Merge pull request 'cjl-dev' (#1) from cjl-dev into master

Reviewed-on: http://120.26.116.243:3000/root/model-lab/pulls/1

.drone.yml

@@ -0,0 +1,39 @@
+kind: pipeline
+type: docker
+name: build-and-run-python
+
+trigger:
+  branch:
+    include:
+      - master
+  event:
+    include:
+      - push
+      - custom
+      - merge_request
+
+steps:
+  - name: build python image
+    image: docker
+    volumes:
+      - name: dockersock
+        path: /var/run/docker.sock
+    commands:
+      # 构建镜像（你需要在项目中准备 scripts/Dockerfile.python）
+      - docker build -t alert-python:latest -f Dockerfile.python .
+
+  - name: run python container
+    image: docker
+    volumes:
+      - name: dockersock
+        path: /var/run/docker.sock
+    commands:
+      - docker stop alert-python || true
+      - docker rm alert-python || true
+      # 启动 Python 服务容器，假设服务监听 8082 端口
+      - docker run -d --restart always --name alert-python --network alert-net -p 8082:8082 alert-python:latest
+
+volumes:
+  - name: dockersock
+    host:
+      path: /var/run/docker.sock

AANN_Train.py

@@ -5,10 +5,6 @@ PCA source code
 @author: rsj zjl
 """
 
-
-
-
-
 import matplotlib.pyplot as plt
 from numba import jit
 import time
@@ -24,6 +20,7 @@ import jenkspy
 import xlrd
 import AANN_Fit
 
+
 class Model(object):
     def __init__(self):
         self.v1 = []
@@ -34,6 +31,7 @@ class Model(object):
         self.maxdata = 0
         self.mindata = 0
 
+
 def AANN(training_Sample, Nodes, num_epochs):
     """
     AANN建模
@@ -59,11 +57,12 @@ def AANN(training_Sample, Nodes, num_epochs):
     print(type(Nodes[0]))
     count, spe, o, v1, v2, w1, w2, sigma = cur_aann(mm, nn, num_epochs, Nodes, mdata)
     reconData = AANN_Fit.AANN_Fit(mdata, v1, v2, w1, w2)
-    r2 = 1-np.sum(np.power((mdata-reconData),2),axis=0)/np.sum(np.power((np.tile(np.average(mdata,axis=0), (mm,1))-reconData),2),axis=0)
+    r2 = 1 - np.sum(np.power((mdata - reconData), 2), axis=0) / np.sum(
+        np.power((np.tile(np.average(mdata, axis=0), (mm, 1)) - reconData), 2), axis=0)
     # 预处理 数据反归一化
     a = maxdata - mindata + np.zeros((mm, nn))
     b = maxdata + mindata + np.zeros((mm, nn))
-    reconData = np.matrix((np.multiply(mdata,a) + b)/2)
+    reconData = np.matrix((np.multiply(mdata, a) + b) / 2)
     Train_X_min = np.min(training_Sample, axis=0)  # 训练值最小值
     Train_X_max = np.max(training_Sample, axis=0)  # 训练值最大值
     Train_X_mean = np.mean(training_Sample, axis=0)  # 训练值平均值
@@ -89,8 +88,8 @@ def AANN(training_Sample, Nodes, num_epochs):
     items = [('Train_X_min', np.around(Train_X_min, decimals=3).tolist()),
              ('Train_X_max', np.around(Train_X_max, decimals=3).tolist()),
              ('Train_X_std', np.around(Train_X_std, decimals=3).tolist()),
-             ('Train_X_mean',np.around(Train_X_mean, decimals=3).tolist()),
-             (('Train_X_bais_max',np.around(Train_X_bais_max, decimals=3).tolist())),
+             ('Train_X_mean', np.around(Train_X_mean, decimals=3).tolist()),
+             (('Train_X_bais_max', np.around(Train_X_bais_max, decimals=3).tolist())),
              (('Train_X_bais_min', np.around(Train_X_bais_min, decimals=3).tolist())),
              (('Train_X_bais_mean', np.around(Train_X_bais_mean, decimals=3).tolist())),
              ('QCUL_95_line', np.around(QCUL_95_line, decimals=3).tolist()),
@@ -111,6 +110,7 @@ def AANN(training_Sample, Nodes, num_epochs):
     result = dict(res_items)  # json.dumps(result)
     return json.dumps(result)
 
+
 @jit(nopython=True, cache=True)
 def cur_aann(mm, nn, num_epochs, Nodes, mdata):
     alpha0 = 0.001
@@ -173,15 +173,15 @@ def cur_aann(mm, nn, num_epochs, Nodes, mdata):
 
     sigma = 3 * np.sum((expectlist - o) * (expectlist - o)) / mm
 
-
     return count, spe, o, v1, v2, w1, w2, sigma
 
+
 def isnumber(limits):
-    flag=True
+    flag = True
     for item in limits:
-        item=item.replace("-","")
-        if(item.isdigit()==False):
-            flag=False
+        item = item.replace("-", "")
+        if (item.isdigit() == False):
+            flag = False
             break
     return flag
 
@@ -204,10 +204,10 @@ def clearmain(info):
         limit = Train_Data["limit"].split(',')
         uplower = Train_Data["uplow"].split(';')
 
-        condition=info["conditon"].replace("=","==").replace(">=",">").replace("<=","<")
-        #percent = info["Hyper_para"]["percent"]
+        condition = info["conditon"].replace("=", "==").replace(">=", ">").replace("<=", "<")
+        # percent = info["Hyper_para"]["percent"]
 
