Example usage for weka.core Instances setClassIndex

List of usage examples for weka.core Instances setClassIndex

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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:com.reactivetechnologies.analytics.mapper.ARFFDataMapper.java

License:Open Source License

@Override
public Dataset mapStringToModel(JsonRequest request) throws ParseException {
    if (!(request instanceof ArffJsonRequest)) {
        throw new ParseException("Not an instance of " + ArffJsonRequest.class, -1);
    }//from www .ja  v  a  2s . c o m
    try {
        ArffJsonRequest arff = (ArffJsonRequest) request;
        ArffReader ar = new ArffReader(new StringReader(request.toString()));
        Instances ins = ar.getData();
        ins.setClassIndex(arff.getClassIndex() >= 0 ? arff.getClassIndex() : ins.numAttributes() - 1);
        return new Dataset(ins);
    } catch (Exception e) {
        ParseException pe = new ParseException("Cannot convert JSON stream to ARFF", -1);
        pe.initCause(e);
        throw pe;
    }
}

From source file:com.reactivetechnologies.analytics.mapper.TEXTDataMapper.java

License:Open Source License

@Override
public Dataset mapStringToModel(JsonRequest request) throws ParseException {

    if (request != null && request.getData() != null && request.getData().length > 0) {
        FastVector fvWekaAttributes = new FastVector(2);
        FastVector nil = null;/* ww w  .j  ava  2 s .  c  o  m*/
        Attribute attr0 = new Attribute("text", nil, 0);
        FastVector fv = new FastVector();
        for (String nominal : request.getClassVars()) {
            fv.addElement(nominal);
        }
        Attribute attr1 = new Attribute("class", fv, 1);

        fvWekaAttributes.addElement(attr0);
        fvWekaAttributes.addElement(attr1);

        Instances ins = new Instances("attr-reln", fvWekaAttributes, request.getData().length);
        ins.setClassIndex(1);
        for (Text s : request.getData()) {
            Instance i = new Instance(2);
            i.setValue(attr0, s.getText());
            i.setValue(attr1, s.getTclass());
            ins.add(i);

        }

        return new Dataset(ins);
    }
    return null;
}

From source file:com.reactivetechnologies.platform.analytics.mapper.JSONDataMapper.java

License:Open Source License

@Override
public TrainModel mapStringToModel(ArffJsonRequest request) throws ParseException {
    try {/* ww  w .  j  a v  a  2s  .c o m*/
        ArffReader ar = new ArffReader(new StringReader(request.toString()));
        Instances ins = ar.getData();
        ins.setClassIndex(request.getClassIndex() >= 0 ? request.getClassIndex() : ins.numAttributes() - 1);
        return new TrainModel(ins);
    } catch (Exception e) {
        ParseException pe = new ParseException("Cannot convert JSON stream to ARFF", -1);
        pe.initCause(e);
        throw pe;
    }
}

From source file:com.relationalcloud.main.ExplanationSingleAttribute.java

License:Open Source License

/**
 * @param args//from  w  w w  .ja va  2s. co  m
 */
@Deprecated
public static void main(String[] args) {

    Properties ini = new Properties();
    try {
        ini.load(new FileInputStream(System.getProperty("prop")));
    } catch (FileNotFoundException e) {
        e.printStackTrace();
    } catch (IOException e) {
        e.printStackTrace();
    }

    // loading properties from file
    String schemaname = ini.getProperty("schemaname");

    String partitioningMethod = ini.getProperty("partitioningMethod");
    String pcol;
    if (partitioningMethod.equals("repGraph")) {
        System.out.println("Replication Graph: using replicated column");
        pcol = ini.getProperty("replicatedPartitionCol");
    } else {
        pcol = ini.getProperty("graphPartitionCol");
    }

    String accessLogTable = ini.getProperty("accessLogTable");
    String numb_trans_to_process = ini.getProperty("numb_trans_to_process");
    String txnLogTable = ini.getProperty("txnLogTable");
    String driver = ini.getProperty("driver");
    String connection = ini.getProperty("conn");
    String user = ini.getProperty("user");
    String password = ini.getProperty("password");

