def on_message(client, userdata, msg):
subtime = time.time()
#print(subtime)
payload = msg.payload
#print(payload)
payload_decoded = pickle.loads(payload)
newAttribute = payload_decoded[0]
#print("att:",newAttribute)
i = payload_decoded[1]
pubTime = payload_decoded[2]
#Load the model and get the predicted results
clf2 = joblib.load("SVM1.model")
#print("loadmodel")
svmResult = clf2.predict(newAttribute)
print(svmResult)
payload = [svmResult, i]
thetime = time.time()
pubTime.append(subtime)
pubTime.append(thetime)
payload.append(pubTime)
payload = pickle.dumps(payload)
pub(pubTopic, pubIp, payload)
print("to ensemble success")
def on_message(client, userdata, msg):
payload = msg.payload
payload_decoded = pickle.loads(payload)
auto_file = open(
'./imageArray' + str(txtF) + '/' + str(payload_decoded) + '.pkl', 'rb')
imageArray = pickle.load(auto_file)
statis_file = open(
'./txtArray' + str(txtF) + '/' + str(payload_decoded) + '.pkl', 'rb')
txtArray = pickle.load(statis_file)
# Transfer data to auto
autoTime = time.time()
autopubTime = [autoTime]
payload = [imageArray, payload_decoded, autopubTime]
payload = pickle.dumps(payload)
pub(topic1, IP1, payload)
# Transfer data to statis
statisTime = time.time()
statispubTime = [statisTime]
payload = [txtArray, payload_decoded, statispubTime]
payload = pickle.dumps(payload)
pub(topic2, IP2, payload)
def f():
global i
print("frame", i)
payload = i
payload = pickle.dumps(payload)
pub("source", "192.168.0.91", payload)
i = i + 1
def on_message(client, userdata, msg):
subtime = time.time()
payload = msg.payload
payload_decoded = pickle.loads(payload)
print("abc:", len(payload_decoded))
newAttribute = payload_decoded[0]
newAttribute = newAttribute.as_matrix()
#print("att:",newAttribute)
i = payload_decoded[1]
pubTime = payload_decoded[2]
# Load the model and get the predicted results
clf2 = pickle.load(open("xbg.pickle.dat", "rb"))
#print("loadmodel")
xgbResult = clf2.predict(newAttribute)
print(xgbResult)
payload = [xgbResult, i]
thetime = time.time()
pubTime.append(subtime)
pubTime.append(thetime)
payload.append(pubTime)
payload = pickle.dumps(payload)
pub(pubTopic, pubIp, payload)
print("to ensemble success")
def on_message(client, userdata, msg):
payload = msg.payload
payload_decoded = pickle.loads(payload)
pubTim = time.time()
pubTime = [pubTim]
payload = [train_df, payload_decoded, pubTime]
payload = pickle.dumps(payload)
pub(topic, IP, payload)
def f():
global i
if(i==500):
return;
print("frame", i)
payload = i
payload = pickle.dumps(payload)
pub("source", "192.168.0.91", payload)
i = i + 1
def on_message(client, userdata, msg):
#subTim = time.time()
# --------Receive a message----------------
global knnList, svmList, xgboostList
topic = msg.topic
payload = msg.payload
payload_decoded = pickle.loads(payload)
if topic == 'fault-ensemble/knn':
knnList.append(payload_decoded)
elif topic == 'fault-ensemble/svm':
svmList.append(payload_decoded)
elif topic == 'fault-ensemble/xgboost':
xgboostList.append(payload_decoded)
print(len(knnList), len(svmList), len(xgboostList))
if (len(knnList) != 0 and len(svmList) != 0 and len(xgboostList) != 0):
print(knnList[0][1])
print(svmList[0][1])
print(xgboostList[0][1])
subTim = time.time()
knnResult = np.array(knnList[0][0])
knnResult = np.transpose([knnResult])
# print(cnnResult)
svmResult = svmList[0][0]
svmResult = np.transpose([svmResult])
# print(svmResult)
xgboostResult = xgboostList[0][0]
xgboostResult = np.transpose([xgboostResult])
attribute = np.concatenate((knnResult, svmResult, xgboostResult),
axis=1)
# Load the xbgoost method, get the prediction, and send it
RF = joblib.load('ensembleTree4.m')
