11.Http11AprProcessor.process(Http11AprProcessor.java:859) StandardEngineValve.invoke(StandardEngineValve.java:109) ErrorReportValve.invoke(ErrorReportValve.java:102) StandardHostValve.invoke(StandardHostValve.java:127) AuthenticatorBase.invoke(AuthenticatorBase.java:558) StandardContextValve.invoke(StandardContextValve.java:191) StandardWrapperValve.invoke(StandardWrapperValve.java:233) ApplicationFilterChain.doFilter(ApplicationFilterChain.java:206) ManagerServlet.doGet(ManagerServlet.java:361) ManagerServlet.reload(ManagerServlet.java:943) StandardContext.reload(StandardContext.java:3228) StandardContext.start(StandardContext.java:4467) StandardContext.listenerStart(StandardContext.java:3972) _Context_ntextInitialized(TestProgram_Context_Listener.java:29) _LibSVM_Test.classify(Weka_LibSVM_Test.java:48) distributionForInstance(LibSVM.java:1489) The full stack trace for the Java exception is: lib folder, renaming them both, putting them into a newly defined CLASSPATH, but nothing works.

I have tried adding both LibSVM.jar and libsvm.jar files into the. This has been really confusing me for the last few hours.

If the latter is the case, how can I resolve naming conflicts in Windows where LibSVM.jar and libsvm.jar are treated as the same file? lib folder.īut do I need libsvm.jar as provided by the download from: But where and what? I am running my application through NetBeans 8 using Tomcat and have recent versions of weka.jar and LibSVM.jar in the application's. I am working on the theory that the cause of the problem is environmental. I don't believe this issue is due to my own code because other classifiers have run successfully with it. However, an exception is being thrown at line:Īnd despite numerous attempts at try.catch blocks around this code, the exception occurring is simply reported as null (which is really helpful) as per full stack trace below. (eval.toSummaryString(' Results = ', false)) String optionsArray = options.split(' ') String options = (classificationType.getParameters()) tClassIndex(train.numAttributes() - 1) ĬlassificationType classificationType = ClassificationTypeDAO.get(6) If I use Weka Explorer to run some training data against testing data using SVM with a linear kernel, everything is fine.īut I need to do this programmatically in my own Java and my current code looks like this: Instances train = new Instances (.)

Weka jar