diff --git a/accent_classification/__pycache__/output_confusion_matrix.cpython-36.pyc b/accent_classification/__pycache__/output_confusion_matrix.cpython-36.pyc
new file mode 100644
index 0000000..e852733
Binary files /dev/null and b/accent_classification/__pycache__/output_confusion_matrix.cpython-36.pyc differ
diff --git a/accent_classification/output_confusion_matrix.py b/accent_classification/output_confusion_matrix.py
index ae92fb2..5dfe2b8 100644
--- a/accent_classification/output_confusion_matrix.py
+++ b/accent_classification/output_confusion_matrix.py
@@ -9,14 +9,6 @@ from sklearn.metrics import accuracy_score
 from sklearn.metrics import confusion_matrix
 
 
-currDir = 'C:\\Users\\Aki\\source\\repos\\rug_VS\\dialect_identification\\dialect_identification'
-sys.path.append(os.path.join(os.path.dirname(sys.path[0]), currDir))
-
-regionLabels  = ['Groningen_and_Drenthe', 'Oost_Overijsel-Gelderland', 'Limburg']
-regionLabels2 = ['Groningen_and_Drenthe', 'Limburg']
-dirOut = currDir + '\\result\\same-utterance_with_cities'
-
-
 def plot_confusion_matrix(cm, classes,
 						  normalize=False,
 						  title='Confusion matrix',
@@ -56,24 +48,32 @@ def plot_confusion_matrix(cm, classes,
 	plt.xlabel('Predicted label', fontsize=_fontsize-4)
 
 
-pred = np.load(dirOut + '\\pred_per_pid_3regions.npy')
+if __name__ == "__main__":
+	currDir = 'C:\\Users\\Aki\\source\\repos\\rug_VS\\dialect_identification\\dialect_identification'
+	sys.path.append(os.path.join(os.path.dirname(sys.path[0]), currDir))
 
-#accuracy = accuracy_score(pred[:, 1], pred[:, 2], normalize=True, sample_weight=None)
-#print('accuracy: {}%'.format(accuracy * 100))
+	regionLabels  = ['Groningen_and_Drenthe', 'Oost_Overijsel-Gelderland', 'Limburg']
+	regionLabels2 = ['Groningen_and_Drenthe', 'Limburg']
+	dirOut = currDir + '\\result\\same-utterance_with_cities'
 
-# confusion matrix
-cm = confusion_matrix(pred[:, 1], pred[:, 2], labels=regionLabels)
-# human perception (2 regions)
-#cm = np.array([[39, 57], [6, 104]])
-# human perception (3 regions)
-#cm = np.array([[22, 14, 52], [23, 21, 52], [5, 5, 100]])
-print(cm)
+	pred = np.load(dirOut + '\\pred_per_pid_3regions.npy')
 
-np.set_printoptions(precision=2)
+	#accuracy = accuracy_score(pred[:, 1], pred[:, 2], normalize=True, sample_weight=None)
+	#print('accuracy: {}%'.format(accuracy * 100))
 
-plt.figure()
-plot_confusion_matrix(cm, classes=['GD', 'OG', 'LB'], normalize=True)
-#plot_confusion_matrix(cm, classes=['GD', 'LB'], normalize=True)
+	# confusion matrix
+	cm = confusion_matrix(pred[:, 1], pred[:, 2], labels=regionLabels)
+	# human perception (2 regions)
+	#cm = np.array([[39, 57], [6, 104]])
+	# human perception (3 regions)
+	#cm = np.array([[22, 14, 52], [23, 21, 52], [5, 5, 100]])
+	print(cm)
 
-#plt.show()
-plt.savefig(dirOut + '\\cm_machine_3regions_normalized.png')
\ No newline at end of file
+	np.set_printoptions(precision=2)
+
+	plt.figure()
+	plot_confusion_matrix(cm, classes=['GD', 'OG', 'LB'], normalize=True)
+	#plot_confusion_matrix(cm, classes=['GD', 'LB'], normalize=True)
+
+	#plt.show()
+	plt.savefig(dirOut + '\\cm_machine_3regions_normalized.png')
\ No newline at end of file