def main1():
random.seed(0)
np.random.seed(0)
ll = Localization(grid_len=10)
# ll.init_data()
inter = Interpolate(ll.sensors, ll.means, ll.stds) #''' args... '''
sensors_inter, means_inter, stds_inter = inter.idw_inter(factor=4)
ll_inter = Localization(grid_len=40)
ll_inter.init_data_direct(ll.covariance, sensors_inter, means_inter,
stds_inter)
def overall():
'''Overall approach
'''
config = 'config/splat_config_40.json'
cov_file = 'dataSplat/1600/cov'
sensor_file = 'dataSplat/1600/sensors'
intruder_hypo_file = 'dataSplat/1600/hypothesis'
primary_hypo_file = 'dataSplat/1600/hypothesis_primary'
secondary_hypo_file = 'dataSplat/1600/hypothesis_secondary'
primary = [123, 1357] # fixed
selectsensor = Localization(config)
selectsensor.init_data(
cov_file, sensor_file,
intruder_hypo_file) # offline step: init intruder hypo
selectsensor.setup_primary_transmitters(
primary, primary_hypo_file) # offline step: construct primary
selectsensor.add_primary(
primary_hypo_file) # offline step: construct intruder + primary
counter = 0
while counter < 3:
time.sleep(1)
# if counter % 1 == 0: # Change secondary
# secondary = random_secondary()
# selectsensor.setup_secondary_transmitters(secondary, secondary_hypo_file) # online step: construct secondary
# selectsensor.add_secondary(secondary_hypo_file) # online step: construct primary + intruder + secondary
# selectsensor.rescale_all_hypothesis()
# selectsensor.transmitters_to_array()
# #results = selectsensor.select_offline_greedy_lazy_gpu(25, 10, o_t_approx_kernal2)
# results = selectsensor.select_offline_greedy_hetero(25, 10, o_t_approx_kernal2)
# print(results[-1])
# #plots.visualize_selection(counter, selectsensor.grid_len, primary, secondary, results[-1], selectsensor.sensors)
'''
def main2():
'''SPLOT
'''
random.seed(0)
np.random.seed(0)
means, stds, locations, wall = read_utah_data(path='dataUtah')
lt = LocationTransform(locations, cell_len=1)
ll = Localization(grid_len=lt.grid_len, case='utah', debug=False)
ll.init_utah(means, stds, locations, lt, wall, interpolate=False)
a, b = 0, 100
num_of_intruders = 2
errors = []
misses = []
false_alarms = []
for i in range(a, b):
# for i in [89]:
if i == 29 or i == 74 or i == 77 or i == 84:
i += 100
print('\n\nTest ', i)
random.seed(i)
np.random.seed(i)
true_powers = [random.uniform(-0, 0) for i in range(num_of_intruders)]
intruders_real, true_indices = generate_intruders_utah(grid_len=ll.grid_len, locations=locations, lt=lt, num=num_of_intruders, min_dist=1, max_dist=3)
intruders, sensor_outputs = set_intruders_utah(true_indices=true_indices, powers=true_powers, means=means, grid_loc=lt.grid_location, ll=ll, randomness=True)
r1 = 10
r2 = 6
threshold = -51
pred_locations = ll.splot_localization(sensor_outputs, intruders, fig=i, R1=r1, R2=r2, threshold=threshold)
print('True', end=' ')
for intru in intruders:
print(intru)
true_locations = ll.convert_to_pos(true_indices)
pred_locations_real = [lt.gridcell_2_real(cell) for cell in pred_locations]
try:
error, miss, false_alarm = ll.compute_error2(intruders_real, pred_locations_real)
if len(error) != 0:
errors.extend(error)
misses.append(miss)
false_alarms.append(false_alarm)
print('\nerror/miss/false = {:.3f}/{}/{}'.format(np.array(error).mean(), miss, false_alarm) )
if ll.debug:
visualize_localization(ll.grid_len, true_locations, pred_locations, i)
except Exception as e:
print(e)
try:
# plot_cdf(errors)
# np.savetxt('visualize/utah/ridge.txt', errors, delimiter=',')
errors = np.array(errors)
print('(mean/max/min) error=({:.3f}/{:.3f}/{:.3f}), miss=({:.3f}/{}/{}), false_alarm=({:.3f}/{}/{})'.format(errors.mean(), errors.max(), errors.min(), \
