.

v4_class.py

 test_mae_loss_average=np.mean(test_mae_loss,axis=2)  # average over features
 classes=np.argmin(test_mae_loss_average,axis=0)
 
+x=[]
+y=[]
+for j in range(NumberOfFailures+1):
+    x.append([])
+    y.append([])
+for j in range(NumberOfFailures+1):
+    for k in range(testRanges[j][0],testRanges[j][1]):
+        if not  classes[k]==j:
+            x[classes[k]].append(k)
+            y[classes[k]].append(x_test[k,0,indexesToPlot[0]]*stdevs[0]+means[0])
+
+
 
 def plotData4():
     NumFeaturesToPlot=len(indexesToPlot)
                 init=end
                 end+=(testRanges[j+1][1]-testRanges[j+1][0])
 
-        for j in range(NumberOfFailures+1):
-            x=[]
-            y=[]
-            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) )
+            #if i==0:
+            #    axes[0].plot(x[j],y[j] ,color=colordot[j],marker='.',markersize=10,linewidth=0,label="Fail detect  type "+str(j) )
 
 
 
-        if i==(NumFeatures-1):
-            axes[i].legend(loc='right')
         s=''
         s+=featureNames[features[indexesToPlot[i]]]
         s+=' '+unitNames[features[indexesToPlot[i]]]
         axes[i].set_ylabel(s)
         axes[i].grid()
+    for j in range(NumberOfFailures+1):
+        axes[0].plot(x[j],y[j] ,color=colordot[j],marker='.',markersize=10,linewidth=0,label="Fail detect  type "+str(j) )
+    axes[0].legend(ncol=4,loc=(0.1,0.98))
+
+        
     axes[NumFeaturesToPlot-1].set_xlabel("Sample number")
     plt.show()