-        count=0
+        count = 0
         ItemsInfo, SamplingTimePeriods = [], []
         Constraint = ""
         for i in range(len(points)):
@@ -221,13 +221,14 @@ def clearmain(info):
                 limits = uplower[i].split(',')
                 if (isnumber(limits) == True):  # 输入上下限正确
                     count += 1
-                    Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[1] + " and "
+                    Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[
+                        1] + " and "
             ItemsInfo.append(iteminfo)
-        if(count!=0):
+        if (count != 0):
             Constraint = Constraint[:len(Constraint) - 4:]
         else:
-            Constraint="1==1"#没有上下限清洗
-        Constraint+=" and ("+condition+")"
+            Constraint = "1==1"  # 没有上下限清洗
+        Constraint += " and (" + condition + ")"
         Constraint = Constraint.replace("\n", " ")
         for i in range(len(times)):
             Eachsampletime = {}
@@ -236,7 +237,7 @@ def clearmain(info):
             Eachsampletime["TerminalTime"] = timess[1]
             SamplingTimePeriods.append(Eachsampletime)
         url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=%d" % (
-        ItemsInfo, SamplingTimePeriods, Constraint, interval, DCount)
+            ItemsInfo, SamplingTimePeriods, Constraint, interval, DCount)
         response = requests.get(url)
         content = json.loads(response.text)
         data = np.array([item for item in content["ClearData"]]).T
@@ -256,24 +257,22 @@ def clearmain(info):
             else:
                 if len(smote_num) != 0:
                     data, *_ = smote(data, smote_index, smote_num, max_value, min_value)
-        Nodes=info["layer"]
-        num_epochs=Train_Data["interval"]
-        result =AANN(data, Nodes, num_epochs)#看看nodes和num_epochs怎么传进来
-        #result = pca(data, percent)
+        Nodes = info["layer"]
+        num_epochs = Train_Data["interval"]
+        result = AANN(data, Nodes, num_epochs)  # 看看nodes和num_epochs怎么传进来
+        # result = pca(data, percent)
         result = result.replace("NaN", "-1")
-        result=json.loads(result)
-        result["BeforeCleanSamNum"]=content["BeforeCleanSamNum"]
-        result["AfterCleanSamNum"]=content["AfterCleanSamNum"]
+        result = json.loads(result)
+        result["BeforeCleanSamNum"] = content["BeforeCleanSamNum"]
+        result["AfterCleanSamNum"] = content["AfterCleanSamNum"]
         result["CleanOrNot"] = True
         return json.dumps(result)
     except Exception as e:
         result = [{"CleanOrNot": False, "msg": traceback.format_exc()}]
         return json.dumps(result, ensure_ascii=False)
 
+
 if __name__ == "__main__":
-    info_str='{"layer":[5,2,5],"Train_Data":{"time":"2020-10-14 04:10:28,2020-10-17 14:53:00;2021-04-07 07:32:47,2021-04-16 08:39:01;2021-06-01 18:48:17,2021-06-03 14:29:40","points":"DH4_40MAG20CT362,DH4_40MAG20AN002GT,DH4_40MAG20CE102,DH4_40MAG20CT312,DH4_40MAG20CT322,DH4_40MAG20CT332","interval":300000,"dead":"1,1,1,1,1,1","limit":"0,0,0,0,0,0","uplow":"null,null;null,null;null,null;null,null;null,null;null,null"},"type":"AANN","conditon":"[DH4_40MAG20CE102]>20","epoch":"10000"}'
+    info_str = '{"layer":[5,2,5],"Train_Data":{"time":"2020-10-14 04:10:28,2020-10-17 14:53:00;2021-04-07 07:32:47,2021-04-16 08:39:01;2021-06-01 18:48:17,2021-06-03 14:29:40","points":"DH4_40MAG20CT362,DH4_40MAG20AN002GT,DH4_40MAG20CE102,DH4_40MAG20CT312,DH4_40MAG20CT322,DH4_40MAG20CT332","interval":300000,"dead":"1,1,1,1,1,1","limit":"0,0,0,0,0,0","uplow":"null,null;null,null;null,null;null,null;null,null;null,null"},"type":"AANN","conditon":"[DH4_40MAG20CE102]>20","epoch":"10000"}'
     info = json.loads(info_str)
     print(clearmain(info))
-
-
-

ANN_Train_offline.py

@@ -5,25 +5,27 @@
  @File    : ANN_train.py
  @Software: PyCharm
 """
-import os
+import datetime
 import json
+import os
 import time
-import datetime
-import requests
+
+import config
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import requests
 import tensorflow as tf
+from sklearn.preprocessing import MinMaxScaler
 from tensorflow.keras import backend
-import matplotlib.pyplot as plt
 from tensorflow.keras import layers
-from sklearn.preprocessing import MinMaxScaler
 from tensorflow.keras.models import load_model
 from tensorflow.keras.models import model_from_json
-import config
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
-def get_history_value(points, time, interval,typedata):
+
+def get_history_value(points, time, interval, typedata):
     # url="http://192.168.1.201:8080/openPlant/getMultiplePointHistorys"
     url = f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat"
     headers = {"Content-Type": "application/json;charset=utf-8"}  # ,"token":get_token()
@@ -41,20 +43,21 @@ def get_history_value(points, time, interval,typedata):
             for row in value:
                 value_group.append(row[1])
         value_array.append(value_group)
-    valuetrs=np.array(value_array)
+    valuetrs = np.array(value_array)
     typeArr = list(enumerate(typedata.split(",")))
     data_x = [(valuetrs.T)[:, item[0]].tolist() for item in typeArr if item[1] == "0"]
     data_y = [(valuetrs.T)[:, item[0]].tolist() for item in typeArr if item[1] == "1"]
     x_data = np.array(data_x).T
     y_data = np.array(data_y).T
-    return x_data,y_data,valuetrs
+    return x_data, y_data, valuetrs
+
 
 def rmse(y_true, y_pred):
     return backend.sqrt(backend.mean(tf.keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
 
 
-def TrainOffline(x_data,y_data,hidden_layers,epochsdata):
-    #计算原来每一列数据的最大值
+def TrainOffline(x_data, y_data, hidden_layers, epochsdata):
+    # 计算原来每一列数据的最大值
     valuetrs = np.hstack((x_data, y_data))
     # valuetrs = valuetrs
     train_max, train_min, train_ave = [], [], []
@@ -128,11 +131,12 @@ def TrainOffline(x_data,y_data,hidden_layers,epochsdata):
     result["weight"] = [model_weight[index].tolist() for index in range(len(model_weight))]
     return json.dumps(result)
 
-def Train(x_data,y_data,hidden_layers,valuetrs,epochsdata):
+
+def Train(x_data, y_data, hidden_layers, valuetrs, epochsdata):
     # 计算原来每一列数据的最大值
-    #x_data = np.array(x_data)
-    #y_data = np.array(y_data)
-    valuetrs=valuetrs
+    # x_data = np.array(x_data)
+    # y_data = np.array(y_data)
+    valuetrs = valuetrs
     train_max, train_min, train_ave = [], [], []
     for row in valuetrs.T:
         train_max.append(np.max(row))
@@ -140,7 +144,7 @@ def Train(x_data,y_data,hidden_layers,valuetrs,epochsdata):
         train_ave.append(np.mean(row))
     mms1 = MinMaxScaler()
     mms2 = MinMaxScaler()
-    mms3=MinMaxScaler()
+    mms3 = MinMaxScaler()
     x_normal = mms1.fit_transform(x_data)
     y_normal = mms2.fit_transform(y_data)
     x_train = x_normal
@@ -156,17 +160,16 @@ def Train(x_data,y_data,hidden_layers,valuetrs,epochsdata):
     model.compile(loss="mse", optimizer="adam", metrics=["accuracy"])
     # 训练模型
     history = model.fit(x_train, y_train, epochs=epochsdata, batch_size=80)
-    path=os.getcwd()+"\\ModelOline";
+    path = os.getcwd() + "\\ModelOline";
     if not os.path.exists(path):
         os.makedirs(path)
-    now=datetime.datetime.now()
-    #filepath=path + "\\M"+"_"+str(now).replace(" ","-").replace(".","-").replace(":","-")+".h5"
-    #model.save(filepath)
-    filepath=model.to_json()
-    model_weight=model.get_weights()
+    now = datetime.datetime.now()
+    # filepath=path + "\\M"+"_"+str(now).replace(" ","-").replace(".","-").replace(":","-")+".h5"
+    # model.save(filepath)
+    filepath = model.to_json()
+    model_weight = model.get_weights()
     test_data = model.predict(x_normal, batch_size=400)
 
