void CoverTree<MetricType, RootPointPolicy, StatisticType>::
DualTreeTraverser<RuleType>::Traverse(
    CoverTree<MetricType, RootPointPolicy, StatisticType>& queryNode,
    CoverTree<MetricType, RootPointPolicy, StatisticType>& referenceNode)
{
  // Start by creating a map and adding the reference root node to it.
  std::map<int, std::vector<DualCoverTreeMapEntry> > refMap;

  DualCoverTreeMapEntry rootRefEntry;

  rootRefEntry.referenceNode = &referenceNode;

  // Perform the evaluation between the roots of either tree.
  rootRefEntry.score = rule.Score(queryNode, referenceNode);
  rootRefEntry.baseCase = rule.BaseCase(queryNode.Point(),
      referenceNode.Point());
  rootRefEntry.traversalInfo = rule.TraversalInfo();

  refMap[referenceNode.Scale()].push_back(rootRefEntry);

  Traverse(queryNode, refMap);
}
Beispiel #2
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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);
  }
}