def handle_data():
req_data = json.loads(request.data)
result = []
routeDatas = req_data['routeDatas']
isSaved = req_data['isSaved']
req_count = len(routeDatas)
print 'req_count =', req_count
i = 0
j = 0
for gps_point in routeDatas:
if i % math.ceil(req_count * 0.05) == 0:
noise_point = DataGenerator.addGaussNoise(gps_point, 0.0002)
poi_point = POI_tagger.get_poi_by_point(noise_point)
feature_point = DataGenerator.generate_feature(poi_point, crf_event_prob_map['value'])
result.append(feature_point)
j += 1
# print j
i += 1
# print 'done'
objid = 'null'
if isSaved:
objid = LeancloudUtils.save_to_leancloud(result)
resp = make_response(json.dumps({'trace_id': objid, 'result': result}), 200)
# resp.headers['Access-Control-Allow-Origin'] = '*'
resp.headers["Access-Control-Allow-Headers"] = "content-type"
return resp
def main():
global args, best_loss
args = parser.parse_args()
model = Model.ColorNet()
model.cuda()
criterion = nn.MSELoss().cuda()
optimizer=torch.optim.Adam(model.parameters(),lr=0.1)
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if args.action = 'train':
train_loader = dGenerator.makeDataLoader(args.data, 'train')
val_loader = dGenerator.makeDataLoader(args.val, 'validate')
for epoch in range(args.epoch):
print('='*10+'epoch '+str(epoch)+'='*10)
adjust_learning_rate(optimizer, epoch)
Run.train(train_loader, model, criterion, optimizer, epoch)
loss = Run.validate(val_loader, model, criterion)
print('loss: '+str(loss)+'\n')
is_best = loss < best_loss
best_loss = min(loss, best_loss)
save_checkpoint({
'epoch': epoch + 1,
'arch': 'inception_v3',
'state_dict': model.state_dict(),
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
}, is_best)
def categorical_test():
path = "../Data/Experiments/"
train_rates = [0, 5, 10, 25]
test_rates = [1, 5, 10, 25, 50, 100, 250, 500]
anoms_rates = []
for train_rate in train_rates:
for test_rate in test_rates:
anoms_rates.append((train_rate, test_rate))
for i in range(len(anoms_rates)):
print(anoms_rates[i])
scores = []
for run in range(RUNS):
print("Run %i" % run)
train_file = path + "%i_train_%i.csv" % (i, anoms_rates[i][0])
test_file = path + "%i_test_%i.csv" % (i, anoms_rates[i][1])
generator.create_shipment_data(10000, 10000, anoms_rates[i][0],
anoms_rates[i][1], train_file,
test_file)
train_data = LogFile(train_file, ",", 0, 1000000, None, "Case")
train_data.remove_attributes(["Anomaly"])
test_data = LogFile(test_file,
",",
0,
1000000,
None,
"Case",
values=train_data.values)
model = edbn.train(train_data)
edbn.test(test_data, path + "Output_%i_%i.csv" % anoms_rates[i],
model, "Anomaly", "0")
output_file = path + "Output_%i_%i.csv" % anoms_rates[i]
output_roc = path + "roc_%i_%i.png" % anoms_rates[i]
output_prec = path + "prec_recall_%i_%i.png" % anoms_rates[i]
score = plt.get_roc_auc(output_file)
scores.append(plt.get_roc_auc(output_file))
print("Score = %f" % score)
with open(path + "results.txt", "a") as fout:
fout.write("Testing:\ntrain rate: %i\ntest rate: %i\n" %
(anoms_rates[i][0], anoms_rates[i][1]))
fout.write("Result: " + str(scores) + "\n")
fout.write("Mean: %f Median: %f\n" %
(np.mean(scores), np.median(scores)))
fout.write("Variance: %f\n\n" % np.var(scores))
def InitDicoAircrafts():
global dico_aircrafts
# lbs
f = open(PATH_RESOURCES + "\\..\\Data\\aircrafts", "r")
dico_aircrafts = eval(f.read())
f.close()
ac = list(dico_aircrafts.keys())[0]
if 'drag_coef' not in dico_aircrafts[ac].keys():
print("generating dico values...")
for ac in dico_aircrafts:
dico_aircrafts[ac]["drag_coef"] = dg.InitDragCoef()
dico_aircrafts[ac]["lift_coef"] = dg.InitLiftCoef()
dico_aircrafts[ac]["surface"] = dg.InitSurface()
f = open(PATH_RESOURCES + "\\..\\Data\\aircrafts", "w+")
f.write(str(dico_aircrafts))
f.close()
else:
print("getting dico values...")