result = RF.predict(attribute)
result = result.tolist()
print("result", result)
pubtime = time.time()
payload = [
decisionNum, result, knnList[0][1], svmList[0][1],
xgboostList[0][1], knnList[0][2], svmList[0][2], xgboostList[0][2],
[subTim, pubtime]
]
payload = pickle.dumps(payload)
pub('faultalarm', "39.100.79.76", payload)
print("success")
del knnList[0]
del svmList[0]
del xgboostList[0]
def on_message(client, userdata, msg):
# ------Receive a message----------------
global cnnList, svmList
topic = msg.topic
payload = msg.payload
payload_decoded = pickle.loads(payload)
if topic == 'wafer-ensemble/cnn':
cnnList.append(payload_decoded)
elif topic == 'wafer-ensemble/svm':
svmList.append(payload_decoded)
print(len(cnnList), len(svmList))
if (len(cnnList) != 0 and len(svmList) != 0):
print(cnnList[0][1])
print(svmList[0][1])
subTim = time.time()
cnnResult = np.array(cnnList[0][0])
cnnResult = np.transpose([cnnResult])
# print(cnnResult)
svmResult = svmList[0][0]
svmResult = np.transpose([svmResult])
# print(svmResult)
attribute = np.concatenate((cnnResult, svmResult), axis=1)
#
RF = joblib.load('ensembleTree4.m')
result = RF.predict(attribute)
result = result.tolist()
print("result", result)
pubtime = time.time()
payload = [
decisionNum, result, cnnList[0][1], svmList[0][1], cnnList[0][2],
svmList[0][2], [subTim, pubtime]
]
payload = pickle.dumps(payload)
pub('waferalarm', "39.100.79.76", payload)
# for re in result:
# if re != "none":
# pubtime = time.time()
# payload = [decisionNum, result, cnnList[0][1], cnnList[0][2], svmList[0][2], [subTim, pubtime]]
#
# payload = pickle.dumps(payload)
# pub('alarm', "192.168.0.135", payload)
# break;
del cnnList[0]
del svmList[0]
def on_message(client, userdata, msg):
#subTim = time.time()
global knnList,svmList,xgboostList
topic = msg.topic
payload=msg.payload
payload_decoded = pickle.loads(payload)
if topic == 'fault-ensemble/knn':
knnList.append(payload_decoded)
elif topic == 'fault-ensemble/svm':
svmList.append(payload_decoded)
elif topic == 'fault-ensemble/xgboost':
xgboostList.append(payload_decoded)
print(len(knnList),len(svmList),len(xgboostList))
if(len(knnList)!=0 and len(svmList)!=0 and len(xgboostList)!=0):
print(knnList[0][1])
print(svmList[0][1])
print(xgboostList[0][1])
if ((knnList[0][1] == svmList[0][1]) and (svmList[0][1]==xgboostList[0][1])):
subTim=time.time()
knnResult = np.array(knnList[0][0])
knnResult = np.transpose([knnResult])
# print(cnnResult)
svmResult = svmList[0][0]
svmResult = np.transpose([svmResult])
# print(svmResult)
xgboostResult = xgboostList[0][0]
xgboostResult = np.transpose([xgboostResult])
attribute = np.concatenate((knnResult, svmResult,xgboostResult), axis=1)
RF = joblib.load('ensembleTree4.m')
result = RF.predict(attribute)
result = result.tolist()
print("result", result)
pubtime = time.time()
payload = [decisionNum, result, knnList[0][1], svmList[0][1],xgboostList[0][1],knnList[0][2], svmList[0][2], xgboostList[0][2],[subTim, pubtime]]
payload = pickle.dumps(payload)
pub('alarm', "192.168.0.135", payload)
print("success")
del knnList[0]
del svmList[0]
del xgboostList[0]
else:
print("bupipei")
def on_message(client, userdata, msg):
subtime = time.time()
payload = msg.payload
payload_decoded = pickle.loads(payload)
train_df = payload_decoded[0] #data
frame = payload_decoded[1]
pubTime = payload_decoded[2]
df = Darapreprocessing(train_df) #data chuli
payload = [df, frame]
thetime = time.time()
pubTime.append(subtime)
pubTime.append(thetime)
payload.append(pubTime)
payload = pickle.dumps(payload)
pub(pubTopic, pubIp, payload)
def on_message(client, userdata, msg):
subTim = time.time()
