Example usage for weka.core NormalizableDistance setInvertSelection

Introduction

In this page you can find the example usage for weka.core NormalizableDistance setInvertSelection.

Prototype

@Override
public void setInvertSelection(boolean value) 

Source Link

Document

Sets whether the matching sense of attribute indices is inverted or not.

Usage

From source file:lu.lippmann.cdb.lab.mds.ClassicMDS.java

License:Open Source License

/**
 * /*from   ww w . j  a  va2  s.c  o m*/
 */
public static CollapsedInstances distanceBetweenInstances(final Instances instances,
        final MDSDistancesEnum distEnum, final int maxInstances, final boolean ignoreClassInDistance)
        throws Exception {
    KmeansResult mapCentroids = null;

    final NormalizableDistance usedDist;
    if (distEnum.equals(MDSDistancesEnum.EUCLIDEAN)) {
        usedDist = new EuclideanDistance(instances);
        //usedDist.setDontNormalize(true);
        //usedDist.setAttributeIndices("1");
        //usedDist.setInvertSelection(true);
    } else if (distEnum.equals(MDSDistancesEnum.MANHATTAN))
        usedDist = new ManhattanDistance(instances);
    else if (distEnum.equals(MDSDistancesEnum.MINKOWSKI)) {
        usedDist = new MinkowskiDistance(instances);
        final String[] parameters = MDSDistancesEnum.MINKOWSKI.getParameters();
        //Change order
        double order = Double.valueOf(parameters[0]).doubleValue();
        ((MinkowskiDistance) usedDist).setOrder(order);
    } else if (distEnum.equals(MDSDistancesEnum.CHEBYSHEV))
        usedDist = new ChebyshevDistance(instances);
    //else if (distEnum.equals(MDSDistancesEnum.DT)) usedDist=new DTDistance(instances);
    else
        throw new IllegalStateException();

    final int numInstances = instances.numInstances();
    final boolean collapsed = (numInstances > maxInstances)
            && (distEnum.equals(MDSDistancesEnum.EUCLIDEAN) || distEnum.equals(MDSDistancesEnum.MANHATTAN));

    SimpleMatrix distances;

    //Ignore class in distance
    if (ignoreClassInDistance && instances.classIndex() != -1) {
        usedDist.setAttributeIndices("" + (instances.classIndex() + 1));
        usedDist.setInvertSelection(true);
    }

    int numCollapsedInstances = numInstances;
    if (collapsed) {
        //Compute distance with centroids using K-means with K=MAX_INSTANCES
        mapCentroids = getSimplifiedInstances(instances, usedDist, maxInstances);

        final List<Instance> centroids = mapCentroids.getCentroids();
        numCollapsedInstances = centroids.size();

        distances = new SimpleMatrix(numCollapsedInstances, numCollapsedInstances);

        for (int i = 0; i < numCollapsedInstances; i++) {
            for (int j = i + 1; j < numCollapsedInstances; j++) {
                double dist = usedDist.distance(centroids.get(i), centroids.get(j));
                distances.set(i, j, dist);
                distances.set(j, i, dist);
            }
        }
    } else {
        distances = new SimpleMatrix(numCollapsedInstances, numCollapsedInstances);
        for (int i = 0; i < numCollapsedInstances; i++) {
            for (int j = i + 1; j < numCollapsedInstances; j++) {
                double dist = usedDist.distance(instances.get(i), instances.get(j));
                distances.set(i, j, dist);
                distances.set(j, i, dist);
            }
        }
    }
    return new CollapsedInstances(instances, mapCentroids, distances, collapsed);
}

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