///////////////////////////////////////////////////////////////////////
        // HOWTO: read ARFF file
        //

        Cell arff = new ArffReader();
        System.out.println("Parameters available in ArffReader [default value]:");
        System.out.print(arff.getAvailableParams());
        
        arff.set("filename", "data/glass.arff");
        arff.set("decisionAttr", "last");           // instead of "last" you can give attribute index, like "0", "1", ...
        
        System.out.println("\nFirst 3 samples from ARFF file:");        
        arff.open();
        System.out.println(arff.next());
        System.out.println(arff.next());
        System.out.println(arff.next());
        arff.close();        

        
        ///////////////////////////////////////////////////////////////////////
        // HOWTO: train and use Rseslib classifier
        //

        Cell learner = new RseslibClassifier("C45");
        learner.set("pruning", "true");
        learner.setSource(arff);
        learner.learn();

        System.out.println("\nFirst sample classified by C45 from Rseslib:");
        learner.open();
        System.out.println(learner.next());
        learner.close();        


        ///////////////////////////////////////////////////////////////////////
        // HOWTO: train and use Weka classifier
        //

        learner = new WekaClassifier("J48");
        System.out.println("\nParameters available in J48 from Weka [default value]:");
        System.out.print(learner.getAvailableParams());

        learner.set("minNumObj", "2");
        learner.setSource(arff);
        learner.learn();

        System.out.println("\nFirst sample classified by J48:");
        learner.open();
        System.out.println(learner.next());
        learner.close();        

        
        ///////////////////////////////////////////////////////////////////////
        // HOWTO: apply Weka filter
        //

        Cell filter = new WekaFilter("attribute.Remove");
        filter.set("attributeIndices", "2-6");
        filter.setSource(arff);

        System.out.println("\nFirst 3 samples from ARFF file after filtering:");
        filter.open();
        System.out.println(filter.next());
        System.out.println(filter.next());
        System.out.println(filter.next());
        filter.close();        

        
        ///////////////////////////////////////////////////////////////////////
        // HOWTO: set and get parameters (2 methods)
        //

        // 1st method:
        arff.set("filename", "data/glass.arff");
        arff.set("decisionAttr", "last");

        // 2nd method:
        Parameters par = new Parameters();
        par.set("filename", "data/glass.arff");
        par.set("decisionAttr", "last");
        arff.setParameters(par);
        System.out.println("\nParameters of the ARFF reader:");
        System.out.println(arff.getParameters());
        
        
        ///////////////////////////////////////////////////////////////////////
        // HOWTO: evaluate a classifier through train & test
        //

        learner = new WekaClassifier("J48");
        learner.set("confidenceFactor", "0.45");

        EvaluatorCell tt = new TrainAndTest(learner);
        tt.setSource(filter);
        tt.set("repetitions", "10");       // run 10 independent trials of train & test
        tt.set("trainPercent", "70");      // in each trial, use randomly selected 70% of data for training
        tt.setRandomSeed(123);             // fix random seed to use the same data splits in every run of tt.learn()
        tt.learn();                        // go!
        System.out.println("\nResult of Train+Test:\n" + tt.report());
        
        
        ///////////////////////////////////////////////////////////////////////
        // HOWTO: run cross-validation for a regression problem
        //

        // already done:   Cell arff = new ArffReader();
        arff.set("filename", "data/cloud.arff");      // decision attribute in cloud.arff is real-valued
        arff.set("decisionAttr", "last");
        learner = new WekaClassifier("SMOreg");

        EvaluatorCell cv = new CrossValidation(learner);
        cv.setSource(arff);
        cv.set("folds", "5");              // 5-fold cross-validation
        cv.set("score", "RMSE");           // use Root Mean Squared Error to measure quality
        cv.learn();
        System.out.println("\nResult of Cross-Validation:     RMSE = " + cv.result()[0]);