    System.out.println("Loading and processing " + schemaname + " traces...");

    // Register jdbcDriver
    try {
        Class.forName(driver);
    } catch (ClassNotFoundException e) {
        e.printStackTrace();
    }

    Connection conn;
    try {
        conn = DriverManager.getConnection(connection + schemaname, user, password);
        conn.setAutoCommit(true);

        Connection infschema_conn = DriverManager.getConnection(connection + "information_schema", user,
                password);

        Schema schema = SchemaLoader.loadSchemaFromDB(infschema_conn, schemaname);

        Statement stmt = conn.createStatement();

        // NOTE: the paramenter numb_trans_to_process is used to limit
        // the number of transactions parsed to determine the which attributes
        // are common in the workload WHERE clauses. This can be a subset of the
        // overall set

        String sqlstring = "SELECT sqlstring FROM `" + txnLogTable + "` LIMIT " + numb_trans_to_process;
        ResultSet res = stmt.executeQuery(sqlstring);

        ExplanationWorkloadPrepocessor wa = new ExplanationWorkloadPrepocessor(schemaname, schema);

        double tstart = System.currentTimeMillis();
        double i = 0;
        while (res.next()) {
            String sql = res.getString(1);
            // PARSE THE STATEMENT
            wa.processSql(sql);
            i++;
        }

        double tend = System.currentTimeMillis();

        System.out.println("Processed " + i + " statements in " + (tend - tstart) + "ms average:"
                + (tend - tstart) / i + "ms per statement");

        System.out.println("ANALISYS RESULTS:\n ");
        wa.printStatsByTableColumn();

        for (String str : wa.getAllTableNames()) {
            if (str == null)
                continue;
            System.out.println("-------------------------------------------");
            System.out.println("ANALYZING TABLE IN USED IN THE TRANSACTION TRACE " + str);
            for (SimpleCount sc : wa.getFeatures(str)) {

                ArrayList<Double> a0 = new ArrayList<Double>();
                ArrayList<Double> a1 = new ArrayList<Double>();

                sqlstring = "SELECT s." + sc.colname + ", g." + pcol + " FROM `" + accessLogTable
                        + "` g, relcloud_" + str + " s WHERE tableid = \"" + str
                        + "\" AND s.relcloud_id = g.tupleid";

                // System.out.println(sqlstring);
                res = stmt.executeQuery(sqlstring);

                while (res.next()) {
                    Object o1 = res.getObject(1);
                    Object o2 = res.getObject(2);
                    if (o1 != null && o2 != null) {
                        a0.add(new Double(o1.hashCode()));
                        a1.add(new Double(o2.hashCode()));
                    }
                }

                if (a0.size() >= 1) {
                    double[] d0 = new double[a0.size()];
                    double[] d1 = new double[a1.size()];

                    boolean unary = true;

                    for (int j = 0; j < a0.size(); j++) {
                        d0[j] = a0.get(j).doubleValue();
                        d1[j] = a1.get(j).doubleValue();
                        if (j > 0 && d1[j - 1] != d1[j])
                            unary = false;
                    }

                    if (unary) {
                        System.out.println("EASY CASE: " + str
                                + " is not partitioned and is stored in partition: " + d1[0]);
                    } else {

                        double correlation = PearsonCorrelation.getPearsonCorrelation(d0, d1);

                        correlationThreshold = Double.parseDouble(ini.getProperty("correlationThreshold"));

                        // if the correlation is high enough proceed to use decision
                        // trees.
                        if (Math.abs(correlation) > correlationThreshold) {
                            System.out.println("Testing " + str + "." + sc.colname + ", " + pcol
                                    + " correlation: " + correlation + " (HIGH)");

                            try {
                                // InstanceQuery query;
                                // query = new InstanceQuery();
                                // query.setUsername("bbb");
                                // query.setPassword("qwer");
                                // query.connectToDatabase();
                                // Instances data = query.retrieveInstances(sqlstring);
                                res.beforeFirst();

                                Instances data = WekaHelper.retrieveInstanceFromResultSet(res);
                                // set the last column to be the classIndex... is this
                                // correct?
                                data.setClassIndex(data.numAttributes() - 1);

                                Instances newData;

                                if (data.attribute(data.numAttributes() - 1).type() == Attribute.NUMERIC) {
                                    NumericToNominal ntn = new NumericToNominal();
                                    String[] options = new String[2];
                                    options[0] = "-R"; // "range"
                                    options[1] = "2"; // first attribute
                                    ntn.setOptions(options); // set options
                                    ntn.setInputFormat(data); // inform filter about dataset
                                    // **AFTER** setting options
                                    newData = Filter.useFilter(data, ntn); // apply fil

                                } else {
                                    StringToNominal ntn = new StringToNominal();
                                    String[] options = new String[2];
                                    options[0] = "-R"; // "range"
                                    options[1] = "2"; // first attribute
                                    ntn.setOptions(options); // set options
                                    ntn.setInputFormat(data); // inform filter about dataset
                                    // **AFTER** setting options
                                    newData = Filter.useFilter(data, ntn); // apply fil

                                }

                                String[] options = new String[1];
                                options[0] = "-P";
                                J48 tree = new J48(); // new instance of tree
                                tree.setOptions(options); // set the options

                                if (!tree.getCapabilities().test(newData)) {
                                    System.err.println("ERROR the FOLLOWING DATA CANNOT BE PROCESED:"
                                            + newData.toSummaryString());
                                    System.err.println("QUERY WAS:" + sqlstring);
                                } else {
                                    long treeTstart = System.currentTimeMillis();
                                    tree.buildClassifier(newData); // build classifier
                                    long treeTend = System.currentTimeMillis();
                                    System.out.println("CLASSIFICATION CONFIDENCE:  "
                                            + tree.getConfidenceFactor() + "\n TREE BUILDING TIME: "
                                            + (treeTend - treeTstart) + "ms \n" + tree.toString());
                                    System.out.println("TREE:" + tree.prefix());
                                }

                            } catch (Exception e) {
                                // TODO Auto-generated catch block
                                e.printStackTrace();
                            }

                        } else {
                            System.out.println("Testing " + str + "." + sc.colname + ", " + pcol
                                    + " correlation: " + correlation + " (LOW)");
                        }
                    }
                }
            }
        }

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

}

From source file:com.relationalcloud.misc.JustifyAgnosticPartitioning.java

License:Open Source License

/**
 * @param args//from   w  w w.j av a2  s  .c o m
 */
public static void main(String[] args) {

    Properties ini = new Properties();
    try {
        ini.load(new FileInputStream(System.getProperty("prop")));
    } catch (FileNotFoundException e) {
        e.printStackTrace();
    } catch (IOException e) {
        e.printStackTrace();
    }

    // Register jdbcDriver
    try {
        Class.forName(ini.getProperty("driver"));
    } catch (ClassNotFoundException e) {
        e.printStackTrace();
    }

    // READ FROM MYSQL THE TPCC TRANSACTION LOG, PARSE EACH STATEMENT AND TEST
    // VARIOUS PARSER FUNCTIONALITIES
    System.out.println("Loading and processing TPCC traces...");

    Connection conn;
    try {

        String schemaname = ini.getProperty("schema");
        String connection = ini.getProperty("conn");
        String user = ini.getProperty("user");
        String password = ini.getProperty("password");
        conn = DriverManager.getConnection(connection + schemaname, user, password);

        Connection infschema_conn = DriverManager.getConnection(connection + "information_schema", user,
                password);

        Schema schema = SchemaLoader.loadSchemaFromDB(infschema_conn, schemaname);

        ExplanationWorkloadPrepocessor wa = new ExplanationWorkloadPrepocessor(schemaname, schema);

        conn.setAutoCommit(true);

        Statement stmt = conn.createStatement();

