C++ TreeType::Parent方法代码示例

double RangeSearchRules<MetricType, TreeType>::Score(const size_t queryIndex,
                                                     TreeType& referenceNode)
{
  // We must get the minimum and maximum distances and store them in this
  // object.
  math::Range distances;

  if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
  {
    // In this situation, we calculate the base case.  So we should check to be
    // sure we haven't already done that.
    double baseCase;
    if (tree::TreeTraits<TreeType>::HasSelfChildren &&
        (referenceNode.Parent() != NULL) &&
        (referenceNode.Point(0) == referenceNode.Parent()->Point(0)))
    {
      // If the tree has self-children and this is a self-child, the base case
      // was already calculated.
      baseCase = referenceNode.Parent()->Stat().LastDistance();
      lastQueryIndex = queryIndex;
      lastReferenceIndex = referenceNode.Point(0);
    }
    else
    {
      // We must calculate the base case by hand.
      baseCase = BaseCase(queryIndex, referenceNode.Point(0));
    }

    // This may be possibly loose for non-ball bound trees.
    distances.Lo() = baseCase - referenceNode.FurthestDescendantDistance();
    distances.Hi() = baseCase + referenceNode.FurthestDescendantDistance();

    // Update last distance calculation.
    referenceNode.Stat().LastDistance() = baseCase;
  }
  else
  {
    distances = referenceNode.RangeDistance(querySet.unsafe_col(queryIndex));
  }

  // If the ranges do not overlap, prune this node.
  if (!distances.Contains(range))
    return DBL_MAX;

  // In this case, all of the points in the reference node will be part of the
  // results.
  if ((distances.Lo() >= range.Lo()) && (distances.Hi() <= range.Hi()))
  {
    AddResult(queryIndex, referenceNode);
    return DBL_MAX; // We don't need to go any deeper.
  }

  // Otherwise the score doesn't matter.  Recursion order is irrelevant in
  // range search.
  return 0.0;
}

  DualTreeKMeansStatistic(TreeType& node) :
      neighbor::NeighborSearchStat<neighbor::NearestNeighborSort>(),
      upperBound(DBL_MAX),
      lowerBound(DBL_MAX),
      owner(size_t(-1)),
      pruned(size_t(-1)),
      staticPruned(false),
      staticUpperBoundMovement(0.0),
      staticLowerBoundMovement(0.0),
      trueParent(node.Parent())
  {
    // Empirically calculate the centroid.
    centroid.zeros(node.Dataset().n_rows);
    for (size_t i = 0; i < node.NumPoints(); ++i)
    {
      // Correct handling of cover tree: don't double-count the point which
      // appears in the children.
      if (tree::TreeTraits<TreeType>::HasSelfChildren && i == 0 &&
          node.NumChildren() > 0)
        continue;
      centroid += node.Dataset().col(node.Point(i));
    }

    for (size_t i = 0; i < node.NumChildren(); ++i)
      centroid += node.Child(i).NumDescendants() *
          node.Child(i).Stat().Centroid();

    centroid /= node.NumDescendants();

    // Set the true children correctly.
    trueChildren.resize(node.NumChildren());
    for (size_t i = 0; i < node.NumChildren(); ++i)
      trueChildren[i] = &node.Child(i);
  }

void CheckFills(const TreeType& tree)
{
  if (tree.IsLeaf())
  {
    BOOST_REQUIRE(tree.Count() >= tree.MinLeafSize() || tree.Parent() == NULL);
    BOOST_REQUIRE(tree.Count() <= tree.MaxLeafSize());
  }
  else
  {
    for (size_t i = 0; i < tree.NumChildren(); i++)
    {
      BOOST_REQUIRE(tree.NumChildren() >= tree.MinNumChildren() ||
                    tree.Parent() == NULL);
      BOOST_REQUIRE(tree.NumChildren() <= tree.MaxNumChildren());
      CheckFills(*tree.Children()[i]);
    }
  }
}

inline double NeighborSearchRules<SortPolicy, MetricType, TreeType>::Score(
    const size_t queryIndex,
    TreeType& referenceNode)
{
  ++scores; // Count number of Score() calls.
  double distance;
  if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
  {
    // The first point in the tree is the centroid.  So we can then calculate
    // the base case between that and the query point.
    double baseCase = -1.0;
    if (tree::TreeTraits<TreeType>::HasSelfChildren)
    {
      // If the parent node is the same, then we have already calculated the
      // base case.
      if ((referenceNode.Parent() != NULL) &&
          (referenceNode.Point(0) == referenceNode.Parent()->Point(0)))
        baseCase = referenceNode.Parent()->Stat().LastDistance();
      else
        baseCase = BaseCase(queryIndex, referenceNode.Point(0));