-
     # 反归一化
     predict_data = mms2.inverse_transform(test_data)
     with tf.compat.v1.Session():
@@ -181,20 +184,20 @@ def Train(x_data,y_data,hidden_layers,valuetrs,epochsdata):
         pre_min.append(np.min(row))
         pre_ave.append(np.mean(row))
         pre_s.append(np.std(row) * 3)
-    result,mms1new,mms2new={},{},{}
-    mms1new["data_max_"]=mms1.data_max_.tolist()
-    mms1new["data_min_"]=mms1.data_min_.tolist()
-    mms1new["data_range_"]=mms1.data_range_.tolist()
-    mms1new["min_"]=mms1.min_.tolist()
-    mms1new["scale_"]=mms1.scale_.tolist()
+    result, mms1new, mms2new = {}, {}, {}
+    mms1new["data_max_"] = mms1.data_max_.tolist()
+    mms1new["data_min_"] = mms1.data_min_.tolist()
+    mms1new["data_range_"] = mms1.data_range_.tolist()
+    mms1new["min_"] = mms1.min_.tolist()
+    mms1new["scale_"] = mms1.scale_.tolist()
     mms2new["data_max_"] = mms2.data_max_.tolist()
     mms2new["data_min_"] = mms2.data_min_.tolist()
     mms2new["data_range_"] = mms2.data_range_.tolist()
     mms2new["min_"] = mms2.min_.tolist()
     mms2new["scale_"] = mms2.scale_.tolist()
-    result["filename"]=filepath
-    result["mms1"]=mms1new
-    result["mms2"]=mms2new
+    result["filename"] = filepath
+    result["mms1"] = mms1new
+    result["mms2"] = mms2new
     result["train_max"] = np.array(train_max).tolist()
     result["train_min"] = np.array(train_min).tolist()
     result["train_ave"] = np.array(train_ave).tolist()
@@ -209,14 +212,15 @@ def Train(x_data,y_data,hidden_layers,valuetrs,epochsdata):
 
 
 def isnumber(limits):
-    flag=True
+    flag = True
     for item in limits:
-        item=item.replace("-","")
-        if(item.isdigit()==False):
-            flag=False
+        item = item.replace("-", "")
+        if (item.isdigit() == False):
+            flag = False
             break
     return flag
 
+
 def clearmain(info):
     try:
         points = info["point"].split(',')
@@ -224,12 +228,12 @@ def clearmain(info):
         epochs = info["iter"]
         layer = info["layer"]
         typedata = info["type"]
-        condition=info["condition"].replace("=","==").replace(">=",">").replace("<=","<")
+        condition = info["condition"].replace("=", "==").replace(">=", ">").replace("<=", "<")
         interval = 300000
         dead = info["dead"].split(',')
         limit = info["limit"].split(',')
         uplower = info["uplow"].split(';')
-        count=0
+        count = 0
         ItemsInfo, SamplingTimePeriods = [], []
         Constraint = ""
         for i in range(len(points)):
@@ -243,13 +247,14 @@ def clearmain(info):
                 limits = uplower[i].split(',')
                 if (isnumber(limits) == True):  # 输入上下限正确
                     count += 1
-                    Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[1] + " and "
+                    Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[
+                        1] + " and "
             ItemsInfo.append(iteminfo)
-        if count!=0:
+        if count != 0:
             Constraint = Constraint[:len(Constraint) - 4:]
         else:
-            Constraint="1==1"#没有上下限清洗
-        Constraint+=" and ("+condition+")"
+            Constraint = "1==1"  # 没有上下限清洗
+        Constraint += " and (" + condition + ")"
         Constraint = Constraint.replace("\n", " ")
         for i in range(len(times)):
             Eachsampletime = {}
@@ -258,19 +263,19 @@ def clearmain(info):
             Eachsampletime["TerminalTime"] = timess[1]
             SamplingTimePeriods.append(Eachsampletime)
         url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=6" % (
-        ItemsInfo, SamplingTimePeriods, Constraint, interval)
+            ItemsInfo, SamplingTimePeriods, Constraint, interval)
         response = requests.get(url)
         content = json.loads(response.text)
         # data =np.array([item for item in content["ClearData"] if item ]).T
         valuetrs = np.array([item for item in content["ClearData"]]).T
         typeArr = list(enumerate(typedata.split(",")))
-        data_x = [valuetrs[:,item[0]].tolist() for item in typeArr if item[1]=="0"]
-        data_y = [valuetrs[:,item[0]].tolist() for item in typeArr if item[1]=="1"]
+        data_x = [valuetrs[:, item[0]].tolist() for item in typeArr if item[1] == "0"]
+        data_y = [valuetrs[:, item[0]].tolist() for item in typeArr if item[1] == "1"]
         data_x = np.array(data_x).T
         data_y = np.array(data_y).T
         result = Train(data_x, data_y, layer, valuetrs, eval(epochs))
-        result["BeforeCleanSamNum"]=content["BeforeCleanSamNum"]
-        result["AfterCleanSamNum"]=content["AfterCleanSamNum"]
+        result["BeforeCleanSamNum"] = content["BeforeCleanSamNum"]
+        result["AfterCleanSamNum"] = content["AfterCleanSamNum"]
         result["CleanOrNot"] = True
         return result
     except Exception as e:
@@ -278,7 +283,6 @@ def clearmain(info):
         return result
 
