Example usage for weka.core Instances classIndex

List of usage examples for weka.core Instances classIndex

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

Prototype


publicint classIndex()

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Document

Returns the class attribute's index.

Usage

From source file:sg.edu.nus.comp.nlp.ims.classifiers.CMultiClassesSVM.java

License:Open Source License

/**
 * get output format/*w ww . j a  va  2s  .com*/
 *
 * @param p_Instances
 *            input format
 */
protected void getOutputFormat(Instances p_Instances) {
    FastVector newAtts, newVals;
    // Compute new attributes
    newAtts = new FastVector(p_Instances.numAttributes());
    for (int j = 0; j < p_Instances.numAttributes(); j++) {
        Attribute att = p_Instances.attribute(j);
        if (j != p_Instances.classIndex()) {
            newAtts.addElement(att.copy());
        } else {
            if (p_Instances.classAttribute().isNumeric()) {
                newAtts.addElement(new Attribute(att.name()));
            } else {
                newVals = new FastVector(2);
                newVals.addElement("negative");
                newVals.addElement("positive");
                newAtts.addElement(new Attribute(att.name(), newVals));
            }
        }
    }

    // Construct new header
    this.m_OutputFormat = new Instances(p_Instances.relationName(), newAtts, 0);
    this.m_OutputFormat.setClassIndex(p_Instances.classIndex());
    if (this.m_IndexOfID >= 0) {
        this.m_OutputFormat.deleteAttributeAt(this.m_IndexOfID);
    }
}

From source file:sg.edu.nus.comp.nlp.ims.classifiers.CMultiClassesSVM.java

License:Open Source License

/**
 * generate instances for classifier classIdx
 *
 * @param p_Instances/*w w w . j a  va 2 s . c o m*/
 *            input instances
 * @param p_ClassIndex
 *            class index
 * @param p_ID2Classes
 *            instance ids
 * @return new instances
 */
protected Instances genInstances(Instances p_Instances, double p_ClassIndex,
        Hashtable<String, ArrayList<Double>> p_ID2Classes) {
    Instances newInsts = new Instances(this.m_OutputFormat, 0);
    for (int i = 0; i < p_Instances.numInstances(); i++) {
        Instance inst = p_Instances.instance(i);
        Instance newInst = null;
        if (SparseInstance.class.isInstance(inst)) {
            newInst = new SparseInstance(inst);
        } else {
            newInst = new Instance(inst);
        }
        if (newInst.value(p_Instances.classIndex()) == p_ClassIndex) {
            newInst.setValue(inst.classIndex(), 1);
        } else {
            if (p_ID2Classes == null || !p_ID2Classes.get(inst.stringValue(this.m_IndexOfID))
                    .contains(new Double(p_ClassIndex))) {
                newInst.setValue(inst.classIndex(), 0);
            } else {
                continue;
            }
        }
        newInst.deleteAttributeAt(this.m_IndexOfID);
        newInst.setDataset(newInsts);
        newInsts.add(newInst);
    }
    return newInsts;
}

From source file:sirius.clustering.main.ClustererClassificationPane.java

License:Open Source License

private void start() {
    //Run Classifier
    if (this.inputDirectoryTextField.getText().length() == 0) {
        JOptionPane.showMessageDialog(parent, "Please set Input Directory to where the clusterer output are!",
                "Evaluate Classifier", JOptionPane.ERROR_MESSAGE);
        return;/*from   w  ww  . j a  v a  2s .  c  o  m*/
    }
    if (m_ClassifierEditor.getValue() == null) {
        JOptionPane.showMessageDialog(parent, "Please choose Classifier!", "Evaluate Classifier",
                JOptionPane.ERROR_MESSAGE);
        return;
    }
    if (validateStatsSettings(1) == false) {
        return;
    }
    if (this.clusteringClassificationThread == null) {
        startButton.setEnabled(false);
        stopButton.setEnabled(true);
        tabbedClassifierPane.setSelectedIndex(0);
        this.clusteringClassificationThread = (new Thread() {
            public void run() {
                //Clear the output text area
                levelOneClassifierOutputTextArea.setText("");
                resultsTableModel.reset();
                //double threshold = Double.parseDouble(classifierOneThresholdTextField.getText());                
                //cross-validation
                int numFolds;
                if (jackKnifeRadioButton.isSelected())
                    numFolds = -1;
                else
                    numFolds = Integer.parseInt(foldsField.getText());
                StringTokenizer st = new StringTokenizer(inputDirectoryTextField.getText(), File.separator);
                String filename = "";
                while (st.hasMoreTokens()) {
                    filename = st.nextToken();
                }
                StringTokenizer st2 = new StringTokenizer(filename, "_.");
                numOfCluster = 0;
                if (st2.countTokens() >= 2) {
                    st2.nextToken();
                    String numOfClusterString = st2.nextToken().replaceAll("cluster", "");
                    try {
                        numOfCluster = Integer.parseInt(numOfClusterString);
                    } catch (NumberFormatException e) {
                        JOptionPane.showMessageDialog(parent,
                                "Please choose the correct file! (Output from Utilize Clusterer)", "ERROR",
                                JOptionPane.ERROR_MESSAGE);
                    }
                }
                Classifier template = (Classifier) m_ClassifierEditor.getValue();