      // Save this evaluation.
      referenceNode.Stat().LastDistance() = baseCase;
    }

    distance = SortPolicy::CombineBest(baseCase,
        referenceNode.FurthestDescendantDistance());
  }
  else
  {
    distance = SortPolicy::BestPointToNodeDistance(querySet.col(queryIndex),
        &referenceNode);
  }

  // Compare against the best k'th distance for this query point so far.
  const double bestDistance = distances(distances.n_rows - 1, queryIndex);

  return (SortPolicy::IsBetter(distance, bestDistance)) ? distance : DBL_MAX;
}

double PellegMooreKMeansRules<MetricType, TreeType>::Score(
    const size_t /* queryIndex */,
    TreeType& referenceNode)
{
  // Obtain the parent's blacklist.  If this is the root node, we'll start with
  // an empty blacklist.  This means that after each iteration, we don't need to
  // reset any statistics.
  if (referenceNode.Parent() == NULL ||
      referenceNode.Parent()->Stat().Blacklist().n_elem == 0)
    referenceNode.Stat().Blacklist().zeros(centroids.n_cols);
  else
    referenceNode.Stat().Blacklist() =
        referenceNode.Parent()->Stat().Blacklist();

  // The query index is a fake index that we won't use, and the reference node
  // holds all of the points in the dataset.  Our goal is to determine whether
  // or not this node is dominated by a single cluster.
  const size_t whitelisted = centroids.n_cols -
      arma::accu(referenceNode.Stat().Blacklist());

  distanceCalculations += whitelisted;

  // Which cluster has minimum distance to the node?
  size_t closestCluster = centroids.n_cols;
  double minMinDistance = DBL_MAX;
  for (size_t i = 0; i < centroids.n_cols; ++i)
  {
    if (referenceNode.Stat().Blacklist()[i] == 0)
    {
      const double minDistance = referenceNode.MinDistance(centroids.col(i));
      if (minDistance < minMinDistance)
      {
        minMinDistance = minDistance;
        closestCluster = i;
      }
    }
  }

  // Now, for every other whitelisted cluster, determine if the closest cluster
  // owns the point.  This calculation is specific to hyperrectangle trees (but,
  // this implementation is specific to kd-trees, so that's okay).  For
  // circular-bound trees, the condition should be simpler and can probably be
  // expressed as a comparison between minimum and maximum distances.
  size_t newBlacklisted = 0;
  for (size_t c = 0; c < centroids.n_cols; ++c)
  {
    if (referenceNode.Stat().Blacklist()[c] == 1 || c == closestCluster)
      continue;

    // This algorithm comes from the proof of Lemma 4 in the extended version
    // of the Pelleg-Moore paper (the CMU tech report, that is).  It has been
    // adapted for speed.
    arma::vec cornerPoint(centroids.n_rows);
    for (size_t d = 0; d < referenceNode.Bound().Dim(); ++d)
    {
      if (centroids(d, c) > centroids(d, closestCluster))
        cornerPoint(d) = referenceNode.Bound()[d].Hi();
      else
        cornerPoint(d) = referenceNode.Bound()[d].Lo();
    }

    const double closestDist = metric.Evaluate(cornerPoint,
        centroids.col(closestCluster));
    const double otherDist = metric.Evaluate(cornerPoint, centroids.col(c));

    distanceCalculations += 3; // One for cornerPoint, then two distances.

    if (closestDist < otherDist)
    {
      // The closest cluster dominates the node with respect to the cluster c.
      // So we can blacklist c.
      referenceNode.Stat().Blacklist()[c] = 1;
      ++newBlacklisted;
    }
  }

  if (whitelisted - newBlacklisted == 1)
  {
    // This node is dominated by the closest cluster.
    counts[closestCluster] += referenceNode.NumDescendants();
    newCentroids.col(closestCluster) += referenceNode.NumDescendants() *
        referenceNode.Stat().Centroid();

    return DBL_MAX;
  }

  // Perform the base case here.
  for (size_t i = 0; i < referenceNode.NumPoints(); ++i)
  {
    size_t bestCluster = centroids.n_cols;
    double bestDistance = DBL_MAX;
    for (size_t c = 0; c < centroids.n_cols; ++c)
    {
      if (referenceNode.Stat().Blacklist()[c] == 1)
        continue;

      ++distanceCalculations;

      // The reference index is the index of the data point.
      const double distance = metric.Evaluate(centroids.col(c),
//.........这里部分代码省略.........