 
-
 def main(info):
     points = info["point"]
     time1 = info["time"]
@@ -293,9 +297,9 @@ def main(info):
 
 if __name__ == "__main__":
     # info_str = '{"time":"2020-01-19 23:26:04,2020-01-25 20:42:16;2020-01-26 16:23:49,2020-02-03 11:36:42;2020-02-05 20:02:49,2020-02-06 05:51:40","condition":"1=1","interval":300000,"dead":"1,1,1","layer":["6"],"point":"JL_D1_10MILLA:SEP_TEMP.PNT,JL_D1_10FSSS20A:HFC10CT301.PNT,JL_D1_10FSSS20A:HFC10CT302.PNT","type":"1,0,0","limit":"0,0,0","uplow":",;,;,","iter":"100"}'
-    info_str = '{"iter":"800","dead":"1,1,1,1","point":"DH4_40HLF10CE101,DH4_40HLF10CT351,DH4_40HLF10CT352,DH4_40HLF10CY101","limit":"0,0,0,0","layer":["5"],"type":"0,0,0,1","time":"2020-10-06 19:17:46,2020-10-12 14:58:19","condition":"1=1","interval":300000,"uplow":"null,null;null,null;null,null;null,null"}'
-    info = json.loads(info_str)
-    result = clearmain(info)
-    print(result)
-    # info = {"iter":"800","Train_Data_X":[[7.0,7.0,7.0,7.0,7.0,7.0,7.0],[8.0,8.0,8.0,8.0,8.0,8.0,8.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0],[2.0,2.0,2.0,2.0,2.0,2.0,2.0],[3.0,3.0,3.0,3.0,3.0,3.0,3.0],[4.0,4.0,4.0,4.0,4.0,4.0,4.0],[5.0,5.0,5.0,5.0,5.0,5.0,5.0],[6.0,6.0,6.0,6.0,6.0,6.0,6.0],[7.0,7.0,7.0,7.0,7.0,7.0,7.0],[8.0,8.0,8.0,8.0,8.0,8.0,8.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0],[2.0,2.0,2.0,2.0,2.0,2.0,2.0]],"hide":["7","5","1"],"Train_Data_Y":[[7.0],[8.0],[1.0],[2.0],[3.0],[4.0],[5.0],[6.0],[7.0],[8.0],[1.0],[2.0]]}
-    # result = TrainOffline(np.array(info["Train_Data_X"]), np.array(info["Train_Data_Y"]),info["hide"], eval(info["iter"]))
+    # info_str = '{"iter":"800","dead":"1,1,1,1","point":"DH4_40HLF10CE101,DH4_40HLF10CT351,DH4_40HLF10CT352,DH4_40HLF10CY101","limit":"0,0,0,0","layer":["5"],"type":"0,0,0,1","time":"2020-10-06 19:17:46,2020-10-12 14:58:19","condition":"1=1","interval":300000,"uplow":"null,null;null,null;null,null;null,null"}'
+    # info = json.loads(info_str)
+    # result = clearmain(info)
+    # print(result)
+    info = {"iter":"800","Train_Data_X":[[7.0,7.0,7.0,7.0,7.0,7.0,7.0],[8.0,8.0,8.0,8.0,8.0,8.0,8.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0],[2.0,2.0,2.0,2.0,2.0,2.0,2.0],[3.0,3.0,3.0,3.0,3.0,3.0,3.0],[4.0,4.0,4.0,4.0,4.0,4.0,4.0],[5.0,5.0,5.0,5.0,5.0,5.0,5.0],[6.0,6.0,6.0,6.0,6.0,6.0,6.0],[7.0,7.0,7.0,7.0,7.0,7.0,7.0],[8.0,8.0,8.0,8.0,8.0,8.0,8.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0],[2.0,2.0,2.0,2.0,2.0,2.0,2.0]],"hide":["7","5","1"],"Train_Data_Y":[[7.0],[8.0],[1.0],[2.0],[3.0],[4.0],[5.0],[6.0],[7.0],[8.0],[1.0],[2.0]]}
+    result = TrainOffline(np.array(info["Train_Data_X"]), np.array(info["Train_Data_Y"]),info["hide"], eval(info["iter"]))

ASSESS.py

@@ -12,7 +12,7 @@ import config
 class HealthyScoringSystem:
     def __init__(self):
         # 读取配置
-        ms = MSSQL(host="172.28.137.230", user="sa", pwd="powerSIS#123", database="ASSESS")
+        ms = MSSQL(host="120.26.116.243", user="root", pwd="123456", database="alert")
 
         conditionlist = ms.ExecQuery(f"SELECT * FROM [ASSESS].[dbo].[conditionlist]")
         pointconfigs = ms.ExecQuery(f"SELECT * FROM [ASSESS].[dbo].[pointconfigs]")

Dockerfile

@@ -0,0 +1,11 @@
+FROM docker.io/python:3.9.20-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["python", "app.py"]

PCA_Test.py

@@ -159,14 +159,14 @@ class MSSQL:
         self.conn.close()
 
 def get_model_by_ID(model_id, version="v-test"):
-    ms = MSSQL(host=config._SQL_IP, user="sa", pwd="powerSIS#123", database="alert")
-    resList = ms.ExecQuery(f"SELECT Model_info FROM [alert].[dbo].[Model_CFG] where model_id={model_id}")
+    ms = MSSQL(host=config._SQL_IP, user="root", pwd="123456", database="alert")
+    resList = ms.ExecQuery(f"SELECT Model_info FROM model_cfg where model_id={model_id}")
     return json.loads(resList[0][0])
 
 
 def get_model_by_id_and_version(model_id, version):
-    ms = MSSQL(host=config._SQL_IP, user="sa", pwd="powerSIS#123", database="alert")
-    resList = ms.ExecQuery(f"SELECT Model_info FROM [alert].[dbo].[model_version] where model_id={model_id} and version='{version}'")
+    ms = MSSQL(host=config._SQL_IP, user="root", pwd="123456", database="alert")
+    resList = ms.ExecQuery(f"SELECT Model_info FROM model_version where model_id={model_id} and version='{version}'")
     return json.loads(resList[0][0])
 
 def pca(model, Data_origin):

PCA_Test_offline.py

@@ -97,8 +97,8 @@ class MSSQL:
         self.conn.close()
 
 def get_model_by_ID(model_id):
-    ms = MSSQL(host=config._SQL_IP, user="sa", pwd="powerSIS#123", database="alert")
-    resList = ms.ExecQuery("SELECT Model_info FROM [alert].[dbo].[Model_CFG] where \"model_id\"="+str(model_id))
+    ms = MSSQL(host="120.26.116.243", user="root", pwd="powerSIS#123", database="alert")
+    resList = ms.ExecQuery("SELECT Model_info FROM model_cfg where \"model_id\"="+str(model_id))
     #return json.loads(resList[0][0])["para"]
     return json.loads(resList[0][0])
 