sum(misses)/(b-a), max(misses), min(misses), sum(false_alarms)/(b-a), max(false_alarms), min(false_alarms) ) )
except Exception as e:
print(e)
def main1():
'''Our localization
'''
random.seed(0)
np.random.seed(0)
means, stds, locations, wall = read_utah_data(path='dataUtah')
lt = LocationTransform(locations, cell_len=1)
ll = Localization(grid_len=lt.grid_len, case='utah', debug=False)
ll.init_utah(means, stds, locations, lt, wall, interpolate=True, percentage=.8)
num_of_intruders = 3
a, b = 0, 100
errors = []
misses = []
false_alarms = []
power_errors = []
ll.counter.num_exper = b-a
ll.counter.time_start()
for i in range(a, b):
# for i in [27, 50]:
if i == 29 or i == 74 or i == 77 or i == 84:
i += 100
print('\n\nTest ', i)
random.seed(i)
np.random.seed(i)
true_powers = [random.uniform(-0, 0) for i in range(num_of_intruders)]
intruders_real, true_indices = generate_intruders_utah(grid_len=ll.grid_len, locations=locations, lt=lt, num=num_of_intruders, min_dist=1, max_dist=3)
intruders, sensor_outputs = set_intruders_utah(true_indices=true_indices, powers=true_powers, means=means, grid_loc=lt.grid_location, ll=ll, randomness=True)
print('True: ' + ''.join(map(lambda x:str(x), intruders)))
pred_locations, pred_power = ll.our_localization(sensor_outputs, intruders, i)
true_locations = ll.convert_to_pos(true_indices)
pred_locations_real = [lt.gridcell_2_real(cell) for cell in pred_locations]
try:
error, miss, false_alarm, power_error = ll.compute_error(intruders_real, true_powers, pred_locations_real, pred_power)
if len(error) != 0:
errors.extend(error)
power_errors.extend(power_error)
misses.append(miss)
false_alarms.append(false_alarm)
print('\nerror/miss/false/power = {:.3f}/{}/{}/{:.3f}'.format(np.array(error).mean(), miss, false_alarm, np.array(power_error).mean()) )
if ll.debug:
visualize_localization(ll.grid_len, true_locations, pred_locations, i)
except Exception as e:
print(e)
try:
# plot_cdf(errors)
# np.savetxt('visualize/utah/0.7.txt', errors, delimiter=',')
errors = np.array(errors)
power_errors = np.array(power_errors)
print('(mean/max/min) error=({:.3f}/{:.3f}/{:.3f}), miss=({:.3f}/{}/{}), false_alarm=({:.3f}/{}/{}), power=({:.3f}/{:.3f}/{:.3f})'.format(errors.mean(), errors.max(), errors.min(), \
sum(misses)/(b-a), max(misses), min(misses), sum(false_alarms)/(b-a), max(false_alarms), min(false_alarms), power_errors.mean(), power_errors.max(), power_errors.min() ) )
ll.counter.time_end()
ratios = ll.counter.procedure_ratios()
print(ratios)
print('Proc-1 time = {:.3f}, Proc-1.1 = {:.3f}， Proc-2-2 time = {:.3f}, Proc-2-3 time = {:.3f}'.format(ll.counter.time1_average(), ll.counter.time2_average(), ll.counter.time3_average(), ll.counter.time4_average()))
except Exception as e:
print(e)
import numpy as np
import pandas as pd
import sys
import time
import random
sys.path.append('../..')
from localize import Localization
from utility_ipsn import generate_intruders
# SelectSensor version on May 31, 2019
ll = Localization(grid_len=50)
ll.init_data('../../data50/homogeneous-200/cov', '../../data50/homogeneous-200/sensors', '../../data50/homogeneous-200/hypothesis')
num_of_intruders = 10
a, b = 0, 50
errors = []
misses = []
false_alarms = []
power_errors = []
start = time.time()
for i in range(a, b):
print('\n\nTest ', i)
random.seed(i)
true_powers = [random.uniform(-2, 2) for i in range(num_of_intruders)]
random.seed(i)
np.random.seed(i)
true_indices, true_powers = generate_intruders(grid_len=ll.grid_len, edge=2, num=num_of_intruders, min_dist=1, powers=true_powers)
intruders, sensor_outputs = ll.set_intruders(true_indices=true_indices, powers=true_powers, randomness=True)
import numpy as np
import pandas as pd
import sys
import time
import random
sys.path.append('../..')