                for (int x = 0; x <= numOfCluster && clusteringClassificationThread != null; x++) {//Test each cluster
                    try {
                        long totalTimeStart = 0, totalTimeElapsed = 0;
                        totalTimeStart = System.currentTimeMillis();
                        statusLabel.setText("Reading in cluster" + x + " file..");
                        String inputFilename = inputDirectoryTextField.getText()
                                .replaceAll("_cluster" + numOfCluster + ".arff", "_cluster" + x + ".arff");
                        String outputScoreFilename = inputDirectoryTextField.getText()
                                .replaceAll("_cluster" + numOfCluster + ".arff", "_cluster" + x + ".score");
                        BufferedWriter output = new BufferedWriter(new FileWriter(outputScoreFilename));
                        Instances inst = new Instances(new FileReader(inputFilename));
                        //Assume that class attribute is the last attribute - This should be the case for all Sirius produced Arff files
                        inst.setClassIndex(inst.numAttributes() - 1);
                        Random random = new Random(1);//Simply set to 1, shall implement the random seed option later
                        inst.randomize(random);
                        if (inst.attribute(inst.classIndex()).isNominal())
                            inst.stratify(numFolds);
                        // for timing
                        ClassifierResults classifierResults = new ClassifierResults(false, 0);
                        String classifierName = m_ClassifierEditor.getValue().getClass().getName();
                        classifierResults.updateList(classifierResults.getClassifierList(), "Classifier: ",
                                classifierName);
                        classifierResults.updateList(classifierResults.getClassifierList(), "Training Data: ",
                                inputFilename);
                        classifierResults.updateList(classifierResults.getClassifierList(), "Time Used: ",
                                "NA");
                        //ArrayList<Double> resultList = new ArrayList<Double>();     
                        if (jackKnifeRadioButton.isSelected() || numFolds > inst.numInstances() - 1)
                            numFolds = inst.numInstances() - 1;
                        for (int fold = 0; fold < numFolds && clusteringClassificationThread != null; fold++) {//Doing cross-validation          
                            statusLabel.setText("Cluster: " + x + " - Training Fold " + (fold + 1) + "..");
                            Instances train = inst.trainCV(numFolds, fold, random);
                            Classifier current = null;
                            try {
                                current = Classifier.makeCopy(template);
                                current.buildClassifier(train);
                                Instances test = inst.testCV(numFolds, fold);
                                statusLabel.setText("Cluster: " + x + " - Testing Fold " + (fold + 1) + "..");
                                for (int jj = 0; jj < test.numInstances(); jj++) {
                                    double[] result = current.distributionForInstance(test.instance(jj));
                                    output.write("Cluster: " + x);
                                    output.newLine();
                                    output.newLine();
                                    output.write(test.instance(jj).stringValue(test.classAttribute()) + ",0="
                                            + result[0]);
                                    output.newLine();
                                }
                            } catch (Exception ex) {
                                ex.printStackTrace();
                                statusLabel.setText("Error in cross-validation!");
                                startButton.setEnabled(true);
                                stopButton.setEnabled(false);
                            }
                        }
                        output.close();
                        totalTimeElapsed = System.currentTimeMillis() - totalTimeStart;
                        classifierResults.updateList(classifierResults.getResultsList(), "Total Time Used: ",
                                Utils.doubleToString(totalTimeElapsed / 60000, 2) + " minutes "
                                        + Utils.doubleToString((totalTimeElapsed / 1000.0) % 60.0, 2)
                                        + " seconds");
                        double threshold = validateFieldAsThreshold(classifierOneThresholdTextField.getText(),
                                "Threshold Field", classifierOneThresholdTextField);
                        String filename2 = inputDirectoryTextField.getText()
                                .replaceAll("_cluster" + numOfCluster + ".arff", "_cluster" + x + ".score");
                        PredictionStats classifierStats = new PredictionStats(filename2, 0, threshold);