DiscreteHilbertValue<TreeElemType>::
DiscreteHilbertValue(const DiscreteHilbertValue& other,
                     TreeType* tree,
                     bool deepCopy) :
    localHilbertValues(NULL),
    ownsLocalHilbertValues(other.ownsLocalHilbertValues),
    numValues(other.NumValues()),
    valueToInsert(NULL),
    ownsValueToInsert(other.ownsValueToInsert)
{
  if (deepCopy)
  {
    // Only leaf nodes own the localHilbertValues dataset.
    // Intermediate nodes store the pointer to the corresponding dataset.
    if (ownsLocalHilbertValues)
      localHilbertValues = new arma::Mat<HilbertElemType>(
          *other.LocalHilbertValues());
    else
      localHilbertValues = NULL;

    // Only the root owns ownsValueToInsert. Other nodes the pointer.
    if (ownsValueToInsert)
      valueToInsert = new arma::Col<HilbertElemType>(
          *other.ValueToInsert());
    else
    {
      assert(tree->Parent() != NULL);
      // Copy the pointer from the parent node.
      valueToInsert = const_cast<arma::Col<HilbertElemType>*>
        (tree->Parent()->AuxiliaryInfo().HilbertValue().ValueToInsert());
    }

    if (tree->NumChildren() == 0)
    {
      // We have to update pointers to the localHilbertValues dataset in
      // intermediate nodes.
      TreeType* node = tree;

      while (node->Parent() != NULL)
      {
        if (node->Parent()->NumChildren() > 1)
        {
          const std::vector<TreeType*> parentChildren =
              node->AuxiliaryInfo().Children(node->Parent());
          // If node is not the last child of its parent, we shouldn't copy
          // the localHilbertValues pointer.
          if (parentChildren[node->Parent()->NumChildren() - 2] == NULL)
            break;
        }
        node->Parent()->AuxiliaryInfo().HilbertValue().LocalHilbertValues() =
          localHilbertValues;
        node = node->Parent();
      }
    }
  }
  else
  {
    localHilbertValues = const_cast<arma::Mat<HilbertElemType>*>
        (other.LocalHilbertValues());
    valueToInsert = const_cast<arma::Col<HilbertElemType>*>
        (other.ValueToInsert());
  }
}

void CheckTrees(TreeType& tree,
                TreeType& xmlTree,
                TreeType& textTree,
                TreeType& binaryTree)
{
  const typename TreeType::Mat* dataset = &tree.Dataset();

  // Make sure that the data matrices are the same.
  if (tree.Parent() == NULL)
  {
    CheckMatrices(*dataset,
                  xmlTree.Dataset(),
                  textTree.Dataset(),
                  binaryTree.Dataset());

    // Also ensure that the other parents are null too.
    BOOST_REQUIRE_EQUAL(xmlTree.Parent(), (TreeType*) NULL);
    BOOST_REQUIRE_EQUAL(textTree.Parent(), (TreeType*) NULL);
    BOOST_REQUIRE_EQUAL(binaryTree.Parent(), (TreeType*) NULL);
  }

  // Make sure the number of children is the same.
  BOOST_REQUIRE_EQUAL(tree.NumChildren(), xmlTree.NumChildren());
  BOOST_REQUIRE_EQUAL(tree.NumChildren(), textTree.NumChildren());
  BOOST_REQUIRE_EQUAL(tree.NumChildren(), binaryTree.NumChildren());

  // Make sure the number of descendants is the same.
  BOOST_REQUIRE_EQUAL(tree.NumDescendants(), xmlTree.NumDescendants());
  BOOST_REQUIRE_EQUAL(tree.NumDescendants(), textTree.NumDescendants());
  BOOST_REQUIRE_EQUAL(tree.NumDescendants(), binaryTree.NumDescendants());

  // Make sure the number of points is the same.
  BOOST_REQUIRE_EQUAL(tree.NumPoints(), xmlTree.NumPoints());
  BOOST_REQUIRE_EQUAL(tree.NumPoints(), textTree.NumPoints());
  BOOST_REQUIRE_EQUAL(tree.NumPoints(), binaryTree.NumPoints());

