Package de.lmu.ifi.dbs.elki.math.linearalgebra

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

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

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

    DoubleMinMax minmax = new DoubleMinMax();
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);
    for(DBID id : attributes.iterDBIDs()) {
      Vector temp = deltas.get(id).minus(mean);
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    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();
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);
    for(DBID id : attributes.iterDBIDs()) {
      Vector temp = deltas.get(id).minus(mean);
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    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
    WritableDataStore<Double> means = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP, Double.class);

    // 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.put(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];
    }
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   * support for other distance functions?
   */
  @Override
  public Matrix processIds(DBIDs ids, Relation<? extends V> database) {
    final int dim = DatabaseUtil.dimensionality(database);
    final CovarianceMatrix cmat = new CovarianceMatrix(dim);
    final V centroid = Centroid.make(database, ids).toVector(database);

    // find maximum distance
    double maxdist = 0.0;
    double stddev = 0.0;
    {
      for(Iterator<DBID> it = ids.iterator(); it.hasNext();) {
        V obj = database.get(it.next());
        double distance = weightDistance.distance(centroid, obj).doubleValue();
        stddev += distance * distance;
        if(distance > maxdist) {
          maxdist = distance;
        }
      }
      if(maxdist == 0.0) {
        maxdist = 1.0;
      }
      // compute standard deviation.
      stddev = Math.sqrt(stddev / ids.size());
    }

    for(Iterator<DBID> it = ids.iterator(); it.hasNext();) {
      V obj = database.get(it.next());
      double distance = weightDistance.distance(centroid, obj).doubleValue();
      double weight = weightfunction.getWeight(distance, maxdist, stddev);
      cmat.put(obj, weight);
    }
    return cmat.destroyToNaiveMatrix();
  }
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   * @return Covariance Matrix
   */
  @Override
  public <D extends NumberDistance<?, ?>> Matrix processQueryResults(Collection<DistanceResultPair<D>> results, Relation<? extends V> database, int k) {
    final int dim = DatabaseUtil.dimensionality(database);
    final CovarianceMatrix cmat = new CovarianceMatrix(dim);

    // avoid bad parameters
    if(k > results.size()) {
      k = results.size();
    }

    // find maximum distance
    double maxdist = 0.0;
    double stddev = 0.0;
    {
      int i = 0;
      for(Iterator<DistanceResultPair<D>> it = results.iterator(); it.hasNext() && i < k; i++) {
        DistanceResultPair<D> res = it.next();
        final double dist;
        if(res instanceof DoubleDistanceResultPair) {
          dist = ((DoubleDistanceResultPair) res).getDoubleDistance();
        }
        else {
          dist = res.getDistance().doubleValue();
        }
        stddev += dist * dist;
        if(dist > maxdist) {
          maxdist = dist;
        }
      }
      if(maxdist == 0.0) {
        maxdist = 1.0;
      }
      stddev = Math.sqrt(stddev / k);
    }

    // calculate weighted PCA
    int i = 0;
    for(Iterator<DistanceResultPair<D>> it = results.iterator(); it.hasNext() && i < k; i++) {
      DistanceResultPair<? extends NumberDistance<?, ?>> res = it.next();
      final double dist;
      if(res instanceof DoubleDistanceResultPair) {
        dist = ((DoubleDistanceResultPair) res).getDoubleDistance();
      }
      else {
        dist = res.getDistance().doubleValue();
      }

      V obj = database.get(res.getDBID());
      double weight = weightfunction.getWeight(dist, maxdist, stddev);
      cmat.put(obj, weight);
    }
    return cmat.destroyToNaiveMatrix();
  }
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    DoubleMinMax mm = new DoubleMinMax();
    // resulting scores
    WritableDataStore<Double> oscores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, Double.class);

    // Compute mean and covariance Matrix
    CovarianceMatrix temp = CovarianceMatrix.make(relation);
    V mean = temp.getMeanVector(relation);
    // debugFine(mean.toString());
    Matrix covarianceMatrix = temp.destroyToNaiveMatrix();
    // debugFine(covarianceMatrix.toString());
    Matrix covarianceTransposed = covarianceMatrix.cheatToAvoidSingularity(SINGULARITY_CHEAT).inverse();

    // Normalization factors for Gaussian PDF
    final double fakt = (1.0 / (Math.sqrt(Math.pow(MathUtil.TWOPI, DatabaseUtil.dimensionality(relation)) * covarianceMatrix.det())));
 
