Package de.lmu.ifi.dbs.elki.database.ids

Examples of de.lmu.ifi.dbs.elki.database.ids.DBIDs

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    Random random = new Random(seed);
    for(int i = 0; i < size; i++) {
      DoubleVector obj = VectorUtil.randomVector(o, random);
      insertions.add(obj);
    }
    DBIDs deletions = db.insert(MultipleObjectsBundle.makeSimple(rep.getDataTypeInformation(), insertions));

    // delete objects
    db.delete(deletions);

    return result;
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    WritableDataStore<Double> errors = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP, Double.class);
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);

    FiniteProgress progress = logger.isVerbose() ? new FiniteProgress("Computing trimmed means", relation.size(), logger) : null;
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      int num = 0;
      double[] values = new double[neighbors.size()];
      // calculate trimmedMean
      for(DBID n : neighbors) {
        values[num] = relation.get(n).doubleValue(1);
        num++;
      }
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          break;
        }
      }
      Clustering<MeanModel<V>> result = new Clustering<MeanModel<V>>("k-Means Clustering", "kmeans-clustering");
      for(int i = 0; i < clusters.size(); i++) {
        DBIDs ids = clusters.get(i);
        MeanModel<V> model = new MeanModel<V>(means.get(i));
        result.addCluster(new Cluster<MeanModel<V>>(ids, model));
      }
      return result;
    }
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  public void addMarker() {
    List<ClusterOrderEntry<D>> order = getClusterOrder();
    // TODO: replace mtag!
    DBIDSelection selContext = context.getSelection();
    if(selContext != null) {
      DBIDs selection = DBIDUtil.ensureSet(selContext.getSelectedIds());

      final double width = plotwidth / order.size();
      int begin = -1;
      for(int j = 0; j < order.size(); j++) {
        DBID id = order.get(j).getID();
        if(selection.contains(id)) {
          if(begin == -1) {
            begin = j;
          }
        }
        else {
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    CovarianceMatrix covmaker = new CovarianceMatrix(DatabaseUtil.dimensionality(attributes));
    WritableDataStore<Vector> deltas = DataStoreUtil.makeStorage(attributes.getDBIDs(), DataStoreFactory.HINT_TEMP, Vector.class);
    for(DBID id : attributes.iterDBIDs()) {
      final O obj = attributes.get(id);
      final DBIDs neighbors = npred.getNeighborDBIDs(id);
      // TODO: remove object itself from neighbors?

      // Mean vector "g"
      Vector mean = Centroid.make(attributes, neighbors);
      // Delta vector "h"
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  public TrivialAllInOne() {
    super();
  }

  public Clustering<Model> run(Relation<?> relation) {
    final DBIDs ids = relation.getDBIDs();
    Clustering<Model> result = new Clustering<Model>("All-in-one trivial Clustering", "allinone-clustering");
    Cluster<Model> c = new Cluster<Model>(ids, ClusterModel.CLUSTER);
    result.addCluster(c);
    return result;
  }
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  public TrivialAllNoise() {
    super();
  }

  public Clustering<Model> run(Relation<?> relation) {
    final DBIDs ids = relation.getDBIDs();
    Clustering<Model> result = new Clustering<Model>("All-in-noise trivial Clustering", "allinnoise-clustering");
    Cluster<Model> c = new Cluster<Model>(ids, true, ClusterModel.CLUSTER);
    result.addCluster(c);
    return result;
  }
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  protected void redraw() {
    addCSSClasses(svgp);
    final double size = context.getStyleLibrary().getSize(StyleLibrary.SELECTION);
    DBIDSelection selContext = context.getSelection();
    if(selContext != null) {
      DBIDs selection = selContext.getSelectedIds();
      for(DBID i : selection) {
        try {
          double[] v = proj.fastProjectDataToRenderSpace(rel.get(i));
          Element dot = svgp.svgCircle(v[0], v[1], size);
          SVGUtil.addCSSClass(dot, MARKER);
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    PCARunner<NV> pcarun = ClassGenericsUtil.parameterizeOrAbort(PCARunner.class, new EmptyParameterization());

    Iterator<Cluster<EMModel<NV>>> ci = clustering.getAllClusters().iterator();
    for(int cnum = 0; cnum < clustering.getAllClusters().size(); cnum++) {
      Cluster<EMModel<NV>> clus = ci.next();
      DBIDs ids = clus.getIDs();

      if(ids.size() > 0) {
        Matrix covmat = clus.getModel().getCovarianceMatrix();
        NV centroid = clus.getModel().getMean();
        Vector cent = new Vector(proj.fastProjectDataToRenderSpace(centroid));

        // Compute the eigenvectors
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  @Override
  public int getStyleForDBID(DBID id) {
    Iterator<Segment> s = selectedSegments.iterator();
    for(int i = 0; s.hasNext(); i++) {
      Segment seg = s.next();
      DBIDs ids = seg.getDBIDs();
      if(ids != null && ids.contains(id)) {
        return i;
      }
    }
    return -2;
  }
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Related Classes of de.lmu.ifi.dbs.elki.database.ids.DBIDs

  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.CASH
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.LMCLUS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ORCLUS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.KMeans
  • de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen
  • de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyChosenInitialMeans
  • de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PROCLUS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.TrivialAllInOne
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