.

v4_class.py

 
 features=['r1 s1','r1 s4','r1 s5','pa1 apiii']
 features=['r1 s1','r1 s4','r1 s5']
+features=['r1 s1','r1 s5']
 featureNames={}
 featureNames['r1 s1']='$T_{evap}$'
 featureNames['r1 s4']='$T_{cond}$'
 
 # Let's plot some features
 
-colorline=['violet','lightcoral','cyan','lime','grey']
-colordot=['darkviolet','red','blue','green','black']
+colorline=['black','violet','lightcoral','cyan','lime','grey']
+colordot=['grey','darkviolet','red','blue','green','black']
 
 #featuresToPlot=['r1 s1','r1 s2','r1 s3','pa1 apiii']
 featuresToPlot=features
 
 
 x_test_predict=[]
-for m in range(1,NumberOfFailures+1):
+for m in range(NumberOfFailures+1):
     x_test_predict.append(model[m].predict(x_test))
 
 x_test_predict=np.array((x_test_predict))
+test_mae_loss =[]
+for m in range(NumberOfFailures+1):
+    test_mae_loss.append(np.mean(np.abs(x_test_predict[m,:,:,:] - x_test), axis=1))
+test_mae_loss=np.array((test_mae_loss))
+test_mae_loss_average=np.mean(test_mae_loss,axis=2)  # average over features
+classes=np.argmin(test_mae_loss_average,axis=0)
 
-print(x_test_predict)
-exit(0)
 
 def plotData4():
     NumFeaturesToPlot=len(indexesToPlot)
         nrows=NumFeaturesToPlot, ncols=1, figsize=(15, 10), dpi=80, facecolor="w", edgecolor="k",sharex=True
     )
     for i in range(NumFeaturesToPlot):
-
-
         init=0
         end=testRanges[0][1]
-        axes[i].plot(range(init,end),x_test[testRanges[0][0]:testRanges[0][1],0,indexesToPlot[i]]*stdevs[i]+means[i],label="No fail", color='black')
-        init=end
-        end+=(testRanges[1][1]-testRanges[1][0])
-        for j in range(1,NumberOfFailures+1):
-            axes[i].plot(range(init,end),x_test[testRanges[j][0]:testRanges[j][1],0,indexesToPlot[i]]*stdevs[i]+means[i],label="Fail type "+str(j), color=colorline[j-1],linewidth=1)
+        for j in range(NumberOfFailures+1):
+            axes[i].plot(range(init,end),x_test[testRanges[j][0]:testRanges[j][1],0,indexesToPlot[i]]*stdevs[i]+means[i],label="Class "+str(j), color=colorline[j],linewidth=1)
             if j<NumberOfFailures:
                 init=end
                 end+=(testRanges[j+1][1]-testRanges[j+1][0])
 
-        for j in range(NumberOfFailures):
+        for j in range(NumberOfFailures+1):
             x=[]
             y=[]
-            for k in anomalous_data_type[j]:
-                if (k+int(options.timesteps))<x_test.shape[0]:
-                    x.append(k+int(options.timesteps))
-                    y.append(x_test[k+int(options.timesteps),0,indexesToPlot[i]]*stdevs[i]+means[i])
-            axes[i].plot(x,y ,color=colordot[j],marker='.',linewidth=0,label="Fail detect  type "+str(j+1) )
+            for k in range(len(classes)):
+                if classes[k]==j:
+                    x.append(k)
+                    y.append(x_test[k,0,indexesToPlot[i]]*stdevs[i]+means[i])
+            axes[i].plot(x,y ,color=colordot[j],marker='.',markersize=10,linewidth=0,label="Fail detect  type "+str(j) )