Example usage for weka.core Instances setClassIndex

Introduction

In this page you can find the example usage for weka.core Instances setClassIndex.

Prototype

public void setClassIndex(int classIndex) 

Source Link

Document

Sets the class index of the set.

Usage

From source file:milk.data.BagStats.java

License:Open Source License

public static void main(String args[]) throws Exception {

    FileReader file = new FileReader(args[0]);
    Instances insts = new Instances(file);
    insts.setClassIndex(insts.numAttributes() - 1);

    Exemplars exs = new Exemplars(insts);

    double av = 0;
    int max = 0;/*from  ww  w. j a v  a 2  s . c o  m*/
    int min = 123333333;
    double[] nums = new double[exs.numExemplars()];
    int pos = 0, neg = 0;

    for (int i = 0; i < exs.numExemplars(); i++) {
        if (exs.exemplar(i).classValue() != 0)
            pos++;
        else
            neg++;

        nums[i] = exs.exemplar(i).getInstances().numInstances();
        av += exs.exemplar(i).getInstances().numInstances();
        if (max < exs.exemplar(i).getInstances().numInstances())
            max = exs.exemplar(i).getInstances().numInstances();
        if (min > exs.exemplar(i).getInstances().numInstances())
            min = exs.exemplar(i).getInstances().numInstances();
    }

    System.out.println("Number of bags: " + exs.numExemplars());
    System.out.println("Number of instances: " + insts.numInstances());
    System.out.println("Number of attributes (without id and class): " + (insts.numAttributes() - 2));
    System.out.println("Average bag size: " + (av / exs.numExemplars()));
    System.out.println("Maximum bag size: " + max);
    System.out.println("Minimum bag size: " + min);
    System.out.println("Number of positive/negative bags: " + pos + "/" + neg);
    int[] order = Utils.sort(nums);
    double median;
    int half = exs.numExemplars() / 2;
    if (half * 2 == exs.numExemplars()) {
        System.out.println("even: " + half);
        median = (nums[order[half - 1]] + nums[order[half]]) / 2.0;
    } else {
        System.out.println("odd: " + half);
        median = nums[order[half]] / 2.0;
    }

    System.out.println("Median bag size: " + median);
}

---

From source file:milk.experiment.MIExperiment.java

License:Open Source License

/**
   * Carries out the next iteration of the experiment.
   *//  w  w w  . ja  va 2s.  c o  m
   * @exception Exception if an error occurs
   */
  public void nextIteration() throws Exception {

      if (m_UsePropertyIterator) {
          if (m_CurrentProperty != m_PropertyNumber) {
              setProperty(0, m_ResultProducer);
              m_CurrentProperty = m_PropertyNumber;
          }
      }

      if (m_CurrentInstances == null) {
          File currentFile = (File) getDatasets().elementAt(m_DatasetNumber);
          Reader reader = new FileReader(currentFile);
          Instances dataInsts = new Instances(new BufferedReader(reader));
          if (m_ClassFirst) {
              dataInsts.setClassIndex(0);
          } else {
              dataInsts.setClassIndex(dataInsts.numAttributes() - 1);
          }
          Exemplars data = new Exemplars(dataInsts, 0);
          m_CurrentInstances = data;
          m_ResultProducer.setInstances(m_CurrentInstances);
      }

      m_ResultProducer.doRun(m_RunNumber);

      advanceCounters();
  }

---

From source file:minorpro.MinorPro.java

public static Instances getDataset(String title) throws Exception {
    Frame ab = null;/*from   w  w  w .  ja va2  s  . co m*/
    FileDialog fd = new FileDialog(ab, title, FileDialog.LOAD);
    fd.setVisible(true);
    String fname = fd.getDirectory() + fd.getFile();
    fd.dispose();
    File f1 = new File(fname);
    Instances dataset = new Instances(new BufferedReader(new FileReader(f1)));
    dataset.setClassIndex(dataset.numAttributes() - 1);
    return dataset;
}

---

From source file:miRdup.WekaModule.java

License:Open Source License

public static void trainModel(File arff, String keyword) {
    dec.setMaximumFractionDigits(3);//w w w. j a  v  a  2  s  .  c om
    System.out.println("\nTraining model on file " + arff);
    try {
        // load data
        DataSource source = new DataSource(arff.toString());
        Instances data = source.getDataSet();
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        PrintWriter pwout = new PrintWriter(new FileWriter(keyword + Main.modelExtension + "Output"));
        PrintWriter pwroc = new PrintWriter(new FileWriter(keyword + Main.modelExtension + "roc.arff"));

