Examples with DoubleRelation de.lmu.ifi.dbs.elki.database.relation.DoubleRelation used on opensource projects

Example 46 with DoubleRelation

use of de.lmu.ifi.dbs.elki.database.relation.DoubleRelation in project elki by elki-project.

the class KMLOutputHandler method writeOutlierResult.

private void writeOutlierResult(XMLStreamWriter xmlw, OutlierResult outlierResult, Database database) throws XMLStreamException {
    Relation<PolygonsObject> polys = database.getRelation(TypeUtil.POLYGON_TYPE);
    Relation<String> labels = DatabaseUtil.guessObjectLabelRepresentation(database);
    xmlw.writeStartDocument();
    xmlw.writeCharacters("\n");
    xmlw.writeStartElement("kml");
    xmlw.writeDefaultNamespace("http://earth.google.com/kml/2.2");
    xmlw.writeStartElement("Document");
    {
        // TODO: can we automatically generate more helpful data here?
        xmlw.writeStartElement("name");
        xmlw.writeCharacters("ELKI KML output for " + outlierResult.getLongName());
        // name
        xmlw.writeEndElement();
        writeNewlineOnDebug(xmlw);
        // TODO: e.g. list the settings in the description?
        xmlw.writeStartElement("description");
        xmlw.writeCharacters("ELKI KML output for " + outlierResult.getLongName());
        // description
        xmlw.writeEndElement();
        writeNewlineOnDebug(xmlw);
    }
    {
        // TODO: generate styles from color scheme
        for (int i = 0; i < NUMSTYLES; i++) {
            Color col = getColorForValue(i / (NUMSTYLES - 1.0));
            xmlw.writeStartElement("Style");
            xmlw.writeAttribute("id", "s" + i);
            writeNewlineOnDebug(xmlw);
            {
                xmlw.writeStartElement("LineStyle");
                xmlw.writeStartElement("width");
                xmlw.writeCharacters("0");
                // width
                xmlw.writeEndElement();
                // LineStyle
                xmlw.writeEndElement();
            }
            writeNewlineOnDebug(xmlw);
            {
                xmlw.writeStartElement("PolyStyle");
                xmlw.writeStartElement("color");
                // KML uses AABBGGRR format!
                xmlw.writeCharacters(String.format("%02x%02x%02x%02x", col.getAlpha(), col.getBlue(), col.getGreen(), col.getRed()));
                // color
                xmlw.writeEndElement();
                // out.writeStartElement("fill");
                // out.writeCharacters("1"); // Default 1
                // out.writeEndElement(); // fill
                xmlw.writeStartElement("outline");
                xmlw.writeCharacters("0");
                // outline
                xmlw.writeEndElement();
                // PolyStyle
                xmlw.writeEndElement();
            }
            writeNewlineOnDebug(xmlw);
            // Style
            xmlw.writeEndElement();
            writeNewlineOnDebug(xmlw);
        }
    }
    DoubleRelation scores = outlierResult.getScores();
    Collection<Relation<?>> otherrel = new LinkedList<>(database.getRelations());
    otherrel.remove(scores);
    otherrel.remove(polys);
    otherrel.remove(labels);
    otherrel.remove(database.getRelation(TypeUtil.DBID));
    ArrayModifiableDBIDs ids = DBIDUtil.newArray(scores.getDBIDs());
    scaling.prepare(outlierResult);
    for (DBIDIter iter = outlierResult.getOrdering().order(ids).iter(); iter.valid(); iter.advance()) {
        double score = scores.doubleValue(iter);
        PolygonsObject poly = polys.get(iter);
        String label = labels.get(iter);
        if (Double.isNaN(score)) {
            LOG.warning("No score for object " + DBIDUtil.toString(iter));
        }
        if (poly == null) {
            LOG.warning("No polygon for object " + DBIDUtil.toString(iter) + " - skipping.");
            continue;
        }
        xmlw.writeStartElement("Placemark");
        {
            xmlw.writeStartElement("name");
            xmlw.writeCharacters(score + " " + label);
            // name
            xmlw.writeEndElement();
            StringBuilder buf = makeDescription(otherrel, iter);
            xmlw.writeStartElement("description");
            xmlw.writeCData("<div>" + buf.toString() + "</div>");
            // description
            xmlw.writeEndElement();
            xmlw.writeStartElement("styleUrl");
            int style = (int) (scaling.getScaled(score) * NUMSTYLES);
            style = Math.max(0, Math.min(style, NUMSTYLES - 1));
            xmlw.writeCharacters("#s" + style);
            // styleUrl
            xmlw.writeEndElement();
        }
        {
            xmlw.writeStartElement("Polygon");
            writeNewlineOnDebug(xmlw);
            if (compat) {
                xmlw.writeStartElement("altitudeMode");
                xmlw.writeCharacters("relativeToGround");
                // close altitude mode
                xmlw.writeEndElement();
                writeNewlineOnDebug(xmlw);
            }
            // First polygon clockwise?
            boolean first = true;
            for (Polygon p : poly.getPolygons()) {
                if (first) {
                    xmlw.writeStartElement("outerBoundaryIs");
                } else {
                    xmlw.writeStartElement("innerBoundaryIs");
                }
                xmlw.writeStartElement("LinearRing");
                xmlw.writeStartElement("coordinates");
                // Reverse anti-clockwise polygons.
                boolean reverse = (p.testClockwise() >= 0);
                ArrayListIter<double[]> it = p.iter();
                if (reverse) {
                    it.seek(p.size() - 1);
                }
                while (it.valid()) {
                    double[] v = it.get();
                    xmlw.writeCharacters(FormatUtil.format(v, ","));
                    if (compat && (v.length == 2)) {
                        xmlw.writeCharacters(",50");
                    }
                    xmlw.writeCharacters(" ");
                    if (!reverse) {
                        it.advance();
                    } else {
                        it.retract();
                    }
                }
                // close coordinates
                xmlw.writeEndElement();
                // close LinearRing
                xmlw.writeEndElement();
                // close *BoundaryIs
                xmlw.writeEndElement();
                first = false;
            }
            writeNewlineOnDebug(xmlw);
            // Polygon
            xmlw.writeEndElement();
        }
        // Placemark
        xmlw.writeEndElement();
        writeNewlineOnDebug(xmlw);
    }
    // Document
    xmlw.writeEndElement();
    // kml
    xmlw.writeEndElement();
    xmlw.writeEndDocument();
}