        String txnLogTable = ini.getProperty("txnLogTable");
        String sqlstring = "SELECT sqlstring FROM `" + txnLogTable + "`";
        ResultSet res = stmt.executeQuery(sqlstring);

        double tstart = System.currentTimeMillis();
        double i = 0;
        while (res.next()) {
            String sql = res.getString(1);
            // PARSE THE STATEMENT
            wa.processSql(sql);
            // System.out.println("SQL: " +sql);
            i++;
        }

        double tend = System.currentTimeMillis();

        String accessLogTable = ini.getProperty("accessLogTable");

        System.out.println("Processed " + i + " statements in " + (tend - tstart) + "ms average:"
                + (tend - tstart) / i + "ms per statement");
        for (String str : wa.getAllTableNames()) {

            System.out.println("-------------------------------------------");
            System.out.println("ANALYZING TABLE " + str);
            for (SimpleCount sc : wa.getFeatures(str)) {

                ArrayList<Double> a0 = new ArrayList<Double>();
                ArrayList<Double> a1 = new ArrayList<Double>();

                sqlstring = "SELECT s." + sc.colname + ", g.partition FROM `" + accessLogTable + "` g, " + str
                        + " s WHERE tableid = \"" + str + "\" AND s.id = g.id";
                System.out.println(sqlstring);
                res = stmt.executeQuery(sqlstring);

                while (res.next()) {
                    a0.add(new Double(res.getObject(1).hashCode()));
                    a1.add(new Double(res.getObject(2).hashCode()));
                }

                if (a0.size() >= 1) {
                    double[] d0 = new double[a0.size()];
                    double[] d1 = new double[a1.size()];

                    boolean unary = true;

                    for (int j = 0; j < a0.size(); j++) {
                        d0[j] = a0.get(j).doubleValue();
                        d1[j] = a1.get(j).doubleValue();
                        if (j > 0 && d1[j - 1] != d1[j])
                            unary = false;
                    }

                    if (unary) {
                        System.out.println("EASY CASE: " + str
                                + " is not partitioned and is stored in partition: " + d1[0]);
                    } else {

                        double correlation = PearsonCorrelation.getPearsonCorrelation(d0, d1);

                        correlationThreshold = Double.parseDouble(ini.getProperty("correlationThreshold"));

                        // if the correlation is high enough proceed to use decision
                        // trees.
                        if (Math.abs(correlation) > correlationThreshold) {
                            System.out.println("Testing " + str + "." + sc.colname
                                    + ", g.partition correlation: " + correlation + " (HIGH)");

                            try {
                                // InstanceQuery query;
                                // query = new InstanceQuery();
                                // query.setUsername("bbb");
                                // query.setPassword("qwer");
                                // query.connectToDatabase();
                                // Instances data = query.retrieveInstances(sqlstring);
                                res.beforeFirst();
                                Instances data = retrieveInstanceFromResultSet(res);
                                // set the last column to be the classIndex... is this
                                // correct?
                                data.setClassIndex(data.numAttributes() - 1);

                                Instances newData;

                                if (data.attribute(data.numAttributes() - 1).type() == Attribute.NUMERIC) {
                                    NumericToNominal ntn = new NumericToNominal();
                                    String[] options = new String[2];
                                    options[0] = "-R"; // "range"
                                    options[1] = "2"; // first attribute
                                    ntn.setOptions(options); // set options
                                    ntn.setInputFormat(data); // inform filter about dataset
                                    // **AFTER** setting options
                                    newData = Filter.useFilter(data, ntn); // apply fil

                                } else {
                                    StringToNominal ntn = new StringToNominal();
                                    String[] options = new String[2];
                                    options[0] = "-R"; // "range"
                                    options[1] = "2"; // first attribute
                                    ntn.setOptions(options); // set options
                                    ntn.setInputFormat(data); // inform filter about dataset
                                    // **AFTER** setting options
                                    newData = Filter.useFilter(data, ntn); // apply fil

                                }

                                String[] options = new String[1];
                                options[0] = "-P";
                                J48 tree = new J48(); // new instance of tree
                                tree.setOptions(options); // set the options

