Main loop for linear scan,

@param relation Data relation
@param iter Iterator
@param obj Query object
@param range Query radius
@param result Output data structure
343128
private void linearScan(Relation<? extends O> relation, DBIDIter iter, O obj, double range, ModifiableDoubleDBIDList result) {
    final PrimitiveDistanceFunction<? super O> rawdist = this.rawdist;
    while(iter.valid()) {
      final double distance = rawdist.distance(obj, relation.get(iter));
      if(distance <= range) {
        result.add(distance, iter);
      }
      iter.advance();
    }
  }
499622
protected List<Map<String, ResultColumn>> processResultIterator(ResultIterator it) throws DatabaseEngineException {
        List<Map<String, ResultColumn>> res = new ArrayList<>();

        Map<String, ResultColumn> temp;
        while ((temp = it.next()) != null) {
            res.add(temp);
        }

        return res;
    }
607181
public PointerPrototypeHierarchyRepresentationResult run(Database db, Relation<O> relation) {
    DistanceQuery<O> dq = DatabaseUtil.precomputedDistanceQuery(db, relation, getDistanceFunction(), LOG);
    final DBIDs ids = relation.getDBIDs();

    // Initialize space for result:
    PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
    Int2ObjectOpenHashMap<ModifiableDBIDs> clusters = new Int2ObjectOpenHashMap<>(ids.size());

    MatrixParadigm mat = new MatrixParadigm(ids);
    ArrayModifiableDBIDs prots = DBIDUtil.newArray(MatrixParadigm.triangleSize(ids.size()));

    MiniMax.initializeMatrices(mat, prots, dq);

    nnChainCore(mat, prots.iter(), dq, builder, clusters);

    return (PointerPrototypeHierarchyRepresentationResult) builder.complete();
  }
776678
function Iterator(db, options) {
  this.db = db;
  this.options = new IteratorOptions(options);
  this.iter = null;
  this.ended = false;
  this.total = 0;
  this.init();
}
106279
private Iterator<Record> rowQuery(final List<Map.Entry<ByteBuffer, Key>> rowKeys, final ReadConsistency consistency,
                                      final DeltaPlacement placement) {
        List<ByteBuffer> keys = Lists.newArrayListWithCapacity(rowKeys.size());
        final Map<ByteBuffer, Key> rawKeyMap = Maps.newHashMap();
        for (Map.Entry<ByteBuffer, Key> entry : rowKeys) {
            keys.add(entry.getKey());
            rawKeyMap.put(entry.getKey(), entry.getValue());
        }

        BlockedDeltaTableDDL tableDDL = placement.getBlockedDeltaTableDDL();

        Statement statement = selectDeltaFrom(tableDDL)
                .where(in(tableDDL.getRowKeyColumnName(), keys))
                .setConsistencyLevel(SorConsistencies.toCql(consistency));

        Iterator<Iterable<Row>> rowGroups = deltaQueryAsync(placement, statement, false, "Failed to read records %s", rawKeyMap.values());

        return Iterators.concat(
                // First iterator reads the row groups found and transforms them to Records
                Iterators.transform(rowGroups, rows -> {
                    ByteBuffer keyBytes = getRawKeyFromRowGroup(rows);
                    Key key = rawKeyMap.remove(keyBytes);
                    assert key != null : "Query returned row with a key out of bound";
                    return newRecordFromCql(key, rows, placement, ByteBufferUtil.bytesToHex(keyBytes));
                }),
                // Second iterator returns an empty Record for each key queried but not found.
                new AbstractIterator<Record>() {
                    private Iterator<Key> _nonExistentKeyIterator;

