Example usage for weka.core Instance value

List of usage examples for weka.core Instance value

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Introduction

In this page you can find the example usage for weka.core Instance value.

Prototype

public double value(Attribute att);

Source Link

Document

Returns an instance's attribute value in internal format.

Usage

From source file:com.relationalcloud.partitioning.explanation.ExplanationHandler.java

License:Open Source License

/**
 * Repeat the selection from the database removing duplicates, since they will
 * only increase the execution time. And run the tuples through the classifier
 * to populate the justifiedpartition column.
 * /*  w  ww.j a  v a 2  s .  co  m*/
 * @param tableProcessed
 * @param classifier
 * @param wa
 * @throws SQLException
 * @throws Exception
 */
public void populateJustifiedColumn(String tableProcessed, Classifier classifier, ArrayList<String> attributes,
        Connection conn, int numbPart, Enumeration enumclassvalues) throws SQLException, Exception {
    if (true) {
        labelTest(tableProcessed, classifier, conn);
        return;
    }

    tableProcessed = removeQuotes(tableProcessed);

    // get from the DB the tuples content and their partitioning column
    String sqlstring = "SELECT distinct g.tupleid, ";
    for (String sc : attributes) {
        sqlstring += "s." + sc + ", ";
    }
    sqlstring += "g." + pcol + " FROM " + "(SELECT distinct tupleid," + pcol + " FROM `" + testingtable
            + "` WHERE tableid = '" + tableProcessed + "') AS g, relcloud_" + tableProcessed + " AS s "
            + "WHERE s.relcloud_id = g.tupleid;";

    System.out.println(sqlstring);
    Statement stmt = conn.createStatement();

    // initializing the testing table to avoid complaints from classifier with
    // an hash partition like distribution
    if (!testingtable.equals(sampledtrainingtable)) {
        int i = 0;

        Object o = enumclassvalues.nextElement();

        // set everything to an existing value to ensure that every field is
        // covered
        stmt.executeUpdate("UPDATE " + testingtable + " SET " + pcol + "=" + o + " WHERE tableid = '"
                + tableProcessed + "'");
        // and than sparkly in a bunch of other values (unsure whether it is
        // required);
        while (enumclassvalues.hasMoreElements()) {
            o = enumclassvalues.nextElement();

            // FIXME there might still be an issue in which tupleid%i do not exists,
            // and thus one of the "o" never appears in the instance...
            stmt.executeUpdate("UPDATE " + testingtable + " SET " + pcol + "=" + o + " WHERE tupleid%"
                    + numbPart + "=" + i + " AND tableid = '" + tableProcessed + "'");
            i++;
        }
    }

    ResultSet res = stmt.executeQuery(sqlstring);
    // create an instance from the resultset
    Instances data_tupleid = WekaHelper.retrieveInstanceFromResultSetComplete(res, dbPropertyFile);
    res.close();

    data_tupleid.setClassIndex(data_tupleid.numAttributes() - 1);
    Instances data_no_tupleid = makeLastNominal(data_tupleid);
    data_no_tupleid.setClassIndex(data_no_tupleid.numAttributes() - 1);
    // remove tupleid from data_no_tupleid, still available in data_tupleid
    data_no_tupleid.deleteAttributeAt(0);

    // if(data_no_tupleid.classAttribute().numValues()>1){
    System.out.println("Running the tuples through the classifier to populate " + explainedPartitionCol);

    // use data that still has the tupleid and newData for the classification
    Enumeration enum_data_tupleid = data_tupleid.enumerateInstances();
    Enumeration enum_data_no_tupleid = data_no_tupleid.enumerateInstances();

    PreparedStatement updateJustCol = conn.prepareStatement("UPDATE `" + testingtable + "` SET `"
            + explainedPartitionCol + "` = ? " + "WHERE tableid = '" + tableProcessed + "' AND tupleid = ?;");

    while (enum_data_tupleid.hasMoreElements() && enum_data_no_tupleid.hasMoreElements()) {