from localize import Localization
from utility import generate_intruders
# SelectSensor version on May 31, 2019
ll = Localization(grid_len=50)
ll.init_data('../../data50/homogeneous-200/cov',
'../../data50/homogeneous-200/sensors',
'../../data50/homogeneous-200/hypothesis')
num_of_intruders = 1
a, b = 0, 50
errors = []
misses = []
false_alarms = []
power_errors = []
start = time.time()
for i in range(a, b):
print('\n\nTest ', i)
random.seed(i)
true_powers = [random.uniform(-2, 2) for i in range(num_of_intruders)]
random.seed(i)
np.random.seed(i)
true_indices, true_powers = generate_intruders(grid_len=ll.grid_len,
edge=2,
transmit_power = {"T1": 30} # 3
full_training_data = 'inter-' + str(gran)
sub_training_data = full_training_data + '_{}'.format(sensor_density) # 4
result_date = '10.22-2' # 5
train_percent = int(gran * gran / (40 * 40) * 100) # 6
output_dir = 'results/{}'.format(result_date)
output_file = 'log' # 7
train = TrainingInfo.naive_factory(data_source, sub_training_data,
train_percent)
# subsample_from_full(train, grid_len, sensor_density, transmit_power) # 8
print(train)
server_support = ServerSupport(train.hostname_loc, output_dir, output_file,
train.tx_calibrate)
ll = Localization(grid_len=grid_len, case=data_source, debug=True)
ll.init_data(train.cov, train.sensors, train.hypothesis, SplatMap)
# init another object named ll_ss add primary
num_authorized = 5 # 9
authorized = Authorized(grid_len=grid_len,
edge=2,
case=data_source,
num=num_authorized)
ll_ss = Localization(grid_len=grid_len,
case=data_source,
debug=True,
authorized=authorized)
ll_ss.init_data(train.cov, train.sensors, train.hypothesis, SplatMap)
true_data_path = Default.true_data_path.format(sensor_density)
hypothesis_file = true_data_path + '/hypothesis'
num_authorized = args.num_authorized[0]
sen_density = args.sen_density[0]
methods = args.methods
repeat = args.repeat
port = args.port[0]
# Client.test_server(Default.server_ip, port)
myinput = Input(num_intruder=num_intruder,
data_source='splat',
methods=methods,
sen_density=sen_density,
num_authorized=num_authorized)
# initialize a Localization object with the ground truth, use it to generate read data
ll = Localization(grid_len=40, case='splat', debug=False)
true_data_path = Default.true_data_path.format(sen_density)
cov_file = true_data_path + '/cov'
sensor_file = true_data_path + '/sensors'
hypothesis_file = true_data_path + '/hypothesis'
print('client side true data: \n{}\n{}\n{}\n'.format(
cov_file, sensor_file, hypothesis_file))
ll.init_data(cov_file, sensor_file, hypothesis_file)
ll.init_truehypo(hypothesis_file)
authorized = Authorized(grid_len=Default.grid_len,
edge=2,
case='splat',
num=num_authorized)
myrange = range(repeat[0], repeat[1])
import numpy as np
import pandas as pd
import sys
import time
import random
sys.path.append('../..')
from localize import Localization
from utility import generate_intruders
# SelectSensor version on May 31, 2019
ll = Localization(grid_len=40)
ll.init_data('../../dataSplat/homogeneous-200/cov',
'../../dataSplat/homogeneous-200/sensors',
'../../dataSplat/homogeneous-200/hypothesis')
num_of_intruders = 3
a, b = 0, 50
errors = []
misses = []
false_alarms = []
power_errors = []
ll.counter.num_exper = b - a
ll.counter.time_start()
for i in range(a, b):
print('\n\nTest ', i)
random.seed(i)
true_powers = [random.uniform(-2, 2) for i in range(num_of_intruders)]
random.seed(i)
np.random.seed(i)