print(dico_aircrafts)
def GetUSPressure(pa):
return dg.PatoPSI(pa)
def GetUSPower(w):
return dg.WToHP(w)
def GetUSSpeed(ms):
return dg.MSToMPH(ms)
def GetUSSurface(m2):
return dg.M2ToFt2(m2)
def GetUSTemp(deg):
return dg.DegToK(deg)
def GetUSDistance(m):
return dg.MToFt(m)
def GetRUMass(lbs):
return dg.LbsToKg(lbs)
def GenerateFile(f_id, f_name, records, start_tstp):
f_type = "US"
if f_name in c.US_AIRCRAFTS:
f_type = "US"
GetDate = GetUSDate
GetMass = GetUSMass
GetDistance = GetUSDistance
GetTemp = GetUSTemp
GetSurface = GetUSSurface
GetPercentage = GetUSPercentage
GetSpeed = GetUSSpeed
GetPower = GetUSPower
GetPressure = GetUSPressure
else:
f_type = "RU"
GetDate = GetRUDate
GetMass = GetRUMass
GetDistance = GetRUDistance
GetTemp = GetRUTemp
GetSurface = GetRUSurface
GetPercentage = GetRUPercentage
GetSpeed = GetRUSpeed
GetPower = GetRUPower
GetPressure = GetRUPressure
if not os.path.exists(c.PATH_RESOURCES + "\\" + c.OUT_FOLDER + "\\" +
c.CURRENT_CONDITION[1] + "\\" + f_type):
os.mkdir(c.PATH_RESOURCES + "\\" + c.OUT_FOLDER + "\\" +
c.CURRENT_CONDITION[1] + "\\" + f_type)
## Header
meta = {}
meta['origin'] = f_type
meta['nb_motor'] = dg.GetMotor(f_name)
meta["drag_coef"] = dg.GetDragCoef(f_name)
meta["lift_coef"] = dg.GetLiftCoef(f_name)
meta["mass"] = GetMass(dg.GetMass(f_name)) # from lbs
meta["mass fuel"] = GetMass(dg.GetMassFuel(f_name)) # from lbs
meta['surface'] = GetSurface(dg.GetSurface(f_name)) # from m2
# for computations only
meta["mass_kg"] = dg.LbsToKg(dg.GetMass(f_name))
meta["mass_fuel_kg"] = dg.LbsToKg(dg.GetMassFuel(f_name))
meta["surface_m2"] = dg.GetSurface(f_name)
## Values
# [degree, degree, m, degree, degree, deg, m, m, degree]
names = [
"longitude", "latitude", "altitude", "roll", "pitch", "yaw", "u", "v",
"heading"
]
dico = GetDico(records, names, f_id, start_tstp)
# get speed
# m / s
#names.append("speed_uv")
dico['vx'], dico['vy'], dico['vz'], dico['speed_uv'] = dg.GetSpeedUV(dico)
# generate air speed
# m / s
names.append("air_speed")
dico['air_x'], dico['air_y'], dico['air_z'], dico[
'air_speed'] = dg.GetAirSpeed(dico)
# then compute engine(s) power
# W
engines, names, dico = dg.GetEnginePower2(names, dico, meta)
# COCKPIT
# celsius
col = "temperature_in"
names.append(col)
dico = dg.GetTemperatureCockpit(dico, col)
# % (e.g., 70)
col = "humidity_in"
names.append(col)
dico = dg.GetHumidityCockpit(dico, col)
# Pa
col = "pressure_in"
names.append(col)
dico = dg.GetPressureCockpit(dico, col)
# BPM
col = "heart_rate"
names.append(col)
dico = dg.GetHeartRate(dico, col)
# % (e.g., 70)
col = "oxygen_mask"
names.append(col)
dico = dg.GetOxygenMask(dico, col)