        Instance tupIDinstance = (Instance) enum_data_tupleid.nextElement();
        Instance instance = (Instance) enum_data_no_tupleid.nextElement();

        double part = classifier.classifyInstance(instance);
        if (part == Instance.missingValue())
            System.err.println("No classification for:" + instance.toString());
        updateJustCol.setInt(1, (int) part);
        updateJustCol.setInt(2, (int) tupIDinstance.value(0));

        // System.out.println(tableProcessed+" "+ instance.value(0) + " " +
        // tupIDinstance.classValue() +" "+ part);

        updateJustCol.execute();
        updateJustCol.clearParameters();

    }

    updateJustCol.close();

}

From source file:com.spread.experiment.tempuntilofficialrelease.ClassificationViaClustering108.java

License:Open Source License

/**
 * Returns class probability distribution for the given instance.
 * /* w  w  w . j  av  a  2 s.com*/
 * @param instance the instance to be classified
 * @return the class probabilities
 * @throws Exception if an error occurred during the prediction
 */
@Override
public double[] distributionForInstance(Instance instance) throws Exception {

    if (m_ZeroR != null) {
        return m_ZeroR.distributionForInstance(instance);
    } else {
        double[] result = new double[instance.numClasses()];

        if (m_ActualClusterer != null) {
            // build new instance
            Instances tempData = m_ClusteringHeader.stringFreeStructure();
            double[] values = new double[tempData.numAttributes()];
            int n = 0;
            for (int i = 0; i < instance.numAttributes(); i++) {
                if (i == instance.classIndex()) {
                    continue;
                }
                if (instance.attribute(i).isString()) {
                    values[n] = tempData.attribute(n).addStringValue(instance.stringValue(i));
                } else if (instance.attribute(i).isRelationValued()) {
                    values[n] = tempData.attribute(n).addRelation(instance.relationalValue(i));
                } else {
                    values[n] = instance.value(i);
                }
                n++;
            }
            Instance newInst = new DenseInstance(instance.weight(), values);
            newInst.setDataset(tempData);

            if (!getLabelAllClusters()) {

                // determine cluster/class
                double r = m_ClustersToClasses[m_ActualClusterer.clusterInstance(newInst)];
                if (r == -1) {
                    return result; // Unclassified
                } else {
                    result[(int) r] = 1.0;
                    return result;
                }
            } else {
                double[] classProbs = new double[instance.numClasses()];
                double[] dist = m_ActualClusterer.distributionForInstance(newInst);
                for (int i = 0; i < dist.length; i++) {
                    for (int j = 0; j < instance.numClasses(); j++) {
                        classProbs[j] += dist[i] * m_ClusterClassProbs[i][j];
                    }
                }
                Utils.normalize(classProbs);
                return classProbs;
            }
        } else {
            return result; // Unclassified
        }
    }
}

From source file:com.tum.classifiertest.FastRandomTree.java

License:Open Source License

/**
 * Computes class distribution of an instance using the FastRandomTree.<p>
 *
 * In Weka's RandomTree, the distributions were normalized so that all
 * probabilities sum to 1; this would abolish the effect of instance weights
 * on voting. In FastRandomForest 0.97 onwards, the distributions are
 * normalized by dividing with the number of instances going into a leaf.<p>
 * //  w  ww  .ja  v  a2  s . com
 * @param instance the instance to compute the distribution for
 * @return the computed class distribution
 * @throws Exception if computation fails
 */
@Override
public double[] distributionForInstance(Instance instance) throws Exception {

    double[] returnedDist = null;

    if (m_Attribute > -1) { // ============================ node is not a leaf

        if (instance.isMissing(m_Attribute)) { // ---------------- missing value

            returnedDist = new double[m_MotherForest.getM_Info().numClasses()];
            // split instance up
            for (int i = 0; i < m_Successors.length; i++) {
                double[] help = m_Successors[i].distributionForInstance(instance);
                if (help != null) {
                    for (int j = 0; j < help.length; j++) {
                        returnedDist[j] += m_Prop[i] * help[j];
                    }
                }
            }

        } else if (m_MotherForest.getM_Info().attribute(m_Attribute).isNominal()) { // ------ nominal