                    @Override
                    protected Record computeNext() {
                        // Lazily return an empty record for each key not found in the previous iterator.
                        // rawKeyMap.iterator() must not be called until the first iterator is completely spent.
                        if (_nonExistentKeyIterator == null) {
                            _nonExistentKeyIterator = rawKeyMap.values().iterator();
                        }
                        return _nonExistentKeyIterator.hasNext() ?
                                emptyRecord(_nonExistentKeyIterator.next()) :
                                endOfData();
                    }
                });
    }
847024
private ScalesResult run(Relation<? extends NumberVector> rel) {
    final int dim = RelationUtil.dimensionality(rel);
    LinearScale[] scales = new LinearScale[dim];
    if(minmax == null) {
      DoubleMinMax mm = new DoubleMinMax();
      for(DBIDIter iditer = rel.iterDBIDs(); iditer.valid(); iditer.advance()) {
        NumberVector vec = rel.get(iditer);
        for(int d = 0; d < dim; d++) {
          final double val = vec.doubleValue(d);
          if(val != val) {
            continue; // NaN
          }
          mm.put(val);
        }
      }
      LinearScale scale = new LinearScale(mm.getMin(), mm.getMax());
      for(int i = 0; i < dim; i++) {
        scales[i] = scale;
      }
    }
    else {
      // Use predefined.
      LinearScale scale = new LinearScale(minmax[0], minmax[1]);
      for(int i = 0; i < dim; i++) {
        scales[i] = scale;
      }
    }
    ScalesResult res = new ScalesResult(scales);
    return res;
  }
372218
def iter(self, obj=None):
        ''
        if obj is None and vd:
            obj = vd.sheet

        for o in self._mappings(obj):
            for k in self.keys():
                for o2 in self[k]:
                    if o == o2:
                        yield (k, o), self[k][o2]
571563
def scan_iter(self, match=None, count=None):
        
        cursor = '0'
        while cursor != 0:
            cursor, data = self.scan(cursor=cursor, match=match, count=count)
            for item in data:
                yield item
238975
public static void iterate(int dimension, int n, int[] size, int[] res, int dimension2, int n2, int[] size2,
                               int[] res2, CoordinateFunction func) {
        if (dimension >= n || dimension2 >= n2) {
            // stop clause
            func.process(ArrayUtil.toLongArray(res), ArrayUtil.toLongArray(res2));
            return;
        }

        if (size2.length != size.length) {
            if (dimension >= size.length)
                return;
            for (int i = 0; i < size[dimension]; i++) {
                if (dimension2 >= size2.length)
                    break;
                for (int j = 0; j < size2[dimension2]; j++) {
                    res[dimension] = i;
                    res2[dimension2] = j;
                    iterate(dimension + 1, n, size, res, dimension2 + 1, n2, size2, res2, func);
                }

            }
        } else {
            if (dimension >= size.length)
                return;

            for (int i = 0; i < size[dimension]; i++) {
                for (int j = 0; j < size2[dimension2]; j++) {
                    if (dimension2 >= size2.length)
                        break;
                    res[dimension] = i;
                    res2[dimension2] = j;
                    iterate(dimension + 1, n, size, res, dimension2 + 1, n2, size2, res2, func);
                }

            }
        }
    }
277218
def _query_iterator(cursor, chunksize, columns, index_col=None,
                        coerce_float=True, parse_dates=None):
        """"""

        while True:
            data = cursor.fetchmany(chunksize)
            if type(data) == tuple:
                data = list(data)
            if not data:
                cursor.close()
                break
            else:
                yield _wrap_result(data, columns, index_col=index_col,
                                   coerce_float=coerce_float,
                                   parse_dates=parse_dates)
1194853
public Result run(Database db, Relation<O> relation) {
    DistanceQuery<O> dq = db.getDistanceQuery(relation, getDistanceFunction());
    ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
    final int size = ids.size();

    if(size > 0x10000) {
      throw new AbortException("This implementation does not scale to data sets larger than " + 0x10000 + " instances (~17 GB RAM), which results in an integer overflow.");
    }
    if(Linkage.SINGLE.equals(linkage)) {
      LOG.verbose("Notice: SLINK is a much faster algorithm for single-linkage clustering!");
    }