@@ -111,7 +111,7 @@ def get_model_by_id(model_id):
         conn = pyodbc.connect(
             r"DRIVER={SQL SERVER NATIVE CLIENT 10.0};SERVER=%s;DATABASE=alert;UID=sa;PWD=powerSIS#123" % config._SQL_IP)  # 连接数据库
     cursor = conn.cursor()  # 获得操作的游标
-    cursor.execute(f"SELECT Model_info FROM [alert].[dbo].[Model_CFG] where model_id={model_id}")
+    cursor.execute(f"SELECT Model_info FROM model_cfg where model_id={model_id}")
     res_list = cursor.fetchall()  # 获取查询的结果
     conn.commit()  # 提交执行
     cursor.close()  # 关闭游标

web.py → app.py

@@ -1,28 +1,31 @@
 # -*- coding: utf-8 -*-
-import traceback
-from flask import Flask
-from flask import request
+import datetime
 import json
-import PCA_Test_offline
-import numpy as np
-import PCA_Test
-import config
 import json
 import sys
-import requests
-import datetime
+import traceback
+
+import PCA_Test
+import PCA_Test_offline
+import config
 import jenkspy
+import numpy as np
+import requests
 import xlrd
+from flask import Flask
+from flask import request
 from numba import jit
 
 app = Flask(__name__)
 
+
 # app.permanent_session_lifetime = timedelta(minutes=5)
 
 @app.route('/', methods=["POST", "GET"])
 def FirstPage():
     return "This is a Flask WebAPi"
 
+
 @app.route('/api/test/Test_offline', methods=["GET"])
 def GetTest_offline():
     try:
@@ -67,6 +70,7 @@ def GetTest_offline():
         result = json.dumps({"msg": traceback.format_exc()}, ensure_ascii=False)
     return result
 
+
 @app.route('/api/predict', methods=["POST"])
 def get_predict():
     try:
@@ -103,6 +107,7 @@ def get_predict():
         result = json.dumps({"msg": traceback.format_exc()}, ensure_ascii=False)
     return result
 
+
 @app.route('/api/test/ANN_Test_offline', methods=["GET", "POST"])
 def GetANNTestOffline():
     try:
@@ -114,6 +119,7 @@ def GetANNTestOffline():
         result = {"error_msg": str(e)}
     return json.dumps(result)
 
+
 @app.route('/api/test/Train', methods=["POST", "GET"])
 def GetTrain():
     result = ""
@@ -127,6 +133,7 @@ def GetTrain():
         result = GMM_train.main(jsonstr)
     return result
 
+
 @app.route('/api/test/ASSESS', methods=["POST", "GET"])
 def GetAssess():
     result = {}
@@ -154,7 +161,7 @@ def GetClearTrain():
         if filename == "PCA":
             import PCA_Train
             result = PCA_Train.clearmain(jsonstr)
-        elif filename == "AANN": #AANN added by rsj 2021-5-3
+        elif filename == "AANN":  # AANN added by rsj 2021-5-3
             import AANN_Train
             result = AANN_Train.clearmain(jsonstr)
         elif filename == "sPCA":
@@ -200,10 +207,10 @@ def GetTest():
             points = Test_Data["points"]
             time1 = Test_Data["time"]
             interval = Test_Data["interval"]
-            #print('1111111',res["para"])
+            # print('1111111',res["para"])
             Data = AANN_Fit.get_history_value(points, time1, interval)
             model = res["para"]["Model_info"]
-            result = AANN_Fit.AANN_Test(model,Data)
+            result = AANN_Fit.AANN_Test(model, Data)
             index = time1.index(",")
             result["time"] = time1[:index:]
 
@@ -213,6 +220,7 @@ def GetTest():
     except Exception as e:
         return json.dumps({"error_msg": traceback.format_exc()})
 
+
 @app.route("/api/aann_clean_test", methods=["POST"])
 def get_aann_clean_test():
     try:
@@ -223,6 +231,7 @@ def get_aann_clean_test():
         result = {"error_msg": str(e)}
     return json.dumps(result)
 
+
 @app.route('/api/test/ANN_Train_offline', methods=["GET", "POST"])
 def GetANNTrainOffline():
     try:
@@ -235,6 +244,7 @@ def GetANNTrainOffline():
         result = {"error_msg": str(e)}
         return json.dumps(result)
 
+
 @app.route('/api/test/ANN_Train', methods=["POST"])
 def GetANNTrain():
     try:
@@ -246,6 +256,7 @@ def GetANNTrain():
         result = {"error": str(e)}
         return json.dumps(result)
 
+
 @app.route('/api/test/ANN_Test', methods=["POST"])
 def GetANNTest():
     try:
@@ -270,6 +281,7 @@ def GetANNTest():
         result["error_msg"] = str(e)
     return json.dumps(result)
 
+
 @app.route('/api/test/PLS_Train_online', methods=["POST", "GET"])
 def getPLSTrainOnline():
     result = ""
@@ -278,6 +290,7 @@ def getPLSTrainOnline():
     result = PLS_Train.main(jsonstr)
     return result
 
+
 @app.route('/api/test/PLS_Test_online', methods=["POST", "GET"])
 def GetPLSTestOnline():
     result = ""
@@ -286,6 +299,7 @@ def GetPLSTestOnline():
     result = PLS_Test.main(info)
     return result
 
+
 @app.route('/api/test/getliner', methods=["POST", "GET"])
 def get_liner():
     data = json.loads(request.data)
@@ -296,6 +310,7 @@ def get_liner():
     y = inputdata[:, 1].reshape(-1, 1)
     return Liner.main(degree, x, y)
 
+
 @app.route('/HeatRate', methods=["POST", "GET"])
 def get_heat_rate():
     data = json.loads(request.args.get("json"))
@@ -303,6 +318,7 @@ def get_heat_rate():
     result = heatrate.main(data)
     return result
 
+
 @app.route('/api/Test_offlinerb', methods=["POST"])
 def get_test_offlinerb():
     info = json.loads(request.data)
@@ -336,6 +352,7 @@ def get_test_offlinerb():
         result["time"] = time1[:index:]
     return json.dumps(result)
 
+
 @app.route("/api/pca_off_train", methods=["POST"])
 def get_pca_off_train():
     try:
@@ -346,6 +363,7 @@ def get_pca_off_train():
         result = json.dumps({"error_msg": traceback.format_exc()})
     return result
 
+
 @app.route('/api/pcamtcltest', methods=["POST"])
 def get_test_pcamtcltest():
     try:
@@ -357,6 +375,7 @@ def get_test_pcamtcltest():
         result = {"error_msg": str(e)}
         return json.dumps(result)
 
+
 @app.route('/api/pcamtcltest_recon', methods=["POST"])
 def get_test_pcamtcltest_recon():
     try:
@@ -368,6 +387,7 @@ def get_test_pcamtcltest_recon():
         result = {"error_msg": str(e)}
         return json.dumps(result)
 
+
 @app.route("/api/mtcltest_recon_exa", methods=["POST"])
 def get_mtcltest_recon_exa():
     try:
@@ -377,7 +397,7 @@ def get_mtcltest_recon_exa():
             import pcamtcltest_recon
             result = pcamtcltest_recon.main(info)
             return json.dumps(result)
-        elif filename =="AANN":
+        elif filename == "AANN":
             import aannmtcltest_recon
             result = aannmtcltest_recon.main(info)
             return json.dumps(result)
@@ -385,6 +405,7 @@ def get_mtcltest_recon_exa():
         result = {"error_msg": str(e)}
         return json.dumps(result)
 
+
 @app.route("/api/ann_clean_test", methods=["POST"])
 def get_ann_clean_test():
     try:
@@ -395,6 +416,7 @@ def get_ann_clean_test():
         result = {"error_msg": str(e)}
     return json.dumps(result)
 
+
 @app.route("/api/test/model_performance", methods=["POST"])
 def get_model_performance():
     info = json.loads(request.data)
@@ -402,6 +424,7 @@ def get_model_performance():
     result = model_performance.main(info)
     return json.dumps(result).encode("utf-8").decode("unicode_escape")
 
+
 @app.route("/api/model_performance_data", methods=["POST"])
 def get_model_performance_data():
     info = json.loads(request.data)
@@ -409,6 +432,7 @@ def get_model_performance_data():
     result = model_performance.data_main(info)
     return json.dumps(result).encode("utf-8").decode("unicode_escape")
 
+
 @app.route("/api/test/spca_train", methods=["POST"])
 def get_spca_train():
     try:
@@ -419,6 +443,7 @@ def get_spca_train():
         result = json.dumps({"error_msg": str(e)})
     return result
 
+
 @app.route("/api/test/spca_test", methods=["POST"])
 def get_spca_test():
     try:
@@ -429,6 +454,7 @@ def get_spca_test():
         result = json.dumps({"error_msg": traceback.format_exc()})
     return result
 
+
 @app.route("/api/test/clean_data", methods=["POST"])
 def get_clean_data():
     try:
@@ -439,6 +465,7 @@ def get_clean_data():
         result = json.dumps({"error_msg": traceback.format_exc()})
     return result
 
+
 @app.route("/api/model_coverage", methods=["POST"])
 def model_coverage():
     try:
@@ -449,6 +476,7 @@ def model_coverage():
         result = json.dumps({"error_msg": traceback.format_exc()})
     return result
 
+
 @app.route('/api/get_smote_data', methods=["POST"])
 def get_smote_data():
     try:
@@ -459,6 +487,7 @@ def get_smote_data():
         res = {"error_msg": traceback.format_exc()}
     return json.dumps(res)
 
+
 @app.route('/api/ae_train', methods=["POST"])
 def ae_train():
     info = json.loads(request.data)
@@ -466,6 +495,7 @@ def ae_train():
     res = train_main(info)
     return res
 
+
 @app.route('/api/ae_test', methods=["POST"])
 def ae_test():
     try:
@@ -476,11 +506,12 @@ def ae_test():
         res = json.dumps({"error_msg": traceback.format_exc()})
     return res
 
+
 @app.route("/api/get_pca_cp_mtcl_test", methods=["POST"])
 def get_pca_cp_mtcl_test():
     try:
         info = json.loads(request.data)
-        from pca import cp_main ##
+        from pca import cp_main  ##
         res = cp_main(info)
     except Exception as e:
         res = json.dumps({"error_msg": traceback.format_exc()})
@@ -489,5 +520,5 @@ def get_pca_cp_mtcl_test():
 
 if __name__ == '__main__':
     a = 1
-    #GetAssess()
-    app.run(host="172.28.137.230", port="8082", threaded=True)
+    # GetAssess()
+    app.run(host="0.0.0.0", port="8082", threaded=True)

app.spec

@@ -0,0 +1,44 @@
+# -*- mode: python ; coding: utf-8 -*-
+
+
+block_cipher = None
+
+
+a = Analysis(['app.py'],
+             pathex=[],
+             binaries=[],
+             datas=[],
+             hiddenimports=[],
+             hookspath=[],
+             hooksconfig={},
+             runtime_hooks=[],
+             excludes=[],
+             win_no_prefer_redirects=False,
+             win_private_assemblies=False,
+             cipher=block_cipher,
+             noarchive=False)
+pyz = PYZ(a.pure, a.zipped_data,
+             cipher=block_cipher)
+
+exe = EXE(pyz,
+          a.scripts, 
+          [],
+          exclude_binaries=True,
+          name='app',
+          debug=False,
+          bootloader_ignore_signals=False,
+          strip=False,
+          upx=True,
+          console=True,
+          disable_windowed_traceback=False,
+          target_arch=None,
+          codesign_identity=None,
+          entitlements_file=None )
+coll = COLLECT(exe,
+               a.binaries,
+               a.zipfiles,
+               a.datas, 
+               strip=False,
+               upx=True,
+               upx_exclude=[],
+               name='app')

cjl勿删/11cf0.json

@@ -1 +0,0 @@
-{"FDR": 0.0020000000000000018, "FAR": 0.0018076923076923077}

cjl勿删/11cf1.json

@@ -1 +0,0 @@
-{"FDR": 0.09199999999999997, "FAR": 0.0028461538461538463}

cjl勿删/11cf10.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0016153846153846153}

cjl勿删/11cf11.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0013846153846153845}

cjl勿删/11cf12.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0015769230769230769}

cjl勿删/11cf2.json

@@ -1 +0,0 @@
-{"FDR": 0.7130000000000001, "FAR": 0.003269230769230769}

cjl勿删/11cf3.json

@@ -1 +0,0 @@
-{"FDR": 0.994, "FAR": 0.0016538461538461537}

cjl勿删/11cf4.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0015769230769230769}

cjl勿删/11cf5.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0016153846153846153}

cjl勿删/11cf6.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0015769230769230769}

cjl勿删/11cf7.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0015384615384615385}

cjl勿删/11cf8.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0016153846153846153}

cjl勿删/11cf9.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0016153846153846153}

cjl勿删/1detection_fan0_1.json

@@ -1 +0,0 @@
-{"FDR": 0.46699999999999997, "FAR": 0.13777777777777778}

cjl勿删/1detection_fan0_5.json

@@ -1 +0,0 @@
-{"FDR": 0.868, "FAR": 0.15344444444444444}

cjl勿删/22cf0.json

@@ -1 +0,0 @@
-{"FDR": 0.007845188284518856, "FAR": 0.001953125}

cjl勿删/22cf1.json

@@ -1 +0,0 @@
-{"FDR": 0.1931106471816284, "FAR": 0.004943390208898103}

cjl勿删/22cf10.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0016729735112527384}