            //returnedDist = m_Successors[(int) instance.value(m_Attribute)]
            //        .distributionForInstance(instance);

            // 0.99: new - binary splits (also) for nominal attributes
            if (instance.value(m_Attribute) == m_SplitPoint) {
                returnedDist = m_Successors[0].distributionForInstance(instance);
            } else {
                returnedDist = m_Successors[1].distributionForInstance(instance);
            }

        } else { // ------------------------------------------ numeric attributes

            if (instance.value(m_Attribute) < m_SplitPoint) {
                returnedDist = m_Successors[0].distributionForInstance(instance);
            } else {
                returnedDist = m_Successors[1].distributionForInstance(instance);
            }
        }

        return returnedDist;

    } else { // =============================================== node is a leaf

        return m_ClassProbs;

    }

}

From source file:control.CosineDistance.java

License:Open Source License

/**
 * Calculates the distance between two instances.
 * //from w ww .j a v  a 2 s  .com
 * @param first    the first instance
 * @param second    the second instance
 * @return       the distance between the two given instances
 */
public double distance(Instance first, Instance second) {

    HashMap<String, Double> fInstance = new HashMap<String, Double>();
    HashMap<String, Double> sInstance = new HashMap<String, Double>();

    for (int i = 0; i < first.numAttributes(); i++) {
        fInstance.put(first.attribute(i).name(), first.value(i));
        sInstance.put(second.attribute(i).name(), second.value(i));
    }

    return 1 - CosineSimilarity.calculateCosineSimilarity(fInstance, sInstance);
}

From source file:control.CosineDistance.java

License:Open Source License

/**
 * Returns true if the value of the given dimension is smaller or equal the
 * value to be compared with./*from w  w  w.ja  v a  2s. co  m*/
 * 
 * @param instance    the instance where the value should be taken of
 * @param dim    the dimension of the value
 * @param value    the value to compare with
 * @return       true if value of instance is smaller or equal value
 */
public boolean valueIsSmallerEqual(Instance instance, int dim, double value) { //This stays
    return instance.value(dim) <= value;
}

From source file:controller.BothClassificationsServlet.java

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    request.setCharacterEncoding("UTF-8");
    String dir = "/data/";
    String path = getServletContext().getRealPath(dir);

    String action = request.getParameter("action");

    switch (action) {
    case "create": {
        String fileName = request.getParameter("file");

        String aux = fileName.substring(0, fileName.indexOf("."));
        String pathInput = path + "/" + request.getParameter("file");
        String pathTrainingOutput = path + "/" + aux + "-training-arff.txt";
        String pathTestOutput = path + "/" + aux + "-test-arff.txt";
        String pathBothClassifications = path + "/" + aux + "-bothClassifications.txt";

        String name = request.getParameter("name");
        int range = Integer.parseInt(request.getParameter("range"));

        int size = Integer.parseInt(request.getParameter("counter"));
        String[] columns = new String[size];
        String[] types = new String[size];
        int[] positions = new int[size];
        int counter = 0;
        for (int i = 0; i < size; i++) {
            if (request.getParameter("column-" + (i + 1)) != null) {
                columns[counter] = request.getParameter("column-" + (i + 1));
                types[counter] = request.getParameter("type-" + (i + 1));
                positions[counter] = Integer.parseInt(request.getParameter("position-" + (i + 1)));
                counter++;/*from  ww w  .ja  v  a  2  s  .  com*/
            }
        }

        FormatFiles.convertTxtToArff(pathInput, pathTrainingOutput, pathTestOutput, name, columns, types,
                positions, counter, range);
        try {
            J48 j48 = new J48();

            BufferedReader readerTraining = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTraining = new Instances(readerTraining);
            instancesTraining.setClassIndex(instancesTraining.numAttributes() - 1);

            j48.buildClassifier(instancesTraining);

            BufferedReader readerTest = new BufferedReader(new FileReader(pathTestOutput));
            //BufferedReader readerTest = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTest = new Instances(readerTest);
            instancesTest.setClassIndex(instancesTest.numAttributes() - 1);

            int correctsDecisionTree = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = j48.classifyInstance(instance);

                if (correctValue == classification) {
                    correctsDecisionTree++;
                }
            }

