Package de.lmu.ifi.dbs.elki.data.model

Examples of de.lmu.ifi.dbs.elki.data.model.OPTICSModel

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   */
  private void drawClusters(List<Cluster<OPTICSModel>> clusters, int depth) {
    final double scale = StyleLibrary.SCALE;
    for(Cluster<OPTICSModel> cluster : clusters) {
      try {
        OPTICSModel model = cluster.getModel();
        final double x1 = plotwidth * ((model.getStartIndex() + .25) / this.optics.getResult().getClusterOrder().size());
        final double x2 = plotwidth * ((model.getEndIndex() + .75) / this.optics.getResult().getClusterOrder().size());
        final double y = plotheight + depth * scale * 0.01;
        Element e = svgp.svgLine(x1, y, x2, y);
        SVGUtil.addCSSClass(e, CSS_BRACKET);
        layer.appendChild(e);
      }
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              }
            }
            if(logger.isDebuggingFine()) {
              logger.debugFine("Found cluster with " + dbids.size() + " new objects, length " + (cstart - cend + 1));
            }
            OPTICSModel model = new OPTICSModel(cstart, cend);
            Cluster<OPTICSModel> cluster = new Cluster<OPTICSModel>("Cluster_" + cstart + "_" + cend, dbids, model, hier);
            // 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()) {
                  hier.add(cluster, clus);
                  iter.remove();
                }
              }
            }
            curclusters.add(cluster);
          }
        }
      }
      // Make sure to advance at least one step
      if(curpos == scan.index) {
        scan.next();
      }
    }
    if(curclusters.size() > 0 || unclaimedids.size() > 0) {
      final Clustering<OPTICSModel> clustering = new Clustering<OPTICSModel>("OPTICS Xi-Clusters", "optics");
      if(unclaimedids.size() > 0) {
        final Cluster<OPTICSModel> allcluster;
        if(clusterOrder.get(clusterOrder.size() - 1).getReachability().isInfiniteDistance()) {
          allcluster = new Cluster<OPTICSModel>("Noise", unclaimedids, true, new OPTICSModel(0, clusterOrder.size() - 1), hier);
        }
        else {
          allcluster = new Cluster<OPTICSModel>("Cluster", unclaimedids, new OPTICSModel(0, clusterOrder.size() - 1), hier);
        }
        for(Cluster<OPTICSModel> cluster : curclusters) {
          hier.add(allcluster, cluster);
        }
        clustering.addCluster(allcluster);
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   */
  private void drawClusters(List<Cluster<OPTICSModel>> clusters, int depth) {
    final double scale = StyleLibrary.SCALE;
    for(Cluster<OPTICSModel> cluster : clusters) {
      try {
        OPTICSModel model = cluster.getModel();
        final double x1 = plotwidth * ((model.getStartIndex() + .25) / this.optics.getResult().getClusterOrder().size());
        final double x2 = plotwidth * ((model.getEndIndex() + .75) / this.optics.getResult().getClusterOrder().size());
        final double y = plotheight + depth * scale * 0.01;
        Element e = svgp.svgLine(x1, y, x2, y);
        SVGUtil.addCSSClass(e, CSS_BRACKET);
        layer.appendChild(e);
      }
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              }
            }
            if(logger.isDebuggingFine()) {
              logger.debugFine("Found cluster with " + dbids.size() + " new objects, length " + (cstart - cend + 1));
            }
            OPTICSModel model = new OPTICSModel(cstart, cend);
            Cluster<OPTICSModel> cluster = new Cluster<OPTICSModel>("Cluster_" + cstart + "_" + cend, dbids, model, hier);
            // 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()) {
                  hier.add(cluster, clus);
                  iter.remove();
                }
              }
            }
            curclusters.add(cluster);
          }
        }
      }
      // Make sure to advance at least one step
      if(curpos == scan.index) {
        scan.next();
      }
    }
    if(curclusters.size() > 0 || unclaimedids.size() > 0) {
      final Clustering<OPTICSModel> clustering = new Clustering<OPTICSModel>("OPTICS Xi-Clusters", "optics");
      if(unclaimedids.size() > 0) {
        final Cluster<OPTICSModel> allcluster;
        if(clusterOrder.get(clusterOrder.size() - 1).getReachability().isInfiniteDistance()) {
          allcluster = new Cluster<OPTICSModel>("Noise", unclaimedids, true, new OPTICSModel(0, clusterOrder.size() - 1), hier);
        }
        else {
          allcluster = new Cluster<OPTICSModel>("Cluster", unclaimedids, new OPTICSModel(0, clusterOrder.size() - 1), hier);
        }
        for(Cluster<OPTICSModel> cluster : curclusters) {
          hier.add(allcluster, cluster);
        }
        clustering.addCluster(allcluster);
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Related Classes of de.lmu.ifi.dbs.elki.data.model.OPTICSModel

  • de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICSXi
  • de.lmu.ifi.dbs.elki.visualization.visualizers.optics.OPTICSClusterVisualization
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