C++ CoverTree::Child方法代码示例

inline void CoverTree<MetricType, StatisticType, MatType, RootPointPolicy>::
    RemoveNewImplicitNodes()
{
  // If we created an implicit node, take its self-child instead (this could
  // happen multiple times).
  while (children[children.size() - 1]->NumChildren() == 1)
  {
    CoverTree* old = children[children.size() - 1];
    children.erase(children.begin() + children.size() - 1);

    // Now take its child.
    children.push_back(&(old->Child(0)));

    // Set its parent and parameters correctly, and rebuild the statistic.
    old->Child(0).Parent() = this;
    old->Child(0).ParentDistance() = old->ParentDistance();
    old->Child(0).DistanceComps() = old->DistanceComps();
    old->Child(0).Stat() = StatisticType(old->Child(0));

    // Remove its child (so it doesn't delete it).
    old->Children().erase(old->Children().begin() + old->Children().size() - 1);

    // Now delete it.
    delete old;
  }
}

CoverTree<MetricType, StatisticType, MatType, RootPointPolicy>::CoverTree(
    const CoverTree& other) :
    dataset((other.parent == NULL && other.localDataset) ?
        new MatType(*other.dataset) : other.dataset),
    point(other.point),
    scale(other.scale),
    base(other.base),
    stat(other.stat),
    numDescendants(other.numDescendants),
    parent(other.parent),
    parentDistance(other.parentDistance),
    furthestDescendantDistance(other.furthestDescendantDistance),
    localMetric(false),
    localDataset(other.parent == NULL && other.localDataset),
    metric(other.metric),
    distanceComps(0)
{
  // Copy each child by hand.
  for (size_t i = 0; i < other.NumChildren(); ++i)
  {
    children.push_back(new CoverTree(other.Child(i)));
    children[i]->Parent() = this;
  }

  // Propagate matrix, but only if we are the root.
  if (parent == NULL && localDataset)
  {
    std::queue<CoverTree*> queue;

    for (size_t i = 0; i < NumChildren(); ++i)
      queue.push(children[i]);

    while (!queue.empty())
    {
      CoverTree* node = queue.front();
      queue.pop();

      node->dataset = dataset;
      for (size_t i = 0; i < node->NumChildren(); ++i)
        queue.push(node->children[i]);
    }
  }
}

CoverTree<MetricType, StatisticType, MatType, RootPointPolicy>::CoverTree(
    const CoverTree& other) :
    dataset((other.parent == NULL) ? new MatType(*other.dataset) : NULL),
    point(other.point),
    scale(other.scale),
    base(other.base),
    stat(other.stat),
    numDescendants(other.numDescendants),
    parent(other.parent),
    parentDistance(other.parentDistance),
    furthestDescendantDistance(other.furthestDescendantDistance),
    localMetric(false),
    localDataset(other.parent == NULL),
    metric(other.metric),
    distanceComps(0)
{
  // Copy each child by hand.
  for (size_t i = 0; i < other.NumChildren(); ++i)
  {
    children.push_back(new CoverTree(other.Child(i)));
    children[i]->Parent() = this;
    children[i]->dataset = this->dataset;
  }
}

void CoverTree<MetricType, StatisticType, MatType, RootPointPolicy>::
SingleTreeTraverser<RuleType>::Traverse(
    const size_t queryIndex,
    CoverTree& referenceNode)
{
  // This is a non-recursive implementation (which should be faster than a
  // recursive implementation).
  typedef CoverTreeMapEntry<MetricType, StatisticType, MatType, RootPointPolicy>
      MapEntryType;

  // We will use this map as a priority queue.  Each key represents the scale,
  // and then the vector is all the nodes in that scale which need to be
  // investigated.  Because no point in a scale can add a point in its own
  // scale, we know that the vector for each scale is final when we get to it.
  // In addition, map is organized in such a way that rbegin() will return the
  // largest scale.
  std::map<int, std::vector<MapEntryType> > mapQueue;

  // Create the score for the children.
  double rootChildScore = rule.Score(queryIndex, referenceNode);

  if (rootChildScore == DBL_MAX)
  {
    numPrunes += referenceNode.NumChildren();
  }
  else
  {
    // Manually add the children of the first node.
    // Often, a ruleset will return without doing any computation on cover trees
    // using TreeTraits::FirstPointIsCentroid; this is an optimization that
    // (theoretically) the compiler should get right.
    double rootBaseCase = rule.BaseCase(queryIndex, referenceNode.Point());

    // Don't add the self-leaf.
    size_t i = 0;
    if (referenceNode.Child(0).NumChildren() == 0)
    {
      ++numPrunes;
      i = 1;
    }

    for (/* i was set above. */; i < referenceNode.NumChildren(); ++i)
    {
      MapEntryType newFrame;
      newFrame.node = &referenceNode.Child(i);
      newFrame.score = rootChildScore;
      newFrame.baseCase = rootBaseCase;
      newFrame.parent = referenceNode.Point();