                        resultsTableModel.add("Cluster " + x, classifierResults, classifierStats);
                        resultsTable.setRowSelectionInterval(x, x);
                        computeStats(numFolds);//compute and display the results                    
                    } catch (Exception e) {
                        e.printStackTrace();
                        statusLabel.setText("Error in reading file!");
                        startButton.setEnabled(true);
                        stopButton.setEnabled(false);
                    }
                } //end of cluster for loop

                resultsTableModel.add("Summary - Equal Weightage", null, null);
                resultsTable.setRowSelectionInterval(numOfCluster + 1, numOfCluster + 1);
                computeStats(numFolds);
                resultsTableModel.add("Summary - Weighted Average", null, null);
                resultsTable.setRowSelectionInterval(numOfCluster + 2, numOfCluster + 2);
                computeStats(numFolds);

                if (clusteringClassificationThread != null)
                    statusLabel.setText("Done!");
                else
                    statusLabel.setText("Interrupted..");
                startButton.setEnabled(true);
                stopButton.setEnabled(false);
                if (classifierOne != null) {
                    levelOneClassifierOutputScrollPane.getVerticalScrollBar()
                            .setValue(levelOneClassifierOutputScrollPane.getVerticalScrollBar().getMaximum());
                }
                clusteringClassificationThread = null;

            }
        });
        this.clusteringClassificationThread.setPriority(Thread.MIN_PRIORITY);
        this.clusteringClassificationThread.start();
    } else {
        JOptionPane.showMessageDialog(parent,
                "Cannot start new job as previous job still running. Click stop to terminate previous job",
                "ERROR", JOptionPane.ERROR_MESSAGE);
    }
}

From source file:sirius.clustering.main.TrainClustererPane.java

License:Open Source License

private Instances removeClass(Instances inst) {//Copied directly from Weka's ClustererPanel.java
    Remove af = new Remove();
    Instances retI = null;//ww  w.  j av  a 2s.c o m
    try {
        if (inst.classIndex() < 0) {
            //do nothing since classindex is not set
            retI = inst;
        } else {
            //remove class attribute
            af.setAttributeIndices("" + (inst.classIndex() + 1));
            af.setInvertSelection(false);
            af.setInputFormat(inst);
            retI = Filter.useFilter(inst, af);
        }
    } catch (Exception e) {
        e.printStackTrace();
    }
    return retI;
}

From source file:sirius.clustering.main.TrainClustererPane.java

License:Open Source License

public void setInstances(Instances inst) {//Directly copied with slight modification from weka's ClustererPanel.java
    String[] attribNames = new String[inst.numAttributes()];
    for (int i = 0; i < inst.numAttributes(); i++) {
        String type = "";
        switch (inst.attribute(i).type()) {
        case Attribute.NOMINAL:
            type = "(Nom) ";
            break;
        case Attribute.NUMERIC:
            type = "(Num) ";
            break;
        case Attribute.STRING:
            type = "(Str) ";
            break;
        case Attribute.DATE:
            type = "(Dat) ";
            break;
        case Attribute.RELATIONAL:
            type = "(Rel) ";
            break;
        default:/*w  w  w. ja  v a2 s.  com*/
            type = "(???) ";
        }
        String attnm = inst.attribute(i).name();
        attribNames[i] = type + attnm;
    }
    m_ClassCombo.setModel(new DefaultComboBoxModel(attribNames));
    if (inst.classIndex() == -1)
        m_ClassCombo.setSelectedIndex(attribNames.length - 1);
    else
        m_ClassCombo.setSelectedIndex(inst.classIndex());
}