        //remove ID row
        Remove rm = new Remove();
        rm.setAttributeIndices("1");
        FilteredClassifier fc = new FilteredClassifier();
        fc.setFilter(rm);

        //            // train model svm
        //            weka.classifiers.functions.LibSVM model = new weka.classifiers.functions.LibSVM();
        //            model.setOptions(weka.core.Utils.splitOptions("-S 0 -K 2 -D 3 -G 0.0 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.0010 -P 0.1 -B"));
        // train model MultilayerPerceptron
        //            weka.classifiers.functions.MultilayerPerceptron model = new weka.classifiers.functions.MultilayerPerceptron();
        //            model.setOptions(weka.core.Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"));
        // train model Adaboost on RIPPER
        //            weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        //            model.setOptions(weka.core.Utils.splitOptions("weka.classifiers.meta.AdaBoostM1 -P 100 -S 1 -I 10 -W weka.classifiers.rules.JRip -- -F 10 -N 2.0 -O 5 -S 1"));
        // train model Adaboost on FURIA
        //            weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        //            model.setOptions(weka.core.Utils.splitOptions("weka.classifiers.meta.AdaBoostM1 -P 100 -S 1 -I 10 -W weka.classifiers.rules.FURIA -- -F 10 -N 2.0 -O 5 -S 1 -p 0 -s 0"));
        //train model Adaboot on J48 trees
        //             weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        //             model.setOptions(
        //                     weka.core.Utils.splitOptions(
        //                     "-P 100 -S 1 -I 10 -W weka.classifiers.trees.J48 -- -C 0.25 -M 2"));
        //train model Adaboot on Random Forest trees
        weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        model.setOptions(weka.core.Utils
                .splitOptions("-P 100 -S 1 -I 10 -W weka.classifiers.trees.RandomForest -- -I 50 -K 0 -S 1"));

        if (Main.debug) {
            System.out.print("Model options: " + model.getClass().getName().trim() + " ");
        }
        System.out.print(model.getClass() + " ");
        for (String s : model.getOptions()) {
            System.out.print(s + " ");
        }

        pwout.print("Model options: " + model.getClass().getName().trim() + " ");
        for (String s : model.getOptions()) {
            pwout.print(s + " ");
        }

        //build model
        //            model.buildClassifier(data);
        fc.setClassifier(model);
        fc.buildClassifier(data);

        // cross validation 10 times on the model
        Evaluation eval = new Evaluation(data);
        //eval.crossValidateModel(model, data, 10, new Random(1));
        StringBuffer sb = new StringBuffer();
        eval.crossValidateModel(fc, data, 10, new Random(1), sb, new Range("first,last"), false);

        //System.out.println(sb);
        pwout.println(sb);
        pwout.flush();

        // output
        pwout.println("\n" + eval.toSummaryString());
        System.out.println(eval.toSummaryString());

        pwout.println(eval.toClassDetailsString());
        System.out.println(eval.toClassDetailsString());

        //calculate importants values
        String ev[] = eval.toClassDetailsString().split("\n");

        String ptmp[] = ev[3].trim().split(" ");
        String ntmp[] = ev[4].trim().split(" ");
        String avgtmp[] = ev[5].trim().split(" ");

        ArrayList<String> p = new ArrayList<String>();
        ArrayList<String> n = new ArrayList<String>();
        ArrayList<String> avg = new ArrayList<String>();

        for (String s : ptmp) {
            if (!s.trim().isEmpty()) {
                p.add(s);
            }
        }
        for (String s : ntmp) {
            if (!s.trim().isEmpty()) {
                n.add(s);
            }
        }
        for (String s : avgtmp) {
            if (!s.trim().isEmpty()) {
                avg.add(s);
            }
        }

        double tp = Double.parseDouble(p.get(0));
        double fp = Double.parseDouble(p.get(1));
        double tn = Double.parseDouble(n.get(0));
        double fn = Double.parseDouble(n.get(1));
        double auc = Double.parseDouble(avg.get(7));

        pwout.println("\nTP=" + tp + "\nFP=" + fp + "\nTN=" + tn + "\nFN=" + fn);
        System.out.println("\nTP=" + tp + "\nFP=" + fp + "\nTN=" + tn + "\nFN=" + fn);