  // Check that each point is the same.
  for (size_t i = 0; i < tree.NumPoints(); ++i)
  {
    BOOST_REQUIRE_EQUAL(tree.Point(i), xmlTree.Point(i));
    BOOST_REQUIRE_EQUAL(tree.Point(i), textTree.Point(i));
    BOOST_REQUIRE_EQUAL(tree.Point(i), binaryTree.Point(i));
  }

  // Check that the parent distance is the same.
  BOOST_REQUIRE_CLOSE(tree.ParentDistance(), xmlTree.ParentDistance(), 1e-8);
  BOOST_REQUIRE_CLOSE(tree.ParentDistance(), textTree.ParentDistance(), 1e-8);
  BOOST_REQUIRE_CLOSE(tree.ParentDistance(), binaryTree.ParentDistance(), 1e-8);

  // Check that the furthest descendant distance is the same.
  BOOST_REQUIRE_CLOSE(tree.FurthestDescendantDistance(),
      xmlTree.FurthestDescendantDistance(), 1e-8);
  BOOST_REQUIRE_CLOSE(tree.FurthestDescendantDistance(),
      textTree.FurthestDescendantDistance(), 1e-8);
  BOOST_REQUIRE_CLOSE(tree.FurthestDescendantDistance(),
      binaryTree.FurthestDescendantDistance(), 1e-8);

  // Check that the minimum bound distance is the same.
  BOOST_REQUIRE_CLOSE(tree.MinimumBoundDistance(),
      xmlTree.MinimumBoundDistance(), 1e-8);
  BOOST_REQUIRE_CLOSE(tree.MinimumBoundDistance(),
      textTree.MinimumBoundDistance(), 1e-8);
  BOOST_REQUIRE_CLOSE(tree.MinimumBoundDistance(),
      binaryTree.MinimumBoundDistance(), 1e-8);

  // Recurse into the children.
  for (size_t i = 0; i < tree.NumChildren(); ++i)
  {
    // Check that the child dataset is the same.
    BOOST_REQUIRE_EQUAL(&xmlTree.Dataset(), &xmlTree.Child(i).Dataset());
    BOOST_REQUIRE_EQUAL(&textTree.Dataset(), &textTree.Child(i).Dataset());
    BOOST_REQUIRE_EQUAL(&binaryTree.Dataset(), &binaryTree.Child(i).Dataset());

    // Make sure the parent link is right.
    BOOST_REQUIRE_EQUAL(xmlTree.Child(i).Parent(), &xmlTree);
    BOOST_REQUIRE_EQUAL(textTree.Child(i).Parent(), &textTree);
    BOOST_REQUIRE_EQUAL(binaryTree.Child(i).Parent(), &binaryTree);

    CheckTrees(tree.Child(i), xmlTree.Child(i), textTree.Child(i),
        binaryTree.Child(i));
  }
}

bool HilbertRTreeSplit<splitOrder>::
SplitNonLeafNode(TreeType* tree, std::vector<bool>& relevels)
{
  // If we are splitting the root node, we need will do things differently so
  // that the constructor and other methods don't confuse the end user by giving
  // an address of another node.
  if (tree->Parent() == NULL)
  {
    // We actually want to copy this way.  Pointers and everything.
    TreeType* copy = new TreeType(*tree, false);
    // Only the root node owns this variable.
    copy->AuxiliaryInfo().HilbertValue().OwnsValueToInsert() = false;
    copy->Parent() = tree;
    tree->NumChildren() = 0;
    tree->NullifyData();
    tree->children[(tree->NumChildren())++] = copy;

    SplitNonLeafNode(copy, relevels);
    return true;
  }

  TreeType* parent = tree->Parent();

  size_t iTree = 0;
  for (iTree = 0; parent->children[iTree] != tree; iTree++);

  // Try to find splitOrder cooperating siblings in order to redistribute
  // children among them and avoid split.
  size_t firstSibling, lastSibling;
  if (FindCooperatingSiblings(parent, iTree, firstSibling, lastSibling))
  {
    RedistributeNodesEvenly(parent, firstSibling, lastSibling);
    return false;
  }

  // We can not find splitOrder cooperating siblings since they are all full.
  // We introduce new one instead.
  size_t iNewSibling = (iTree + splitOrder < parent->NumChildren() ?
                        iTree + splitOrder : parent->NumChildren());

  for (size_t i = parent->NumChildren(); i > iNewSibling ; i--)
    parent->children[i] = parent->children[i - 1];

  parent->NumChildren()++;

  parent->children[iNewSibling] = new TreeType(parent);

  lastSibling = (iTree + splitOrder < parent->NumChildren() ?
                 iTree + splitOrder : parent->NumChildren() - 1);
  firstSibling = (lastSibling > splitOrder ?
                  lastSibling - splitOrder : 0);

  assert(lastSibling - firstSibling <= splitOrder);
  assert(firstSibling >= 0);
  assert(lastSibling < parent->NumChildren());