From source file:smo2.SMO.java

License:Open Source License

/**
 * Method for building the classifier. Implements a one-against-one wrapper
 * for multi-class problems./*from  w  w  w  . jav a2s. c  o m*/
 *
 * @param insts
 *            the set of training instances
 * @exception Exception
 *                if the classifier can't be built successfully
 */
public void buildClassifier(Instances insts) throws Exception {

    if (!m_checksTurnedOff) {
        if (insts.checkForStringAttributes()) {
            throw new UnsupportedAttributeTypeException("Cannot handle string attributes!");
        }
        if (insts.classAttribute().isNumeric()) {
            throw new UnsupportedClassTypeException(
                    "mySMO can't handle a numeric class! Use" + "SMOreg for performing regression.");
        }
        insts = new Instances(insts);
        insts.deleteWithMissingClass();
        if (insts.numInstances() == 0) {
            throw new Exception("No training instances without a missing class!");
        }

        /*
         * Removes all the instances with weight equal to 0. MUST be done
         * since condition (8) of Keerthi's paper is made with the assertion
         * Ci > 0 (See equation (3a).
         */
        Instances data = new Instances(insts, insts.numInstances());
        for (int i = 0; i < insts.numInstances(); i++) {
            if (insts.instance(i).weight() > 0)
                data.add(insts.instance(i));
        }
        if (data.numInstances() == 0) {
            throw new Exception("No training instances left after removing "
                    + "instance with either a weight null or a missing class!");
        }
        insts = data;

    }

    m_onlyNumeric = true;
    if (!m_checksTurnedOff) {
        for (int i = 0; i < insts.numAttributes(); i++) {
            if (i != insts.classIndex()) {
                if (!insts.attribute(i).isNumeric()) {
                    m_onlyNumeric = false;
                    break;
                }
            }
        }
    }

    if (!m_checksTurnedOff) {
        m_Missing = new ReplaceMissingValues();
        m_Missing.setInputFormat(insts);
        insts = Filter.useFilter(insts, m_Missing);
    } else {
        m_Missing = null;
    }

    if (!m_onlyNumeric) {
        m_NominalToBinary = new NominalToBinary();
        m_NominalToBinary.setInputFormat(insts);
        insts = Filter.useFilter(insts, m_NominalToBinary);
    } else {
        m_NominalToBinary = null;
    }

    if (m_filterType == FILTER_STANDARDIZE) {
        m_Filter = new Standardize();
        m_Filter.setInputFormat(insts);
        insts = Filter.useFilter(insts, m_Filter);
    } else if (m_filterType == FILTER_NORMALIZE) {
        m_Filter = new Normalize();
        m_Filter.setInputFormat(insts);
        insts = Filter.useFilter(insts, m_Filter);
    } else {
        m_Filter = null;
    }

    m_classIndex = insts.classIndex();
    m_classAttribute = insts.classAttribute();

    // Generate subsets representing each class
    Instances[] subsets = new Instances[insts.numClasses()];
    for (int i = 0; i < insts.numClasses(); i++) {
        subsets[i] = new Instances(insts, insts.numInstances());
    }
    for (int j = 0; j < insts.numInstances(); j++) {
        Instance inst = insts.instance(j);
        subsets[(int) inst.classValue()].add(inst);
    }
    for (int i = 0; i < insts.numClasses(); i++) {
        subsets[i].compactify();
    }

    // Build the binary classifiers
    Random rand = new Random(m_randomSeed);
    m_classifiers = new BinarymySMO[insts.numClasses()][insts.numClasses()];
    for (int i = 0; i < insts.numClasses(); i++) {
        for (int j = i + 1; j < insts.numClasses(); j++) {
            m_classifiers[i][j] = new BinarymySMO();
            Instances data = new Instances(insts, insts.numInstances());
            for (int k = 0; k < subsets[i].numInstances(); k++) {
                data.add(subsets[i].instance(k));
            }
            for (int k = 0; k < subsets[j].numInstances(); k++) {
                data.add(subsets[j].instance(k));
            }
            data.compactify();
            data.randomize(rand);
            m_classifiers[i][j].buildClassifier(data, i, j, m_fitLogisticModels, m_numFolds, m_randomSeed);
        }
    }
}