        //specificity, sensitivity, Mathew's correlation, Prediction accuracy
        double sp = ((tn) / (tn + fp));
        double se = ((tp) / (tp + fn));
        double acc = ((tp + tn) / (tp + tn + fp + fn));
        double mcc = ((tp * tn) - (fp * fn)) / Math.sqrt((tp + fp) * (tn + fn) * (tp + fn) * tn + fp);

        String output = "\nse=" + dec.format(se).replace(",", ".") + "\nsp=" + dec.format(sp).replace(",", ".")
                + "\nACC=" + dec.format(acc).replace(",", ".") + "\nMCC=" + dec.format(mcc).replace(",", ".")
                + "\nAUC=" + dec.format(auc).replace(",", ".");

        pwout.println(output);
        System.out.println(output);

        pwout.println(eval.toMatrixString());
        System.out.println(eval.toMatrixString());

        pwout.flush();
        pwout.close();

        //Saving model
        System.out.println("Model saved: " + keyword + Main.modelExtension);
        weka.core.SerializationHelper.write(keyword + Main.modelExtension, fc.getClassifier() /*model*/);

        // get curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);
        pwroc.print(result.toString());
        pwroc.flush();
        pwroc.close();

        // draw curve
        //rocCurve(eval);
    } catch (Exception e) {
        e.printStackTrace();
    }
}

---

From source file:miRdup.WekaModule.java

License:Open Source License

public static void testModel(File testarff, String predictionsFile, String classifier, boolean predictMiRNA) {
    System.out.println("Testing model on " + predictionsFile + " adapted in " + testarff
            + ". Submitted to model " + classifier);

    try {/* w  ww .j av a  2s  .c om*/
        //add predictions sequences to object
        ArrayList<MirnaObject> alobj = new ArrayList<MirnaObject>();
        BufferedReader br = null;
        try {
            br = new BufferedReader(new FileReader(predictionsFile + ".folded"));
        } catch (FileNotFoundException fileNotFoundException) {
            br = new BufferedReader(new FileReader(predictionsFile));
        }
        BufferedReader br2 = new BufferedReader(new FileReader(testarff));
        String line2 = br2.readLine();
        while (!line2.startsWith("@data")) {
            line2 = br2.readLine();
        }
        String line = " ";
        int cpt = 0;
        while (br.ready()) {
            line = br.readLine();
            line2 = br2.readLine();
            String[] tab = line.split("\t");
            MirnaObject m = new MirnaObject();
            m.setArff(line2);
            m.setId(cpt++);
            m.setIdName(tab[0]);
            m.setMatureSequence(tab[1]);
            m.setPrecursorSequence(tab[2]);
            m.setStructure(tab[3]);
            alobj.add(m);
        }
        br.close();
        br2.close();

        // load data
        DataSource source = new DataSource(testarff.toString());
        Instances data = source.getDataSet();
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }
        //remove ID row
        data.deleteAttributeAt(0);
        //load model
        Classifier model = (Classifier) weka.core.SerializationHelper.read(classifier);

        // evaluate dataset on the model
        Evaluation eval = new Evaluation(data);

        eval.evaluateModel(model, data);

        FastVector fv = eval.predictions();

        // output
        PrintWriter pw = new PrintWriter(new FileWriter(predictionsFile + "." + classifier + ".miRdup.txt"));
        PrintWriter pwt = new PrintWriter(
                new FileWriter(predictionsFile + "." + classifier + ".miRdup.tab.txt"));
        PrintWriter pwout = new PrintWriter(
                new FileWriter(predictionsFile + "." + classifier + ".miRdupOutput.txt"));

        for (int i = 0; i < fv.size(); i++) {
            //System.out.println(fv.elementAt(i).toString());
            String[] tab = fv.elementAt(i).toString().split(" ");
            int actual = Integer.valueOf(tab[1].substring(0, 1));
            int predicted = Integer.valueOf(tab[2].substring(0, 1));
            double score = 0.0;
            boolean validated = false;
            if (actual == predicted) { //case validated
                int s = tab[4].length();
                try {
                    score = Double.valueOf(tab[4]);
                    //score = Double.valueOf(tab[4].substring(0, s - 1));
                } catch (NumberFormatException numberFormatException) {
                    score = 0.0;
                }