  // Redistribute children among (splitOrder + 1) cooperating siblings evenly.
  RedistributeNodesEvenly(parent, firstSibling, lastSibling);

  if (parent->NumChildren() == parent->MaxNumChildren() + 1)
    SplitNonLeafNode(parent, relevels);
  return false;
}

void HilbertRTreeSplit<splitOrder>::
RedistributePointsEvenly(TreeType* parent,
                         const size_t firstSibling,
                         const size_t lastSibling)
{
  size_t numPoints = 0;
  size_t numPointsPerNode, numRestPoints;

  for (size_t i = firstSibling; i <= lastSibling; i++)
    numPoints += parent->Child(i).NumPoints();

  numPointsPerNode = numPoints / (lastSibling - firstSibling + 1);
  numRestPoints = numPoints % (lastSibling - firstSibling + 1);

  std::vector<size_t> points(numPoints);

  // Copy children's points in order to redistribute them.
  size_t iPoint = 0;
  for (size_t i = firstSibling; i <= lastSibling; i++)
  {
    for (size_t j = 0; j < parent->Child(i).NumPoints(); j++)
      points[iPoint++] = parent->Child(i).Point(j);
  }

  iPoint = 0;
  for (size_t i = firstSibling; i <= lastSibling; i++)
  {
    // Since we redistribute points of a sibling we should recalculate the
    // bound.
    parent->Child(i).Bound().Clear();

    size_t j;
    for (j = 0; j < numPointsPerNode; j++)
    {
      parent->Child(i).Bound() |= parent->Dataset().col(points[iPoint]);
      parent->Child(i).Point(j) = points[iPoint];
      iPoint++;
    }
    if (numRestPoints > 0)
    {
      parent->Child(i).Bound() |= parent->Dataset().col(points[iPoint]);
      parent->Child(i).Point(j) = points[iPoint];
      parent->Child(i).Count() = numPointsPerNode + 1;
      numRestPoints--;
      iPoint++;
    }
    else
    {
      parent->Child(i).Count() = numPointsPerNode;
    }
    parent->Child(i).numDescendants = parent->Child(i).Count();

    assert(parent->Child(i).NumPoints() <=
           parent->Child(i).MaxLeafSize());
  }

  // Fix the largest Hilbert values of the siblings.
  parent->AuxiliaryInfo().HilbertValue().RedistributeHilbertValues(parent,
      firstSibling, lastSibling);

  TreeType* root = parent;

  while (root != NULL)
  {
    root->AuxiliaryInfo().HilbertValue().UpdateLargestValue(root);
    root = root->Parent();
  }
}

inline double NeighborSearchRules<SortPolicy, MetricType, TreeType>::
    CalculateBound(TreeType& queryNode) const
{
  // We have five possible bounds, and we must take the best of them all.  We
  // don't use min/max here, but instead "best/worst", because this is general
  // to the nearest-neighbors/furthest-neighbors cases.  For nearest neighbors,
  // min = best, max = worst.
  //
  // (1) worst ( worst_{all points p in queryNode} D_p[k],
  //             worst_{all children c in queryNode} B(c) );
  // (2) best_{all points p in queryNode} D_p[k] + worst child distance +
  //        worst descendant distance;
  // (3) best_{all children c in queryNode} B(c) +
  //      2 ( worst descendant distance of queryNode -
  //          worst descendant distance of c );
  // (4) B_1(parent of queryNode)
  // (5) B_2(parent of queryNode);
  //
  // D_p[k] is the current k'th candidate distance for point p.
  // So we will loop over the points in queryNode and the children in queryNode
  // to calculate all five of these quantities.

