Example usage for weka.filters.unsupervised.attribute AddCluster setInputFormat

Introduction

In this page you can find the example usage for weka.filters.unsupervised.attribute AddCluster setInputFormat.

Prototype

@Override
public boolean setInputFormat(Instances instanceInfo) throws Exception 

Source Link

Document

Sets the format of the input instances.

Usage

From source file:eu.cassandra.appliance.IsolatedApplianceExtractor.java

License:Apache License

/**
 * This is an auxiliary function that prepares the clustering data set. The
 * events must be translated to instances of the data set that can be used for
 * clustering.//  w w  w .  ja v  a2s  . c om
 * 
 * @param isolated
 *          The list of the events containing an isolated appliance.
 * @return The instances of the data
 * @throws Exception
 */
private Instances createInstances(ArrayList<Event> isolated) throws Exception {
    // Initializing auxiliary variables namely the attributes of the data set
    Attribute id = new Attribute("id");
    Attribute pDiffRise = new Attribute("pDiffRise");
    Attribute qDiffRise = new Attribute("qDiffRise");
    Attribute pDiffReduce = new Attribute("pDiffReduce");
    Attribute qDiffReduce = new Attribute("qDiffReduce");

    ArrayList<Attribute> attr = new ArrayList<Attribute>();
    attr.add(id);
    attr.add(pDiffRise);
    attr.add(qDiffRise);
    attr.add(pDiffReduce);
    attr.add(qDiffReduce);

    Instances instances = new Instances("Isolated", attr, 0);

    // Each event is translated to an instance with the above attributes
    for (Event event : isolated) {

        Instance inst = new DenseInstance(5);
        inst.setValue(id, event.getId());
        inst.setValue(pDiffRise, event.getRisingPoints().get(0).getPDiff());
        inst.setValue(qDiffRise, event.getRisingPoints().get(0).getQDiff());
        inst.setValue(pDiffReduce, event.getReductionPoints().get(0).getPDiff());
        inst.setValue(qDiffReduce, event.getReductionPoints().get(0).getQDiff());

        instances.add(inst);

    }

    int n = Constants.MAX_CLUSTERS_NUMBER;
    Instances newInst = null;

    System.out.println("Instances: " + instances.toSummaryString());
    System.out.println("Max Clusters: " + n);

    // Create the addcluster filter of Weka and the set up the hierarchical
    // clusterer.
    AddCluster addcluster = new AddCluster();

    if (instances.size() > Constants.KMEANS_LIMIT_NUMBER || instances.size() == 0) {

        HierarchicalClusterer clusterer = new HierarchicalClusterer();

        String[] opt = { "-N", "" + n + "", "-P", "-D", "-L", "AVERAGE" };

        clusterer.setDistanceFunction(new EuclideanDistance());
        clusterer.setNumClusters(n);
        clusterer.setOptions(opt);
        clusterer.setPrintNewick(true);
        clusterer.setDebug(true);

        // clusterer.getOptions();

        addcluster.setClusterer(clusterer);
        addcluster.setInputFormat(instances);
        addcluster.setIgnoredAttributeIndices("1");

        // Cluster data set
        newInst = Filter.useFilter(instances, addcluster);

    } else {

        SimpleKMeans kmeans = new SimpleKMeans();

        kmeans.setSeed(10);

        // This is the important parameter to set
        kmeans.setPreserveInstancesOrder(true);
        kmeans.setNumClusters(n);
        kmeans.buildClusterer(instances);

        addcluster.setClusterer(kmeans);
        addcluster.setInputFormat(instances);
        addcluster.setIgnoredAttributeIndices("1");

        // Cluster data set
        newInst = Filter.useFilter(instances, addcluster);

    }

    return newInst;

}

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From source file:eu.cassandra.appliance.IsolatedEventsExtractor.java

License:Apache License

/**
 * This is an auxiliary function that prepares the clustering data set. The
 * events must be translated to instances of the data set that can be used for
 * clustering.//www  .ja  v  a  2  s  . c o  m
 * 
 * @param isolated
 *          The list of the events containing an isolated appliance.
 * @return The instances of the data
 * @throws Exception
 */
private Instances createInstances(ArrayList<Event> isolated) throws Exception {
    // Initializing auxiliary variables namely the attributes of the data set
    Attribute id = new Attribute("id");
    Attribute pDiffRise = new Attribute("pDiffRise");
    Attribute qDiffRise = new Attribute("qDiffRise");
    Attribute pDiffReduce = new Attribute("pDiffReduce");
    Attribute qDiffReduce = new Attribute("qDiffReduce");
    Attribute duration = new Attribute("duration");

    ArrayList<Attribute> attr = new ArrayList<Attribute>();
    attr.add(id);
    attr.add(pDiffRise);
    attr.add(qDiffRise);
    attr.add(pDiffReduce);
    attr.add(qDiffReduce);
    attr.add(duration);

    Instances instances = new Instances("Isolated", attr, 0);

    // Each event is translated to an instance with the above attributes
    for (Event event : isolated) {

        Instance inst = new DenseInstance(6);
        inst.setValue(id, event.getId());
        inst.setValue(pDiffRise, event.getRisingPoints().get(0).getPDiff());
        inst.setValue(qDiffRise, event.getRisingPoints().get(0).getQDiff());
        inst.setValue(pDiffReduce, event.getReductionPoints().get(0).getPDiff());
        inst.setValue(qDiffReduce, event.getReductionPoints().get(0).getQDiff());
        inst.setValue(duration, event.getEndMinute() - event.getStartMinute());
        instances.add(inst);

