, pendulum_mass, cart_mass):
Controller.__init__(self, pendulum_length, pendulum_mass, cart_mass)
self.decisionTree = tree.DecisionTreeRegressor()
self.dataset_generator = DatasetBlindGenerator(pendulum_mass,
cart_mass,
pendulum_length)
def learn(self, number):
dataset = self.dataset_generator.generateRandomDataset(number)
self.decisionTree = self.decisionTree.fit(dataset.data, dataset.target)
with open("test.dot", 'w') as f:
f = tree.export_graphviz(self.decisionTree, out_file=f)
def calculate_force(self, angular_position, angular_velocity,
cart_position, cart_velocity):
force = self.dataset_generator.get_force()
move_left_response = self.decisionTree.predict([
angular_position, angular_velocity, cart_position, cart_velocity,
-force
])[0]
move_right_response = self.decisionTree.predict([
angular_position, angular_velocity, cart_position, cart_velocity,
force
])[0]
if self.good_positioning(angular_position):
return self.return_to_start_point(force, cart_position,
cart_velocity)
else:
return -force if abs(move_left_response) < abs(
move_right_response) else force
def good_positioning(self, position):
good_position = 1.5 * self.dataset_generator.get_radian()
return abs(position) <= good_position
def return_to_start_point(self, force, cart_position, cart_velocity):
return self.sign(cart_position) * force
def sign(self, x):
return 1 if x >= 0 else -1
def moving_too_fast(self, cart_velocity, force):
return math.fabs(cart_velocity) > force / 10
def slow_down(self, cart_velocity, force):
return -self.sign(cart_velocity) * force
class ClassifierRegressionController(Controller):
NAME = "Classifier Regression Controller"
def __init__(self,pendulum_length,pendulum_mass, cart_mass):
Controller.__init__(self,pendulum_length,pendulum_mass,cart_mass)
self.decisionTree = tree.DecisionTreeRegressor()
self.dataset_generator = DatasetBlindGenerator(pendulum_mass, cart_mass,pendulum_length)
def learn(self, number):
dataset = self.dataset_generator.generateRandomDataset(number)
self.decisionTree = self.decisionTree.fit(dataset.data, dataset.target)
with open("test.dot", 'w') as f:
f = tree.export_graphviz(self.decisionTree, out_file=f)
def calculate_force(self, angular_position, angular_velocity, cart_position, cart_velocity):
force = self.dataset_generator.get_force()
move_left_response = self.decisionTree.predict([angular_position,angular_velocity,cart_position,cart_velocity,-force])[0]
move_right_response = self.decisionTree.predict([angular_position,angular_velocity,cart_position,cart_velocity,force])[0]
if self.good_positioning(angular_position):
return self.return_to_start_point(force,cart_position,cart_velocity)
else:
return -force if abs(move_left_response) < abs(move_right_response) else force
def good_positioning(self, position):
good_position = 1.5* self.dataset_generator.get_radian()
return abs(position) <= good_position
def return_to_start_point(self, force, cart_position,cart_velocity):
return self.sign(cart_position) * force
def sign(self,x):
return 1 if x >= 0 else -1
def moving_too_fast(self, cart_velocity,force):
return math.fabs(cart_velocity) > force / 10
def slow_down(self, cart_velocity, force):
return -self.sign(cart_velocity)*force
def __init__(self, pendulum_length, pendulum_mass, cart_mass):
Controller.__init__(self, pendulum_length, pendulum_mass, cart_mass)
self.decisionTree = tree.DecisionTreeRegressor()
self.dataset_generator = DatasetBlindGenerator(pendulum_mass,
cart_mass,
pendulum_length)
def __init__(self,pendulum_length,pendulum_mass, cart_mass):
Controller.__init__(self,pendulum_length,pendulum_mass,cart_mass)
self.decisionTree = tree.DecisionTreeRegressor()
self.dataset_generator = DatasetBlindGenerator(pendulum_mass, cart_mass,pendulum_length)