  // Hm, can we populate our distances vector with estimates from the parent?
  // This is written specifically for the cover tree and assumes only one point
  // in a node.
//  if (queryNode.Parent() != NULL && queryNode.NumPoints() > 0)
//  {
//    size_t parentIndexStart = 0;
//    for (size_t i = 0; i < neighbors.n_rows; ++i)
//    {
//      const double pointDistance = distances(i, queryNode.Point(0));
//      if (pointDistance == DBL_MAX)
//      {
//      // Cool, can we take an estimate from the parent?
//        const double parentWorstBound = distances(distances.n_rows - 1,
//              queryNode.Parent()->Point(0));
//        if (parentWorstBound != DBL_MAX)
//        {
//          const double parentAdjustedDistance = parentWorstBound +
//              queryNode.ParentDistance();
//          distances(i, queryNode.Point(0)) = parentAdjustedDistance;
//        }
//      }
//    }
//  }

  double worstPointDistance = SortPolicy::BestDistance();
  double bestPointDistance = SortPolicy::WorstDistance();

  // Loop over all points in this node to find the best and worst distance
  // candidates (for (1) and (2)).
  for (size_t i = 0; i < queryNode.NumPoints(); ++i)
  {
    const double distance = distances(distances.n_rows - 1,
        queryNode.Point(i));
    if (SortPolicy::IsBetter(distance, bestPointDistance))
      bestPointDistance = distance;
    if (SortPolicy::IsBetter(worstPointDistance, distance))
      worstPointDistance = distance;
  }

  // Loop over all the children in this node to find the worst bound (for (1))
  // and the best bound with the correcting factor for descendant distances (for
  // (3)).
  double worstChildBound = SortPolicy::BestDistance();
  double bestAdjustedChildBound = SortPolicy::WorstDistance();
  const double queryMaxDescendantDistance =
      queryNode.FurthestDescendantDistance();

  for (size_t i = 0; i < queryNode.NumChildren(); ++i)
  {
    const double firstBound = queryNode.Child(i).Stat().FirstBound();
    const double secondBound = queryNode.Child(i).Stat().SecondBound();
    const double childMaxDescendantDistance =
        queryNode.Child(i).FurthestDescendantDistance();

    if (SortPolicy::IsBetter(worstChildBound, firstBound))
      worstChildBound = firstBound;

    // Now calculate adjustment for maximum descendant distances.
    const double adjustedBound = SortPolicy::CombineWorst(secondBound,
        2 * (queryMaxDescendantDistance - childMaxDescendantDistance));
    if (SortPolicy::IsBetter(adjustedBound, bestAdjustedChildBound))
      bestAdjustedChildBound = adjustedBound;
  }

  // This is bound (1).
  const double firstBound =
      (SortPolicy::IsBetter(worstPointDistance, worstChildBound)) ?
      worstChildBound : worstPointDistance;

  // This is bound (2).
  const double secondBound = SortPolicy::CombineWorst(
      SortPolicy::CombineWorst(bestPointDistance, queryMaxDescendantDistance),
      queryNode.FurthestPointDistance());

  // Bound (3) is bestAdjustedChildBound.

  // Bounds (4) and (5) are the parent bounds.
  const double fourthBound = (queryNode.Parent() != NULL) ?
//.........这里部分代码省略.........

inline double NeighborSearchRules<SortPolicy, MetricType, TreeType>::Score(
    TreeType& queryNode,
    TreeType& referenceNode)
{
  ++scores; // Count number of Score() calls.

  // Update our bound.
  const double bestDistance = CalculateBound(queryNode);

  // Use the traversal info to see if a parent-child or parent-parent prune is
  // possible.  This is a looser bound than we could make, but it might be
  // sufficient.
  const double queryParentDist = queryNode.ParentDistance();
  const double queryDescDist = queryNode.FurthestDescendantDistance();
  const double refParentDist = referenceNode.ParentDistance();
  const double refDescDist = referenceNode.FurthestDescendantDistance();
  const double score = traversalInfo.LastScore();
  double adjustedScore;

  // We want to set adjustedScore to be the distance between the centroid of the
  // last query node and last reference node.  We will do this by adjusting the
  // last score.  In some cases, we can just use the last base case.
  if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
  {
    adjustedScore = traversalInfo.LastBaseCase();
  }
  else if (score == 0.0) // Nothing we can do here.
  {
    adjustedScore = 0.0;
  }
  else
  {
    // The last score is equal to the distance between the centroids minus the
    // radii of the query and reference bounds along the axis of the line
    // between the two centroids.  In the best case, these radii are the
    // furthest descendant distances, but that is not always true.  It would
    // take too long to calculate the exact radii, so we are forced to use
    // MinimumBoundDistance() as a lower-bound approximation.
    const double lastQueryDescDist =
        traversalInfo.LastQueryNode()->MinimumBoundDistance();
    const double lastRefDescDist =
        traversalInfo.LastReferenceNode()->MinimumBoundDistance();
    adjustedScore = SortPolicy::CombineWorst(score, lastQueryDescDist);
    adjustedScore = SortPolicy::CombineWorst(score, lastRefDescDist);
  }