            Evaluation eval = new Evaluation(instancesTraining);
            eval.evaluateModel(j48, instancesTest);

            PrintWriter writer = new PrintWriter(
                    new BufferedWriter(new FileWriter(pathBothClassifications, false)));

            writer.println("?rvore de Deciso\n\n");

            writer.println(j48.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            NaiveBayes naiveBayes = new NaiveBayes();

            naiveBayes.buildClassifier(instancesTraining);

            eval = new Evaluation(instancesTraining);
            eval.evaluateModel(naiveBayes, instancesTest);

            int correctsNaiveBayes = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = naiveBayes.classifyInstance(instance);

                if (correctValue == classification) {
                    correctsNaiveBayes++;
                }
            }

            writer.println("Naive Bayes\n\n");

            writer.println(naiveBayes.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            writer.close();

            response.sendRedirect("BothClassifications?action=view&correctsDecisionTree=" + correctsDecisionTree
                    + "&correctsNaiveBayes=" + correctsNaiveBayes + "&totalTest=" + instancesTest.size()
                    + "&totalTrainig=" + instancesTraining.size() + "&range=" + range + "&fileName=" + aux
                    + "-bothClassifications.txt");
        } catch (Exception e) {
            System.out.println(e.getMessage());
            response.sendRedirect("Navigation?action=decisionTree");
        }

        break;
    }
    default:
        response.sendError(404);
    }
}

From source file:controller.DecisionTreeServlet.java

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    request.setCharacterEncoding("UTF-8");
    String dir = "/data/";
    String path = getServletContext().getRealPath(dir);

    String action = request.getParameter("action");

    switch (action) {
    case "create": {
        String fileName = request.getParameter("file");

        String aux = fileName.substring(0, fileName.indexOf("."));
        String pathInput = path + "/" + request.getParameter("file");
        String pathTrainingOutput = path + "/" + aux + "-training-arff.txt";
        String pathTestOutput = path + "/" + aux + "-test-arff.txt";
        String pathDecisionTree = path + "/" + aux + "-decisionTree.txt";

        String name = request.getParameter("name");
        int range = Integer.parseInt(request.getParameter("range"));

        int size = Integer.parseInt(request.getParameter("counter"));
        String[] columns = new String[size];
        String[] types = new String[size];
        int[] positions = new int[size];
        int counter = 0;
        for (int i = 0; i < size; i++) {
            if (request.getParameter("column-" + (i + 1)) != null) {
                columns[counter] = request.getParameter("column-" + (i + 1));
                types[counter] = request.getParameter("type-" + (i + 1));
                positions[counter] = Integer.parseInt(request.getParameter("position-" + (i + 1)));
                counter++;//from  w  ww.j a v  a  2 s .  c  o m
            }
        }

        FormatFiles.convertTxtToArff(pathInput, pathTrainingOutput, pathTestOutput, name, columns, types,
                positions, counter, range);
        try {
            J48 j48 = new J48();

            BufferedReader readerTraining = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTraining = new Instances(readerTraining);
            instancesTraining.setClassIndex(instancesTraining.numAttributes() - 1);

            j48.buildClassifier(instancesTraining);

            BufferedReader readerTest = new BufferedReader(new FileReader(pathTestOutput));
            //BufferedReader readerTest = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTest = new Instances(readerTest);
            instancesTest.setClassIndex(instancesTest.numAttributes() - 1);

            int corrects = 0;
            int truePositive = 0;
            int trueNegative = 0;
            int falsePositive = 0;
            int falseNegative = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = j48.classifyInstance(instance);

                if (correctValue == classification) {
                    corrects++;
                }
                if (correctValue == 1 && classification == 1) {
                    truePositive++;
                }
                if (correctValue == 1 && classification == 0) {
                    falseNegative++;
                }
                if (correctValue == 0 && classification == 1) {
                    falsePositive++;
                }
                if (correctValue == 0 && classification == 0) {
                    trueNegative++;
                }
            }

