Classification Iris data set

@@ -72,7 +72,7 @@ if __name__ == "__main__":
     print("File to read evaluation true classes: " + str(args.inputEvaluationClasses))
     print("Path to place output files: " + str(args.outputPath))
     print("File to write evaluation report: " + str(args.outputFile))
-    print("Classifier: " + str(args.outputFile))
+    print("Classifier: " + str(args.classifier))
 
     # Start time
     t0 = time()
@@ -123,7 +123,7 @@ if __name__ == "__main__":
     if args.classifier == "MultinomialNB":
         classifier = MultinomialNB()
     elif args.classifier == "SVM":
-        classifier = SVC()
+        classifier = SVC(kernel="linear")
     elif args.classifier == "DecisionTree":
         classifier = DecisionTreeClassifier()
     elif args.classifier == "Perceptron":
@@ -154,9 +154,9 @@ if __name__ == "__main__":
             oFile.write("{}".format(classifier.coef_))
             oFile.write('Confidence scores: \n')
             oFile.write("{}".format(confidence_scores))
-            if args.classifier == "SVM":
-                oFile.write('Number of support vectors per class: {}\n'.format(classifier.n_support_))
-                oFile.write('Support vectors: {}\n'.format(classifier.support_vectors_))
+        if args.classifier == "SVM":
+            oFile.write('Number of support vectors per class: {}\n'.format(classifier.n_support_))
+            oFile.write('Support vectors: {}\n'.format(classifier.support_vectors_))
 
     print("     Saving evaluation report done!")