      // Put it into the map.
      mapQueue[newFrame.node->Scale()].push_back(newFrame);
    }
  }

  // Now begin the iteration through the map, but only if it has anything in it.
  if (mapQueue.empty())
    return;
  typename std::map<int, std::vector<MapEntryType> >::reverse_iterator rit =
      mapQueue.rbegin();

  // We will treat the leaves differently (below).
  while ((*rit).first != INT_MIN)
  {
    // Get a reference to the current scale.
    std::vector<MapEntryType>& scaleVector = (*rit).second;

    // Before traversing all the points in this scale, sort by score.
    std::sort(scaleVector.begin(), scaleVector.end());

    // Now loop over each element.
    for (size_t i = 0; i < scaleVector.size(); ++i)
    {
      // Get a reference to the current element.
      const MapEntryType& frame = scaleVector.at(i);

      CoverTree* node = frame.node;
      const double score = frame.score;
      const size_t parent = frame.parent;
      const size_t point = node->Point();
      double baseCase = frame.baseCase;

      // First we recalculate the score of this node to find if we can prune it.
      if (rule.Rescore(queryIndex, *node, score) == DBL_MAX)
      {
        ++numPrunes;
        continue;
      }

      // Create the score for the children.
      const double childScore = rule.Score(queryIndex, *node);

      // Now if this childScore is DBL_MAX we can prune all children.  In this
      // recursion setup pruning is all or nothing for children.
      if (childScore == DBL_MAX)
      {
        numPrunes += node->NumChildren();
        continue;
      }

      // If we are a self-child, the base case has already been evaluated.
//.........这里部分代码省略.........

CoverTree<MetricType, StatisticType, MatType, RootPointPolicy>::CoverTree(
    MatType&& data,
    MetricType& metric,
    const ElemType base) :
    dataset(new MatType(std::move(data))),
    point(RootPointPolicy::ChooseRoot(dataset)),
    scale(INT_MAX),
    base(base),
    numDescendants(0),
    parent(NULL),
    parentDistance(0),
    furthestDescendantDistance(0),
    localMetric(false),
    localDataset(true),
    metric(&metric),
    distanceComps(0)
{
  // If there is only one point or zero points in the dataset... uh, we're done.
  // Technically, if the dataset has zero points, our node is not correct...
  if (dataset->n_cols <= 1)
    return;

  // Kick off the building.  Create the indices array and the distances array.
  arma::Col<size_t> indices = arma::linspace<arma::Col<size_t> >(1,
      dataset->n_cols - 1, dataset->n_cols - 1);
  // This is now [1 2 3 4 ... n].  We must be sure that our point does not
  // occur.
  if (point != 0)
    indices[point - 1] = 0; // Put 0 back into the set; remove what was there.

  arma::vec distances(dataset->n_cols - 1);

  // Build the initial distances.
  ComputeDistances(point, indices, distances, dataset->n_cols - 1);

  // Create the children.
  size_t farSetSize = 0;
  size_t usedSetSize = 0;
  CreateChildren(indices, distances, dataset->n_cols - 1, farSetSize,
      usedSetSize);

  // If we ended up creating only one child, remove the implicit node.
  while (children.size() == 1)
  {
    // Prepare to delete the implicit child node.
    CoverTree* old = children[0];

    // Now take its children and set their parent correctly.
    children.erase(children.begin());
    for (size_t i = 0; i < old->NumChildren(); ++i)
    {
      children.push_back(&(old->Child(i)));

      // Set its parent correctly, and rebuild the statistic.
      old->Child(i).Parent() = this;
      old->Child(i).Stat() = StatisticType(old->Child(i));
    }

    // Remove all the children so they don't get erased.
    old->Children().clear();

    // Reduce our own scale.
    scale = old->Scale();

    // Now delete it.
    delete old;
  }

  // Use the furthest descendant distance to determine the scale of the root
  // node.
  scale = (int) ceil(log(furthestDescendantDistance) / log(base));

  // Initialize statistic.
  stat = StatisticType(*this);

  Log::Info << distanceComps << " distance computations during tree "
      << "construction." << std::endl;
}

void CoverTree<MetricType, RootPointPolicy, StatisticType>::
DualTreeTraverser<RuleType>::Traverse(
    CoverTree<MetricType, RootPointPolicy, StatisticType>& queryNode,
    std::map<int, std::vector<DualCoverTreeMapEntry> >& referenceMap)
{
  if (referenceMap.size() == 0)
    return; // Nothing to do!

  // First recurse down the reference nodes as necessary.
  ReferenceRecursion(queryNode, referenceMap);

  // Did the map get emptied?
  if (referenceMap.size() == 0)
    return; // Nothing to do!

