.

RESULTS.txt

@@ -3,7 +3,7 @@ timesteps      features                        f1-score   FN
 n=12  ['r1 s1','r1 s4','r1 s5','pa1 apiii']   0.966  [51 0 0 0 65]
 n=12  ['r1 s1','r1 s4','r1 s5']    0.9799   [0 0 0 0 57]
 n=18   ['r1 s1','r1 s4','r1 s5']  0.996   [0 0 0 0 10]
-n=24  error
+n=24   ['r1 s1','r1 s4','r1 s5']    mal
 
 5-22   poques dades
 n=12  ['r1 s1','r1 s4','r1 s5','pa1 apiii']    
@@ -19,7 +19,7 @@ n=24  error
 n=6 ['r1 s1','r1 s4','r1 s5']   0.941     [0 0 0 0 103]
 n=12  ['r1 s1','r1 s4','r1 s5','pa1 apiii']   0.951  [0 0 0 0 86]
 n=12  ['r1 s1','r1 s4','r1 s5']    0.934   [0 0 0 0 112]
-n=18   error
+n=18    MAAAL !!!!  0.61005 [0, 0, 42, 254, 390]
 n=24   ['r1 s1','r1 s4','r1 s5']  0.943   [0 0 0 0 92]
 
 
@@ -27,8 +27,10 @@ n=24   ['r1 s1','r1 s4','r1 s5']  0.943   [0 0 0 0 92]
 
 30
 
-n=6  ['r1 s1','r1 s4','r1 s5']   error   [0 0 0 0 112]
-n=12  ['r1 s1','r1 s4','r1 s5','pa1 apiii']     error  [0 0 0 0 86]
-n=12  ['r1 s1','r1 s4','r1 s5']   error   [0 0 0 0 112]
-n=18     error
-n=24   ['r1 s1','r1 s4','r1 s5']   error   [0 0 0 0 92]
+n=12  ['r1 s1','r1 s4','r1 s5']   0.6947    [0, 0, 1, 423, 150]
+n=24   ['r1 s1','r1 s4','r1 s5']  0.7041  [0, 0, 0, 411, 97]
+
+
+32   (s'estan posant més dades) 
+n=12  ['r1 s1','r1 s4','r1 s5']   0.995  [0, 0, 0, 4, 0]
+

v2_unsupervised.py

@@ -378,27 +378,29 @@ def anomalyMetric(labels,ranges):
     TP=[]
     FN=[]
     TPFP=[]
-    A=[]
-    B=[]
-    COUNTER=[]
-    ASSIGNED=[]
+    ASSIGNED_CLASS=[]
+    for i in  range(NumberOfFailures+1):
+        ASSIGNED_CLASS.append(-1)
+    UNASSIGNED_CLASS=[]
     for i in range(NumberOfFailures+1):
-        A.append(i)
         TP.append([])
         FN.append([])
         TPFP.append([])
-        COUNTER.append(Counter(labels[ranges[i][0]:ranges[i][1]]))
-
-    while len(A)>0:
-        a=A.pop(0)
-        Class=COUNTER[a].most_common(1)[0][0]
-        if Class in ASSIGNED:
-            B.append(Class)
-        else:
-            ASSIGNED.append(Class)       
-
-    # AKI
-    #lab.append(b.most_common(1)[0][0])
+        Class=Counter(labels[ranges[i][0]:ranges[i][1]]).most_common(1)[0][0]
+        if not Class in ASSIGNED_CLASS:
+            ASSIGNED_CLASS[i]=Class
+
+    for i in range(NumberOfFailures+1):
+        if not i in ASSIGNED_CLASS:
+            UNASSIGNED_CLASS.append(i)
+
+    for i in range(NumberOfFailures+1):
+        if ASSIGNED_CLASS[i]==-1:
+            ASSIGNED_CLASS[i]=UNASSIGNED_CLASS.pop(0)
+
+
+    for i in range(NumberOfFailures+1):
+        lab.append(ASSIGNED_CLASS[i])
 
     for i in range(NumberOfFailures+1):
         counttp=0