Examples with DoubleVector de.lmu.ifi.dbs.elki.data.DoubleVector used on opensource projects

Example 56 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class AggarwalYuNaiveTest method testAggarwalYuNaive.

@Test
public void testAggarwalYuNaive() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);
    OutlierResult result = // 
    new ELKIBuilder<AggarwalYuNaive<DoubleVector>>(AggarwalYuNaive.class).with(AggarwalYuNaive.Parameterizer.K_ID, // 
    2).with(AggarwalYuNaive.Parameterizer.PHI_ID, // 
    8).build().run(db);
    testAUC(db, "Noise", result, 0.9007777777777);
    testSingleScore(result, 945, -2.862640213982);
}

Example 57 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class UnweightedDiscreteUncertainObject method getCenterOfMass.

@Override
public DoubleVector getCenterOfMass() {
    final int dim = getDimensionality();
    // Unweighted average.
    double[] meanVals = new double[dim];
    for (int i = 0; i < samples.length; i++) {
        DoubleVector vals = samples[i];
        for (int d = 0; d < dim; d++) {
            meanVals[d] += vals.doubleValue(d);
        }
    }
    for (int d = 0; d < dim; d++) {
        meanVals[d] /= samples.length;
    }
    return DoubleVector.wrap(meanVals);
}

Example 58 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class WeightedDiscreteUncertainObject method getCenterOfMass.

@Override
public DoubleVector getCenterOfMass() {
    final int dim = getDimensionality();
    // Weighted average.
    double[] meanVals = new double[dim];
    double weightSum = 0.;
    for (int i = 0; i < samples.length; i++) {
        DoubleVector v = samples[i];
        final double w = weights[i];
        for (int d = 0; d < dim; d++) {
            meanVals[d] += v.doubleValue(d) * w;
        }
        weightSum += w;
    }
    for (int d = 0; d < dim; d++) {
        meanVals[d] /= weightSum;
    }
    return DoubleVector.wrap(meanVals);
}

Example 59 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class RepresentativeUncertainClustering method run.

