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

Example 51 with KNNList

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

the class EvaluateRankingQuality method run.

@Override
public HistogramResult run(Database database) {
    final Relation<V> relation = database.getRelation(getInputTypeRestriction()[0]);
    final DistanceQuery<V> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
    final KNNQuery<V> knnQuery = database.getKNNQuery(distQuery, relation.size());
    if (LOG.isVerbose()) {
        LOG.verbose("Preprocessing clusters...");
    }
    // Cluster by labels
    Collection<Cluster<Model>> split = (new ByLabelOrAllInOneClustering()).run(database).getAllClusters();
    // Compute cluster averages and covariance matrix
    HashMap<Cluster<?>, double[]> averages = new HashMap<>(split.size());
    HashMap<Cluster<?>, double[][]> covmats = new HashMap<>(split.size());
    for (Cluster<?> clus : split) {
        CovarianceMatrix covmat = CovarianceMatrix.make(relation, clus.getIDs());
        averages.put(clus, covmat.getMeanVector());
        covmats.put(clus, covmat.destroyToPopulationMatrix());
    }
    MeanVarianceStaticHistogram hist = new MeanVarianceStaticHistogram(numbins, 0.0, 1.0);
    if (LOG.isVerbose()) {
        LOG.verbose("Processing points...");
    }
    FiniteProgress rocloop = LOG.isVerbose() ? new FiniteProgress("Computing ROC AUC values", relation.size(), LOG) : null;
    ROCEvaluation roc = new ROCEvaluation();
    // sort neighbors
    for (Cluster<?> clus : split) {
        ModifiableDoubleDBIDList cmem = DBIDUtil.newDistanceDBIDList(clus.size());
        double[] av = averages.get(clus);
        double[][] covm = covmats.get(clus);
        for (DBIDIter iter = clus.getIDs().iter(); iter.valid(); iter.advance()) {
            double d = mahalanobisDistance(covm, relation.get(iter).toArray(), av);
            cmem.add(d, iter);
        }
        cmem.sort();
        for (DBIDArrayIter it = cmem.iter(); it.valid(); it.advance()) {
            KNNList knn = knnQuery.getKNNForDBID(it, relation.size());
            double result = EvaluateClustering.evaluateRanking(roc, clus, knn);
            hist.put(((double) it.getOffset()) / clus.size(), result);
            LOG.incrementProcessed(rocloop);
        }
    }
    LOG.ensureCompleted(rocloop);
    // Collections.sort(results);
    // Transform Histogram into a Double Vector array.
    Collection<double[]> res = new ArrayList<>(relation.size());
    for (ObjHistogram.Iter<MeanVariance> iter = hist.iter(); iter.valid(); iter.advance()) {
        res.add(new double[] { iter.getCenter(), iter.getValue().getCount(), iter.getValue().getMean(), iter.getValue().getSampleVariance() });
    }
    return new HistogramResult("Ranking Quality Histogram", "ranking-histogram", res);
}

Example 52 with KNNList

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

the class KNNDistancesSampler method run.

/**
 * Provides an order of the kNN-distances for all objects within the specified
 * database.
 *
 * @param database Database
 * @param relation Relation
 * @return Result
 */
public KNNDistanceOrderResult run(Database database, Relation<O> relation) {
    final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction());
    final KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, k + 1);
    final int size = (int) ((sample <= 1.) ? Math.ceil(relation.size() * sample) : sample);
    DBIDs sample = DBIDUtil.randomSample(relation.getDBIDs(), size, rnd);
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Sampling kNN distances", size, LOG) : null;
    double[] knnDistances = new double[size];
    int i = 0;
    for (DBIDIter iditer = sample.iter(); iditer.valid(); iditer.advance(), i++) {
        final KNNList neighbors = knnQuery.getKNNForDBID(iditer, k + 1);
        knnDistances[i] = neighbors.getKNNDistance();
        LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);
    return new KNNDistanceOrderResult(knnDistances, k);
}

Example 53 with KNNList

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

the class LinearScanPrimitiveDistanceKNNQuery 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.toKNNList());
    }
    return result;
}

Example 54 with KNNList

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

the class LinearScanRKNNQuery method getRKNNForDBID.

@Override
public DoubleDBIDList getRKNNForDBID(DBIDRef id, int k) {
    ModifiableDoubleDBIDList rNNList = 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()) {
            if (DBIDUtil.equal(n, id)) {
                rNNList.add(n.doubleValue(), iter);
            }
        }
        i++;
    }
    rNNList.sort();
    return rNNList;
}

Example 55 with KNNList

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

the class LinearScanRKNNQuery method getRKNNForObject.

@Override
public DoubleDBIDList getRKNNForObject(O obj, int k) {
    ModifiableDoubleDBIDList rNNlist = DBIDUtil.newDistanceDBIDList();
    ArrayDBIDs allIDs = DBIDUtil.ensureArray(relation.getDBIDs());
    List<? extends KNNList> kNNLists = knnQuery.getKNNForBulkDBIDs(allIDs, k);
    int i = 0;
    for (DBIDIter iter = allIDs.iter(); iter.valid(); iter.advance()) {
        KNNList knn = kNNLists.get(i);
        int last = Math.min(k - 1, knn.size() - 1);
        double dist = distanceQuery.distance(obj, iter);
        if (last < k - 1 || dist <= knn.get(last).doubleValue()) {
            rNNlist.add(dist, iter);
        }
        i++;
    }
    rNNlist.sort();
    return rNNlist;
}