Examples with KNNList de.lmu.ifi.dbs.elki.database.ids.KNNList used on opensource projects

Example 41 with KNNList

use of de.lmu.ifi.dbs.elki.database.ids.KNNList in project elki by elki-project.

the class RankingQualityHistogram method run.

/**
 * Process a database
 *
 * @param database Database to process
 * @param relation Relation to process
 * @return Histogram of ranking qualities
 */
public HistogramResult run(Database database, Relation<O> relation) {
    final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction());
    final KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, relation.size());
    if (LOG.isVerbose()) {
        LOG.verbose("Preprocessing clusters...");
    }
    // Cluster by labels
    Collection<Cluster<Model>> split = (new ByLabelOrAllInOneClustering()).run(database).getAllClusters();
    DoubleStaticHistogram hist = new DoubleStaticHistogram(numbins, 0.0, 1.0);
    if (LOG.isVerbose()) {
        LOG.verbose("Processing points...");
    }
    FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("Computing ROC AUC values", relation.size(), LOG) : null;
    ROCEvaluation roc = new ROCEvaluation();
    MeanVariance mv = new MeanVariance();
    // sort neighbors
    for (Cluster<?> clus : split) {
        for (DBIDIter iter = clus.getIDs().iter(); iter.valid(); iter.advance()) {
            KNNList knn = knnQuery.getKNNForDBID(iter, relation.size());
            double result = EvaluateClustering.evaluateRanking(roc, clus, knn);
            mv.put(result);
            hist.increment(result, 1. / relation.size());
            LOG.incrementProcessed(progress);
        }
    }
    LOG.ensureCompleted(progress);
    // Transform Histogram into a Double Vector array.
    Collection<double[]> res = new ArrayList<>(relation.size());
    for (DoubleStaticHistogram.Iter iter = hist.iter(); iter.valid(); iter.advance()) {
        res.add(new double[] { iter.getCenter(), iter.getValue() });
    }
    HistogramResult result = new HistogramResult("Ranking Quality Histogram", "ranking-histogram", res);
    result.addHeader("Mean: " + mv.getMean() + " Variance: " + mv.getSampleVariance());
    return result;
}

Example 42 with KNNList

use of de.lmu.ifi.dbs.elki.database.ids.KNNList in project elki by elki-project.

the class ALIDEstimator method estimate.

@Override
public double estimate(KNNQuery<?> knnq, DBIDRef cur, int k) {
    int a = 0;
    double sum = 0;
    final KNNList kl = knnq.getKNNForDBID(cur, k);
    final double w = kl.getKNNDistance();
    final double halfw = 0.5 * w;
    for (DoubleDBIDListIter it = kl.iter(); it.valid(); it.advance()) {
        if (it.doubleValue() <= 0. || DBIDUtil.equal(cur, it)) {
            continue;
        }
        final double v = it.doubleValue();
        sum += v < halfw ? FastMath.log(v / w) : FastMath.log1p((v - w) / w);
        ++a;
        final double nw = w - v;
        final double halfnw = 0.5 * nw;
        for (DoubleDBIDListIter it2 = knnq.getKNNForDBID(it, k).iter(); it2.valid() && it2.doubleValue() <= nw; it2.advance()) {
            if (it2.doubleValue() <= 0. || DBIDUtil.equal(it, it2)) {
                continue;
            }
            final double v2 = it2.doubleValue();
            sum += v2 < halfnw ? FastMath.log(v2 / nw) : FastMath.log1p((v2 - nw) / nw);
            ++a;
        }
    }
    return -a / sum;
}

Example 43 with KNNList

use of de.lmu.ifi.dbs.elki.database.ids.KNNList in project elki by elki-project.

the class LinearScanRKNNQuery method getRKNNForBulkDBIDs.

@Override
public List<? extends DoubleDBIDList> getRKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
    List<ModifiableDoubleDBIDList> rNNList = new ArrayList<>(ids.size());
    for (int i = 0; i < ids.size(); i++) {
        rNNList.add(DBIDUtil.newDistanceDBIDList());
    }
    ArrayDBIDs allIDs = DBIDUtil.ensureArray(relation.getDBIDs());
    List<? extends KNNList> kNNList = knnQuery.getKNNForBulkDBIDs(allIDs, k);
    int i = 0;
    for (DBIDIter iter = allIDs.iter(); iter.valid(); iter.advance()) {
        KNNList knn = kNNList.get(i);
        for (DoubleDBIDListIter n = knn.iter(); n.valid(); n.advance()) {
            int j = 0;
            for (DBIDIter iter2 = ids.iter(); iter2.valid(); iter2.advance()) {
                if (DBIDUtil.equal(n, iter2)) {
                    ModifiableDoubleDBIDList rNN = rNNList.get(j);
                    rNN.add(n.doubleValue(), iter);
                }
                j++;
            }
        }
        i++;
    }
    for (int j = 0; j < ids.size(); j++) {
        rNNList.get(j).sort();
    }
    return rNNList;
}

Example 44 with KNNList

use of de.lmu.ifi.dbs.elki.database.ids.KNNList in project elki by elki-project.

the class LinearScanDistanceKNNQuery method getKNNForBulkDBIDs.

@Override
public List<KNNList> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
    final int size = ids.size();
    final List<KNNHeap> heaps = new ArrayList<>(size);
    for (int i = 0; i < size; i++) {
        heaps.add(DBIDUtil.newHeap(k));
    }
    linearScanBatchKNN(ids, heaps);
    // Serialize heaps
    List<KNNList> result = new ArrayList<>(size);
    for (KNNHeap heap : heaps) {
        result.add(heap.toKNNList());
    }
    return result;
}

Example 45 with KNNList

use of de.lmu.ifi.dbs.elki.database.ids.KNNList in project elki by elki-project.

the class LinearScanEuclideanDistanceKNNQuery method getKNNForBulkDBIDs.

@Override
public List<KNNList> getKNNForBulkDBIDs(ArrayDBIDs ids, int k) {
    final Relation<? extends O> relation = getRelation();
    final int size = ids.size();
    final List<KNNHeap> heaps = new ArrayList<>(size);
    List<O> objs = new ArrayList<>(size);
    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
        heaps.add(DBIDUtil.newHeap(k));
        objs.add(relation.get(iter));
    }
    linearScanBatchKNN(objs, heaps);
    List<KNNList> result = new ArrayList<>(heaps.size());
    for (KNNHeap heap : heaps) {
        result.add(heap.toKNNListSqrt());
    }
    return result;
}