Example 47 with DoubleRelation

use of de.lmu.ifi.dbs.elki.database.relation.DoubleRelation in project elki by elki-project.

the class OPTICSOF method run.

/**
 * Perform OPTICS-based outlier detection.
 *
 * @param database Database
 * @param relation Relation
 * @return Outlier detection result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    DistanceQuery<O> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
    KNNQuery<O> knnQuery = database.getKNNQuery(distQuery, minpts);
    RangeQuery<O> rangeQuery = database.getRangeQuery(distQuery);
    DBIDs ids = relation.getDBIDs();
    // FIXME: implicit preprocessor.
    WritableDataStore<KNNList> nMinPts = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, KNNList.class);
    WritableDoubleDataStore coreDistance = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    WritableIntegerDataStore minPtsNeighborhoodSize = DataStoreUtil.makeIntegerStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, -1);
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        KNNList minptsNeighbours = knnQuery.getKNNForDBID(iditer, minpts);
        double d = minptsNeighbours.getKNNDistance();
        nMinPts.put(iditer, minptsNeighbours);
        coreDistance.putDouble(iditer, d);
        minPtsNeighborhoodSize.put(iditer, rangeQuery.getRangeForDBID(iditer, d).size());
    }
    // Pass 2
    WritableDataStore<List<Double>> reachDistance = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, List.class);
    WritableDoubleDataStore lrds = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        List<Double> core = new ArrayList<>();
        double lrd = 0;
        // TODO: optimize for double distances
        for (DoubleDBIDListIter neighbor = nMinPts.get(iditer).iter(); neighbor.valid(); neighbor.advance()) {
            double coreDist = coreDistance.doubleValue(neighbor);
            double dist = distQuery.distance(iditer, neighbor);
            double rd = MathUtil.max(coreDist, dist);
            lrd = rd + lrd;
            core.add(rd);
        }
        lrd = minPtsNeighborhoodSize.intValue(iditer) / lrd;
        reachDistance.put(iditer, core);
        lrds.putDouble(iditer, lrd);
    }
    // Pass 3
    DoubleMinMax ofminmax = new DoubleMinMax();
    WritableDoubleDataStore ofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_STATIC);
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        double of = 0;
        for (DBIDIter neighbor = nMinPts.get(iditer).iter(); neighbor.valid(); neighbor.advance()) {
            double lrd = lrds.doubleValue(iditer);
            double lrdN = lrds.doubleValue(neighbor);
            of = of + lrdN / lrd;
        }
        of = of / minPtsNeighborhoodSize.intValue(iditer);
        ofs.putDouble(iditer, of);
        // update minimum and maximum
        ofminmax.put(of);
    }
    // Build result representation.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("OPTICS Outlier Scores", "optics-outlier", ofs, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(ofminmax.getMin(), ofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    return new OutlierResult(scoreMeta, scoreResult);
}

Example 48 with DoubleRelation

use of de.lmu.ifi.dbs.elki.database.relation.DoubleRelation in project elki by elki-project.

the class CBLOF method run.