cjl勿删/22cf11.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0015537229592446516}

cjl勿删/22cf2.json

@@ -1 +0,0 @@
-{"FDR": 0.770440251572327, "FAR": 0.001912960306073649}

cjl勿删/22cf3.json

@@ -1,28 +0,0 @@
-    try:
-        for i in range(1, 3):
-            k = i
-            model = pca_train_k.pca(train_data, k)
-            if al_type == "SPE":
-                limit = model["QCUL_95"]
-            elif al_type == "FAI":
-                limit = model["Kesi_95"]
-            else:
-                limit = model["T2CUL_95"]
-            _, test_data, f_m = get_test_data_1(train_data, samples, amplitudes, fault_index, 1)
-            data = (test_data - model["Train_X_mean"]) / model["Train_X_std"]
-            t_r = get_rb_pca(data, model, limit, al_type, f_m)
-            result.append(t_r)
-    except Exception as e:
-        with open('log.log', "a") as f:
-            f.write(f"{str(datetime.datetime.now())}{traceback.format_exc()}")
-
-
-
-# for index in range(data.shape[0]):
-            #     line = data[index] @ m @ data[index].T
-            #     lines.append(line)
-            # x = list(range(data.shape[0]))
-            # limits_line = list(repeat(limit, data.shape[0]))
-            # plt.plot(x, lines)
-            # plt.plot(x, limits_line)
-            # plt.title(f'k={k},limit={limit}')

cjl勿删/22cf4.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0015949599266318435}

cjl勿删/22cf5.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0017141034840149885}

cjl勿删/22cf6.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0016721076518831117}

cjl勿删/22cf7.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0013954786491766676}

cjl勿删/22cf8.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.0015545280612244898}

cjl勿删/22cf9.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.001634117178158629}

cjl勿删/2detection_fan0_1.json

@@ -1 +0,0 @@
-{"FDR": 0.926, "FAR": 0.0006666666666666666}

cjl勿删/2detection_fan0_5.json

@@ -1 +0,0 @@
-{"FDR": 0.984, "FAR": 0.0071111111111111115}

cjl勿删/3detection_fan0_1.json

@@ -1 +0,0 @@
-{"FDR": 0.17000000000000004, "FAR": 0.025444444444444443}

cjl勿删/3detection_fan0_5.json

@@ -1 +0,0 @@
-{"FDR": 0.821, "FAR": 0.018}

cjl勿删/4detection_fan0_1.json

@@ -1 +0,0 @@
-{"FDR": 0.5720000000000001, "FAR": 0.008}

cjl勿删/4detection_fan0_5.json

@@ -1 +0,0 @@
-{"FDR": 0.921, "FAR": 0.008222222222222223}

cjl勿删/5detection_fan0_1.json

@@ -1 +0,0 @@
-{"FDR": 0.783, "FAR": 0.0035555555555555557}

cjl勿删/5detection_fan0_5.json

@@ -1 +0,0 @@
-{"FDR": 0.958, "FAR": 0.0012222222222222222}

cjl勿删/ae_train.py

@@ -1,95 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
- @Time    : 2020/5/29 13:52
- @Author  : 杰森·家乐森
- @File    : ae_train.py
- @Software: PyCharm
-"""
-
-import time
-import json
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-from sklearn.metrics import r2_score
-from tensorflow.keras import backend
-from tensorflow.keras.models import Model
-from sklearn.preprocessing import MinMaxScaler
-from tensorflow.keras.layers import Dense, Input
-
-
-def rmse(y_true, y_pred):
-    return backend.sqrt(backend.mean(tf.keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
-
-
-def train(layers, data, test, epoch):
-    """
-    自编码训练函数
-    :param data: 归一化数据
-    :param layers: 网络结构，int类型数组
-    :param epoch: 训练次数
-    :return:
-    """
-    mms = MinMaxScaler()
-    data = mms.fit_transform(data)
-    test = mms.transform(test)
-    mid = len(layers) // 2 - 1
-    layer = [Input(shape=(layers[0],))]
-    for i in range(len(layers) - 1):
-        if i == mid:
-            layer.append(Dense(layers[i + 1])(layer[i]))
-        else:
-            layer.append(Dense(layers[i + 1], activation="sigmoid")(layer[i]))
-    autoencoder = Model(layer[0], layer[-1])
-    autoencoder.compile(optimizer="adam", loss="mse",
-                        metrics=['accuracy'])
-    autoencoder.fit(data, data, epochs=epoch, batch_size=32, shuffle=True)
-    test_data = autoencoder.predict(data, batch_size=400)
-    test_out = autoencoder.predict(test, batch_size=400)
-    with tf.Session():
-        spe = rmse(data, test_data).eval()
-        limit = 3 * spe
-        mse = rmse(test, test_out).eval()
-    # r2 = r2_score(data, test_data)
-    r2 = r2_score(test, test_out)
-    weight = autoencoder.get_weights()
-    weights = []
-    bias = []
-    is_weight = True
-    model = {
-        "weights": weights,
-        "bias": bias
-    }
-    for i in range(len(weight)):
-        if is_weight:
-            weights.append(weight[i].tolist())
-            is_weight = False
-        else:
-            bias.append(weight[i].tolist())
-            is_weight = True
-    return model, float(limit), r2, float(mse)
-
-
-if __name__ == '__main__':
-    with open('data.json', 'r') as f:
-        data = np.array(json.load(f)['data'])
-    layer_arr = [
-        [10, 1, 10],
-        [10, 8, 1, 8, 10],
-        [10, 8, 6, 1, 6, 8, 10]
-    ]
-    for i in range(len(layer_arr)):
-        k = len(layer_arr[i]) // 2
-        for j in range(5):
-            layer_arr[i][k] = j + 1
-            model, limit, r2, mse = train(layer_arr[i], data[:3000, :], data, 5000)
-            model_config = {
-                "layer": layer_arr[i],
-                "r2": r2,
-                "mse": mse,
-                "model": model,
-                "spe": limit
-            }
-            with open('model%d_%d.json' % (len(layer_arr[i]), j + 1), 'w') as f:
-                f.write(json.dumps(model_config))
-    print('OK')