                validated = true;
            } else {// case not validated
                int s = tab[5].length();
                try {
                    score = Double.valueOf(tab[5]);
                    //score = Double.valueOf(tab[5].substring(0, s - 1));
                } catch (NumberFormatException numberFormatException) {
                    score = 0.0;
                }
                validated = false;
            }
            MirnaObject m = alobj.get(i);
            m.setActual(actual);
            m.setPredicted(predicted);
            m.setScore(score);
            m.setValidated(validated);
            m.setNeedPrediction(predictMiRNA);
            String predictionMiRNA = "";
            if (predictMiRNA && validated == false) {
                predictionMiRNA = miRdupPredictor.Predictor.predictionBySequence(m.getPrecursorSequence(),
                        classifier, classifier + ".miRdupPrediction.txt");
                try {
                    m.setPredictedmiRNA(predictionMiRNA.split(",")[0]);
                    m.setPredictedmiRNAstar(predictionMiRNA.split(",")[1]);
                } catch (Exception e) {
                    m.setPredictedmiRNA(predictionMiRNA);
                    m.setPredictedmiRNAstar(predictionMiRNA);
                }
            }

            pw.println(m.toStringFullPredictions());
            pwt.println(m.toStringPredictions());
            if (i % 100 == 0) {
                pw.flush();
                pwt.flush();
            }
        }

        //System.out.println(eval.toSummaryString("\nSummary results of predictions\n======\n", false));
        String[] out = eval.toSummaryString("\nSummary results of predictions\n======\n", false).split("\n");
        String info = out[0] + "\n" + out[1] + "\n" + out[2] + "\n" + out[4] + "\n" + out[5] + "\n" + out[6]
                + "\n" + out[7] + "\n" + out[11] + "\n";
        System.out.println(info);
        //System.out.println("Predicted position of the miRNA by miRdup:"+predictionMiRNA);
        pwout.println(
                "File " + predictionsFile + " adapted in " + testarff + " submitted to model " + classifier);
        pwout.println(info);

        pw.flush();
        pw.close();
        pwt.flush();
        pwt.close();
        pwout.flush();
        pwout.close();

        System.out.println("Results in " + predictionsFile + "." + classifier + ".miRdup.txt");

        // draw curve
        //rocCurve(eval);
    } catch (Exception e) {
        e.printStackTrace();
    }

}

---

From source file:miRdup.WekaModule.java

License:Open Source License

public static String testModel(File testarff, String classifier) {
    // System.out.println("Testing model on "+testarff+". Submitted to model "+classifier);
    try {//from  w  w w.  j  a  v  a2 s  .  c o m

        // load data
        DataSource source = new DataSource(testarff.toString());
        Instances data = source.getDataSet();
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        //load model
        Classifier model = (Classifier) weka.core.SerializationHelper.read(classifier);

        // evaluate dataset on the model
        Evaluation eval = new Evaluation(data);

        eval.evaluateModel(model, data);
        FastVector fv = eval.predictions();

        //calculate importants values
        String ev[] = eval.toClassDetailsString().split("\n");

        String p = ev[3].trim();
        String n = ev[4].trim();

        double tp = Double.parseDouble(p.substring(0, 6).trim());
        double fp = 0;
        try {
            fp = Double.parseDouble(p.substring(11, 16).trim());
        } catch (Exception exception) {
            fp = Double.parseDouble(p.substring(7, 16).trim());
        }
        double tn = Double.parseDouble(n.substring(0, 6).trim());
        double fn = 0;
        try {
            fn = Double.parseDouble(n.substring(11, 16).trim());
        } catch (Exception exception) {
            fn = Double.parseDouble(n.substring(7, 16).trim());
        }

        //System.out.println("\nTP="+tp+"\nFP="+fp+"\nTN="+tn+"\nFN="+fn);
        //specificity, sensitivity, Mathew's correlation, Prediction accuracy
        double sp = ((tn) / (tn + fp));
        double se = ((tp) / (tp + fn));
        double acc = ((tp + tn) / (tp + tn + fp + fn));
        double mcc = ((tp * tn) - (fp * fn)) / Math.sqrt((tp + fp) * (tn + fn) * (tp + fn) * tn + fp);
        //            System.out.println("\nse="+se+"\nsp="+sp+"\nACC="+dec.format(acc).replace(",", ".")+"\nMCC="+dec.format(mcc).replace(",", "."));
        //            System.out.println(eval.toMatrixString());