## convert values if necessary
## use agnostic function (so check them again!)
dico['altitude'] = GetDistance(dico['altitude'])
dico['u'] = GetDistance(dico['u'])
dico['v'] = GetDistance(dico['v'])
dico['air_speed'] = GetSpeed(dico['air_speed'])
for e in engines:
dico[e] = GetPower(dico[e])
dico['temperature_in'] = GetTemp(dico['temperature_in'])
dico['humidity_in'] = GetPercentage(dico['humidity_in'])
dico['pressure_in'] = GetPressure(dico['pressure_in'])
dico['oxygen_mask'] = GetPercentage(dico['oxygen_mask'])
names.insert(0, "timestamp")
names.remove("u")
names.remove("v")
f = None
head = GetHeader(f_id, f_name, start_tstp, meta)
if c.CURRENT_CONDITION[0] == c.NORMAL[0]:
f = open(
c.PATH_RESOURCES + "\\" + c.OUT_FOLDER + "\\" +
c.CURRENT_CONDITION[1] + "\\" + f_type + "\\" +
str(c.num_condition) + "_" + f_id + "_" + f_name + ".csv", "w+")
f.write(head)
LogDico(f, names, dico)
f.close()
return
elif c.CURRENT_CONDITION[0] == c.MISSING_HEADER[0]:
f = open(
c.PATH_RESOURCES + "\\" + c.OUT_FOLDER + "\\" +
c.CURRENT_CONDITION[1] + "\\" + f_type + "\\" +
str(c.num_condition) + "_" + f_id + "_" + f_name + ".csv", "w+")
#f.write(head)
LogDico(f, names, dico)
f.close()
return
elif c.CURRENT_CONDITION[0] == c.INCOMPLETE_HEADER[0]:
tab = head.split('\n')
for cur in range(0, len(tab)):
t2 = [x for i, x in enumerate(tab) if i != cur]
s = "\n".join(t2)
f = open(
c.PATH_RESOURCES + "\\" + c.OUT_FOLDER + "\\" +
c.CURRENT_CONDITION[1] + "\\" + f_type + "\\" +
str(c.num_condition) + "_" + str(cur) + f_id + "_" + f_name +
".csv", "w+")
f.write(head)
LogDico(f, names, dico)
f.close()
return
elif c.CURRENT_CONDITION[0] == c.MISSING_COLUMN[0]:
for removed in range(0, len(names)):
print(str(removed) + " / " + str(len(names)))
names2 = list(names)
names2.pop(removed)
dico2 = {}
for cur in names2:
dico2[cur] = dico[cur]
f = open(
c.PATH_RESOURCES + "\\" + c.OUT_FOLDER + "\\" +
c.CURRENT_CONDITION[1] + "\\" + f_type + "\\" +
str(c.num_condition) + "_" + str(removed) + f_id + "_" +
f_name + ".csv", "w+")
f.write(head)
LogDico(f, names2, dico2)
f.close()
return
else:
f = open(c.PATH_RESOURCES + "\\" + c.OUT_FOLDER + "\\" +
c.CURRENT_CONDITION[1] + "\\" + f_type + "\\" +
str(c.num_condition) + "_" + f_id + "_" + f_name + ".csv",
"w+",
encoding='utf-8')
f.write(head)
LogDico(f, names, dico)
f.close()
return
train_rates = [0, 5, 10, 25]
test_rates = [1, 5, 10, 25, 50, 100, 250, 500]
anoms_rates = []
for train_rate in train_rates:
for test_rate in test_rates:
anoms_rates.append((train_rate, test_rate))
for i in range(len(anoms_rates)):
print(anoms_rates[i])
scores = []
for run in range(1):
print("Run %i" % run)
train_file = path + "%i_train_%i.csv" % (i, anoms_rates[i][0])
test_file = path + "%i_test_%i.csv" % (i, anoms_rates[i][1])
generator.create_shipment_data(10000, 10000, anoms_rates[i][0],
anoms_rates[i][1], train_file,
test_file)
train_date = LogFile(train_file, ",", 0, 1000000, None, "Case")
train_date.remove_attributes(["Anomaly"])
test_date = LogFile(test_file,
",",
0,
1000000,
None,
"Case",
string_2_int=train_date.string_2_int,
int_2_string=train_date.int_2_string)
model = edbn.train(train_date)
edbn.test(test_date, path + "Output_%i_%i.csv" % anoms_rates[i],
model, "Anomaly", "0")
# https://aviation.stackexchange.com/questions/46518/what-is-the-typical-value-of-maximum-lift-coefficient-for-aerobatic-aircraft LIFTS = [x * 0.1 for x in range(10, 20, 1)] LIFT_FACTOR = 0.4 # kg MASS = [x for x in range(10000, 20000, 500)] MASS_FACTOR = 1000 # https://en.wikipedia.org/wiki/Wing_loading # same for drag (asked Philippe) # m^2 WING_AREA = [x for x in range(20, 25, 1)] WING_FACTOR = 1 # m AIR_LEVELS = [dg.FtToM(x) for x in [-1000, 1600, 6600, 26000]] # m EARTH_RADIUS = 6371 * 1000 # C T_COCKPIT = 25 # https://www.researchgate.net/publication/276074552_Simulation_for_temperature_control_of_a_military_aircraft_cockpit_to_avoid_pilot%27s_thermal_stress # > +10 deg (sometimes > +45 deg) compared to ambient temp. T_NOISE = 15 T_EVENT = +20 # https://www.google.com/search?client=firefox-b-d&q=heart+rate+data+bpm+fighter+jet+pilot BPM = 90 BPM_NOISE = 20 BPM_EVENT = +40