    // Compute the initial (lower triangular) distance matrix.
    double[] scratch = new double[triangleSize(size)];
    DBIDArrayIter ix = ids.iter(), iy = ids.iter();
    // Position counter - must agree with computeOffset!
    int pos = 0;
    boolean square = Linkage.WARD.equals(linkage) && !getDistanceFunction().isSquared();
    for(int x = 0; ix.valid(); x++, ix.advance()) {
      iy.seek(0);
      for(int y = 0; y < x; y++, iy.advance()) {
        scratch[pos] = dq.distance(ix, iy);
        // Ward uses variances -- i.e. squared values
        if(square) {
          scratch[pos] *= scratch[pos];
        }
        pos++;
      }
    }

    // Initialize space for result:
    double[] height = new double[size];
    Arrays.fill(height, Double.POSITIVE_INFINITY);
    // Parent node, to track merges
    // have every object point to itself initially
    ArrayModifiableDBIDs parent = DBIDUtil.newArray(ids);
    // Active clusters, when not trivial.
    Int2ReferenceMap<ModifiableDBIDs> clusters = new Int2ReferenceOpenHashMap<>();

    // Repeat until everything merged, except the desired number of clusters:
    final int stop = size - numclusters;
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Agglomerative clustering", stop, LOG) : null;
    for(int i = 0; i < stop; i++) {
      double min = Double.POSITIVE_INFINITY;
      int minx = -1, miny = -1;
      for(int x = 0; x < size; x++) {
        if(height[x] < Double.POSITIVE_INFINITY) {
          continue;
        }
        final int xbase = triangleSize(x);
        for(int y = 0; y < x; y++) {
          if(height[y] < Double.POSITIVE_INFINITY) {
            continue;
          }
          final int idx = xbase + y;
          if(scratch[idx] < min) {
            min = scratch[idx];
            minx = x;
            miny = y;
          }
        }
      }
      assert (minx >= 0 && miny >= 0);
      // Avoid allocating memory, by reusing existing iterators:
      ix.seek(minx);
      iy.seek(miny);
      // Perform merge in data structure: x -> y
      // Since y < x, prefer keeping y, dropping x.
      height[minx] = min;
      parent.set(minx, iy);
      // Merge into cluster
      ModifiableDBIDs cx = clusters.get(minx);
      ModifiableDBIDs cy = clusters.get(miny);
      int sizex = 1, sizey = 1; // cluster sizes, for averaging
      if(cy == null) {
        cy = DBIDUtil.newHashSet();
        cy.add(iy);
      }
      else {
        sizey = cy.size();
      }
      if(cx == null) {
        cy.add(ix);
      }
      else {
        sizex = cx.size();
        cy.addDBIDs(cx);
        clusters.remove(minx);
      }
      clusters.put(miny, cy);

      // Update distance matrix. Note: miny < minx

      // Implementation note: most will not need sizej, and could save the
      // hashmap lookup.
      final int xbase = triangleSize(minx), ybase = triangleSize(miny);
      // Write to (y, j), with j < y
      for(int j = 0; j < miny; j++) {
        if(height[j] < Double.POSITIVE_INFINITY) {
          continue;
        }
        final DBIDs idsj = clusters.get(j);
        final int sizej = (idsj == null) ? 1 : idsj.size();
        scratch[ybase + j] = linkage.combine(sizex, scratch[xbase + j], sizey, scratch[ybase + j], sizej, min);
      }
      // Write to (j, y), with y < j < x
      for(int j = miny + 1; j < minx; j++) {
        if(height[j] < Double.POSITIVE_INFINITY) {
          continue;
        }
        final int jbase = triangleSize(j);
        final DBIDs idsj = clusters.get(j);
        final int sizej = (idsj == null) ? 1 : idsj.size();
        scratch[jbase + miny] = linkage.combine(sizex, scratch[xbase + j], sizey, scratch[jbase + miny], sizej, min);
      }
      // Write to (j, y), with y < x < j
      for(int j = minx + 1; j < size; j++) {
        if(height[j] < Double.POSITIVE_INFINITY) {
          continue;
        }
        final DBIDs idsj = clusters.get(j);
        final int sizej = (idsj == null) ? 1 : idsj.size();
        final int jbase = triangleSize(j);
        scratch[jbase + miny] = linkage.combine(sizex, scratch[jbase + minx], sizey, scratch[jbase + miny], sizej, min);
      }
      LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);