        String out = dec.format(acc).replace(",", ".");
        System.out.println(out);
        return out;
    } catch (Exception e) {
        e.printStackTrace();
        return "";
    }

}

---

From source file:miRdup.WekaModule.java

License:Open Source License

public static void attributeSelection(File arff, String outfile) {
    // load data// w ww.j  ava  2  s  .  c o  m
    try {
        PrintWriter pw = new PrintWriter(new FileWriter(outfile));
        DataSource source = new DataSource(arff.toString());
        Instances data = source.getDataSet();
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        AttributeSelection attrsel = new AttributeSelection();
        weka.attributeSelection.InfoGainAttributeEval eval = new weka.attributeSelection.InfoGainAttributeEval();

        weka.attributeSelection.Ranker rank = new weka.attributeSelection.Ranker();
        rank.setOptions(weka.core.Utils.splitOptions("-T -1.7976931348623157E308 -N -1"));
        if (Main.debug) {
            System.out.print("Model options: " + rank.getClass().getName().trim() + " ");
        }
        for (String s : rank.getOptions()) {
            System.out.print(s + " ");
        }
        attrsel.setEvaluator(eval);
        attrsel.setSearch(rank);
        attrsel.setFolds(10);

        attrsel.SelectAttributes(data);
        //attrsel.CrossValidateAttributes();

        System.out.println(attrsel.toResultsString());
        pw.println(attrsel.toResultsString());

        //evaluation.crossValidateModel(classifier, data, 10, new Random(1));
        pw.flush();
        pw.close();

    } catch (Exception e) {
        e.printStackTrace();
    }
}

---

From source file:ml.ann.MainPTR.java

public static void main(String[] args) throws FileNotFoundException, IOException, Exception {
    boolean randomWeight;
    double weightawal = 0.0;
    double learningRate = 0.0001;
    double threshold = 0.00;
    double momentum = 0.00;
    int maxEpoch = 100000;
    int nCrossValidate = 2;

    m_nominalToBinaryFilter = new NominalToBinary();
    m_normalize = new Normalize();

    Scanner in = new Scanner(System.in);
    System.out.println("Lokasi file: ");

    String filepath = in.nextLine();
    filepath = "test-arffs/iris.arff";
    System.out.println("--- Algoritma ---");
    System.out.println("1. Perceptron Training Rule");
    System.out.println("2. Delta Rule Incremental");
    System.out.println("3. Delta Rule Batch");
    System.out.println("Pilihan Algoritma (1/2/3) : ");
    int choice = in.nextInt();
    String temp = in.nextLine();/*www.j ava 2  s .  c  o  m*/

    System.out.println("Apakah Anda ingin memasukkan nilai weight awal? (YES/NO)");
    String isRandom = in.nextLine();
    System.out.println("Apakah Anda ingin memasukkan konfigurasi? (YES/NO)");
    String config = in.nextLine();

    if (config.equalsIgnoreCase("yes")) {
        System.out.print("Masukkan nilai learning rate: ");
        learningRate = in.nextDouble();
        System.out.print("Masukkan nilai threshold: ");
        threshold = in.nextDouble();
        System.out.print("Masukkan nilai momentum: ");
        momentum = in.nextDouble();
        System.out.print("Masukkan jumlah epoch: ");
        threshold = in.nextInt();
        System.out.print("Masukkan jumlah folds untuk crossvalidate: ");
        nCrossValidate = in.nextInt();
    }

    randomWeight = isRandom.equalsIgnoreCase("yes");

    if (randomWeight) {
        System.out.print("Masukkan nilai weight awal: ");
        weightawal = Double.valueOf(in.nextLine());
    }

    //print config
    if (isRandom.equalsIgnoreCase("yes")) {
        System.out.print("isRandom | ");
    } else {
        System.out.print("Weight " + weightawal + " | ");
    }

