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

Example 21 with DoubleDBIDListIter

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

the class SimplifiedLOF method computeSimplifiedLRDs.

/**
 * Compute the simplified reachability densities.
 *
 * @param ids IDs to process
 * @param knnq kNN query class
 * @param lrds Density output
 */
private void computeSimplifiedLRDs(DBIDs ids, KNNQuery<O> knnq, WritableDoubleDataStore lrds) {
    FiniteProgress lrdsProgress = LOG.isVerbose() ? new FiniteProgress("Densities", ids.size(), LOG) : null;
    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
        final KNNList neighbors = knnq.getKNNForDBID(iter, k);
        double sum = 0.0;
        int count = 0;
        for (DoubleDBIDListIter neighbor = neighbors.iter(); neighbor.valid(); neighbor.advance()) {
            if (DBIDUtil.equal(neighbor, iter)) {
                continue;
            }
            sum += neighbor.doubleValue();
            count++;
        }
        // Avoid division by 0
        final double lrd = (sum > 0) ? (count / sum) : Double.POSITIVE_INFINITY;
        lrds.putDouble(iter, lrd);
        LOG.incrementProcessed(lrdsProgress);
    }
    LOG.ensureCompleted(lrdsProgress);
}

Example 22 with DoubleDBIDListIter

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

the class VarianceOfVolume method computeVOVs.

/**
 * Compute variance of volumes.
 *
 * @param knnq KNN query
 * @param ids IDs to process
 * @param vols Volumes
 * @param vovs Variance of Volume storage
 * @param vovminmax Score minimum/maximum tracker
 */
private void computeVOVs(KNNQuery<O> knnq, DBIDs ids, DoubleDataStore vols, WritableDoubleDataStore vovs, DoubleMinMax vovminmax) {
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Variance of Volume", ids.size(), LOG) : null;
    boolean warned = false;
    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
        KNNList knns = knnq.getKNNForDBID(iter, k);
        DoubleDBIDListIter it = knns.iter();
        double vbar = 0.;
        for (; it.valid(); it.advance()) {
            vbar += vols.doubleValue(it);
        }
        // Average
        vbar /= knns.size();
        double vov = 0.;
        for (it.seek(0); it.valid(); it.advance()) {
            double v = vols.doubleValue(it) - vbar;
            vov += v * v;
        }
        if (!(vov < Double.POSITIVE_INFINITY) && !warned) {
            LOG.warning("Variance of Volumes has hit double precision limits, results are not reliable.");
            warned = true;
        }
        vov = (knns.size() > 1 && vov < Double.POSITIVE_INFINITY) ? vov / (knns.size() - 1) : Double.POSITIVE_INFINITY;
        vovs.putDouble(iter, vov);
        // update minimum and maximum
        vovminmax.put(vov);
        LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);
}

Example 23 with DoubleDBIDListIter

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

the class KNNSOS method run.

/**
 * Run the algorithm.
 *
 * @param relation data relation
 * @return outlier detection result
 */
public OutlierResult run(Relation<O> relation) {
    // Query size
    final int k1 = k + 1;
    final double perplexity = k / 3.;
    KNNQuery<O> knnq = relation.getKNNQuery(getDistanceFunction(), k1);
    final double logPerp = FastMath.log(perplexity);
    double[] p = new double[k + 10];
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("KNNSOS scores", relation.size(), LOG) : null;
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_DB, 1.);
    for (DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
        KNNList knns = knnq.getKNNForDBID(it, k1);
        if (p.length < knns.size() + 1) {
            p = new double[knns.size() + 10];
        }
        final DoubleDBIDListIter ki = knns.iter();
        // Compute affinities
        SOS.computePi(it, ki, p, perplexity, logPerp);
        // Normalization factor:
        double s = SOS.sumOfProbabilities(it, ki, p);
        if (s > 0) {
            ISOS.nominateNeighbors(it, ki, p, 1. / s, scores);
        }
        LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);
    // Find minimum and maximum.
    DoubleMinMax minmax = ISOS.transformScores(scores, relation.getDBIDs(), logPerp, phi);
    DoubleRelation scoreres = new MaterializedDoubleRelation("kNN Stoachastic Outlier Selection", "knnsos-outlier", scores, relation.getDBIDs());
    OutlierScoreMeta meta = new ProbabilisticOutlierScore(minmax.getMin(), minmax.getMax(), 0.);
    return new OutlierResult(meta, scoreres);
}