    // Build the clustering result
    final Clustering<Model> dendrogram = new Clustering<>("Hierarchical-Clustering", "hierarchical-clustering");
    for(int x = 0; x < size; x++) {
      if(height[x] < Double.POSITIVE_INFINITY) {
        DBIDs cids = clusters.get(x);
        if(cids == null) {
          ix.seek(x);
          cids = DBIDUtil.deref(ix);
        }
        Cluster<Model> cluster = new Cluster<>("Cluster", cids);
        dendrogram.addToplevelCluster(cluster);
      }
    }

    return dendrogram;
  }
942237
public OutlierResult run(Relation<? extends NumberVector> relation) {
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT);
    DoubleMinMax minmax = new DoubleMinMax();
    Mean m = new Mean();
    for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
      m.reset();
      NumberVector nv = relation.get(iditer);
      for (int i = 0; i < nv.getDimensionality(); i++) {
        m.put(nv.doubleValue(i));
      }
      final double score = m.getMean();
      scores.putDouble(iditer, score);
      minmax.put(score);
    }
    DoubleRelation scoreres = new MaterializedDoubleRelation("Trivial mean score", "mean-outlier", scores, relation.getDBIDs());
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax());
    return new OutlierResult(meta, scoreres);
  }
558706
public OutlierResult run(Relation<V> relation) {
    final int dim = RelationUtil.dimensionality(relation);
    final int size = relation.size();
    ArrayList<ArrayList<DBIDs>> ranges = buildRanges(relation);

    ArrayList<int[]> Rk;
    // Build a list of all subspaces
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Subspace size", k, LOG) : null;
    // R1 initial one-dimensional subspaces.
    Rk = new ArrayList<>();
    // Set of all dim*phi ranges
    ArrayList<IntIntPair> q = new ArrayList<>();
    for(int i = 0; i < dim; i++) {
      for(int j = 0; j < phi; j++) {
        q.add(new IntIntPair(i, j));
        Rk.add(new int[] { i, j });
      }
    }
    LOG.incrementProcessed(prog);

    // build Ri
    for(int i = 2; i <= k; i++) {
      ArrayList<int[]> Rnew = new ArrayList<>();

      for(int j = 0; j < Rk.size(); j++) {
        int[] c = Rk.get(j);
        for(IntIntPair pair : q) {
          boolean invalid = false;
          for(int t = 0; t < c.length; t += 2) {
            if(c[t] == pair.first) {
              invalid = true;
              break;
            }
          }
          if(!invalid) {
            int[] neu = Arrays.copyOf(c, c.length + 2);
            neu[c.length] = pair.first;
            neu[c.length + 1] = pair.second;
            Rnew.add(neu);
          }
        }
      }
      Rk = Rnew;
      LOG.incrementProcessed(prog);
    }
    if(prog != null) {
      prog.setProcessed(k, LOG);
    }
    LOG.ensureCompleted(prog);

    WritableDoubleDataStore sparsity = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC);
    // calculate the sparsity coefficient
    for(int[] sub : Rk) {
      DBIDs ids = computeSubspace(sub, ranges);
      final double sparsityC = sparsity(ids.size(), size, k, phi);

