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

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

726
727
728
729
730
731
732
733
734
735
736
737
738
   *         interval
   * @throws UnableToComplyException if an error according to the database
   *         occurs
   */
  private Database buildDerivatorDB(Relation<ParameterizationFunction> relation, CASHInterval interval) throws UnableToComplyException {
    DBIDs ids = interval.getIDs();
    ProxyDatabase proxy = new ProxyDatabase(ids);
    int dim = relation.get(ids.iterator().next()).getDimensionality();
    SimpleTypeInformation<DoubleVector> type = new VectorFieldTypeInformation<DoubleVector>(DoubleVector.class, dim, new DoubleVector(new double[dim]));
    MaterializedRelation<DoubleVector> prep = new MaterializedRelation<DoubleVector>(proxy, type, ids);
    proxy.addRelation(prep);

    // Project
View Full Code Here
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
      // Expand multiple steps
      FiniteProgress progress = logger.isVerbose() ? new FiniteProgress("Expanding neighborhoods", database.size(), logger) : null;
      for(final DBID id : database.iterDBIDs()) {
        ModifiableDBIDs res = DBIDUtil.newHashSet(id);
        DBIDs todo = id;
        for(int i = 0; i < steps; i++) {
          ModifiableDBIDs ntodo = DBIDUtil.newHashSet();
          for(final DBID oid : todo) {
            DBIDs add = innerinst.getNeighborDBIDs(oid);
            if(add != null) {
              for (DBID nid: add) {
                if (res.contains(add)) {
                  continue;
                }
View Full Code Here
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
    Iterator<Cluster<Model>> ci = clustering.getAllClusters().iterator();

    for(int cnum = 0; cnum < clustering.getAllClusters().size(); cnum++) {
      Cluster<?> clus = ci.next();

      final DBIDs ids = clus.getIDs();
      ConvexHull2D hull = new ConvexHull2D();

      for(DBID clpnum : ids) {
        double[] projP = proj.fastProjectDataToRenderSpace(rel.get(clpnum).getColumnVector());
        hull.add(new Vector(projP));
      }
      Polygon chres = hull.getHull();

      // Plot the convex hull:
      if(chres != null) {
        SVGPath path = new SVGPath(chres);
        // Approximate area (using bounding box)
        double hullarea = SpatialUtil.volume(chres);
        final double relativeArea = (projarea - hullarea) / projarea;
        final double relativeSize = (double) ids.size() / rel.size();
        opacity = Math.sqrt(relativeSize * relativeArea);

        hulls = path.makeElement(svgp);
        addCSSClasses(svgp, cnum, opacity);
        SVGUtil.addCSSClass(hulls, CONVEXHULL + cnum);
View Full Code Here
49
50
51
52
53
54
55
56
57
58
59
    this.store = store;
  }

  @Override
  public DBIDs getNeighborDBIDs(DBID reference) {
    DBIDs neighbors = store.get(reference);
    if(neighbors != null) {
      return neighbors;
    }
    else {
      // Use just the object itself.
View Full Code Here
91
92
93
94
95
96
97
98
99
100
101
  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);
View Full Code Here
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
    for(DBID id : relation.iterDBIDs()) {
      double sum = 0;
      double maxDist = 0;
      int cnt = 0;

      final DBIDs neighbors = npred.getNeighborDBIDs(id);
      for(DBID neighbor : neighbors) {
        if(id.equals(neighbor)) {
          continue;
        }
        double dist = distFunc.distance(id, neighbor).doubleValue();
        sum += dist;
        cnt++;
        maxDist = Math.max(maxDist, dist);
      }
      if(cnt > 1) {
        modifiedDistance.put(id, ((sum - maxDist) / (cnt - 1)));
      }
      else {
        // Use regular distance when the d-tilde trick is undefined.
        // Note: this can be 0 when there were no neighbors.
        modifiedDistance.put(id, maxDist);
      }
    }

    // Second step - compute actual SLOM values
    DoubleMinMax slomminmax = new DoubleMinMax();
    WritableDataStore<Double> sloms = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);

    for(DBID id : relation.iterDBIDs()) {
      double sum = 0;
      int cnt = 0;

      final DBIDs neighbors = npred.getNeighborDBIDs(id);
      for(DBID neighbor : neighbors) {
        if(neighbor.equals(id)) {
          continue;
        }
        sum += modifiedDistance.get(neighbor);
        cnt++;
      }
      double slom;
      if(cnt > 0) {
        // With and without the object itself:
        double avgPlus = (sum + modifiedDistance.get(id)) / (cnt + 1);
        double avg = sum / cnt;

        double beta = 0;
        for(DBID neighbor : neighbors) {
          final double dist = modifiedDistance.get(neighbor).doubleValue();
          if(dist > avgPlus) {
            beta += 1;
          }
          else if(dist < avgPlus) {
            beta -= 1;
          }
        }
        // Include object itself
        if(!neighbors.contains(id)) {
          final double dist = modifiedDistance.get(id).doubleValue();
          if(dist > avgPlus) {
            beta += 1;
          }
          else if(dist < avgPlus) {
View Full Code Here
104
105
106
107
108
109
110
111
112
113
114
    CovarianceMatrix covm = new CovarianceMatrix(2);
    for(DBID id : relation.iterDBIDs()) {
      final double local = relation.get(id).doubleValue(1);
      // Compute mean of neighbors
      Mean mean = new Mean();
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      for(DBID n : neighbors) {
        if(id.equals(n)) {
          continue;
        }
        mean.put(relation.get(n).doubleValue(1));
View Full Code Here
92
93
94
95
96
97
98
99
100
101
102
    // Add starting object
    result.add(new DoubleObjPair<DBID>(computeWeight(0), reference));
    seen.add(reference);
    // Extend.
    DBIDs cur = reference;
    for(int i = 1; i <= steps; i++) {
      final double weight = computeWeight(i);
      // Collect newly discovered IDs
      ModifiableDBIDs add = DBIDUtil.newHashSet();
      for(DBID id : cur) {
View Full Code Here
59
60
61
62
63
64
65
66
67
68
69
70
    this.inner = inner;
  }

  @Override
  public Collection<DoubleObjPair<DBID>> getWeightedNeighbors(DBID reference) {
    DBIDs neighbors = inner.getNeighborDBIDs(reference);
    ArrayList<DoubleObjPair<DBID>> adapted = new ArrayList<DoubleObjPair<DBID>>(neighbors.size());
    for(DBID id : neighbors) {
      adapted.add(new DoubleObjPair<DBID>(1.0, id));
    }
    return adapted;
  }
View Full Code Here
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
    WritableDataStore<Double> lofs = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);
    DoubleMinMax lofminmax = new DoubleMinMax();

    // Compute densities
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      double avg = 0;
      for(DBID n : neighbors) {
        avg += distFunc.distance(id, n).doubleValue();
      }
      double lrd = 1 / (avg / neighbors.size());
      if (Double.isNaN(lrd)) {
        lrd = 0;
      }
      lrds.put(id, lrd);
    }

    // Compute density quotients
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      double avg = 0;
      for(DBID n : neighbors) {
        avg += lrds.get(n);
      }
      final double lrd = (avg / neighbors.size()) / lrds.get(id);
      if (!Double.isNaN(lrd)) {
        lofs.put(id, lrd);
        lofminmax.put(lrd);
      } else {
        lofs.put(id, 0.0);
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.