Examples with Attribute weka.core.Attribute used on opensource projects

Example 31 with Attribute

use of weka.core.Attribute in project chuidiang-ejemplos by chuidiang.

the class GenerateTestVessels method getData.

public static Instances getData() throws Exception {
    ArrayList<Attribute> atts = new ArrayList<>();
    Instances data;
    double[] vals;
    // Atributos, diciendo nombre y posibles valores. Si no se indica
    // nada, son numéricos.
    atts.add(new Attribute("type", Arrays.asList(types)));
    atts.add(new Attribute("size"));
    atts.add(new Attribute("vmax"));
    atts.add(new Attribute("vmin"));
    atts.add(new Attribute("mmsi", Arrays.asList("si", "no")));
    // Creamos el conjunto de datos, de momento vacío, indicando qué atributos
    // van a llevar.
    data = new Instances("MyData", atts, 0);
    // pateras
    for (int i = 0; i < 10; i++) {
        vals = getPatera(data.numAttributes());
        data.add(new DenseInstance(1.0, vals));
    }
    // lanchas
    for (int i = 0; i < 10; i++) {
        vals = getLancha(data.numAttributes());
        data.add(new DenseInstance(1.0, vals));
    }
    // barco
    for (int i = 0; i < 10; i++) {
        vals = getBarco(data.numAttributes());
        data.add(new DenseInstance(1.0, vals));
    }
    data.setClassIndex(0);
    return data;
}

Example 32 with Attribute

use of weka.core.Attribute in project mcib3d-core by mcib3d.

the class Attributes method addAttribute.

public void addAttribute(String name) {
    Attribute att = new Attribute(name);
    atts.add(att);
    attributes.put(name, att);
}

Example 33 with Attribute

use of weka.core.Attribute in project iobserve-analysis by research-iobserve.

the class JUnitTestsHierarchicalClustering method setupTestData.

/**
 * Create test data sets and their expected clustering results.
 *
 * @throws IOException
 *             when failing to write a CSV file to CSVOUTPUTPATH
 */
@Before
public void setupTestData() throws IOException {
    // Create Attribute names for the test data sets.
    final FastVector attVector = new FastVector(1);
    attVector.addElement(new Attribute("Attribute1"));
    attVector.addElement(new Attribute("Attribute2"));
    // Create a data set with two similar instances i1 and i2 with two attributes.
    final Instance i1one = new Instance(1.0, new double[] { 1.0, 2.0 });
    final Instance i2one = new Instance(1.0, new double[] { 1.5, 2.1 });
    final Instances instancesOne = new Instances("Instances", attVector, 2);
    instancesOne.add(i1one);
    instancesOne.add(i2one);
    i1one.setDataset(instancesOne);
    i2one.setDataset(instancesOne);
    this.setTestInstancesOneCluster(instancesOne);
    // Create expected result.
    final Map<Integer, List<Pair<Instance, Double>>> expResOne = new HashMap<>();
    expResOne.put(0, new LinkedList<Pair<Instance, Double>>());
    expResOne.get(0).add(new Pair<>(i1one, 1.0));
    expResOne.get(0).add(new Pair<>(i2one, 1.0));
    this.setExpectedResultsOneCluster(expResOne);
    /*
         * Create a data set with two similar instances i1 and i2 and one different instance i3 with
         * two attributes.
         */
    final Instance i1two = new Instance(1.0, new double[] { 1.0, 2.0 });
    final Instance i2two = new Instance(1.0, new double[] { 1.5, 2.1 });
    final Instance i3two = new Instance(1.0, new double[] { 1000.0, 1200.0 });
    final Instances instancesTwo = new Instances("Instances", attVector, 3);
    instancesTwo.add(i1two);
    instancesTwo.add(i2two);
    instancesTwo.add(i3two);
    i1two.setDataset(instancesTwo);
    i2two.setDataset(instancesTwo);
    i3two.setDataset(instancesTwo);
    this.setTestInstancesTwoClusters(instancesTwo);
    // Create expected result.
    final Map<Integer, List<Pair<Instance, Double>>> expResTwo = new HashMap<>();
    expResTwo.put(0, new LinkedList<Pair<Instance, Double>>());
    expResTwo.put(1, new LinkedList<Pair<Instance, Double>>());
    expResTwo.get(0).add(new Pair<>(i1two, 1.0));
    expResTwo.get(0).add(new Pair<>(i2two, 1.0));
    expResTwo.get(1).add(new Pair<>(i3two, 1.0));
    this.setExpectedResultsTwoCluster(expResTwo);
    // Write a CSV file for a clustering result which expects two clusters.
    final CSVSinkFilter csvFilter = new CSVSinkFilter();
    final Map<Double, List<Instance>> clusteringKVs = csvFilter.convertClusteringResultsToKVPair(expResTwo);
    csvFilter.createCSVFromClusteringResult(JUnitTestsHierarchicalClustering.CSVOUTPUTPATH, clusteringKVs);
}

