Training, crossvalidation and testing structural domain dataset

@@ -208,12 +208,12 @@ if __name__ == "__main__":
         print("      Done!")
 
     print("   Number of training classes: {}".format(len(y_train)))
-    print("   Number of training class RI: {}".format(y_train.count('DOM')))
+    print("   Number of training class DOM: {}".format(y_train.count('DOM')))
     print("   Number of training class OTHER: {}".format(y_train.count('OTHER')))
     print("   Shape of training matrix: {}".format(X_train.shape))
 
     print("   Number of testing classes: {}".format(len(y_test)))
-    print("   Number of testing class RI: {}".format(y_test.count('DOM')))
+    print("   Number of testing class DOM: {}".format(y_test.count('DOM')))
     print("   Number of testing class OTHER: {}".format(y_test.count('OTHER')))
     print("   Shape of testing matrix: {}".format(X_test.shape))
 
@@ -276,13 +276,13 @@ if __name__ == "__main__":
     print("   Done!")
 
     print("Training...")
-    classifier.fit(X_train, y_train)
+    myClassifier.fit(X_train, y_train)
     print("   Done!")
 
     print("Testing (prediction in new data)...")
     if args.reduction is not None:
         X_test = reduc.transform(X_test)
-    y_pred = classifier.predict(X_test)
+    y_pred = myClassifier.predict(X_test)
     print("   Done!")
 
     print("Saving report...")