cjl勿删/autoencoder1.json

@@ -1,109 +0,0 @@
-{
-  "model": {
-    "weights": [
-      [
-        [
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-          1.161040186882019
-        ],
-        [
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-        ],
-        [
-          -0.7668898105621338,
-          1.2049490213394165
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-          -1.8514107465744019
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-        [
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-          0.8509989380836487
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-          1.7956933975219727,
-          0.868218183517456
-        ],
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-          -2.00811767578125,
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-        ]
-      ],
-      [
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-          -7.338461875915527,
-          5.048229217529297,
-          -2.2446556091308594,
-          -11.515352249145508
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-          -7.772740840911865,
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-          -2.1539909839630127,
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-          1.64174222946167,
-          -1.6902512311935425,
-          1.0922235250473022,
-          -0.36473581194877625,
-          -1.7786568403244019
-        ]
-      ]
-    ],
-    "bias": [
-      [
-        -0.12595483660697937,
-        -0.46088284254074097
-      ],
-      [
-        -2.623779535293579,
-        1.970546007156372,
-        -1.4591680765151978,
-        -0.1846103072166443,
-        0.5310178995132446,
-        -5.116513729095459,
-        4.508279800415039,
-        -3.150614023208618,
-        0.5747373700141907,
-        3.220733165740967
-      ]
-    ]
-  },
-  "spe": 0.17004422843456268,
-  "cov": [
-    [
-      22.631289381543212,
-      -3.8047325457751078
-    ],
-    [
-      -3.8047325457751078,
-      23.33727614059547
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-  ],
-  "h_mean": [
-    0.5599127411842346,
-    0.538157045841217
-  ],
-  "limit_95": 6.011475027591657,
-  "limit_99": 9.251043652650376
-}

cjl勿删/autoencoder2.json

@@ -1 +0,0 @@
-{"model": {"weights": [[[-0.8356160521507263, 0.3092801868915558, -1.2909915447235107, 1.6997143030166626, -0.027433719485998154], [1.1384114027023315, 0.9021164774894714, 0.39725518226623535, -1.7175440788269043, -1.1450926065444946], [-0.5036107897758484, 0.9385666251182556, -1.476340651512146, 0.5507526993751526, -0.1961209625005722], [0.6321204304695129, -0.1261664777994156, 0.3924834430217743, -1.504025936126709, -1.271727204322815], [-1.4868829250335693, -2.190732479095459, 2.3284878730773926, -0.6612717509269714, 1.4683117866516113], [0.3885435163974762, 1.6360031366348267, -0.27096644043922424, -0.6505817770957947, 0.9483583569526672], [0.6231023669242859, 0.44627267122268677, -1.5316928625106812, -1.2492015361785889, -3.0080792903900146], [0.2088644951581955, 2.1375482082366943, -0.5260413289070129, -1.106449842453003, 1.1216826438903809], [1.161962628364563, -1.5129520893096924, -0.9803615212440491, 0.8483885526657104, -1.1303361654281616], [-0.3191094696521759, -1.0919346809387207, 0.6873847842216492, 1.3502848148345947, 1.280632734298706]], [[2.3238298892974854, -0.4271831512451172], [-3.0681726932525635, 1.4455369710922241], [3.102893114089966, 2.151425361633301], [-1.994104266166687, -2.915308952331543], [-0.658724844455719, 3.064145565032959]], [[4.225986480712891, -3.9873640537261963, -3.688408136367798, -10.041897773742676, -4.217791557312012], [-3.526071310043335, 9.990665435791016, -3.4148740768432617, -1.254059076309204, 4.050563335418701]], [[-2.1572444438934326, 1.85689115524292, -1.498786449432373, 1.785080075263977, 1.9605295658111572, -3.683119535446167, 3.6307613849639893, -1.7958505153656006, 0.5846244692802429, 1.6343960762023926], [-2.4148974418640137, 2.371803045272827, -1.5240622758865356, 0.8642465472221375, 1.2768983840942383, 2.6133084297180176, -3.3966615200042725, 1.2262998819351196, -2.612510919570923, -4.64479923248291], [3.0364913940429688, -3.201200008392334, 2.247159719467163, -3.5875837802886963, -5.923583030700684, -3.643436908721924, 3.541846990585327, -2.394259452819824, 0.3634031414985657, 1.1130130290985107], [5.331246376037598, -5.80121374130249, 3.4133965969085693, 1.897201418876648, 1.3766930103302002, -0.1153433546423912, 0.14817313849925995, -0.06482663750648499, -0.26036545634269714, -0.28526535630226135], [0.09504624456167221, -0.30022457242012024, 0.09537942707538605, -0.5121979117393494, -1.3358240127563477, 4.144266605377197, -4.262178421020508, 3.3335225582122803, 0.04168545454740524, -1.972151756286621]]], "bias": [[0.506768524646759, 1.2093656063079834, -0.8774029612541199, -0.9476589560508728, -0.5230045318603516], [0.29825013875961304, -1.4348657131195068], [0.0223399605602026, -0.46129781007766724, 4.27229642868042, 1.873478889465332, -0.6493011713027954], [-1.0499862432479858, 0.8853863477706909, -0.5634945034980774, 0.2314375638961792, 0.9517301917076111, -0.668080747127533, 0.9133151769638062, -0.1797475665807724, 1.027596116065979, 1.3987751007080078]]}, "spe": 0.10670268908143044, "cov": [[31.721639732741355, -6.242508859273951], [-6.2425088592739515, 26.28154801896884]], "h_mean": [0.2731820046901703, 0.5072057843208313], "limit_95": 6.011475027591657, "limit_99": 9.251043652650376}

cjl勿删/autoencoder3.json

@@ -1,387 +0,0 @@
-{
-  "model": {
-    "weights": [
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cjl勿删/cf.json

@@ -1 +0,0 @@
-{"FDR": 0.0, "FAR": 0.00034615384615384613}

cjl勿删/cf0.json

@@ -1 +0,0 @@
-{"FDR": 0.0, "FAR": 0.0003846153846153846}

cjl勿删/cf1.json

@@ -1 +0,0 @@
-{"FDR": 0.0020000000000000018, "FAR": 0.0004230769230769231}

cjl勿删/cf10.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.00034615384615384613}

cjl勿删/cf11.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.00034615384615384613}

cjl勿删/cf2.json

@@ -1 +0,0 @@
-{"FDR": 0.013000000000000012, "FAR": 0.00046153846153846153}

cjl勿删/cf3.json

@@ -1 +0,0 @@
-{"FDR": 0.14700000000000002, "FAR": 0.0017307692307692308}

cjl勿删/cf4.json

@@ -1 +0,0 @@
-{"FDR": 0.862, "FAR": 0.001346153846153846}

cjl勿删/cf5.json

@@ -1 +0,0 @@
-{"FDR": 0.998, "FAR": 0.0003846153846153846}

cjl勿删/cf6.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.00034615384615384613}

cjl勿删/cf7.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.00034615384615384613}

cjl勿删/cf8.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.00034615384615384613}

cjl勿删/cf9.json

@@ -1 +0,0 @@
-{"FDR": 1.0, "FAR": 0.00034615384615384613}