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   * support for other distance functions?
   */
  @Override
  public Matrix processIds(DBIDs ids, Relation<? extends V> database) {
    final int dim = DatabaseUtil.dimensionality(database);
    final CovarianceMatrix cmat = new CovarianceMatrix(dim);
    final V centroid = Centroid.make(database, ids).toVector(database);

    // find maximum distance
    double maxdist = 0.0;
    double stddev = 0.0;
    {
      for(Iterator<DBID> it = ids.iterator(); it.hasNext();) {
        V obj = database.get(it.next());
        double distance = weightDistance.distance(centroid, obj).doubleValue();
        stddev += distance * distance;
        if(distance > maxdist) {
          maxdist = distance;
        }
      }
      if(maxdist == 0.0) {
        maxdist = 1.0;
      }
      // compute standard deviation.
      stddev = Math.sqrt(stddev / ids.size());
    }

    for(Iterator<DBID> it = ids.iterator(); it.hasNext();) {
      V obj = database.get(it.next());
      double distance = weightDistance.distance(centroid, obj).doubleValue();
      double weight = weightfunction.getWeight(distance, maxdist, stddev);
      cmat.put(obj, weight);
    }
    return cmat.destroyToNaiveMatrix();
  }
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   * @return Covariance Matrix
   */
  @Override
  public <D extends NumberDistance<?, ?>> Matrix processQueryResults(Collection<DistanceResultPair<D>> results, Relation<? extends V> database, int k) {
    final int dim = DatabaseUtil.dimensionality(database);
    final CovarianceMatrix cmat = new CovarianceMatrix(dim);

    // avoid bad parameters
    if(k > results.size()) {
      k = results.size();
    }

    // find maximum distance
    double maxdist = 0.0;
    double stddev = 0.0;
    {
      int i = 0;
      for(Iterator<DistanceResultPair<D>> it = results.iterator(); it.hasNext() && i < k; i++) {
        DistanceResultPair<D> res = it.next();
        final double dist;
        if(res instanceof DoubleDistanceResultPair) {
          dist = ((DoubleDistanceResultPair) res).getDoubleDistance();
        }
        else {
          dist = res.getDistance().doubleValue();
        }
        stddev += dist * dist;
        if(dist > maxdist) {
          maxdist = dist;
        }
      }
      if(maxdist == 0.0) {
        maxdist = 1.0;
      }
      stddev = Math.sqrt(stddev / k);
    }

    // calculate weighted PCA
    int i = 0;
    for(Iterator<DistanceResultPair<D>> it = results.iterator(); it.hasNext() && i < k; i++) {
      DistanceResultPair<? extends NumberDistance<?, ?>> res = it.next();
      final double dist;
      if(res instanceof DoubleDistanceResultPair) {
        dist = ((DoubleDistanceResultPair) res).getDoubleDistance();
      }
      else {
        dist = res.getDistance().doubleValue();
      }

      V obj = database.get(res.getDBID());
      double weight = weightfunction.getWeight(dist, maxdist, stddev);
      cmat.put(obj, weight);
    }
    return cmat.destroyToNaiveMatrix();
  }
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    if(logger.isDebugging()) {
      logger.debug("Dimensionality: " + DatabaseUtil.dimensionality(attributes));
    }
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(spatial);

    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"
      Vector delta = obj.getColumnVector().minus(mean);
      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);
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    DoubleMinMax mm = new DoubleMinMax();
    // resulting scores
    WritableDoubleDataStore oscores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT);

    // Compute mean and covariance Matrix
    CovarianceMatrix temp = CovarianceMatrix.make(relation);
    Vector mean = temp.getMeanVector(relation).getColumnVector();
    // debugFine(mean.toString());
    Matrix covarianceMatrix = temp.destroyToNaiveMatrix();
    // debugFine(covarianceMatrix.toString());
    Matrix covarianceTransposed = covarianceMatrix.cheatToAvoidSingularity(SINGULARITY_CHEAT).inverse();

    // Normalization factors for Gaussian PDF
    final double fakt = (1.0 / (Math.sqrt(Math.pow(MathUtil.TWOPI, DatabaseUtil.dimensionality(relation)) * covarianceMatrix.det())));
 
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Related Classes of de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix

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  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMeanMultipleAttributes
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMedianMultipleAttributes
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuScatterplotOutlier
  • de.lmu.ifi.dbs.elki.math.linearalgebra.pca.WeightedCovarianceMatrixBuilder
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