Examples of CovarianceMatrix

    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
    WritableDoubleDataStore means = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP);


    // Calculate average of neighborhood for each object and perform a linear
    // regression using the covariance matrix
    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));
      }
      final double m;
      if(mean.getCount() > 0) {
        m = mean.getMean();
      }
      else {
        // if object id has no neighbors ==> avg = non-spatial attribute of id
        m = local;
      }
      // Store the mean for the score calculation
      means.putDouble(id, m);
      covm.put(new double[] { local, m });
    }
    // Finalize covariance matrix, compute linear regression
    final double slope, inter;
    {
      double[] meanv = covm.getMeanVector().getArrayRef();
      Matrix fmat = covm.destroyToSampleMatrix();
      final double covxx = fmat.get(0, 0);
      final double covxy = fmat.get(0, 1);
      slope = covxy / covxx;
      inter = meanv[1] - slope * meanv[0];
    }

    if(logger.isDebugging()) {
      logger.debug("Dimensionality: " + dim);
    }
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(spatial);


    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);
          for(int d = 0; d < dim; d++) {
            data[d][i] = nobj.doubleValue(d + 1);
          }
          i++;
        }
        double[] md = new double[dim];
        for(int d = 0; d < dim; d++) {
          md[d] = QuickSelect.median(data[d]);
        }
        median = new Vector(md);
      }


      // Delta vector "h"
      Vector delta = obj.getColumnVector().minus(median);
      deltas.put(id, delta);
      covmaker.put(delta);
    }
    // Finalize covariance matrix:
    Vector mean = covmaker.getMeanVector();
    Matrix cmati = covmaker.destroyToSampleMatrix().inverse();


    DoubleMinMax minmax = new DoubleMinMax();
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC);
    for(DBID id : attributes.iterDBIDs()) {
      Vector temp = deltas.get(id).minus(mean);

Examples of CovarianceMatrix

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix