Example usage for weka.core Instance numAttributes

Introduction

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

Prototype

public int numAttributes();

Source Link

Document

Returns the number of attributes.

Usage

From source file:org.if4071.myann.TopologyModel.java

public void insertDataToInputNodes(Instance inputData) {
    for (int i = 0; i < inputData.numAttributes() - 1; i++) {
        nodes.get(i).setOutput(inputData.value(i));
    }// w w  w.  j  a  v a2 s . com
}

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From source file:org.iobserve.analysis.behavior.clustering.birch.model.ClusteringFeature.java

License:Apache License

/** Constructor for a clustering feature
 * representing an instance vector.//w w w .j av a 2s . c o m
 * @param instance the instance to represent.
 */
public ClusteringFeature(final Instance instance) {
    this.number = 1;
    this.linearSum = new double[instance.numAttributes()];
    this.squareSum = new double[instance.numAttributes()];

    for (int i = 0; i < instance.numAttributes(); i++) {
        this.linearSum[i] = instance.value(i);
        this.squareSum[i] = this.linearSum[i] * this.linearSum[i];
    }
}

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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.
 *///  w  w  w.ja va  2  s. c o 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);
}

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

License:Apache License

/**
 * create a BehaviorModel from Instance.
 *
 * @param instances//from ww w .ja v a 2 s  . c om
 *            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

@Override
protected void execute(final Map<Integer, List<Pair<Instance, Double>>> clustering) throws Exception {
    /**/*from  w w  w  .  j a v a2 s. c om*/
     * simply pick the first instance of every cluster lookup attributes to build a new
     * instances Object
     */
    Instance instance = clustering.entrySet().iterator().next().getValue().get(0).getElement1();
    final FastVector attributes = new FastVector();
    for (int j = 0; j < instance.numAttributes(); j++) {
        attributes.addElement(instance.attribute(j));
    }

    final Instances result = new Instances("Clustering Result", attributes, clustering.size());

    for (final List<Pair<Instance, Double>> entry : clustering.values()) {
        if (!entry.isEmpty()) {
            instance = entry.get(0).getElement1();
            result.add(instance);
        }
    }

    if (ClusterMerger.LOGGER.isDebugEnabled()) {
        this.printInstances(result);
    }
    this.outputPort.send(result);
}

---

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  ava2s.  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 ww.j a  v a2  s .  c  o  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);
    }
}

---

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

License:Open Source License

/**
 * {@inheritDoc}//from   w ww  .  ja va 2 s.c om
 */
@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}/*w w  w  .  j av a2s  .c  o  m*/
 */
@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.ops.BuildTrainingData.java

License:Open Source License

/**
 * {@inheritDoc}//from w  w w. j  a v  a  2 s.  co m
 */
@Override
public Instances compute(RandomAccessibleInterval<LabelingType<L>> lab, Img<T> img, Instances r) {
    Random rand = new Random();

    double[] extent = new double[lab.numDimensions()];
    for (int d = 0; d < m_dimIndices.length; d++) {
        extent[m_dimIndices[d]] = lab.max(m_dimIndices[d]);
    }
    RectangleRegionOfInterest roi = new RectangleRegionOfInterest(new double[lab.numDimensions()], extent);

    Cursor<LabelingType<L>> labCur = roi.getIterableIntervalOverROI(lab).localizingCursor();
    OutOfBounds<T> imgRA = new OutOfBoundsBorder<T>(img);

    LabelRegions<L> regions = new LabelRegions<L>(lab);
    // get the class distributions
    Map<L, Double> classDistr = null;
    if (m_balanceInstancePerClass) {
        long sum = 0;
        long area;
        Collection<L> labels = regions.getExistingLabels();
        classDistr = new HashMap<L, Double>(labels.size());
        for (L label : labels) {
            area = regions.getLabelRegion(label).size();
            sum += area;
            classDistr.put(label, new Double(area));
        }
        // determine the new sampling rate for each class individually
        double instancesPerClass = (double) sum / (double) labels.size();
        for (L label : labels) {
            Double sampleRate = instancesPerClass / classDistr.get(label) * m_samplingRate;
            classDistr.put(label, sampleRate);
        }
    }

    long[] tmpPos = new long[imgRA.numDimensions()];
    while (labCur.hasNext()) {
        labCur.fwd();
        for (int d = 0; d < m_dimIndices.length; d++) {
            imgRA.setPosition(labCur.getLongPosition(m_dimIndices[d]), m_dimIndices[d]);
            if (imgRA.isOutOfBounds()) {
                imgRA.localize(tmpPos);
                NodeLogger.getLogger(getClass()).warn("Labeling reaches beyond the feature image. Position "
                        + Arrays.toString(tmpPos) + " skipped.");
                continue;
            }

        }
        if (!labCur.get().isEmpty()) {

            if (m_balanceInstancePerClass) {
                if (rand.nextDouble() >= classDistr.get(labCur.get().iterator().next())) {
                    continue;
                }
            } else {
                if (rand.nextDouble() >= m_samplingRate) {
                    continue;
                }
            }

            double[] featVec = new double[(int) img.dimension(m_featDim)];
            for (int f = 0; f < img.dimension(m_featDim); f++) {
                imgRA.setPosition(f, m_featDim);
                featVec[f] = imgRA.get().getRealDouble();
            }
            for (L classLabel : labCur.get()) {
                Instance instance = new DenseInstance(1.0, featVec);
                instance.insertAttributeAt(instance.numAttributes());
                instance.setDataset(r);
                instance.setClassValue(classLabel.toString());

                r.add(instance);

            }
        }
    }
    return r;
}

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