Example 34 with Attribute

use of weka.core.Attribute in project iobserve-analysis by research-iobserve.

the class AbstractClustering method createInstances.

/**
 * It transforms the user sessions(userSessions in form of counts of their called operation
 * signatures) to Weka instances that can be used for the clustering.
 *
 * @param countModel
 *            contains the userSessions in form of counts of called operation signatures
 * @param listOfDistinctOperationSignatures
 *            contains the extracted distinct operation signatures of the input
 *            entryCallSequenceModel
 * @return the Weka instances that hold the data that is used for the clustering
 */
protected Instances createInstances(final List<UserSessionAsCountsOfCalls> countModel, final List<String> listOfDistinctOperationSignatures) {
    final int numberOfDistinctOperationSignatures = listOfDistinctOperationSignatures.size();
    final FastVector fvWekaAttributes = new FastVector(numberOfDistinctOperationSignatures);
    for (int i = 0; i < numberOfDistinctOperationSignatures; i++) {
        final String attributeName = "Attribute" + i;
        final Attribute attribute = new Attribute(attributeName);
        fvWekaAttributes.addElement(attribute);
    }
    final Instances clusterSet = new Instances("CallCounts", fvWekaAttributes, countModel.size());
    for (final UserSessionAsCountsOfCalls userSession : countModel) {
        int indexOfAttribute = 0;
        final Instance instance = new Instance(numberOfDistinctOperationSignatures);
        for (int row = 0; row < listOfDistinctOperationSignatures.size(); row++) {
            instance.setValue((Attribute) fvWekaAttributes.elementAt(indexOfAttribute), userSession.getAbsoluteCountOfCalls()[row]);
            indexOfAttribute++;
        }
        clusterSet.add(instance);
    }
    return clusterSet;
}

Example 35 with Attribute

use of weka.core.Attribute in project mzmine2 by mzmine.

the class ClusteringTask method createVariableWekaDataset.

/**
 * Creates the weka data set for clustering of variables (metabolites)
 *
 * @param rawData Data extracted from selected Raw data files and rows.
 * @return Weka library data set
 */
private Instances createVariableWekaDataset(double[][] rawData) {
    FastVector attributes = new FastVector();
    for (int i = 0; i < this.selectedRawDataFiles.length; i++) {
        String varName = "Var" + i;
        Attribute var = new Attribute(varName);
        attributes.addElement(var);
    }
    if (clusteringStep.getModule().getClass().equals(HierarClusterer.class)) {
        Attribute name = new Attribute("name", (FastVector) null);
        attributes.addElement(name);
    }
    Instances data = new Instances("Dataset", attributes, 0);
    for (int i = 0; i < selectedRows.length; i++) {
        double[] values = new double[data.numAttributes()];
        System.arraycopy(rawData[i], 0, values, 0, rawData[0].length);
        if (clusteringStep.getModule().getClass().equals(HierarClusterer.class)) {
            DecimalFormat twoDForm = new DecimalFormat("#.##");
            double MZ = Double.valueOf(twoDForm.format(selectedRows[i].getAverageMZ()));
            double RT = Double.valueOf(twoDForm.format(selectedRows[i].getAverageRT()));
            String rowName = "MZ->" + MZ + "/RT->" + RT;
            values[data.numAttributes() - 1] = data.attribute("name").addStringValue(rowName);
        }
        Instance inst = new SparseInstance(1.0, values);
        data.add(inst);
    }
    return data;
}