.

v5_class.py

@@ -42,7 +42,7 @@ parser.add_option("-p", "--plot", dest="plot", help="Only plot data (false)", de
 # 5.  Open door
 
 
-NumberOfFailures=3  # So far, we have only data for the first 4 types of failures
+NumberOfFailures=4  # So far, we have only data for the first 4 types of failures
 datafiles=[[],[]]   # 0 for train,  1 for test
 for i in range(NumberOfFailures+1):
     datafiles[0].append([])
@@ -53,21 +53,21 @@ datafiles[0][0]=['2024-08-07_5_','2024-08-08_5_','2025-01-25_5_','2025-01-26_5_'
 datafiles[0][1]=['2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_'] 
 datafiles[0][2]=['2024-12-18_5_','2024-12-21_5_','2024-12-22_5_','2024-12-23_5_','2024-12-24_5_'] 
 datafiles[0][3]=['2024-12-28_5_','2024-12-29_5_','2024-12-30_5_'] 
-#datafiles[0][4]=['2025-02-05_5_','2025-02-10_5_']
+datafiles[0][4]=['2025-02-13_5_']
 
 if options.transition:
     datafiles[1][0]=['2025-01-27_5_','2025-01-28_5_'] 
     datafiles[1][1]=['2024-12-14_5_','2024-12-15_5_','2024-12-16_5_']  # with TRANSITION
     datafiles[1][2]=['2024-12-17_5_','2024-12-19_5_','2024-12-25_5_','2024-12-26_5_'] # with TRANSITION
     datafiles[1][3]=['2024-12-27_5_','2024-12-31_5_','2025-01-01_5_'] # with TRANSITION
-    #datafiles[1][4]=['2025-02-05_5_','2025-02-10_5_']
+    datafiles[1][4]=['2025-02-12_5_','2025-02-13_5_']
 
 else:
     datafiles[1][0]=['2025-01-27_5_','2025-01-28_5_'] 
     datafiles[1][1]=['2024-12-14_5_','2024-12-15_5_'] 
     datafiles[1][2]=['2024-12-19_5_','2024-12-25_5_','2024-12-26_5_'] 
     datafiles[1][3]=['2024-12-31_5_','2025-01-01_5_'] 
-    #datafiles[1][4]=['2025-02-05_5_','2025-02-10_5_']
+    datafiles[1][4]=['2025-02-13_5_']
  
 
 #datafiles[0][4]=['2025-02-05_5_'] 
@@ -318,8 +318,8 @@ def plotData():
 
 
 #   2nd scenario. Go over anomalies and classify it by less error
-datalist=[dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3]]
-#datalist=[dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]]
+#datalist=[dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3]]
+datalist=[dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]]
 x_test=create_sequences(datalist[0],int(options.timesteps))
 for i in range(1,len(datalist)):
     x_test=np.vstack((x_test,create_sequences(datalist[i],int(options.timesteps))))
@@ -336,7 +336,7 @@ if options.plot:
     plotData()
     exit(0)
 
-testClasses=[0,1,2,3]
+testClasses=[0,1,2,3,4]
 
 if not len(testClasses)==len(testRanges):
     print("ERROR:  testClasses and testRanges must have same length")
@@ -460,7 +460,6 @@ def anomalyMetric(classes,testranges,testclasses):
     print("F1-Score: ",F1)
 
 anomalyMetric(classes,testRanges,testClasses)
-plotData4()
 
 # Compute delay until correct detection for a list of ranges (when transition data exists)
 def computeDelay(l,classes,testRanges,testClasses):
@@ -471,18 +470,21 @@ def computeDelay(l,classes,testRanges,testClasses):
         start=testRanges[i][0]
         count=0
         while start<testRanges[i][1]:
-            start+=1
             if classes[start]==testClasses[i]:
                 count+=1
-            if count==NoFailsInARow:
+            if count==NoFailsInARow or start==(testRanges[i][1]-1):
                 count=0
                 #print(start,start-testRanges[i][0]-NoFailsInARow+timesteps)
                 d[ind]=start-testRanges[i][0]-NoFailsInARow+timesteps
                 break
+            start+=1
         ind+=1
+    print(d)
     return(d.mean())
 
-d=computeDelay([2,3],classes,testRanges,testClasses)
+print(testRanges)
+d=computeDelay([2,3,4],classes,testRanges,testClasses)
 print("mean delay: ",d)
+plotData4()