    }

    int n = Constants.MAX_CLUSTERS_NUMBER;
    Instances newInst = null;

    log.info("Instances: " + instances.toSummaryString());
    log.info("Max Clusters: " + n);

    // Create the addcluster filter of Weka and the set up the hierarchical
    // clusterer.
    AddCluster addcluster = new AddCluster();

    if (instances.size() > Constants.KMEANS_LIMIT_NUMBER || instances.size() == 0) {

        HierarchicalClusterer clusterer = new HierarchicalClusterer();

        String[] opt = { "-N", "" + n + "", "-P", "-D", "-L", "AVERAGE" };

        clusterer.setDistanceFunction(new EuclideanDistance());
        clusterer.setNumClusters(n);
        clusterer.setOptions(opt);
        clusterer.setPrintNewick(true);
        clusterer.setDebug(true);

        // clusterer.getOptions();

        addcluster.setClusterer(clusterer);
        addcluster.setInputFormat(instances);
        addcluster.setIgnoredAttributeIndices("1");

        // Cluster data set
        newInst = Filter.useFilter(instances, addcluster);

    } else {

        SimpleKMeans kmeans = new SimpleKMeans();

        kmeans.setSeed(10);

        // This is the important parameter to set
        kmeans.setPreserveInstancesOrder(true);
        kmeans.setNumClusters(n);
        kmeans.buildClusterer(instances);

        addcluster.setClusterer(kmeans);
        addcluster.setInputFormat(instances);
        addcluster.setIgnoredAttributeIndices("1");

        // Cluster data set
        newInst = Filter.useFilter(instances, addcluster);

    }

    return newInst;

}

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From source file:eu.cassandra.utils.Utils.java

License:Apache License

/**
 * This function is used in order to create clusters of points of interest
 * based on the active power difference they have.
 * //from   w  w w  .  java  2  s  . c  om
 * @param pois
 *          The list of points of interest that will be clustered.
 * @return The newly created clusters with the points that are comprising
 *         them.
 * @throws Exception
 */
public static ArrayList<ArrayList<PointOfInterest>> clusterPoints(ArrayList<PointOfInterest> pois, int bias)
        throws Exception {
    // Initialize the auxiliary variables
    ArrayList<ArrayList<PointOfInterest>> result = new ArrayList<ArrayList<PointOfInterest>>();

    // Estimating the number of clusters that will be created
    int numberOfClusters = (int) (Math.ceil((double) pois.size() / (double) Constants.MAX_POINTS_OF_INTEREST))
            + bias;

    log.info("Clusters: " + pois.size() + " / " + Constants.MAX_POINTS_OF_INTEREST + " + " + bias + " = "
            + numberOfClusters);

    // Create a new empty list of points for each cluster
    for (int i = 0; i < numberOfClusters; i++)
        result.add(new ArrayList<PointOfInterest>());

    // Initializing auxiliary variables namely the attributes of the data set
    Attribute id = new Attribute("id");
    Attribute pDiffRise = new Attribute("pDiff");

    ArrayList<Attribute> attr = new ArrayList<Attribute>();
    attr.add(id);
    attr.add(pDiffRise);

    Instances instances = new Instances("Points of Interest", attr, 0);

    // Each event is translated to an instance with the above attributes
    for (int i = 0; i < pois.size(); i++) {

        Instance inst = new DenseInstance(2);
        inst.setValue(id, i);
        inst.setValue(pDiffRise, Math.abs(pois.get(i).getPDiff()));

        instances.add(inst);

    }

    // System.out.println(instances.toString());

    Instances newInst = null;

    log.debug("Instances: " + instances.toSummaryString());

    // Create the addcluster filter of Weka and the set up the hierarchical
    // clusterer.
    AddCluster addcluster = new AddCluster();

    SimpleKMeans kmeans = new SimpleKMeans();

    kmeans.setSeed(numberOfClusters);

    // This is the important parameter to set
    kmeans.setPreserveInstancesOrder(true);
    kmeans.setNumClusters(numberOfClusters);
    kmeans.buildClusterer(instances);

    addcluster.setClusterer(kmeans);
    addcluster.setInputFormat(instances);
    addcluster.setIgnoredAttributeIndices("1");

    // Cluster data set
    newInst = Filter.useFilter(instances, addcluster);

    // System.out.println(newInst.toString());

    // Parse through the dataset to see where each point is placed in the
    // clusters.
    for (int i = 0; i < newInst.size(); i++) {

        String cluster = newInst.get(i).stringValue(newInst.attribute(2));

        cluster = cluster.replace("cluster", "");

        log.debug("Point of Interest: " + i + " Cluster: " + cluster);

        result.get(Integer.parseInt(cluster) - 1).add(pois.get(i));
    }

    // Sorting the each cluster points by their minutes.
    for (int i = result.size() - 1; i >= 0; i--) {
        if (result.get(i).size() == 0)
            result.remove(i);
        else
            Collections.sort(result.get(i), Constants.comp);
    }

    // Sorting the all clusters by their active power.

    Collections.sort(result, Constants.comp5);

    return result;
}

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