                                if (!tree.getCapabilities().test(newData)) {
                                    System.err.println("ERROR the FOLLOWING DATA CANNOT BE PROCESED:"
                                            + newData.toSummaryString());
                                    System.err.println("QUERY WAS:" + sqlstring);
                                } else {
                                    tree.buildClassifier(newData); // build classifier

                                }
                                System.out.println("CLASSIFICATION CONFIDENCE:  " + tree.getConfidenceFactor()
                                        + "\n " + tree.toString());

                            } catch (Exception e) {
                                // TODO Auto-generated catch block
                                e.printStackTrace();
                            }

                        } else {
                            System.out.println("Testing " + str + "." + sc.colname
                                    + ", g.partition correlation: " + correlation + " (LOW)");
                        }
                    }
                }
            }
        }

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

}

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  .c  o  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.relationalcloud.partitioning.explanation.ExplanationHandler.java

License:Open Source License

/**
 * Fetch from the database the content of the table and the partition lables,
 * and prepares a Weka Instance by sampling and cleaning it
 * //from  w  w w .  j  a  v a2  s  . com
 * @param tableProcessed
 * @param arraySc
 * @param conn
 * @return
 */
public Instances generateInstancesForTable(String tabname, ArrayList<SimpleCount> arraySc, Connection conn) {

    tabname = removeQuotes(tabname);

    Statement stmt;
    try {
        stmt = conn.createStatement();

        ResultSet test = stmt
                .executeQuery("SELECT count(*) FROM " + sampledtrainingtable + " WHERE " + pcol + " is null");

        // safety check, verifies that there are no nulls in input table.
        if (test.next() && test.getInt(1) > 0)
            throw new Exception("Table " + sampledtrainingtable + " contains nulls in " + pcol);

        // get from the DB the tuples content and their partitioning column
        String sqlstring = "SELECT ";
        for (SimpleCount sc : arraySc) {
            sqlstring += "s." + sc.colname + ", ";
        }

        sqlstring += "g." + pcol + " FROM " + "(SELECT tupleid," + pcol + " FROM `" + sampledtrainingtable
                + "` WHERE tableid = '" + tabname + "') AS g, relcloud_" + tabname + " AS s "
                + "WHERE s.relcloud_id = g.tupleid";

        System.out.println(sqlstring);

        ResultSet res = stmt.executeQuery(sqlstring);

        // create an instance from the resultset
        Instances data = WekaHelper.retrieveInstanceFromResultSetComplete(res, dbPropertyFile);
        res.close();

        // prepare the data, by setting class attributed and sampling if required
        data = makeLastNominal(data);
        data.setClassIndex(data.numAttributes() - 1);
        data = sampleTraining(Double.parseDouble(ini.getProperty("Explanation.j48SamplingThreshold")), data);

        System.out.println(data.toSummaryString());

        return data;

    } catch (SQLException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    } catch (Exception e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
    return null;
}

From source file:com.sensyscal.activityrecognition2.utils.Classifiers.java

License:LGPL

public static int customKnnClassifier(double[] newInstanceArray) {
    // TODO Auto-generated method stub
    ts1 = new Timestamp(System.currentTimeMillis());
    int activityId = 0;
    String classLabel = "";
    ArrayList<Attribute> atts = new ArrayList<Attribute>();
    ArrayList<String> classVal = new ArrayList<String>();
    classVal.add("STANDING");
    classVal.add("SITTING");
    classVal.add("LYINGDOWN");
    classVal.add("WALKING");
    atts.add(new Attribute("class", classVal));
    atts.add(new Attribute("1_1_2_1"));
    atts.add(new Attribute("1_1_3_1"));
    atts.add(new Attribute("1_1_9_2"));
    atts.add(new Attribute("2_1_3_1"));
    atts.add(new Attribute("2_1_4_1"));
    atts.add(new Attribute("2_1_9_2"));
    Instances dataUnlabeled = new Instances("TestInstances", atts, 0);
    dataUnlabeled.add(new DenseInstance(1.0, newInstanceArray));
    dataUnlabeled.setClassIndex(0);
    try {//from  ww  w  . j  av a  2 s .  c om
        activityId = (int) (MonitoringWorkerThread.cls.classifyInstance(dataUnlabeled.firstInstance()));
        classLabel = dataUnlabeled.firstInstance().classAttribute().value(activityId);
    } catch (Exception e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
    ts = new Timestamp(System.currentTimeMillis());
    // Log.e("classifyActivity Knn," -> Impiegati:
    // "+(ts.getTime()-ts1.getTime())+" ms;\n");
    return getActivityIDofClassLabel(classLabel);
}