From source file:svmal.SVMStrategymulti.java

public static Instances PatternsToInstances2(Pattern[] patts, int positiveLabel) {
    Instances result = new Instances(patts[0].dataset(), 0, 0);
    int classIndex = result.classIndex();
    for (Pattern orig : patts) {
        double[] vals = orig.toDoubleArray();
        Instance2 inst2;//www .  j a  v  a  2s .c  om
        if (vals[classIndex] == positiveLabel)
            vals[classIndex] = 1;
        else
            vals[classIndex] = 0;
        inst2 = new Instance2(orig.weight(), vals);
        inst2.setIndex(orig.id());
        inst2.setDataset(result);
        result.add(inst2);
    }
    return result;
}

From source file:tr.gov.ulakbim.jDenetX.experiments.wrappers.EvalActiveBoostingID.java

License:Open Source License

public Instances clusteredInstances(Instances data) {
    if (data == null) {
        throw new NullPointerException("Data is null at clusteredInstances method");
    }//from ww w .j  av  a 2s . c o m
    Instances sampled_data = data;
    for (int i = 0; i < sampled_data.numInstances(); i++) {
        sampled_data.remove(i);
    }

    SimpleKMeans sKmeans = new SimpleKMeans();
    data.setClassIndex(data.numAttributes() - 1);
    Remove filter = new Remove();
    filter.setAttributeIndices("" + (data.classIndex() + 1));
    List assignments = new ArrayList();

    try {
        filter.setInputFormat(data);
        Instances dataClusterer = Filter.useFilter(data, filter);
        String[] options = new String[3];
        options[0] = "-I"; // max. iterations
        options[1] = "500";
        options[2] = "-O";
        sKmeans.setNumClusters(data.numClasses());
        sKmeans.setOptions(options);
        sKmeans.buildClusterer(dataClusterer);
        System.out.println("Kmeans\n:" + sKmeans);
        System.out.println(Arrays.toString(sKmeans.getAssignments()));
        assignments = Arrays.asList(sKmeans.getAssignments());
    } catch (Exception e) {
        e.printStackTrace();
    }
    System.out.println("Assignments\n: " + assignments);
    ClusterEvaluation eval = new ClusterEvaluation();
    eval.setClusterer(sKmeans);
    try {
        eval.evaluateClusterer(data);
    } catch (Exception e) {
        e.printStackTrace();
    }
    int classesToClustersMap[] = eval.getClassesToClusters();
    for (int i = 0; i < classesToClustersMap.length; i++) {
        if (assignments.get(i).equals(((Integer) classesToClustersMap[(int) data.get(i).classValue()]))) {
            ((Instances) sampled_data).add(data.get(i));
        }
    }
    return ((Instances) sampled_data);
}

From source file:tr.gov.ulakbim.jDenetX.experiments.wrappers.EvalActiveBoostingID.java

License:Open Source License

public static Instances clusterInstances(Instances data) {
    XMeans xmeans = new XMeans();
    Remove filter = new Remove();
    Instances dataClusterer = null;/*w w  w  . ja  v a  2  s  .co  m*/
    if (data == null) {
        throw new NullPointerException("Data is null at clusteredInstances method");
    }
    //Get the attributes from the data for creating the sampled_data object

    ArrayList<Attribute> attrList = new ArrayList<Attribute>();
    Enumeration attributes = data.enumerateAttributes();
    while (attributes.hasMoreElements()) {
        attrList.add((Attribute) attributes.nextElement());
    }

    Instances sampled_data = new Instances(data.relationName(), attrList, 0);
    data.setClassIndex(data.numAttributes() - 1);
    sampled_data.setClassIndex(data.numAttributes() - 1);
    filter.setAttributeIndices("" + (data.classIndex() + 1));
    data.remove(0);//In Wavelet Stream of MOA always the first element comes without class

