def main(argv):
try:
opts, args = getopt.getopt(argv, "")
except getopt.GetoptError:
print 'Usage:\tlearn_offline.py <inputfile>'
sys.exit(2)
filename = args[0] if len(
args) > 0 and not args[0] == "" else "./training_data.txt"
if os.path.isfile(filename):
print "Parsing file: {filename}".format(filename=filename)
# Calculate the weights
theta_dot_fi_function = 'Duty_fi_linear_no_constant'
v_fi_function = 'Duty_fi_linear_no_constant'
learner = Linear_learner(theta_dot_fi_function, v_fi_function)
theta_dot_weights = learner.fit_theta_dot_from_file(filename)
v_weights = learner.fit_v_from_file(filename)
print "theta_dot_weights:\t[{weight1}, {weight2}]".format(
weight1=theta_dot_weights[0, 0], weight2=theta_dot_weights[0, 1])
print "v_weights:\t\t[{weight1}, {weight2}]".format(
weight1=v_weights[0, 0], weight2=v_weights[0, 1])
else:
print "ERROR: {filename} not found.".format(filename=filename)
sys.exit(2)
def donot_test_linear_no_constant(self):
theta_dot_fi_function = 'Duty_fi_linear_no_constant'
v_fi_function = 'Duty_fi_linear_no_constant'
learner = Linear_learner(theta_dot_fi_function, v_fi_function)
rp = RosPack()
filepath = rp.get_path('kinematics') + "/tests/test_training_set.txt"
theta_dot_weights = learner.fit_theta_dot_from_file(filepath)
v_weights = learner.fit_v_from_file(filepath)
def test_linear_no_constant(self):
theta_dot_fi_function = 'Duty_fi_linear_no_constant'
v_fi_function = 'Duty_fi_linear_no_constant'
learner = Linear_learner(theta_dot_fi_function, v_fi_function)
rp = RosPack()
filepath = rp.get_path('kinematics') + "/tests/test_training_set.txt"
theta_dot_weights = learner.fit_theta_dot_from_file(filepath)
v_weights = learner.fit_v_from_file(filepath)
np.testing.assert_almost_equal(v_weights, np.matrix([1.0, 1.0]))
np.testing.assert_almost_equal(theta_dot_weights, np.matrix([-1, 1]))
def main(argv):
try:
opts, args = getopt.getopt(argv,"")
except getopt.GetoptError:
print 'Usage:\tlearn_offline.py <inputfile>'
sys.exit(2)
filename = args[0] if len(args) > 0 and not args[0] == "" else "./training_data.txt"
if os.path.isfile(filename):
print "Parsing file: {filename}".format(filename=filename)
# Calculate the weights
theta_dot_fi_function = 'Duty_fi_linear_no_constant'
v_fi_function = 'Duty_fi_linear_no_constant'
learner = Linear_learner(theta_dot_fi_function, v_fi_function)
theta_dot_weights = learner.fit_theta_dot_from_file(filename)
v_weights = learner.fit_v_from_file(filename)
print "theta_dot_weights:\t[{weight1}, {weight2}]".format(weight1=theta_dot_weights[0,0], weight2=theta_dot_weights[0,1])
print "v_weights:\t\t[{weight1}, {weight2}]".format(weight1=v_weights[0,0], weight2=v_weights[0,1])
else:
print "ERROR: {filename} not found.".format(filename=filename)
sys.exit(2)
def __init__(self):
self.node_name = 'kinematics_learning_node'
self.mutex = Lock()
# Read parameters
fi_theta_dot_function = self.setupParameter(
'~fi_theta_dot_function_param', 'Duty_fi_theta_dot_naive')
fi_v_function = self.setupParameter('~fi_v_function_param',
'Duty_fi_v_naive')
self.theta_dot_noZeros = self.setupParameter(
'~learner_number_of_zero_entries', 0)
self.v_noZeros = self.setupParameter('~learner_number_of_zero_entries',
0)
self.noThetaDotSamples = 2 * self.setupParameter(
'~learner_number_of_theta_dot_samples_per_direction', 1000)
self.noVsamples = self.setupParameter('~learner_number_of_v_samples',
1000)
self.theta_dot_threshold = self.setupParameter(
'~learner_theta_dot_threshold', 0.1)
self.v_disp_threshold = pow(
self.setupParameter('~learner_v_disp_threshold', 0.01), 2)
theta_dot_regularizer = self.setupParameter(
'~learner_theta_dot_regularizer', 0.1)
v_regularizer = self.setupParameter('~learner_v_regularizer', 0.1)
# Setup the kinematics learning model
self.kl = Linear_learner.Linear_learner(fi_theta_dot_function,
fi_v_function,
theta_dot_regularizer,
v_regularizer)
# Setup publishers and subscribers
self.pub_theta_dot_kinematics_weights = rospy.Publisher(
"~theta_dot_kinematics_weights", KinematicsWeights, queue_size=1)
self.pub_v_kinematics_weights = rospy.Publisher(
"~v_kinematics_weights", KinematicsWeights, queue_size=1)
self.sub_theta_dot_sample = rospy.Subscriber(
"~theta_dot_sample", ThetaDotSample, self.thetaDotSampleCallback)
self.sub_v_sample = rospy.Subscriber("~v_sample", Vsample,
self.vSampleCallback)
self.theta_dot_negative_index = self.theta_dot_noZeros
self.theta_dot_positive_index = self.theta_dot_negative_index + self.noThetaDotSamples / 2
self.v_index = self.v_noZeros
self.theta_dot_d_L = zeros(
(self.theta_dot_noZeros + self.noThetaDotSamples, 1))
self.theta_dot_d_R = zeros(
(self.theta_dot_noZeros + self.noThetaDotSamples, 1))
self.theta_dot_dt = zeros(
(self.theta_dot_noZeros + self.noThetaDotSamples, 1))
self.theta_dot_dt[:self.
theta_dot_noZeros, :] = 1 # initialize dt=1 for the dummy zero motion samples
self.theta_dot_theta_angle_pose_delta = zeros(
(self.theta_dot_noZeros + self.noThetaDotSamples, 1))
self.v_d_L = zeros((self.v_noZeros + self.noVsamples, 1))
self.v_d_R = zeros((self.v_noZeros + self.noVsamples, 1))
self.v_dt = zeros((self.v_noZeros + self.noVsamples, 1))
self.v_dt[:self.
v_noZeros, :] = 1 # initialize dt=1 for the dummy zero motion samples
self.v_theta_angle_pose_delta = zeros(
(self.v_noZeros + self.noVsamples, 1))
self.v_x_axis_pose_delta = zeros((self.v_noZeros + self.noVsamples, 1))
self.v_y_axis_pose_delta = zeros((self.v_noZeros + self.noVsamples, 1))
rospy.loginfo("[%s] has started", self.node_name)
