Example usage for weka.core Instance value

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:org.iobserve.analysis.behavior.clustering.hierarchical.ElbowMethod.java

License:Apache License

/**
 * Calculate within-cluster sum-of-square (WSS) for a given cluster.
 *
 * @param cluster//  ww w.j  a v a2 s .  c o  m
 *            Calculate the WSS for this cluster.
 * @return WSS
 **/
public double calcWSS(final List<Integer> cluster) {

    final DistanceFunction distanceFunction = this.hierarchicalClusterer.getDistanceFunction();
    final double[] sumAttValues = new double[this.instances.numAttributes()];
    for (int i = 0; i < cluster.size(); i++) {
        final Instance instance = this.instances.instance(cluster.get(i));
        // Sum up all values of all instances.
        for (int j = 0; j < this.instances.numAttributes(); j++) {
            sumAttValues[j] += instance.value(j);
        }
    }
    // Get average value of each attribute value.
    for (int j = 0; j < sumAttValues.length; j++) {
        sumAttValues[j] /= cluster.size();
    }

    /*
     * Create a centroid of this cluster by setting the average attributes of this cluster as
     * its own.
     */
    final Instance centroid = (Instance) this.instances.instance(cluster.get(0)).copy();
    for (int j = 0; j < this.instances.numAttributes(); j++) {
        centroid.setValue(j, sumAttValues[j]);
    }
    // Sum up distances of each data point in cluster to centroid to get WSS.
    double clusterWSS = 0.0;
    for (int i = 0; i < cluster.size(); i++) {
        final Instance instance = this.instances.instance(cluster.get(i));
        clusterWSS += Math.pow(distanceFunction.distance(centroid, instance), 2);
    }
    return clusterWSS;
}

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From source file:org.iobserve.analysis.behavior.clustering.hierarchical.GapStatisticMethod.java

License:Apache License

/**
 * Finds min and max values for each attribute of the instances.
 */// ww w.  j  a v a 2  s .co  m
private void findBoundaryValues() {
    final List<Double> minBoundaryValues = new ArrayList<>();
    final List<Double> maxBoundaryValues = new ArrayList<>();

    for (int i = 0; i < this.instances.numInstances(); i++) {
        final Instance instance = this.instances.instance(i);
        for (int j = 0; j < instance.numAttributes(); j++) {
            // Initialization of both min and max lists.
            if (minBoundaryValues.size() == j) {
                minBoundaryValues.add(j, Double.POSITIVE_INFINITY);
            }
            if (maxBoundaryValues.size() == j) {
                maxBoundaryValues.add(j, Double.NEGATIVE_INFINITY);
            }
            final double minValue = minBoundaryValues.get(j);
            final double maxValue = maxBoundaryValues.get(j);
            final double attributeValue = instance.value(j);
            if (attributeValue < minValue) {
                minBoundaryValues.remove(j);
                minBoundaryValues.add(j, attributeValue);
            }
            if (attributeValue >= maxValue) {
                maxBoundaryValues.remove(j);
                maxBoundaryValues.add(j, attributeValue);
            }
        }
    }
    this.setMinAttributeValues(minBoundaryValues);
    this.setMaxAttributeValues(maxBoundaryValues);
}

---

From source file:org.iobserve.analysis.behavior.clustering.hierarchical.GapStatisticMethod.java

License:Apache License

/**
 * Calculated error sum-of-squares (ESS) for a given cluster.
 *
 * @param cluster/*  w  w  w . jav  a2 s .co  m*/
 *            Calculate the ESS for this cluster
 * @return ESS
 **/
public double calcESS(final List<Integer> cluster) {

    if ((cluster.size() == 0) || (cluster.size() == 1)) {
        return 0.0;
    }
    final DistanceFunction distanceFunction = this.hierarchicalClusterer.getDistanceFunction();
    final double[] sumAttValues = new double[this.instances.numAttributes()];
    for (int i = 0; i < cluster.size(); i++) {
        final Instance instance = this.instances.instance(cluster.get(i));
        // Sum up all values of all instances.
        for (int j = 0; j < this.instances.numAttributes(); j++) {
            sumAttValues[j] += instance.value(j);
        }
    }
    // Get average value of each attribute value.
    for (int j = 0; j < this.instances.numAttributes(); j++) {
        sumAttValues[j] /= cluster.size();
    }