/**
 * Runs the CBLOF algorithm on the given database.
 *
 * @param database Database to query
 * @param relation Data to process
 * @return CBLOF outlier result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    StepProgress stepprog = LOG.isVerbose() ? new StepProgress("CBLOF", 3) : null;
    DBIDs ids = relation.getDBIDs();
    LOG.beginStep(stepprog, 1, "Computing clustering.");
    Clustering<MeanModel> clustering = clusteringAlgorithm.run(database);
    LOG.beginStep(stepprog, 2, "Computing boundary between large and small clusters.");
    List<? extends Cluster<MeanModel>> clusters = clustering.getAllClusters();
    Collections.sort(clusters, new Comparator<Cluster<MeanModel>>() {

        @Override
        public int compare(Cluster<MeanModel> o1, Cluster<MeanModel> o2) {
            // Sort in descending order by size
            return Integer.compare(o2.size(), o1.size());
        }
    });
    int clusterBoundary = getClusterBoundary(relation, clusters);
    List<? extends Cluster<MeanModel>> largeClusters = clusters.subList(0, clusterBoundary + 1);
    List<? extends Cluster<MeanModel>> smallClusters = clusters.subList(clusterBoundary + 1, clusters.size());
    LOG.beginStep(stepprog, 3, "Computing Cluster-Based Local Outlier Factors (CBLOF).");
    WritableDoubleDataStore cblofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_DB);
    DoubleMinMax cblofMinMax = new DoubleMinMax();
    computeCBLOFs(relation, distance, cblofs, cblofMinMax, largeClusters, smallClusters);
    LOG.setCompleted(stepprog);
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Cluster-Based Local Outlier Factor", "cblof-outlier", cblofs, ids);
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(cblofMinMax.getMin(), cblofMinMax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    return new OutlierResult(scoreMeta, scoreResult);
}

Example 49 with DoubleRelation

use of de.lmu.ifi.dbs.elki.database.relation.DoubleRelation in project elki by elki-project.

the class ParallelKNNWeightOutlier method run.

/**
 * Run the parallel kNN weight outlier detector.
 *
 * @param database Database to process
 * @param relation Relation to analyze
 * @return Outlier detection result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    DBIDs ids = relation.getDBIDs();
    WritableDoubleDataStore store = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_DB);
    DistanceQuery<O> distq = database.getDistanceQuery(relation, getDistanceFunction());
    KNNQuery<O> knnq = database.getKNNQuery(distq, k + 1);
    // Find kNN
    KNNProcessor<O> knnm = new KNNProcessor<>(k + 1, knnq);
    SharedObject<KNNList> knnv = new SharedObject<>();
    knnm.connectKNNOutput(knnv);
    // Extract outlier score
    KNNWeightProcessor kdistm = new KNNWeightProcessor(k + 1);
    SharedDouble kdistv = new SharedDouble();
    kdistm.connectKNNInput(knnv);
    kdistm.connectOutput(kdistv);
    // Store in output result
    WriteDoubleDataStoreProcessor storem = new WriteDoubleDataStoreProcessor(store);
    storem.connectInput(kdistv);
    // And gather statistics for metadata
    DoubleMinMaxProcessor mmm = new DoubleMinMaxProcessor();
    mmm.connectInput(kdistv);
    ParallelExecutor.run(ids, knnm, kdistm, storem, mmm);
    DoubleMinMax minmax = mmm.getMinMax();
    DoubleRelation scoreres = new MaterializedDoubleRelation("kNN weight Outlier Score", "knnw-outlier", store, ids);
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0., Double.POSITIVE_INFINITY, 0.);
    return new OutlierResult(meta, scoreres);
}

Example 50 with DoubleRelation

use of de.lmu.ifi.dbs.elki.database.relation.DoubleRelation 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);
}