  // Now, reduce the scale of the query node by recursing.  But we can't recurse
  // if the query node is a leaf node.
  if ((queryNode.Scale() != INT_MIN) &&
      (queryNode.Scale() >= (*referenceMap.rbegin()).first))
  {
    // Recurse into the non-self-children first.  The recursion order cannot
    // affect the runtime of the algorithm, because each query child recursion's
    // results are separate and independent.  I don't think this is true in
    // every case, and we may have to modify this section to consider scores in
    // the future.
    for (size_t i = 1; i < queryNode.NumChildren(); ++i)
    {
      // We need a copy of the map for this child.
      std::map<int, std::vector<DualCoverTreeMapEntry> > childMap;
      PruneMap(queryNode.Child(i), referenceMap, childMap);
      Traverse(queryNode.Child(i), childMap);
    }
    std::map<int, std::vector<DualCoverTreeMapEntry> > selfChildMap;
    PruneMap(queryNode.Child(0), referenceMap, selfChildMap);
    Traverse(queryNode.Child(0), selfChildMap);
  }

  if (queryNode.Scale() != INT_MIN)
    return; // No need to evaluate base cases at this level.  It's all done.

  // If we have made it this far, all we have is a bunch of base case
  // evaluations to do.
  Log::Assert((*referenceMap.begin()).first == INT_MIN);
  Log::Assert(queryNode.Scale() == INT_MIN);
  std::vector<DualCoverTreeMapEntry>& pointVector =
      (*referenceMap.begin()).second;

  for (size_t i = 0; i < pointVector.size(); ++i)
  {
    // Get a reference to the frame.
    const DualCoverTreeMapEntry& frame = pointVector[i];

    CoverTree<MetricType, RootPointPolicy, StatisticType>* refNode =
        frame.referenceNode;

    // If the point is the same as both parents, then we have already done this
    // base case.
    if ((refNode->Point() == refNode->Parent()->Point()) &&
        (queryNode.Point() == queryNode.Parent()->Point()))
    {
      ++numPrunes;
      continue;
    }

    // Score the node, to see if we can prune it, after restoring the traversal
    // info.
    rule.TraversalInfo() = frame.traversalInfo;
    double score = rule.Score(queryNode, *refNode);

    if (score == DBL_MAX)
    {
      ++numPrunes;
      continue;
    }

    // If not, compute the base case.
    rule.BaseCase(queryNode.Point(), pointVector[i].referenceNode->Point());
  }
}

void CoverTree<MetricType, RootPointPolicy, StatisticType>::
DualTreeTraverser<RuleType>::ReferenceRecursion(
    CoverTree& queryNode,
    std::map<int, std::vector<DualCoverTreeMapEntry> >& referenceMap)
{
  // First, reduce the maximum scale in the reference map down to the scale of
  // the query node.
  while (!referenceMap.empty())
  {
    // Hacky bullshit to imitate jl cover tree.
    if (queryNode.Parent() == NULL && (*referenceMap.rbegin()).first <
        queryNode.Scale())
      break;
    if (queryNode.Parent() != NULL && (*referenceMap.rbegin()).first <=
        queryNode.Scale())
      break;
    // If the query node's scale is INT_MIN and the reference map's maximum
    // scale is INT_MIN, don't try to recurse...
    if ((queryNode.Scale() == INT_MIN) &&
       ((*referenceMap.rbegin()).first == INT_MIN))
      break;

    // Get a reference to the current largest scale.
    std::vector<DualCoverTreeMapEntry>& scaleVector = (*referenceMap.rbegin()).second;

    // Before traversing all the points in this scale, sort by score.
    std::sort(scaleVector.begin(), scaleVector.end());

    // Now loop over each element.
    for (size_t i = 0; i < scaleVector.size(); ++i)
    {
      // Get a reference to the current element.
      const DualCoverTreeMapEntry& frame = scaleVector.at(i);

      CoverTree<MetricType, RootPointPolicy, StatisticType>* refNode =
          frame.referenceNode;

      // Create the score for the children.
      double score = rule.Rescore(queryNode, *refNode, frame.score);

      // Now if this childScore is DBL_MAX we can prune all children.  In this
      // recursion setup pruning is all or nothing for children.
      if (score == DBL_MAX)
      {
        ++numPrunes;
        continue;
      }

      // If it is not pruned, we must evaluate the base case.

      // Add the children.
      for (size_t j = 0; j < refNode->NumChildren(); ++j)
      {
        rule.TraversalInfo() = frame.traversalInfo;
        double childScore = rule.Score(queryNode, refNode->Child(j));
        if (childScore == DBL_MAX)
        {
          ++numPrunes;
          continue;
        }

        // It wasn't pruned; evaluate the base case.
        const double baseCase = rule.BaseCase(queryNode.Point(),
            refNode->Child(j).Point());

        DualCoverTreeMapEntry newFrame;
        newFrame.referenceNode = &refNode->Child(j);
        newFrame.score = childScore; // Use the score of the parent.
        newFrame.baseCase = baseCase;
        newFrame.traversalInfo = rule.TraversalInfo();

        referenceMap[newFrame.referenceNode->Scale()].push_back(newFrame);
      }
    }

    // Now clear the memory for this scale; it isn't needed anymore.
    referenceMap.erase((*referenceMap.rbegin()).first);
  }
}