    /*
     * Create a centroid of this cluster by setting the average attributes of this cluster as
     * its own.
     */
    final Instance centroid = (Instance) this.instances.instance(cluster.get(0)).copy();
    for (int j = 0; j < this.instances.numAttributes(); j++) {
        centroid.setValue(j, sumAttValues[j]);
    }
    // Sum up distances of each data point in cluster to centroid to get ESS.
    double clusterESS = 0.0;
    for (int i = 0; i < cluster.size(); i++) {
        final Instance instance = this.instances.instance(cluster.get(i));
        clusterESS += distanceFunction.distance(centroid, instance);
    }
    return clusterESS / cluster.size();
}

---

From source file:org.iobserve.analysis.behavior.filter.BehaviorModelCreationStage.java

License:Apache License

/**
 * create a BehaviorModel from Instance.
 *
 * @param instances/* w  w  w  .  j  a v a2s .c  o m*/
 *            instances containing the attribute names
 * @param instance
 *            instance containing the attributes
 * @return behavior model if relevant
 */
private Optional<BehaviorModel> createBehaviorModel(final Instances instances, final Instance instance) {
    final int size = instance.numAttributes();
    final BehaviorModel behaviorModel = new BehaviorModel();

    for (int i = 0; i < size; i++) {
        final Attribute attribute = instances.attribute(i);

        final String attributeName = attribute.name();
        final Double attributeValue = instance.value(attribute);

        if (this.matchEdge(attributeName)) {
            final Optional<EntryCallEdge> edge = this.createEdge(attributeName, attributeValue);

            if (edge.isPresent()) {
                behaviorModel.addEdge(edge.get(), true);
            }

        } else if (this.matchNode(attributeName)) {
            final Optional<EntryCallNode> node = this.createNode(attributeName, attributeValue);

            if (node.isPresent()) {
                behaviorModel.addNode(node.get(), true);
            }
        }
    }

    if (behaviorModel.getEdges().isEmpty() && behaviorModel.getNodes().isEmpty()) {
        return Optional.empty();
    }

    return Optional.of(behaviorModel);
}

---

From source file:org.iobserve.analysis.behavior.filter.ClusterMerger.java

License:Apache License

private void printInstances(final Instances instances) {
    for (int i = 0; i < instances.numInstances(); i++) {
        ClusterMerger.LOGGER.debug("***************************");
        ClusterMerger.LOGGER.debug("Cluster {}", i);
        ClusterMerger.LOGGER.debug("***************************");
        final Instance instance = instances.instance(i);
        for (int a = 0; a < instance.numAttributes(); a++) {
            ClusterMerger.LOGGER.debug("{} : {}", instances.attribute(a).name(), instance.value(a));
        }/*w w w  .j  a v a 2  s. c  o m*/
    }
}

---

From source file:org.iobserve.analysis.behavior.filter.VectorQuantizationClusteringStage.java

License:Apache License

private void printInstances(final ClusteringResults results) {
    results.printClusteringResults();//from   w  w  w. j  a v  a2  s  . co  m
    final Instances centroids = results.getClusteringMetrics().getCentroids();
    for (int i = 0; i < centroids.numInstances(); i++) {
        String logString = "";
        logString += "***************************";
        logString += "Cluster " + i;
        logString += "***************************";
        final Instance instance = centroids.instance(i);
        for (int a = 0; a < instance.numAttributes(); a++) {
            logString += centroids.attribute(a).name() + " : " + instance.value(a);
        }
        VectorQuantizationClusteringStage.LOGGER.info(logString);
    }
}

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From source file:org.knime.knip.suise.node.boundarymodel.contourdata.IntervalRule.java

