Examples with DoubleDataStore de.lmu.ifi.dbs.elki.database.datastore.DoubleDataStore used on opensource projects

Example 1 with DoubleDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.DoubleDataStore in project elki by elki-project.

the class AbstractDBOutlier method run.

/**
 * Runs the algorithm in the timed evaluation part.
 *
 * @param database Database to process
 * @param relation Relation to process
 * @return Outlier result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    // Run the actual score process
    DoubleDataStore dbodscore = computeOutlierScores(database, relation, d);
    // Build result representation.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Density-Based Outlier Detection", "db-outlier", dbodscore, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new ProbabilisticOutlierScore();
    return new OutlierResult(scoreMeta, scoreResult);
}

Example 2 with DoubleDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.DoubleDataStore in project elki by elki-project.

the class IDOS method run.

/**
 * Run the algorithm
 *
 * @param database Database
 * @param relation Data relation
 * @return Outlier result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    StepProgress stepprog = LOG.isVerbose() ? new StepProgress("IDOS", 3) : null;
    if (stepprog != null) {
        stepprog.beginStep(1, "Precomputing neighborhoods", LOG);
    }
    KNNQuery<O> knnQ = DatabaseUtil.precomputedKNNQuery(database, relation, getDistanceFunction(), Math.max(k_c, k_r) + 1);
    DBIDs ids = relation.getDBIDs();
    if (stepprog != null) {
        stepprog.beginStep(2, "Computing intrinsic dimensionalities", LOG);
    }
    DoubleDataStore intDims = computeIDs(ids, knnQ);
    if (stepprog != null) {
        stepprog.beginStep(3, "Computing IDOS scores", LOG);
    }
    DoubleMinMax idosminmax = new DoubleMinMax();
    DoubleDataStore ldms = computeIDOS(ids, knnQ, intDims, idosminmax);
    if (stepprog != null) {
        stepprog.setCompleted(LOG);
    }
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Intrinsic Dimensionality Outlier Score", "idos", ldms, ids);
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(idosminmax.getMin(), idosminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    return new OutlierResult(scoreMeta, scoreResult);
}

Example 3 with DoubleDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.DoubleDataStore in project elki by elki-project.

the class OPTICSXi method extractClusters.

