def main7():
'''Weighted Error
'''
# DIR = 'output7' # 100 hypothesis
DIR2 = 'interpolate10' # 1600 hypothesis interpolated
DIR3 = 'output8' # 1600 hypothesis
_, itwom_inter = read_all_data(DIR2)
fspl_true, itwom_true = read_all_data(DIR3)
clean_all_itwom(itwom_true, fspl_true)
mean, median, std, coarse_mean, coarse_median, coarse_std, fine_mean, fine_median, fine_std = customized_error(
itwom_inter, itwom_true)
print('\nmean = {:.3f}, median = {:.3f}, std = {:.3f}\ncoar mean = {:.3f}, coar median = {:.3f}, coar std = {:.3f}\nfine mean = {:.3f}, fine median = {:.3f}, fine std = {:.3f}'.format(\
mean, median, std, coarse_mean, coarse_median, coarse_std, fine_mean, fine_median, fine_std))
def main3():
'''fix tx
'''
original_dir_10 = 'output7'
original_dir_40 = 'output8'
fix_dir_10 = 'output15'
fix_dir_40 = 'output16'
fspl10_fix, itwom10_fix = read_all_data(fix_dir_10)
fspl40_fix, itwom40_fix = read_all_data(fix_dir_40)
size = len(fspl10_fix)
for i in range(size):
tx = '{:04}'.format(i)
fix_one_tx(original_dir_10, tx, fspl10_fix[i], itwom10_fix[i])
size = len(fspl40_fix)
for i in range(size):
tx = '{:04}'.format(i)
fix_one_tx(original_dir_40, tx, fspl40_fix[i], itwom40_fix[i])
def main6():
DIR = 'output13' # 100 hypothesis
DIR2 = 'interpolate13' # 1600 hypothesis interpolated
DIR3 = 'output14' # 1600 hypothesis
fspl, itwom = read_all_data(DIR)
clean_all_itwom(itwom, fspl)
fspl_inter = interpolate_idw(fspl, factor=4)
itwom_inter = interpolate_idw(itwom, factor=4)
fspl_true, itwom_true = read_all_data(DIR3)
clean_all_itwom(itwom_true, fspl_true)
mean, median, root = compute_error(fspl_inter, fspl_true)
print(
'FSPL:\nmean absolute error = {}\nmedian absolute error = {}\nroot mean squared error = {}\n'
.format(mean, median, root))
mean, median, root = compute_error(itwom_inter, itwom_true)
print(
'ITWOM:\nmean absolute error = {}\nmedian absolute error = {}\nroot mean squared error = {}\n\n'
.format(mean, median, root))
write_all_data(fspl_inter, itwom_inter, DIR2)
def main1():
# granularity5 = 'output9'
# granularity10 = 'output7'
# granularity20 = 'output10'
# txmg = TxMultiGran(4, 5, debug=True)
# txs = [(4, 5), (8, 10)] # the same tx in grid length 10 and 20
# grid_lens = [10, 20]
# granularity_directory = [granularity10, granularity20]
# for tx, grid_len, directory in zip(txs, grid_lens, granularity_directory):
# index = indexconvert(tx, grid_len)
# txfile = directory + '/' + '{:04}'.format(index)
# itwom = read_clean_itwom(txfile)
# txmg.add_sensor_data(grid_len, itwom)
# txmg.combine_sensor_data()
DIR0 = 'output9' # 25 hypotheses
DIR1 = 'output7' # 100 hypotheses
DIR2 = 'output10' # 400 hypotheses
DIR3 = 'output8' # 1600 hypotheses
DIR4 = 'interpolate8' # 1600 hypotheses interpolated
# DIR1 = 'output9' # 25 hypotheses
# DIR2 = 'output7' # 100 hypotheses
# DIR3 = 'interpolate9' # 400 hypotheses interpolated
# DIR4 = 'output10' # 400 hypotheses
# txfiles = sorted(glob.glob(DIR1 + '/*'))
# txs = []
# factors = [2, 4] # factors in grid_len
# directories = [DIR2, DIR3]
# TxMultiGran.TX_GRID_LEN = 20
# txfiles = sorted(glob.glob(DIR2 + '/*'))
# txs = []
# factors = [0.5, 2] # factors in grid_len
# directories = [DIR1, DIR3]
# TxMultiGran.TX_GRID_LEN = 40
# txfiles = sorted(glob.glob(DIR3 + '/*'))
# txs = []
# factors = [0.25, 0.5] # factors in grid_len
# directories = [DIR1, DIR2]
TxMultiGran.TX_GRID_LEN = 40
txfiles = sorted(glob.glob(DIR3 + '/*'))
txs = []
factors = [0.125, 0.25, 0.5] # factors in grid_len
directories = [DIR0, DIR1, DIR2]
target_grid_len = 40
for txfile in txfiles:
tx_1dindex = get_tx_index(txfile) # 1d index of TX
try:
itwom = read_clean_itwom(txfile)
except:
print(txfile)
continue
grid_len = int(math.sqrt(len(itwom)))
x, y = indexconvert(tx_1dindex, grid_len)
txmg = TxMultiGran(x, y, debug=True)
txmg.add_sensor_data(grid_len, itwom)
for factor, directory in zip(factors, directories):
fine_grid_len = int(grid_len * factor) # multi-granularity sensor
fine_x = int(x * factor)
fine_y = int(y * factor)
txfile = directory + '/{:04}'.format(
indexconvert((fine_x, fine_y), fine_grid_len))
itwom = read_clean_itwom(txfile)
txmg.add_sensor_data(fine_grid_len, itwom)
txmg.combine_sensor_data()
txs.append(txmg)
itwom_inter = MultiIntepolate.idw_interpolate(
txs, target_grid_len=target_grid_len)
fspl_true, itwom_true = read_all_data(DIR3)
clean_all_itwom(itwom_true, fspl_true)
mean, median, root = compute_error(itwom_inter, itwom_true)
print(
'ITWOM:\nmean absolute error = {}\nmedian absolute error = {}\nroot mean squared error = {}'
.format(mean, median, root))
mean, median, std, coarse_mean, coarse_median, coarse_std, fine_mean, fine_median, fine_std = customized_error(
itwom_inter,
itwom_true,
dist_th=8,
factor=int(target_grid_len / TxMultiGran.TX_GRID_LEN))
print('mean = {:.3f}, median = {:.3f}, std = {:.3f}\ncoar mean = {:.3f}, coar median = {:.3f}, coar std = {:.3f}\nfine mean = {:.3f}, fine median = {:.3f}, fine std = {:.3f}'.format(\
mean, median, std, coarse_mean, coarse_median, coarse_std, fine_mean, fine_median, fine_std))
write_all_itwom(itwom_inter, DIR4)
print(Global.GRAN_LEVEL)