Examples with DoubleMinMax de.lmu.ifi.dbs.elki.math.DoubleMinMax used on opensource projects

Example 46 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax 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 47 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class DistanceStatisticsWithClasses method exactMinMax.

/**
 * Compute the exact maximum and minimum.
 *
 * @param relation Relation to process
 * @param distFunc Distance function
 * @return Exact maximum and minimum
 */
private DoubleMinMax exactMinMax(Relation<O> relation, DistanceQuery<O> distFunc) {
    final FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("Exact fitting distance computations", relation.size(), LOG) : null;
    DoubleMinMax minmax = new DoubleMinMax();
    // find exact minimum and maximum first.
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        for (DBIDIter iditer2 = relation.iterDBIDs(); iditer2.valid(); iditer2.advance()) {
            // skip the point itself.
            if (DBIDUtil.equal(iditer, iditer2)) {
                continue;
            }
            double d = distFunc.distance(iditer, iditer2);
            minmax.put(d);
        }
        LOG.incrementProcessed(progress);
    }
    LOG.ensureCompleted(progress);
    return minmax;
}

Example 48 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax 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 49 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax 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 50 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class LOF method run.

/**
 * Runs the LOF algorithm on the given database.
 *
 * @param database Database to query
 * @param relation Data to process
 * @return LOF outlier result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    StepProgress stepprog = LOG.isVerbose() ? new StepProgress("LOF", 3) : null;
    DBIDs ids = relation.getDBIDs();
    LOG.beginStep(stepprog, 1, "Materializing nearest-neighbor sets.");
    KNNQuery<O> knnq = DatabaseUtil.precomputedKNNQuery(database, relation, getDistanceFunction(), k);
    // Compute LRDs
    LOG.beginStep(stepprog, 2, "Computing Local Reachability Densities (LRD).");
    WritableDoubleDataStore lrds = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    computeLRDs(knnq, ids, lrds);
    // compute LOF_SCORE of each db object
    LOG.beginStep(stepprog, 3, "Computing Local Outlier Factors (LOF).");
    WritableDoubleDataStore lofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_DB);
    // track the maximum value for normalization.
    DoubleMinMax lofminmax = new DoubleMinMax();
    computeLOFScores(knnq, ids, lrds, lofs, lofminmax);
    LOG.setCompleted(stepprog);
    // Build result representation.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Local Outlier Factor", "lof-outlier", lofs, ids);
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(lofminmax.getMin(), lofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    return new OutlierResult(scoreMeta, scoreResult);
}