Example 24 with DoubleDBIDListIter

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

the class MaterializedKNNAndRKNNPreprocessorTest method testKNNQueries.

private void testKNNQueries(Relation<DoubleVector> rep, KNNQuery<DoubleVector> lin_knn_query, KNNQuery<DoubleVector> preproc_knn_query, int k) {
    ArrayDBIDs sample = DBIDUtil.ensureArray(rep.getDBIDs());
    List<? extends KNNList> lin_knn_ids = lin_knn_query.getKNNForBulkDBIDs(sample, k);
    List<? extends KNNList> preproc_knn_ids = preproc_knn_query.getKNNForBulkDBIDs(sample, k);
    for (int i = 0; i < rep.size(); i++) {
        KNNList lin_knn = lin_knn_ids.get(i);
        KNNList pre_knn = preproc_knn_ids.get(i);
        DoubleDBIDListIter lin = lin_knn.iter(), pre = pre_knn.iter();
        for (; lin.valid() && pre.valid(); lin.advance(), pre.advance(), i++) {
            assertTrue(DBIDUtil.equal(lin, pre) || lin.doubleValue() == pre.doubleValue());
        }
        assertEquals("kNN sizes do not agree.", lin_knn.size(), pre_knn.size());
        for (int j = 0; j < lin_knn.size(); j++) {
            assertTrue("kNNs of linear scan and preprocessor do not match!", DBIDUtil.equal(lin_knn.get(j), pre_knn.get(j)));
            assertEquals("kNNs of linear scan and preprocessor do not match!", lin_knn.get(j).doubleValue(), pre_knn.get(j).doubleValue(), 0.);
        }
    }
}

Example 25 with DoubleDBIDListIter

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

the class MaterializedKNNAndRKNNPreprocessorTest method testRKNNQueries.

private void testRKNNQueries(Relation<DoubleVector> rep, RKNNQuery<DoubleVector> lin_rknn_query, RKNNQuery<DoubleVector> preproc_rknn_query, int k) {
    ArrayDBIDs sample = DBIDUtil.ensureArray(rep.getDBIDs());
    List<? extends DoubleDBIDList> lin_rknn_ids = lin_rknn_query.getRKNNForBulkDBIDs(sample, k);
    List<? extends DoubleDBIDList> preproc_rknn_ids = preproc_rknn_query.getRKNNForBulkDBIDs(sample, k);
    for (int i = 0; i < rep.size(); i++) {
        DoubleDBIDList lin_rknn = lin_rknn_ids.get(i);
        DoubleDBIDList pre_rknn = preproc_rknn_ids.get(i);
        DoubleDBIDListIter lin = lin_rknn.iter(), pre = pre_rknn.iter();
        for (; lin.valid() && pre.valid(); lin.advance(), pre.advance(), i++) {
            assertTrue(DBIDUtil.equal(lin, pre) || lin.doubleValue() == pre.doubleValue());
        }
        assertEquals("rkNN sizes do not agree for k=" + k, lin_rknn.size(), pre_rknn.size());
        for (int j = 0; j < lin_rknn.size(); j++) {
            assertTrue("rkNNs of linear scan and preprocessor do not match!", DBIDUtil.equal(lin_rknn.get(j), pre_rknn.get(j)));
            assertEquals("rkNNs of linear scan and preprocessor do not match!", lin_rknn.get(j).doubleValue(), pre_rknn.get(j).doubleValue(), 0.);
        }
    }
}