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)