.

v5_class.py

@@ -125,7 +125,6 @@ for i in range(NumberOfFailures+1):
     dftemp=[]
     for f in datafiles[1][i]:
         print("                 ", f)
-        #df1 = pd.read_csv('./data/'+f+'.csv', parse_dates=['datetime'], dayfirst=True, index_col='datetime')
         df1 = pd.read_csv('./data/'+f+'.csv')
         dftemp.append(df1)
     df_list[1][i]=pd.concat(dftemp)
@@ -152,8 +151,7 @@ for i in range(NumberOfFailures+1):
     dataframe[1][i].dropna(inplace=True)
 
 
-# Train data is first 2/3 of data. Not exactly. L
-# Test data is: last 1/3 of data 
+# Train data i [0] and test data is [1]
 dataTrain=[]
 dataTest=[]
 for i in range(NumberOfFailures+1):
@@ -161,6 +159,10 @@ for i in range(NumberOfFailures+1):
     dataTest.append(dataframe[1][i])
 
 # Calculate means and stdev
+
+
+
+
 a=dataTrain[0]
 for i in range(1,NumberOfFailures+1):
     a=np.vstack((a,dataTrain[i]))
@@ -298,6 +300,10 @@ def plotData():
         s+=featureNames[features[indexesToPlot[i]]]
         s+=' '+unitNames[features[indexesToPlot[i]]]
         if NumFeaturesToPlot==1:
+
+
+
+
             axes.set_ylabel(s)
             axes.grid()
         else:
@@ -347,7 +353,7 @@ for m in range(NumberOfFailures+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)
+classes=np.argmin(test_mae_loss_average,axis=0)    # Choose the minimum loss
 
 x=[]
 y=[]