Iris dataset for automatic clasification

@@ -140,6 +140,7 @@ if __name__ == "__main__":
     classifier.fit(dataTraining, trueTrainingClasses)
     classifier.fit(dataTraining, trueTrainingClasses)
     print("   Prediction...")
     print("   Prediction...")
     y_pred = classifier.predict(dataEvaluation)
     y_pred = classifier.predict(dataEvaluation)
+    confidence_scores = classifier.decision_function(dataEvaluation)
     print("     Training and predition done!")
     print("     Training and predition done!")
 
 
     # for i in range(len(trueClasses)):
     # for i in range(len(trueClasses)):
@@ -162,9 +163,8 @@ if __name__ == "__main__":
         oFile.write('Classification report: \n')
         oFile.write('Classification report: \n')
         oFile.write(classification_report(trueEvaluationClasses, y_pred) + '\n')
         oFile.write(classification_report(trueEvaluationClasses, y_pred) + '\n')
         if options.classifier == "Perceptron":
         if options.classifier == "Perceptron":
-            oFile.write("Perceptron\n")
-            for coef in classifier.coefs_:
-                oFile.write("coef.shape: {}\n".format(coef.shape))
+            oFile.write("{}".format(confidence_scores))
+
     print("     Saving test report done!")
     print("     Saving test report done!")
 
 
     print("Training and test done in: %fs" % (time() - t0))
     print("Training and test done in: %fs" % (time() - t0))