Iris dataset for automatic clasification

@@ -135,7 +135,7 @@ if __name__ == "__main__":
     elif options.classifier == "DecisionTree":
     elif options.classifier == "DecisionTree":
         classifier = DecisionTreeClassifier()
         classifier = DecisionTreeClassifier()
     elif options.classifier == "MLPClassifier":
     elif options.classifier == "MLPClassifier":
-        classifier = MLPClassifier(solver='lbfgs')
+        classifier = MLPClassifier(solver='lbfgs', hidden_layer_sizes=(3))
 
 
 
 
     print("   Training...")
     print("   Training...")
@@ -163,10 +163,10 @@ if __name__ == "__main__":
         oFile.write(str(confusion_matrix(trueEvaluationClasses, y_pred)) + '\n')
         oFile.write(str(confusion_matrix(trueEvaluationClasses, y_pred)) + '\n')
         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')
-        oFile.write("Weight matrices:\n")
         if options.classifier == "MLPClassifier":
         if options.classifier == "MLPClassifier":
+            oFile.write("Weight matrices\n")
             for coef in classifier.coefs_:
             for coef in classifier.coefs_:
-                oFile.write("{}".format(coef))
+                oFile.write("coef.shape: {}".format(coef.shape))
     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))