            Evaluation eval = new Evaluation(instancesTraining);
            eval.evaluateModel(j48, instancesTest);

            PrintWriter writer = new PrintWriter(new BufferedWriter(new FileWriter(pathDecisionTree, false)));

            writer.println(j48.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            writer.close();

            response.sendRedirect("DecisionTree?action=view&corrects=" + corrects + "&totalTest="
                    + instancesTest.size() + "&totalTrainig=" + instancesTraining.size() + "&truePositive="
                    + truePositive + "&trueNegative=" + trueNegative + "&falsePositive=" + falsePositive
                    + "&falseNegative=" + falseNegative + "&fileName=" + aux + "-decisionTree.txt");
        } catch (Exception e) {
            System.out.println(e.getMessage());
            response.sendRedirect("Navigation?action=decisionTree");
        }

        break;
    }
    default:
        response.sendError(404);
    }
}

From source file:controller.NaiveBayesServlet.java

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    request.setCharacterEncoding("UTF-8");
    String dir = "/data/";
    String path = getServletContext().getRealPath(dir);

    String action = request.getParameter("action");

    switch (action) {
    case "create": {
        String fileName = request.getParameter("file");

        String aux = fileName.substring(0, fileName.indexOf("."));
        String pathInput = path + "/" + request.getParameter("file");
        String pathTrainingOutput = path + "/" + aux + "-training-arff.txt";
        String pathTestOutput = path + "/" + aux + "-test-arff.txt";
        String pathNaivebayes = path + "/" + aux + "-naiveBayes.txt";

        String name = request.getParameter("name");
        int range = Integer.parseInt(request.getParameter("range"));

        int size = Integer.parseInt(request.getParameter("counter"));
        String[] columns = new String[size];
        String[] types = new String[size];
        int[] positions = new int[size];
        int counter = 0;

        for (int i = 0; i < size; i++) {
            if (request.getParameter("column-" + (i + 1)) != null) {
                columns[counter] = request.getParameter("column-" + (i + 1));
                types[counter] = request.getParameter("type-" + (i + 1));
                positions[counter] = Integer.parseInt(request.getParameter("position-" + (i + 1)));
                counter++;/*from   ww w  . j  a va2s.  c  o m*/
            }
        }

        FormatFiles.convertTxtToArff(pathInput, pathTrainingOutput, pathTestOutput, name, columns, types,
                positions, counter, range);

        try {
            NaiveBayes naiveBayes = new NaiveBayes();

            BufferedReader readerTraining = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTraining = new Instances(readerTraining);
            instancesTraining.setClassIndex(instancesTraining.numAttributes() - 1);

            naiveBayes.buildClassifier(instancesTraining);

            BufferedReader readerTest = new BufferedReader(new FileReader(pathTestOutput));
            //BufferedReader readerTest = new BufferedReader(new FileReader(pathTrainingOutput));
            Instances instancesTest = new Instances(readerTest);
            instancesTest.setClassIndex(instancesTest.numAttributes() - 1);

            Evaluation eval = new Evaluation(instancesTraining);
            eval.evaluateModel(naiveBayes, instancesTest);

            int corrects = 0;
            int truePositive = 0;
            int trueNegative = 0;
            int falsePositive = 0;
            int falseNegative = 0;

            for (int i = 0; i < instancesTest.size(); i++) {
                Instance instance = instancesTest.get(i);
                double correctValue = instance.value(instance.attribute(instancesTest.numAttributes() - 1));
                double classification = naiveBayes.classifyInstance(instance);

                if (correctValue == classification) {
                    corrects++;
                }
                if (correctValue == 1 && classification == 1) {
                    truePositive++;
                }
                if (correctValue == 1 && classification == 0) {
                    falseNegative++;
                }
                if (correctValue == 0 && classification == 1) {
                    falsePositive++;
                }
                if (correctValue == 0 && classification == 0) {
                    trueNegative++;
                }
            }