      if(sparsityC < 0) {
        for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
          double prev = sparsity.doubleValue(iter);
          if(Double.isNaN(prev) || sparsityC < prev) {
            sparsity.putDouble(iter, sparsityC);
          }
        }
      }
    }
    DoubleMinMax minmax = new DoubleMinMax();
    for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
      double val = sparsity.doubleValue(iditer);
      if(Double.isNaN(val)) {
        sparsity.putDouble(iditer, 0.0);
        val = 0.0;
      }
      minmax.put(val);
    }
    DoubleRelation scoreResult = new MaterializedDoubleRelation("AggarwalYuNaive", "aggarwal-yu-outlier", sparsity, relation.getDBIDs());
    OutlierScoreMeta meta = new InvertedOutlierScoreMeta(minmax.getMin(), minmax.getMax(), Double.NEGATIVE_INFINITY, 0.0);
    return new OutlierResult(meta, scoreResult);
  }
576970
function (obj, iterator, context) {
    var results = [];
    if (!obj) return results;
    if (Array.prototype.map && obj.map === Array.prototype.map) {
      return obj.map(iterator, context);
    }
    each(obj, function (value, index, list) {
      results[results.length] = iterator.call(context, value, index, list);
    });
    if (obj.length === +obj.length) results.length = obj.length;
    return results;
  }
467950
public void each(VectorProcedure procedure) {
        VectorIterator it = iterator();

        while (it.hasNext()) {
            double x = it.next();
            int i = it.index();
            procedure.apply(i, x);
        }
    }
596087
public PointerHierarchyRepresentationResult run(Database db, Relation<O> relation) {
    if(SingleLinkage.class.isInstance(linkage)) {
      LOG.verbose("Notice: SLINK is a much faster algorithm for single-linkage clustering!");
    }
    final DBIDs ids = relation.getDBIDs();
    final int size = ids.size();
    DistanceQuery<O> dq = db.getDistanceQuery(relation, getDistanceFunction());

    // Compute the initial (lower triangular) distance matrix.
    MatrixParadigm mat = new MatrixParadigm(ids);
    initializeDistanceMatrix(mat, dq, linkage);

    // Initialize space for result:
    PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());

    // Repeat until everything merged into 1 cluster
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Agglomerative clustering", size - 1, LOG) : null;
    // Use end to shrink the matrix virtually as the tailing objects disappear
    DBIDArrayIter ix = mat.ix;
    for(int i = 1, end = size; i < size; i++) {
      end = shrinkActiveSet(ix, builder, end, //
          findMerge(end, mat, builder));
      LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);

    return builder.complete();
  }
771899
public OutlierResult run(Database database, Relation<? extends NumberVector> relation) {
    @SuppressWarnings("unchecked")
    PrimitiveDistanceQuery<? super NumberVector> distq = (PrimitiveDistanceQuery<? super NumberVector>) database.getDistanceQuery(relation, distanceFunction);
    Collection<? extends NumberVector> refPoints = refp.getReferencePoints(relation);
    if(refPoints.isEmpty()) {
      throw new AbortException("Cannot compute ROS without reference points!");
    }

    DBIDs ids = relation.getDBIDs();
    if(k >= ids.size()) {
      throw new AbortException("k must not be chosen larger than the database size!");
    }
    // storage of distance/score values.
    WritableDoubleDataStore rbod_score = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_STATIC | DataStoreFactory.HINT_HOT, Double.NaN);

    // Compute density estimation:
    for(NumberVector refPoint : refPoints) {
      DoubleDBIDList referenceDists = computeDistanceVector(refPoint, relation, distq);
      updateDensities(rbod_score, referenceDists);
    }
    // compute maximum density
    DoubleMinMax mm = new DoubleMinMax();
    for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
      mm.put(rbod_score.doubleValue(iditer));
    }
    // compute ROS
    double scale = mm.getMax() > 0. ? 1. / mm.getMax() : 1.;
    mm.reset(); // Reuse
    for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
      double score = 1 - (rbod_score.doubleValue(iditer) * scale);
      mm.put(score);
      rbod_score.putDouble(iditer, score);
    }

    DoubleRelation scoreResult = new MaterializedDoubleRelation("Reference-points Outlier Scores", "reference-outlier", rbod_score, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new BasicOutlierScoreMeta(mm.getMin(), mm.getMax(), 0., 1., 0.);
    OutlierResult result = new OutlierResult(scoreMeta, scoreResult);
    // adds reference points to the result. header information for the
    // visualizer to find the reference points in the result
    result.addChildResult(new ReferencePointsResult<>("Reference points", "reference-points", refPoints));
    return result;
  }