    System.out.print("L.rate " + learningRate + " | ");
    System.out.print("Max Epoch " + maxEpoch + " | ");
    System.out.print("Threshold " + threshold + " | ");
    System.out.print("Momentum " + momentum + " | ");
    System.out.print("Folds " + nCrossValidate + " | ");
    System.out.println();

    FileReader trainreader = new FileReader(filepath);
    Instances train = new Instances(trainreader);
    train.setClassIndex(train.numAttributes() - 1);

    m_nominalToBinaryFilter.setInputFormat(train);
    train = new Instances(Filter.useFilter(train, m_nominalToBinaryFilter));

    m_normalize.setInputFormat(train);
    train = new Instances(Filter.useFilter(train, m_normalize));

    MultiClassPTR tempMulti = new MultiClassPTR(choice, randomWeight, learningRate, maxEpoch, threshold);
    tempMulti.buildClassifier(train);

    Evaluation eval = new Evaluation(new Instances(train));
    eval.evaluateModel(tempMulti, train);
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
}

---

From source file:ml.WekaBatteryPredictionExample.java

License:Open Source License

private static Instances loadDatasetFromTxt(String txtFile) throws IOException {
    ArrayList<Attribute> atts = new ArrayList<>(2);
    atts.add(new Attribute("time_charged", Attribute.NUMERIC));
    atts.add(new Attribute("battery_lasted_time", Attribute.NUMERIC));
    Instances data = new Instances("battery-prediction-training-set", atts, 0);
    data.setClassIndex(1);

    File file = new File(txtFile);
    FileReader fr = new FileReader(file);
    BufferedReader br = new BufferedReader(fr);
    String line;//from   w  w  w  . j a v  a  2s  . com
    while ((line = br.readLine()) != null) {
        String[] values = line.split(",");
        double[] newInst = new double[2];
        newInst[0] = Double.valueOf(values[0]);
        newInst[1] = Double.valueOf(values[1]);

        data.add(new DenseInstance(1.0, newInst));
    }
    br.close();
    fr.close();

    return data;
}

---

From source file:mlpoc.MLPOC.java

/**
 * @param args the command line arguments
 *//*from ww  w .j av a  2 s . c om*/
public static void main(String[] args) {
    try {
        // TODO code application logic here
        BufferedReader br;
        br = new BufferedReader(
                new FileReader("D:/Extra/B.E Project/agrodeploy/webapp/Data/ClusterAutotrain12.arff"));
        Instances training_data = new Instances(br);
        br.close();
        training_data.setClassIndex(training_data.numAttributes() - 1);
        br = new BufferedReader(new FileReader("D:/Extra/B.E Project/agrodeploy/webapp/Data/TestFinal.arff"));
        Instances testing_data = new Instances(br);
        br.close();
        testing_data.setClassIndex(testing_data.numAttributes() - 1);
        String summary = training_data.toSummaryString();
        int number_samples = training_data.numInstances();
        int number_attributes_per_sample = training_data.numAttributes();
        System.out.println("Number of attributes in model = " + number_attributes_per_sample);
        System.out.println("Number of samples = " + number_samples);
        System.out.println("Summary: " + summary);
        System.out.println();

        J48 j48 = new J48();
        FilteredClassifier fc = new FilteredClassifier();
        fc.setClassifier(j48);
        fc.buildClassifier(training_data);
        System.out.println("Testing instances: " + testing_data.numInstances());
        for (int i = 0; i < testing_data.numInstances(); i++) {
            double pred = fc.classifyInstance(testing_data.instance(i));
            String s1 = testing_data.classAttribute().value((int) pred);
            System.out.println(testing_data.instance(i) + " Predicted value: " + s1);
        }
        Evaluation crossValidate = crossValidate(
                "D:/Extra/B.E Project/agrodeploy/webapp/Data/ClusterAutotrain12.arff");

        DataSource source = new DataSource(
                "D:/Extra/B.E Project/agrodeploy/webapp/Data/ClusterAutotrain12.arff");
        Instances data = source.getDataSet();
        System.out.println(data.numInstances());
        data.setClassIndex(data.numAttributes() - 1);

        // 1. meta-classifier
        useClassifier(data);

        // 2. filter
        useFilter(data);
    } catch (Exception ex) {
        Logger.getLogger(MLPOC.class.getName()).log(Level.SEVERE, null, ex);
    }
}

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