  // Assemble an adjusted score.  For nearest neighbor search, this adjusted
  // score is a lower bound on MinDistance(queryNode, referenceNode) that is
  // assembled without actually calculating MinDistance().  For furthest
  // neighbor search, it is an upper bound on
  // MaxDistance(queryNode, referenceNode).  If the traversalInfo isn't usable
  // then the node should not be pruned by this.
  if (traversalInfo.LastQueryNode() == queryNode.Parent())
  {
    const double queryAdjust = queryParentDist + queryDescDist;
    adjustedScore = SortPolicy::CombineBest(adjustedScore, queryAdjust);
  }
  else if (traversalInfo.LastQueryNode() == &queryNode)
  {
    adjustedScore = SortPolicy::CombineBest(adjustedScore, queryDescDist);
  }
  else
  {
    // The last query node wasn't this query node or its parent.  So we force
    // the adjustedScore to be such that this combination can't be pruned here,
    // because we don't really know anything about it.

    // It would be possible to modify this section to try and make a prune based
    // on the query descendant distance and the distance between the query node
    // and last traversal query node, but this case doesn't actually happen for
    // kd-trees or cover trees.
    adjustedScore = SortPolicy::BestDistance();
  }

  if (traversalInfo.LastReferenceNode() == referenceNode.Parent())
  {
    const double refAdjust = refParentDist + refDescDist;
    adjustedScore = SortPolicy::CombineBest(adjustedScore, refAdjust);
  }
  else if (traversalInfo.LastReferenceNode() == &referenceNode)
  {
    adjustedScore = SortPolicy::CombineBest(adjustedScore, refDescDist);
  }
  else
  {
    // The last reference node wasn't this reference node or its parent.  So we
    // force the adjustedScore to be such that this combination can't be pruned
    // here, because we don't really know anything about it.

    // It would be possible to modify this section to try and make a prune based
    // on the reference descendant distance and the distance between the
    // reference node and last traversal reference node, but this case doesn't
    // actually happen for kd-trees or cover trees.
    adjustedScore = SortPolicy::BestDistance();
  }

  // Can we prune?
  if (SortPolicy::IsBetter(bestDistance, adjustedScore))
  {
    if (!(tree::TreeTraits<TreeType>::FirstPointIsCentroid && score == 0.0))
//.........这里部分代码省略.........

double FastMKSRules<KernelType, TreeType>::CalculateBound(TreeType& queryNode)
const
{
    // We have four possible bounds -- just like NeighborSearchRules, but they are
    // slightly different in this context.
    //
    // (1) min ( min_{all points p in queryNode} P_p[k],
    //           min_{all children c in queryNode} B(c) );
    // (2) max_{all points p in queryNode} P_p[k] + (worst child distance + worst
    //           descendant distance) sqrt(K(I_p[k], I_p[k]));
    // (3) max_{all children c in queryNode} B(c) + <-- not done yet.  ignored.
    // (4) B(parent of queryNode);
    double worstPointKernel = DBL_MAX;
    double bestAdjustedPointKernel = -DBL_MAX;

    const double queryDescendantDistance = queryNode.FurthestDescendantDistance();

    // Loop over all points in this node to find the best and worst.
    for (size_t i = 0; i < queryNode.NumPoints(); ++i)
    {
        const size_t point = queryNode.Point(i);
        if (products(products.n_rows - 1, point) < worstPointKernel)
            worstPointKernel = products(products.n_rows - 1, point);

        if (products(products.n_rows - 1, point) == -DBL_MAX)
            continue; // Avoid underflow.

        // This should be (queryDescendantDistance + centroidDistance) for any tree
        // but it works for cover trees since centroidDistance = 0 for cover trees.
        const double candidateKernel = products(products.n_rows - 1, point) -
                                       queryDescendantDistance *
                                       referenceKernels[indices(indices.n_rows - 1, point)];

        if (candidateKernel > bestAdjustedPointKernel)
            bestAdjustedPointKernel = candidateKernel;
    }

    // Loop over all the children in the node.
    double worstChildKernel = DBL_MAX;

    for (size_t i = 0; i < queryNode.NumChildren(); ++i)
    {
        if (queryNode.Child(i).Stat().Bound() < worstChildKernel)
            worstChildKernel = queryNode.Child(i).Stat().Bound();
    }