    try {
        filter.setInputFormat(data);
        dataClusterer = Filter.useFilter(data, filter);
        String[] options = new String[4];
        options[0] = "-L"; // max. iterations
        options[1] = Integer.toString(noOfClassesInPool - 1);
        if (noOfClassesInPool > 2) {
            options[1] = Integer.toString(noOfClassesInPool - 1);
            xmeans.setMinNumClusters(noOfClassesInPool - 1);
        } else {
            options[1] = Integer.toString(noOfClassesInPool);
            xmeans.setMinNumClusters(noOfClassesInPool);
        }
        xmeans.setMaxNumClusters(data.numClasses() + 1);
        System.out.println("No of classes in the pool: " + noOfClassesInPool);
        xmeans.setUseKDTree(true);
        //xmeans.setOptions(options);
        xmeans.buildClusterer(dataClusterer);
        System.out.println("Xmeans\n:" + xmeans);
    } catch (Exception e) {
        e.printStackTrace();
    }
    //System.out.println("Assignments\n: " + assignments);
    ClusterEvaluation eval = new ClusterEvaluation();
    eval.setClusterer(xmeans);
    try {
        eval.evaluateClusterer(data);
        int classesToClustersMap[] = eval.getClassesToClusters();
        //check the classes to cluster map
        int clusterNo = 0;
        for (int i = 0; i < data.size(); i++) {
            clusterNo = xmeans.clusterInstance(dataClusterer.get(i));
            //Check if the class value of instance and class value of cluster matches
            if ((int) data.get(i).classValue() == classesToClustersMap[clusterNo]) {
                sampled_data.add(data.get(i));
            }
        }
    } catch (Exception e) {
        e.printStackTrace();
    }
    return ((Instances) sampled_data);
}

From source file:tubes2ai.AIJKNaiveBayes.java

@Override
public void buildClassifier(Instances i) throws Exception {
    //        throw new UnsupportedOperationException("Not supported yet."); //To change body of generated methods, choose Tools | Templates.
    getCapabilities().testWithFail(i);/*  w  w w .  j av a2 s.  c o  m*/

    nAttribute = i.numAttributes();
    classIndex = i.classIndex();
    Instance inst;
    Attribute att;
    int n_instance = i.numInstances();
    //inisialisasi matrix 3x3;
    //pertama cari ada berapa value di kelas
    nClassValue = i.attribute(classIndex).numValues();

    freq = new int[nAttribute][][];
    prob = new double[nAttribute][][];

    int a = 0;
    while (a < nAttribute) {
        int nValue = i.attribute(a).numValues();
        if (a != classIndex) {
            freq[a] = new int[nValue][nClassValue];
            prob[a] = new double[nValue][nClassValue];
        } else {
            freq[a] = new int[1][nClassValue];
            prob[a] = new double[1][nClassValue];
        }
        a++;
    }

    //System.out.println("beres buat matriks");
    //inisialisasi matriks sama nilai 0
    a = 0;
    int b;
    int c;
    while (a < nAttribute) { //outlook dkk
        b = 0;
        int nValue = i.attribute(a).numValues();
        //System.out.println("row "+a);
        while (b < nValue) {
            c = 0;
            //System.out.println("row1 "+b);
            if (a == classIndex) {
                //System.out.println("row2 "+c);
                freq[a][0][b] = 0;
            } else {
                while (c < nClassValue) {
                    //System.out.println("row2 "+c);
                    freq[a][b][c] = 0;
                    c++;
                }
            }
            b++;
        }
        a++;
    }

    //System.out.println("beres inisialisasi 0");

    a = 0;
    int val;
    int classValue;
    while (a < n_instance) {
        inst = i.get(a);
        b = 0;
        classValue = (int) inst.value(classIndex);
        while (b < nAttribute) {
            val = (int) inst.value(b);
            if (b == classIndex) {
                freq[b][0][classValue]++;
            } else {
                freq[b][val][classValue]++;
            }
            b++;
        }
        a++;
    }
    //System.out.println("beres frekuensi!!!!");

    a = 0;
    while (a < nAttribute) {
        b = 0;
        int nValue = i.attribute(a).numValues();
        //System.out.println("row "+a);
        while (b < nValue) {
            //System.out.println("row1 "+b);
            if (a != classIndex) {
                c = 0;
                while (c < nClassValue) {
                    //System.out.println("freq "+freq[a][b][c]);
                    //System.out.println("freq_index "+freq[classIndex][0][c]);
                    prob[a][b][c] = (double) (freq[a][b][c]) / (double) (freq[classIndex][0][c]);
                    //System.out.println("prob ["+a+"]["+b+"]["+c+"] "+ prob[a][b][c]);
                    c++;
                }
            } else {
                prob[a][0][b] = (double) freq[a][0][b] / i.numInstances();
                //System.out.println("prob ["+a+"][0]["+b+"] "+ prob[a][0][b]);
            }
            b++;
        }
        a++;
    }
    //System.out.println("beres prob!!!!");

}