def optimize_hypers(self, comp, vals, labels):
# First the GP to observations
mygp = gp.GP(self.cov_func.__name__)
mygp.real_init(comp.shape[1], vals)
mygp.optimize_hypers(comp, vals)
self.mean = mygp.mean
self.ls = mygp.ls
self.amp2 = mygp.amp2
self.noise = mygp.noise
# Now the GP to times
timegp = gp.GP(self.cov_func.__name__)
timegp.real_init(comp.shape[1], labels)
timegp.optimize_hypers(comp, labels)
self.constraint_mean = timegp.mean
self.constraint_amp2 = timegp.amp2
self.constraint_noise = timegp.noise
self.constraint_ls = timegp.ls
# Save hyperparameter samples
self.hyper_samples.append((self.mean, self.noise, self.amp2, self.ls))
self.constraint_hyper_samples.append(
(self.constraint_mean, self.constraint_noise, self.constraint_amp2,
self.constraint_ls))
self.dump_hypers()
def __init__(self):
# read the bag file LOCATION from a json file:
try:
with open('bagfile_location.json') as f:
data = json.load(f)
self.bagfile = data["bagfile_path"]
except IOError:
print(
"Please create a JSON file containing the location of your " +
"bag file. \nCreate a dictionary, set the key \"bagfile_path\" equal "
+
"to the path, and save to a file named \"bagfile_location.json\"."
)
return
# variable for sensor topic
self.sensor_topic = '/kf1/simulated_sensor/raw'
self.counter = 0
print("Reading bagfile \"" + self.bagfile + "\"")
self.bag = rosbag.Bag(self.bagfile)
sensor_msg_cnt = self.bag.get_message_count(self.sensor_topic)
print("Read %d messages from the bagfile. " % sensor_msg_cnt)
# initialize arrays to contain gps_data and sensor_data
self.gps_data = np.zeros((sensor_msg_cnt, 2))
self.sensor_data = np.zeros((sensor_msg_cnt, 1))
# initialize variables
self.cur_data = None
self.cur_data_lat = None
self.cur_data_long = None
self.gaussian = gp.GP()
# subscribers
self.sensor = rospy.Subscriber("/kf1/simulated_sensor/raw",
Measurement, self.callback)
#self.goto = rospy.Subscriber("/kf1/waypoint_goto_result", Int8, self.goto_hotspot)
#self.goto = rospy.Subscriber("/kf1/waypoint_goto_result", Int8, self.callback_chloro)
print("after callback")
rospy.wait_for_message("/kf1/simulated_sensor/raw", Measurement)
#rospy.wait_for_message("/kf1/waypoint_goto_result", Int8)
print("received message")
self.go_to = rospy.ServiceProxy('/kf1/goto', Goto)
#self.set_waypoint(self.go_to)
self.controller()
def optimize_hypers(self, comp, vals):
mygp = gp.GP(self.cov_func.__name__)
mygp.real_init(comp.shape[1], vals)
mygp.optimize_hypers(comp, vals)
self.mean = mygp.mean
self.ls = mygp.ls
self.amp2 = mygp.amp2
self.noise = mygp.noise
# Save hyperparameter samples
self.hyper_samples.append((self.mean, self.noise, self.amp2, self.ls))
self.dump_hypers()
return
generation = 100
skip = 5
input_path = args.input_path
output_path = args.output_path
if args.population:
population = args.population
if args.perturbation:
perturbation = args.perturbation
if args.generation:
generation = args.generation
if args.skip:
skip = args.skip
print("* Scanning original malware sample")
fbytes = open(input_path, "rb").read()
original = gp.origin(input_path, fbytes)
with open(output_path, "a") as wf:
wf.write("original file: " + input_path + "\nVT result: " +
str(original.vt_result) + "\nVT detection list:" +
str(original.vt_dlist) + "\n\n")
print("\nOriginal file: " + input_path)
print("VirusTotal detection rate: " + str(original.vt_result))
print("")
print("* Starting GP malware generation\n")
print("* 1 generation\n")
g = gp.GP(fbytes, population, perturbation, output_path, skip)
g.generation(original, generation)
self.bag.close()
def show_sensor_data(self, save_to_file=False):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(self.gps_data[:, 0], self.gps_data[:, 1], self.sensor_data)
if save_to_file:
plt.savefig("sensor_data.pdf")
else:
plt.show()
if __name__ == "__main__":
rospy.init_node('gp_movement', anonymous=True)
n = node()
print("after node")
process = gp.GP(n.gps_data, n.sensor_data)
print("after gp")
model = process.gaussian_proc()
while not rospy.is_shutdown():
rospy.spin()
#data_sub = ()