/**
 * This run method will do the wrapping.
 *
 * Its called from {@link AbstractAlgorithm#run(Database)} and performs the
 * call to the algorithms particular run method as well as the storing and
 * comparison of the resulting Clusterings.
 *
 * @param database Database
 * @param relation Data relation of uncertain objects
 * @return Clustering result
 */
public Clustering<?> run(Database database, Relation<? extends UncertainObject> relation) {
    ResultHierarchy hierarchy = database.getHierarchy();
    ArrayList<Clustering<?>> clusterings = new ArrayList<>();
    final int dim = RelationUtil.dimensionality(relation);
    DBIDs ids = relation.getDBIDs();
    // To collect samples
    Result samples = new BasicResult("Samples", "samples");
    // Step 1: Cluster sampled possible worlds:
    Random rand = random.getSingleThreadedRandom();
    FiniteProgress sampleP = LOG.isVerbose() ? new FiniteProgress("Clustering samples", numsamples, LOG) : null;
    for (int i = 0; i < numsamples; i++) {
        WritableDataStore<DoubleVector> store = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_DB, DoubleVector.class);
        for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
            store.put(iter, relation.get(iter).drawSample(rand));
        }
        clusterings.add(runClusteringAlgorithm(hierarchy, samples, ids, store, dim, "Sample " + i));
        LOG.incrementProcessed(sampleP);
    }
    LOG.ensureCompleted(sampleP);
    // Step 2: perform the meta clustering (on samples only).
    DBIDRange rids = DBIDFactory.FACTORY.generateStaticDBIDRange(clusterings.size());
    WritableDataStore<Clustering<?>> datastore = DataStoreUtil.makeStorage(rids, DataStoreFactory.HINT_DB, Clustering.class);
    {
        Iterator<Clustering<?>> it2 = clusterings.iterator();
        for (DBIDIter iter = rids.iter(); iter.valid(); iter.advance()) {
            datastore.put(iter, it2.next());
        }
    }
    assert (rids.size() == clusterings.size());
    // Build a relation, and a distance matrix.
    Relation<Clustering<?>> crel = new MaterializedRelation<Clustering<?>>(Clustering.TYPE, rids, "Clusterings", datastore);
    PrecomputedDistanceMatrix<Clustering<?>> mat = new PrecomputedDistanceMatrix<>(crel, rids, distance);
    mat.initialize();
    ProxyDatabase d = new ProxyDatabase(rids, crel);
    d.getHierarchy().add(crel, mat);
    Clustering<?> c = metaAlgorithm.run(d);
    // Detach from database
    d.getHierarchy().remove(d, c);
    // Evaluation
    Result reps = new BasicResult("Representants", "representative");
    hierarchy.add(relation, reps);
    DistanceQuery<Clustering<?>> dq = mat.getDistanceQuery(distance);
    List<? extends Cluster<?>> cl = c.getAllClusters();
    List<DoubleObjPair<Clustering<?>>> evaluated = new ArrayList<>(cl.size());
    for (Cluster<?> clus : cl) {
        double besttau = Double.POSITIVE_INFINITY;
        Clustering<?> bestc = null;
        for (DBIDIter it1 = clus.getIDs().iter(); it1.valid(); it1.advance()) {
            double tau = 0.;
            Clustering<?> curc = crel.get(it1);
            for (DBIDIter it2 = clus.getIDs().iter(); it2.valid(); it2.advance()) {
                if (DBIDUtil.equal(it1, it2)) {
                    continue;
                }
                double di = dq.distance(curc, it2);
                tau = di > tau ? di : tau;
            }
            // Cluster member with the least maximum distance.
            if (tau < besttau) {
                besttau = tau;
                bestc = curc;
            }
        }
        if (bestc == null) {
            // E.g. degenerate empty clusters
            continue;
        }
        // Global tau:
        double gtau = 0.;
        for (DBIDIter it2 = crel.iterDBIDs(); it2.valid(); it2.advance()) {
            double di = dq.distance(bestc, it2);
            gtau = di > gtau ? di : gtau;
        }
        final double cprob = computeConfidence(clus.size(), crel.size());
        // Build an evaluation result
        hierarchy.add(bestc, new RepresentativenessEvaluation(gtau, besttau, cprob));
        evaluated.add(new DoubleObjPair<Clustering<?>>(cprob, bestc));
    }
    // Sort evaluated results by confidence:
    Collections.sort(evaluated, Collections.reverseOrder());
    for (DoubleObjPair<Clustering<?>> pair : evaluated) {
        // Attach parent relation (= sample) to the representative samples.
        for (It<Relation<?>> it = hierarchy.iterParents(pair.second).filter(Relation.class); it.valid(); it.advance()) {
            hierarchy.add(reps, it.get());
        }
    }
    // Add the random samples below the representative results only:
    if (keep) {
        hierarchy.add(relation, samples);
    } else {
        hierarchy.removeSubtree(samples);
    }
    return c;
}

Example 60 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class RepresentativeUncertainClustering method runClusteringAlgorithm.

/**
 * Run a clustering algorithm on a single instance.
 *
 * @param parent Parent result to attach to
 * @param ids Object IDs to process
 * @param store Input data
 * @param dim Dimensionality
 * @param title Title of relation
 * @return Clustering result
 */
protected Clustering<?> runClusteringAlgorithm(ResultHierarchy hierarchy, Result parent, DBIDs ids, DataStore<DoubleVector> store, int dim, String title) {
    SimpleTypeInformation<DoubleVector> t = new VectorFieldTypeInformation<>(DoubleVector.FACTORY, dim);
    Relation<DoubleVector> sample = new MaterializedRelation<>(t, ids, title, store);
    ProxyDatabase d = new ProxyDatabase(ids, sample);
    Clustering<?> clusterResult = samplesAlgorithm.run(d);
    d.getHierarchy().remove(sample);
    d.getHierarchy().remove(clusterResult);
    hierarchy.add(parent, sample);
    hierarchy.add(sample, clusterResult);
    return clusterResult;
}