/**
 * Extract clusters from a cluster order result.
 *
 * @param clusterOrderResult cluster order result
 * @param relation Relation
 * @param ixi Parameter 1 - Xi
 * @param minpts Parameter minPts
 */
private Clustering<OPTICSModel> extractClusters(ClusterOrder clusterOrderResult, Relation<?> relation, double ixi, int minpts) {
    ArrayDBIDs clusterOrder = clusterOrderResult.ids;
    DoubleDataStore reach = clusterOrderResult.reachability;
    DBIDArrayIter tmp = clusterOrder.iter();
    DBIDVar tmp2 = DBIDUtil.newVar();
    double mib = 0.0;
    List<SteepArea> salist = keepsteep ? new ArrayList<SteepArea>() : null;
    List<SteepDownArea> sdaset = new ArrayList<>();
    final Clustering<OPTICSModel> clustering = new Clustering<>("OPTICS Xi-Clusters", "optics");
    HashSet<Cluster<OPTICSModel>> curclusters = new HashSet<>();
    HashSetModifiableDBIDs unclaimedids = DBIDUtil.newHashSet(relation.getDBIDs());
    FiniteProgress scanprog = LOG.isVerbose() ? new FiniteProgress("OPTICS Xi cluster extraction", clusterOrder.size(), LOG) : null;
    for (SteepScanPosition scan = new SteepScanPosition(clusterOrderResult); scan.hasNext(); ) {
        if (scanprog != null) {
            scanprog.setProcessed(scan.index, LOG);
        }
        // Update maximum-inbetween
        mib = MathUtil.max(mib, scan.getReachability());
        // The last point cannot be the start of a steep area.
        if (!scan.next.valid()) {
            break;
        }
        // Xi-steep down area
        if (scan.steepDown(ixi)) {
            // Update mib values with current mib and filter
            updateFilterSDASet(mib, sdaset, ixi);
            final double startval = scan.getReachability();
            mib = 0.;
            int startsteep = scan.index, endsteep = scan.index;
            for (scan.next(); scan.hasNext(); scan.next()) {
                // still steep - continue.
                if (scan.steepDown(ixi)) {
                    endsteep = scan.index;
                    continue;
                }
                // Always stop looking after minpts "flat" steps.
                if (!scan.steepDown(1.0) || scan.index - endsteep > minpts) {
                    break;
                }
            }
            final SteepDownArea sda = new SteepDownArea(startsteep, endsteep, startval, 0);
            if (LOG.isDebuggingFinest()) {
                LOG.debugFinest("New steep down area: " + sda.toString());
            }
            sdaset.add(sda);
            if (salist != null) {
                salist.add(sda);
            }
            continue;
        }
        // Xi-steep up area
        if (scan.steepUp(ixi)) {
            // Update mib values with current mib and filter
            updateFilterSDASet(mib, sdaset, ixi);
            final SteepUpArea sua;
            // Compute steep-up area
            {
                int startsteep = scan.index, endsteep = scan.index;
                mib = scan.getReachability();
                double esuccr = scan.getNextReachability();
                // Find end of steep-up-area, eventually updating mib again
                while (!Double.isInfinite(esuccr) && scan.hasNext()) {
                    scan.next();
                    // still steep - continue.
                    if (scan.steepUp(ixi)) {
                        endsteep = scan.index;
                        mib = scan.getReachability();
                        esuccr = scan.getNextReachability();
                        continue;
                    }
                    // Stop looking after minpts non-up steps.
                    if (!scan.steepUp(1.0) || scan.index - endsteep > minpts) {
                        break;
                    }
                }
                if (Double.isInfinite(esuccr)) {
                    scan.next();
                }
                sua = new SteepUpArea(startsteep, endsteep, esuccr);
                if (LOG.isDebuggingFinest()) {
                    LOG.debugFinest("New steep up area: " + sua.toString());
                }
                if (salist != null) {
                    salist.add(sua);
                }
            }
            // Validate and computer clusters
            // LOG.debug("SDA size:"+sdaset.size()+" "+sdaset);
            ListIterator<SteepDownArea> sdaiter = sdaset.listIterator(sdaset.size());
            // Iterate backwards for correct hierarchy generation.
            while (sdaiter.hasPrevious()) {
                SteepDownArea sda = sdaiter.previous();
                if (LOG.isDebuggingFinest()) {
                    LOG.debugFinest("Comparing: eU=" + mib + " SDA: " + sda.toString());
                }
                // Condition 3b: end-of-steep-up > maximum-in-between lower
                if (mib * ixi < sda.getMib()) {
                    if (LOG.isDebuggingFinest()) {
                        LOG.debugFinest("mib * ixi = " + mib * ixi + " >= sda.getMib() = " + sda.getMib());
                    }
                    continue;
                }
                // By default, clusters cover both the steep up and steep down area
                int cstart = sda.getStartIndex(), cend = MathUtil.min(sua.getEndIndex(), clusterOrder.size() - 1);
                // However, we sometimes have to adjust this (Condition 4):
                {
                    // Case b)
                    if (sda.getMaximum() * ixi >= sua.getMaximum()) {
                        while (// 
                        cstart < cend && reach.doubleValue(tmp.seek(cstart + 1)) > sua.getMaximum()) {
                            cstart++;
                        }
                    } else // Case c)
                    if (sua.getMaximum() * ixi >= sda.getMaximum()) {
                        while (// 
                        cend > cstart && reach.doubleValue(tmp.seek(cend - 1)) > sda.getMaximum()) {
                            cend--;
                        }
                    }
                // Case a) is the default
                }
                // removes common artifacts from the Xi method
                if (!nocorrect) {
                    simplify: while (cend > cstart) {
                        clusterOrderResult.predecessor.assignVar(tmp.seek(cend), tmp2);
                        for (int i = cstart; i < cend; i++) {
                            if (DBIDUtil.equal(tmp2, tmp.seek(i))) {
                                break simplify;
                            }
                        }
                        // Not found.
                        --cend;
                    }
                }
                // Condition 3a: obey minpts
                if (cend - cstart + 1 < minpts) {
                    if (LOG.isDebuggingFinest()) {
                        LOG.debugFinest("MinPts not satisfied.");
                    }
                    continue;
                }
                // Build the cluster
                ModifiableDBIDs dbids = DBIDUtil.newArray();
                for (int idx = cstart; idx <= cend; idx++) {
                    tmp.seek(idx);
                    // Collect only unclaimed IDs.
                    if (unclaimedids.remove(tmp)) {
                        dbids.add(tmp);
                    }
                }
                if (LOG.isDebuggingFine()) {
                    LOG.debugFine("Found cluster with " + dbids.size() + " new objects, length " + (cend - cstart + 1));
                }
                OPTICSModel model = new OPTICSModel(cstart, cend);
                Cluster<OPTICSModel> cluster = new Cluster<>("Cluster_" + cstart + "_" + cend, dbids, model);
                // Build the hierarchy
                {
                    Iterator<Cluster<OPTICSModel>> iter = curclusters.iterator();
                    while (iter.hasNext()) {
                        Cluster<OPTICSModel> clus = iter.next();
                        OPTICSModel omodel = clus.getModel();
                        if (model.getStartIndex() <= omodel.getStartIndex() && omodel.getEndIndex() <= model.getEndIndex()) {
                            clustering.addChildCluster(cluster, clus);
                            iter.remove();
                        }
                    }
                }
                curclusters.add(cluster);
            }
            continue;
        }
        // Flat - advance anyway.
        scan.next();
    }
    if (scanprog != null) {
        scanprog.setProcessed(clusterOrder.size(), LOG);
    }
    if (!unclaimedids.isEmpty()) {
        boolean noise = reach.doubleValue(tmp.seek(clusterOrder.size() - 1)) >= Double.POSITIVE_INFINITY;
        Cluster<OPTICSModel> allcluster = new Cluster<>(noise ? "Noise" : "Cluster", unclaimedids, noise, new OPTICSModel(0, clusterOrder.size() - 1));
        for (Cluster<OPTICSModel> cluster : curclusters) {
            clustering.addChildCluster(allcluster, cluster);
        }
        clustering.addToplevelCluster(allcluster);
    } else {
        for (Cluster<OPTICSModel> cluster : curclusters) {
            clustering.addToplevelCluster(cluster);
        }
    }
    clustering.addChildResult(clusterOrderResult);
    if (salist != null) {
        clusterOrderResult.addChildResult(new SteepAreaResult(salist));
    }
    return clustering;
}