.

plotFScore_v5.py

+# Csar Fdez, UdL, 2025
+# Plots paper's bar plots of FSCore
+import matplotlib.pyplot as plt
+import numpy as np
+
+F={}
+for i in range(4,29,4):
+    F[i]=np.ones(6)
+for i in range(4,29,4):
+    for j in range(6):
+        F[i][j]=np.random.uniform(0.8,1)
+
+
+# https://stackoverflow.com/questions/10369681/how-to-plot-bar-graphs-with-same-x-coordinates-side-by-side-dodged
+
+print(F)

v5_class.py

 # 5.  Open door
 
 
-NumberOfFailures=4  # So far, we have only data for the first 4 types of failures
+NumberOfFailures=3  # 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([])
 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]=['2025-02-13_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]=['2025-02-12_5_','2025-02-13_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]=['2025-02-13_5_']
+    #datafiles[1][4]=['2025-02-13_5_']
  
 
 #datafiles[0][4]=['2025-02-05_5_'] 
 features=['r1 s5']
 # Feature combination suggested by AKO
 #features=['r1 s1','r1 s4','r1 s5','pa1 apiii']
-features=['r1 s1','r1 s4','r1 s5']
+#features=['r1 s1','r1 s4','r1 s5']
 #features=['r1 s1','r1 s5','pa1 apiii']
 #features=['r1 s5','pa1 apiii']
 #features=['r1 s1','r1 s5']
-#features=['r1 s5']
+features=['r1 s5']
 
 
 
 
 
 #   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))))
     plotData()
     exit(0)
 
-testClasses=[0,1,2,3,4]
+testClasses=[0,1,2,3]
 
 if not len(testClasses)==len(testRanges):
     print("ERROR:  testClasses and testRanges must have same length")
     print("F1-Score: ",F1)
 
 anomalyMetric(classes,testRanges,testClasses)
-
+#plotData4()
+exit(0)
 # Compute delay until correct detection for a list of ranges (when transition data exists)
 def computeDelay(l,classes,testRanges,testClasses):
     d=np.zeros(len(l))
     print(d)
     return(d.mean())
 
-print(testRanges)
 d=computeDelay([2,3,4],classes,testRanges,testClasses)
-print("mean delay: ",d)
-plotData4()