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

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

211
212
213
214
215
216
217
218
219
220
221
    // noise cluster containing all noise objects over all partitions
    Cluster<Model> noise = null;

    // iterate over correlation dimensions
    for(Cluster<Model> clus : copacResult.getAllClusters()) {
      DBIDs group = clus.getIDs();
      if(clus.getModel() != null && clus.getModel() instanceof DimensionModel) {
        int correlationDimension = ((DimensionModel) clus.getModel()).getDimension();

        ListParameterization parameters = pcaParameters(correlationDimension);
        Class<PCAFilteredRunner<V>> cls = ClassGenericsUtil.uglyCastIntoSubclass(PCAFilteredRunner.class);
View Full Code Here
131
132
133
134
135
136
137
138
139
140
141
    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;
View Full Code Here
90
91
92
93
94
95
96
97
98
99
100
  @Override
  protected void redraw() {
    DBIDSelection selContext = context.getSelection();
    if(selContext != null) {
      DBIDs selection = selContext.getSelectedIds();

      for(DBID objId : selection) {
        double[] yPos = proj.fastProjectDataToRenderSpace(relation.get(objId));

        SVGPath path = new SVGPath();
View Full Code Here
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
  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 {
View Full Code Here
238
239
240
241
242
243
244
245
246
247
248
      if(pair.getKey() == DatabaseUtil.dimensionality(relation)) {
        // Make a Noise cluster
        result.addCluster(new Cluster<Model>(pair.getValue(), true, ClusterModel.CLUSTER));
      }
      else {
        DBIDs partids = pair.getValue();
        ProxyDatabase proxy = new ProxyDatabase(partids, relation);
       
        ClusteringAlgorithm<Clustering<Model>> partitionAlgorithm = getPartitionAlgorithm(query);
        if(logger.isVerbose()) {
          logger.verbose("Running " + partitionAlgorithm.getClass().getName() + " on partition [corrDim = " + pair.getKey() + "]...");
View Full Code Here
138
139
140
141
142
143
144
145
146
147
148
149
    this.centroid = centroid;
    this.nf = nf;

    // determine standard deviation
    double variance = 0;
    DBIDs ids = db.getDBIDs();
    for (DBID id : ids) {
      double distance = distance(db.get(id).getColumnVector());
      variance += distance * distance;
    }
    standardDeviation = Math.sqrt(variance / ids.size());
  }
View Full Code Here
95
96
97
98
99
100
101
102
103
104
105
  @Override
  protected void redraw() {
    addCSSClasses(svgp);
    DBIDSelection selContext = context.getSelection();
    if(selContext != null) {
      DBIDs selection = selContext.getSelectedIds();
      ConvexHull2D hull = new ConvexHull2D();
      for(DBID i : selection) {
        try {
          hull.add(new Vector(proj.fastProjectDataToRenderSpace(rel.get(i))));
        }
View Full Code Here
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
    CovarianceMatrix covmaker = new CovarianceMatrix(dim);
    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);
      // Compute the median vector
      final Vector median;
      {
        double[][] data = new double[dim][neighbors.size()];
        int i = 0;
        // Load data
        for(DBID n : neighbors) {
          // TODO: skip object itself within neighbors?
          O nobj = attributes.get(n);
View Full Code Here
93
94
95
96
97
98
99
100
101
102
103
104
105
106
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);

    MeanVariance mv = new MeanVariance();
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      final double median;
      {
        double[] fi = new double[neighbors.size()];
        // calculate and store Median of neighborhood
        int c = 0;
        for(DBID n : neighbors) {
          if(id.equals(n)) {
            continue;
View Full Code Here
158
159
160
161
162
163
164
165
166
167
168
169
170
171
    // initialization phase
    if(logger.isVerbose()) {
      logger.verbose("1. Initialization phase...");
    }
    int sampleSize = Math.min(relation.size(), k_i * k);
    DBIDs sampleSet = DBIDUtil.randomSample(relation.getDBIDs(), sampleSize, random.nextLong());

    int medoidSize = Math.min(relation.size(), m_i * k);
    DBIDs medoids = greedy(distFunc, sampleSet, medoidSize, random);

    if(logger.isDebugging()) {
      StringBuffer msg = new StringBuffer();
      msg.append("\n");
      msg.append("sampleSize ").append(sampleSize).append("\n");
View Full Code Here
TOP

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
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.