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

Examples of de.lmu.ifi.dbs.elki.math.Mean

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    // 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;
      }
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    // calculate neighborhood average of normalized attribute values.
    for(DBID id : relation.iterDBIDs()) {
      // Compute global z score
      final double globalZ = (relation.get(id).doubleValue(1) - globalmv.getMean()) / globalmv.getNaiveStddev();
      // Compute local average z score
      Mean localm = new Mean();
      for(DBID n : npred.getNeighborDBIDs(id)) {
        if(id.equals(n)) {
          continue;
        }
        localm.put((relation.get(n).doubleValue(1) - globalmv.getMean()) / globalmv.getNaiveStddev());
      }
      // if neighors.size == 0
      final double localZ;
      if(localm.getCount() > 0) {
        localZ = localm.getMean();
      }
      else {
        // if s has no neighbors => Wzi = zi
        localZ = globalZ;
      }
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      final double tm;
      if(num > 0) {
        int left = (int) Math.floor(p * (num - 1));
        int right = (int) Math.floor((1 - p) * (num - 1));
        Arrays.sort(values, 0, num);
        Mean mean = new Mean();
        for(int i = left; i <= right; i++) {
          mean.put(values[i]);
        }
        tm = mean.getMean();
      }
      else {
        tm = relation.get(id).doubleValue(1);
      }
      // Error: deviation from trimmed mean
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    MeanVariance zmv = new MeanVariance();
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      // Compute Mean of neighborhood
      Mean localmean = new Mean();
      for(DBID n : neighbors) {
        if(id.equals(n)) {
          continue;
        }
        else {
          localmean.put(relation.get(n).doubleValue(1));
        }
      }
      final double localdiff;
      if(localmean.getCount() > 0) {
        localdiff = relation.get(id).doubleValue(1) - localmean.getMean();
      }
      else {
        localdiff = 0.0;
      }
      scores.put(id, localdiff);
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    // calculate neighborhood average of normalized attribute values.
    for(DBID id : relation.iterDBIDs()) {
      // Compute global z score
      final double globalZ = (relation.get(id).doubleValue(1) - globalmv.getMean()) / globalmv.getNaiveStddev();
      // Compute local average z score
      Mean localm = new Mean();
      for(DBID n : npred.getNeighborDBIDs(id)) {
        if(id.equals(n)) {
          continue;
        }
        localm.put((relation.get(n).doubleValue(1) - globalmv.getMean()) / globalmv.getNaiveStddev());
      }
      // if neighors.size == 0
      final double localZ;
      if(localm.getCount() > 0) {
        localZ = localm.getMean();
      }
      else {
        // if s has no neighbors => Wzi = zi
        localZ = globalZ;
      }
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      final double tm;
      if(num > 0) {
        int left = (int) Math.floor(p * (num - 1));
        int right = (int) Math.floor((1 - p) * (num - 1));
        Arrays.sort(values, 0, num);
        Mean mean = new Mean();
        for(int i = left; i <= right; i++) {
          mean.put(values[i]);
        }
        tm = mean.getMean();
      }
      else {
        tm = relation.get(id).doubleValue(1);
      }
      // Error: deviation from trimmed mean
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    MeanVariance zmv = new MeanVariance();
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      // Compute Mean of neighborhood
      Mean localmean = new Mean();
      for(DBID n : neighbors) {
        if(id.equals(n)) {
          continue;
        }
        else {
          localmean.put(relation.get(n).doubleValue(1));
        }
      }
      final double localdiff;
      if(localmean.getCount() > 0) {
        localdiff = relation.get(id).doubleValue(1) - localmean.getMean();
      }
      else {
        localdiff = 0.0;
      }
      scores.putDouble(id, localdiff);
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    // 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;
      }
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    final DistanceResultPair<D> x = referenceDists.get(index);
    final double xDist = x.getDistance().doubleValue();

    int lef = index - 1;
    int rig = index + 1;
    Mean mean = new Mean();
    double lef_d = (lef >= 0) ? referenceDists.get(lef).getDistance().doubleValue() : Double.NEGATIVE_INFINITY;
    double rig_d = (rig < referenceDists.size()) ? referenceDists.get(rig).getDistance().doubleValue() : Double.NEGATIVE_INFINITY;
    while(mean.getCount() < k) {
      if(lef >= 0 && rig < referenceDists.size()) {
        // Prefer n or m?
        if(Math.abs(lef_d - xDist) < Math.abs(rig_d - xDist)) {
          mean.put(Math.abs(lef_d - xDist));
          // Update n
          lef--;
          lef_d = (lef >= 0) ? referenceDists.get(lef).getDistance().doubleValue() : Double.NEGATIVE_INFINITY;
        }
        else {
          mean.put(Math.abs(rig_d - xDist));
          // Update right
          rig++;
          rig_d = (rig < referenceDists.size()) ? referenceDists.get(rig).getDistance().doubleValue() : Double.NEGATIVE_INFINITY;
        }
      }
      else {
        if(lef >= 0) {
          // Choose left, since right is not available.
          mean.put(Math.abs(lef_d - xDist));
          // update left
          lef--;
          lef_d = (lef >= 0) ? referenceDists.get(lef).getDistance().doubleValue() : Double.NEGATIVE_INFINITY;
        }
        else if(rig < referenceDists.size()) {
          // Choose right, since left is not available
          mean.put(Math.abs(rig_d - xDist));
          // Update right
          rig++;
          rig_d = (rig < referenceDists.size()) ? referenceDists.get(rig).getDistance().doubleValue() : Double.NEGATIVE_INFINITY;
        }
        else {
          // Not enough objects in database?
          throw new IndexOutOfBoundsException();
        }
      }
    }

    return 1.0 / mean.getMean();
  }
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Related Classes of de.lmu.ifi.dbs.elki.math.Mean

  • de.lmu.ifi.dbs.elki.algorithm.outlier.ReferenceBasedOutlierDetection
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMoranScatterplotOutlier
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuScatterplotOutlier
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuZTestOutlier
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.TrimmedMeanApproach
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