    // Now assemble bound (1).
    const double firstBound = (worstPointKernel < worstChildKernel) ?
                              worstPointKernel : worstChildKernel;

    // Bound (2) is bestAdjustedPointKernel.
    const double fourthBound = (queryNode.Parent() == NULL) ? -DBL_MAX :
                               queryNode.Parent()->Stat().Bound();

    // Pick the best of these bounds.
    const double interA = (firstBound > bestAdjustedPointKernel) ? firstBound :
                          bestAdjustedPointKernel;
//  const double interA = 0.0;
    const double interB = fourthBound;

    return (interA > interB) ? interA : interB;
}

double FastMKSRules<KernelType, TreeType>::Score(TreeType& queryNode,
        TreeType& referenceNode)
{
    // Update and get the query node's bound.
    queryNode.Stat().Bound() = CalculateBound(queryNode);
    const double bestKernel = queryNode.Stat().Bound();

    // First, see if we can make a parent-child or parent-parent prune.  These
    // four bounds on the maximum kernel value are looser than the bound normally
    // used, but they can prevent a base case from needing to be calculated.

    // Convenience caching so lines are shorter.
    const double queryParentDist = queryNode.ParentDistance();
    const double queryDescDist = queryNode.FurthestDescendantDistance();
    const double refParentDist = referenceNode.ParentDistance();
    const double refDescDist = referenceNode.FurthestDescendantDistance();
    double adjustedScore = traversalInfo.LastBaseCase();

    const double queryDistBound = (queryParentDist + queryDescDist);
    const double refDistBound = (refParentDist + refDescDist);
    double dualQueryTerm;
    double dualRefTerm;

    // The parent-child and parent-parent prunes work by applying the same pruning
    // condition as when the parent node was used, except they are tighter because
    //    queryDistBound < queryNode.Parent()->FurthestDescendantDistance()
    // and
    //    refDistBound < referenceNode.Parent()->FurthestDescendantDistance()
    // so we construct the same bounds that were used when Score() was called with
    // the parents, except with the tighter distance bounds.  Sometimes this
    // allows us to prune nodes without evaluating the base cases between them.
    if (traversalInfo.LastQueryNode() == queryNode.Parent())
    {
        // We can assume that queryNode.Parent() != NULL, because at the root node
        // combination, the traversalInfo.LastQueryNode() pointer will _not_ be
        // NULL.  We also should be guaranteed that
        // traversalInfo.LastReferenceNode() is either the reference node or the
        // parent of the reference node.
        adjustedScore += queryDistBound *
                         traversalInfo.LastReferenceNode()->Stat().SelfKernel();
        dualQueryTerm = queryDistBound;
    }
    else
    {
        // The query parent could be NULL, which does weird things and we have to
        // consider.
        if (traversalInfo.LastReferenceNode() != NULL)
        {
            adjustedScore += queryDescDist *
                             traversalInfo.LastReferenceNode()->Stat().SelfKernel();
            dualQueryTerm = queryDescDist;
        }
        else
        {
            // This makes it so a child-parent (or parent-parent) prune is not
            // possible.
            dualQueryTerm = 0.0;
            adjustedScore = bestKernel;
        }
    }

    if (traversalInfo.LastReferenceNode() == referenceNode.Parent())
    {
        // We can assume that referenceNode.Parent() != NULL, because at the root
        // node combination, the traversalInfo.LastReferenceNode() pointer will
        // _not_ be NULL.
        adjustedScore += refDistBound *
                         traversalInfo.LastQueryNode()->Stat().SelfKernel();
        dualRefTerm = refDistBound;
    }
    else
    {
        // The reference parent could be NULL, which does weird things and we have
        // to consider.
        if (traversalInfo.LastQueryNode() != NULL)
        {
            adjustedScore += refDescDist *
                             traversalInfo.LastQueryNode()->Stat().SelfKernel();
            dualRefTerm = refDescDist;
        }
        else
        {
            // This makes it so a child-parent (or parent-parent) prune is not
            // possible.
            dualRefTerm = 0.0;
            adjustedScore = bestKernel;
        }
    }

    // Now add the dual term.
    adjustedScore += (dualQueryTerm * dualRefTerm);

    if (adjustedScore < bestKernel)
    {
        // It is not possible that this node combination can contain a point
        // combination with kernel value better than the minimum kernel value to
        // improve any of the results, so we can prune it.
        return DBL_MAX;
    }

//.........这里部分代码省略.........