# -------Receive a message----------------
global cnnList, svmList
topic = msg.topic
payload = msg.payload
payload_decoded = pickle.loads(payload)
if topic == 'wafer-ensemble/cnn':
cnnList.append(payload_decoded)
elif topic == 'wafer-ensemble/svm':
svmList.append(payload_decoded)
print(len(cnnList), len(svmList))
if (len(cnnList) != 0 and len(svmList) != 0):
cnnResult = np.array(cnnList[0][0])
cnnResult = np.transpose([cnnResult])
#print(cnnResult)
svmResult = svmList[0][0]
svmResult = np.transpose([svmResult])
#print(svmResult)
attribute = np.concatenate((cnnResult, svmResult), axis=1)
print(attribute)
# Load the xbgoost method, get the prediction, and send it
RF = joblib.load('ensembleTree4.m')
result = RF.predict(attribute)
print("result", result)
i = 0
for re in result:
i = i + 1
if re != "none":
print("in:", i)
pubtime = time.time()
payload = [
decisionNum, result, cnnList[0][1], cnnList[0][2],
svmList[0][2], [subTim, pubtime]
]
payload = pickle.dumps(payload)
pub('alarm', "192.168.0.135", payload)
print("pubSuccess")
break
print("out:", i)
del cnnList[0]
del svmList[0]
def on_message(client, userdata, msg):
subtime = time.time()
payload = msg.payload
payload_decoded = pickle.loads(payload)
newAttribute = payload_decoded[0]
i = payload_decoded[1]
pubTime = payload_decoded[2]
#Load the model and get the predicted results
labels = []
for newAtt in newAttribute:
labels.append(classify(newAtt))
print("iii")
print(labels)
payload = [labels, i]
thetime = time.time()
pubTime.append(subtime)
pubTime.append(thetime)
payload.append(pubTime)
payload = pickle.dumps(payload)
pub(pubTopic, pubIp, payload)
def on_message(client, userdata, msg):
subtime = time.time()
payload = msg.payload
payload_decoded = pickle.loads(payload)
newAttribute = payload_decoded[0]
#print("att:",newAttribute[:2])
i = payload_decoded[1]
pubTime = payload_decoded[2]
#Load the model and get the predicted results
clf2 = load_model("binary_model.h5")
#print("loadmodel")
LSTMResult = clf2.predict_classes(newAttribute)
print(LSTMResult)
payload = [decisionNum, LSTMResult, i]
thetime = time.time()
pubTime.append(subtime)
pubTime.append(thetime)
payload.append(pubTime)
payload = pickle.dumps(payload)
#pub(pubTopic, pubIp, payload)
pub('alarm', "39.100.79.76", payload)
def on_message(client, userdata, msg):
# -------Receive a message----------------
subTim = time.time()
payload = msg.payload
payload_decoded = pickle.loads(payload)
# print(payload_decoded)
df = payload_decoded[0]
frame = payload_decoded[1]
pubTime = payload_decoded[2]
#-------------------------------------------Feature extraction----------------------------------------
new_x = extractFeature(df)
#------------------------------------------pub message--------------------------------------
payload = [new_x, frame]
thetime = time.time()
pubTime.append(subTim)
pubTime.append(thetime)
payload.append(pubTime)
payload = pickle.dumps(payload)
pub(topic1, ip1, payload)
print("knn success")
# Transfer data to SVM
pub(topic2, ip2, payload)
print("svm success")
#xgb
pub(topic3, ip3, payload)
print("xgb success")
def on_message(client, userdata, msg):
# -------Receive a message----------------
subTim = time.time()
payload = msg.payload
payload_decoded = pickle.loads(payload)
#print(payload_decoded)
imageArray = payload_decoded[0]
frame = payload_decoded[1]
pubTime = payload_decoded[2]
newAttribute = []
for image in imageArray:
newAttribute.append(feature_extract(image))
payload = [newAttribute, frame]
j = 0
thetime = time.time()
pubTime.append(subTim)
pubTime.append(thetime)
payload.append(pubTime)
payload = pickle.dumps(payload)
pub(pubTopic, pubIp, payload)