            PrintWriter writer = new PrintWriter(new BufferedWriter(new FileWriter(pathNaivebayes, false)));

            writer.println(naiveBayes.toString());

            writer.println("");
            writer.println("");
            writer.println("Results");
            writer.println(eval.toSummaryString());

            writer.close();

            response.sendRedirect(
                    "NaiveBayes?action=view&corrects=" + corrects + "&totalTest=" + instancesTest.size()
                            + "&totalTrainig=" + instancesTraining.size() + "&range=" + range + "&truePositive="
                            + truePositive + "&trueNegative=" + trueNegative + "&falsePositive=" + falsePositive
                            + "&falseNegative=" + falseNegative + "&fileName=" + aux + "-naiveBayes.txt");

        } catch (Exception e) {
            System.out.println(e.getMessage());
            response.sendRedirect("Navigation?action=naiveBayes");
        }

        break;
    }
    default:
        response.sendError(404);
    }

}

From source file:core.ClusterEvaluationEX.java

License:Open Source License

/**
 * num??/*from   w  w  w. j a va 2 s  .c om*/
 * Returns the Centroids
 * */
public Instances getCentroids(int num) {
    FastVector atts = new FastVector();
    Attribute clusterID = new Attribute("clusterID");
    atts.addElement(clusterID);
    Instances data = new Instances("centroids", atts, m_numClusters);
    for (int i = 0; i < ID.numAttributes() - 1; i++) {
        Attribute att = new Attribute("Subject" + String.valueOf(i));
        atts.addElement(att);
    }
    double[] map = new double[m_numClusters];
    double[] temp = new double[m_clusterAssignments.length];
    System.arraycopy(m_clusterAssignments, 0, temp, 0, m_clusterAssignments.length);
    int n = map.length;
    for (int i = 0; i < m_clusterAssignments.length; i++) {
        double id = temp[i];
        if (id == -1)
            continue;
        boolean flag = true;
        for (int j = 0; j < temp.length; j++) {
            if (temp[j] == id) {
                temp[j] = -1;
            }
        }
        if (flag && n != -1) {
            map[map.length - n] = id + num;
            n--;
        } else if (n != -1) {
            continue;
        } else {
            break;
        }
    }
    for (int i = 0; i < map.length; i++) {
        double id = map[i];
        double[] averatts = new double[ID.numAttributes()];
        int count = 0;
        for (int j = 0; j < ID.numInstances(); j++) {
            Instance iter = ID.instance(j);
            if (iter.value(0) == id) {
                averatts = CommonMethords.add(averatts, iter.toDoubleArray());
                count++;
            }
        }
        averatts = CommonMethords.calAver(averatts, count);
        Instance ins = new Instance(1, averatts);
        data.add(ins);
    }
    return data;
}

From source file:core.DatabaseSaverEx.java

License:Open Source License

/**
 * inserts the given instance into the table.
 * /*ww  w  .j  a  va  2  s .  c  om*/
 * @param inst the instance to insert
 * @throws Exception if something goes wrong
 */
public void writeInstance(Instance inst) throws Exception {

    StringBuffer insert = new StringBuffer();
    insert.append("INSERT INTO ");
    insert.append(m_tableName);
    insert.append(" VALUES ( ");
    if (m_id) {
        insert.append(m_count);
        insert.append(", ");
        m_count++;
    }
    for (int j = 0; j < inst.numAttributes(); j++) {
        if (inst.isMissing(j))
            insert.append("NULL");
        else {
            if ((inst.attribute(j)).isDate())
                insert.append("'" + m_DateFormat.format((long) inst.value(j)) + "'");
            else if ((inst.attribute(j)).isNumeric())
                insert.append(inst.value(j));
            else {
                String stringInsert = "'" + inst.stringValue(j) + "'";
                if (stringInsert.length() > 2)
                    stringInsert = stringInsert.replaceAll("''", "'");
                insert.append(stringInsert);
            }
        }
        if (j != inst.numAttributes() - 1)
            insert.append(", ");
    }
    insert.append(" )");
    //System.out.println(insert.toString());
    if (m_DataBaseConnection.update(insert.toString()) < 1) {
        throw new IOException("Tuple cannot be inserted.");
    } else {
        m_DataBaseConnection.close();
    }
}