From source file:com.sensyscal.activityrecognition2.utils.Classifiers.java

License:LGPL

public static int customJRipClassifier(double[] newInstanceArray) {
    // TODO Auto-generated method stub
    ts1 = new Timestamp(System.currentTimeMillis());
    int activityId = 0;
    String classLabel = "";
    ArrayList<Attribute> atts = new ArrayList<Attribute>();
    ArrayList<String> classVal = new ArrayList<String>();
    classVal.add("STANDING");
    classVal.add("WALKING");
    classVal.add("SITTING");
    classVal.add("LYINGDOWN");
    atts.add(new Attribute("class", classVal));
    atts.add(new Attribute("1_1_2_1"));
    atts.add(new Attribute("1_1_3_1"));
    atts.add(new Attribute("1_1_9_2"));
    atts.add(new Attribute("2_1_3_1"));
    atts.add(new Attribute("2_1_4_1"));
    atts.add(new Attribute("2_1_9_2"));

    Instances dataUnlabeled = new Instances("TestInstances", atts, 0);
    dataUnlabeled.add(new DenseInstance(1.0, newInstanceArray));
    dataUnlabeled.setClassIndex(0);
    try {/*www  .j a  v a  2  s . co m*/
        activityId = (int) MonitoringWorkerThread.cls.classifyInstance(dataUnlabeled.firstInstance());
        Log.i("classifyActivity JRip ---->", activityId + "");
        classLabel = dataUnlabeled.firstInstance().classAttribute().value((int) activityId);
    } catch (Exception e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
    ts = new Timestamp(System.currentTimeMillis());
    // Log.i("classifyActivity JRip"," -> Impiegati: "+(ts.getTime()-ts1.getTime())+" ms;\n");
    return getActivityIDofClassLabel(classLabel);
}

From source file:com.sensyscal.activityrecognition2.utils.Classifiers.java

License:LGPL

public static int customJ48Classifier(double[] newInstanceArray) {
    // TODO Auto-generated method stub
    ts1 = new Timestamp(System.currentTimeMillis());
    int activityId = 0;
    String classLabel = "";
    ArrayList<Attribute> atts = new ArrayList<Attribute>();
    ArrayList<String> classVal = new ArrayList<String>();
    classVal.add("STANDING");
    classVal.add("SITTING");
    classVal.add("LYINGDOWN");
    classVal.add("WALKING");
    atts.add(new Attribute("class", classVal));
    atts.add(new Attribute("1_1_2_1"));
    atts.add(new Attribute("1_1_3_1"));
    atts.add(new Attribute("1_1_9_2"));
    atts.add(new Attribute("2_1_3_1"));
    atts.add(new Attribute("2_1_4_1"));
    atts.add(new Attribute("2_1_9_2"));
    Instances dataUnlabeled = new Instances("TestInstances", atts, 0);
    dataUnlabeled.add(new DenseInstance(1.0, newInstanceArray));
    dataUnlabeled.setClassIndex(0);
    try {//from w w w . j  a v a2 s . com
        activityId = (int) getJ48ActivityId(
                MonitoringWorkerThread.cls.classifyInstance(dataUnlabeled.firstInstance()));
        classLabel = dataUnlabeled.firstInstance().classAttribute().value((int) activityId);
    } catch (Exception e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
    ts = new Timestamp(System.currentTimeMillis());
    // Log.e("classifyActivity J48"," -> Impiegati: "+(ts.getTime()-ts1.getTime())+" ms;\n");
    return activityId;// getActivityIDofClassLabel(classLabel);
}