License:Open Source License

/**
 * {@inheritDoc}/*from   ww w.ja  v  a2  s  .co m*/
 */
@Override
public void updateClassifier(Instance instance) throws Exception {
    if (m_min == null || m_max == null) {
        m_min = new DenseInstance(1.0, instance.toDoubleArray());
        m_max = new DenseInstance(1.0, instance.toDoubleArray());
    } else {
        for (int a = 0; a < instance.numAttributes() - 1; a++) {
            m_min.setValue(a, Math.min(instance.value(a), m_min.value(a)));
            m_max.setValue(a, Math.max(instance.value(a), m_max.value(a)));
        }
    }

}

---

From source file:org.knime.knip.suise.node.boundarymodel.contourdata.IntervalRule.java

License:Open Source License

/**
 * {@inheritDoc}/*from w ww .j  a  v  a2s. com*/
 */
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
    for (int a = 0; a < instance.numAttributes() - 1; a++) {
        if (instance.value(a) < m_min.value(a) || instance.value(a) > m_max.value(a)) {
            return new double[] { 1, 0 };
        }
    }
    return new double[] { 0, 1 };

    // int fit = 0;
    // for (int a = 0; a < instance.numAttributes() - 1; a++) {
    // if (instance.value(a) >= m_min.value(a)
    // || instance.value(a) <= m_max.value(a)) {
    // fit++;
    // }
    // }
    // double r = (double) fit / (instance.numAttributes() - 1);
    // return new double[] { 1 - r, r };

}

---

From source file:org.knime.knip.suise.node.boundarymodel.contourdata.IRI.java

License:Open Source License

/**
 * {@inheritDoc}/*from   ww  w . j  av a2  s.  c o  m*/
 */
@Override
public void buildClassifier(Instances miData) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(miData);

    final Instances tmpMiData = new Instances(miData);
    final Instances flatData = toSingleInstanceDataset(miData, null);

    int numPosBags = 0;
    for (Instance bag : miData) {
        if (bag.value(2) == 1) {
            numPosBags++;
        }
    }

    int remainingNumPosBags = numPosBags;
    Future<Pair<IntervalRule, Double>>[] futures = new Future[m_numThreads];
    ExecutorService pool = Executors.newFixedThreadPool(m_numThreads);

    while (remainingNumPosBags / (double) numPosBags > 1 - m_coverRate) {

        final int numIterations = ((int) (m_sampleRate * remainingNumPosBags)) / m_numThreads + 1;

        for (int t = 0; t < m_numThreads; t++) {
            futures[t] = pool.submit(new Callable<Pair<IntervalRule, Double>>() {
                @Override
                public Pair<IntervalRule, Double> call() throws Exception {
                    return createRule(flatData, tmpMiData, numIterations);
                }
            });
        }

        // select the best rule from the threads
        double score = -Double.MAX_VALUE;
        IntervalRule rule = null;
        for (int f = 0; f < futures.length; f++) {
            if (futures[f].get().getB() > score) {
                score = futures[f].get().getB();
                rule = futures[f].get().getA();
            }
        }

        m_rules.add(rule);

        // only keep the bags whose instances are not covered by this rule
        Instances tmp = new Instances(tmpMiData);
        tmpMiData.clear();
        boolean covered;
        remainingNumPosBags = 0;
        for (Instance bag : tmp) {
            covered = false;
            for (Instance inst : bag.relationalValue(1)) {
                double[] distr;
                distr = rule.distributionForInstance(inst);
                if (distr[1] > distr[0]) {
                    covered = true;
                    break;
                }
            }
            if (!covered) {
                tmpMiData.add(bag);
                if (bag.value(2) == 1) {
                    remainingNumPosBags++;
                }
            }
        }
        flatData.clear();
        toSingleInstanceDataset(tmpMiData, flatData);
    }

    pool.shutdown();

}

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From source file:org.lappsgrid.eval.metric.BinaryGoldStandardAttribute.java

License:Apache License

public boolean isCorrect(Instance instance) {
    return gs_att.value((int) instance.value(gs_att)).equals(CORRECT);
}

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