C++ TreeType::NumPoints方法代码示例

 DTBStat(const TreeType& node) :
     maxNeighborDistance(DBL_MAX),
     minNeighborDistance(DBL_MAX),
     bound(DBL_MAX),
     componentMembership(
         ((node.NumPoints() == 1) && (node.NumChildren() == 0)) ?
           node.Point(0) : -1) { }

void NeighborSearchRules<
    SortPolicy,
    MetricType,
    TreeType>::
UpdateAfterRecursion(TreeType& queryNode, TreeType& /* referenceNode */)
{
  // Find the worst distance that the children found (including any points), and
  // update the bound accordingly.
  double worstDistance = SortPolicy::BestDistance();

  // First look through children nodes.
  for (size_t i = 0; i < queryNode.NumChildren(); ++i)
  {
    if (SortPolicy::IsBetter(worstDistance, queryNode.Child(i).Stat().Bound()))
      worstDistance = queryNode.Child(i).Stat().Bound();
  }

  // Now look through children points.
  for (size_t i = 0; i < queryNode.NumPoints(); ++i)
  {
    if (SortPolicy::IsBetter(worstDistance,
        distances(distances.n_rows - 1, queryNode.Point(i))))
      worstDistance = distances(distances.n_rows - 1, queryNode.Point(i));
  }

  // Take the worst distance from all of these, and update our bound to reflect
  // that.
  queryNode.Stat().Bound() = worstDistance;
}

  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 GreedySingleTreeTraverser<TreeType, RuleType>::Traverse(
    const size_t queryIndex,
    TreeType& referenceNode)
{
  // Run the base case as necessary for all the points in the reference node.
  for (size_t i = 0; i < referenceNode.NumPoints(); ++i)
    rule.BaseCase(queryIndex, referenceNode.Point(i));

  size_t bestChild = rule.GetBestChild(queryIndex, referenceNode);
  size_t numDescendants;

  // Check that referencenode is not a leaf node while calculating number of
  // descendants of it's best child.
  if (!referenceNode.IsLeaf())
    numDescendants = referenceNode.Child(bestChild).NumDescendants();
  else
    numDescendants = referenceNode.NumPoints();

  // If number of descendants are more than minBaseCases than we can go along
  // with best child otherwise we need to traverse for each descendant to
  // ensure that we calculate at least minBaseCases number of base cases.
  if (!referenceNode.IsLeaf())
  {
    if (numDescendants > minBaseCases)
    {
      // We are prunning all but one child.
      numPrunes += referenceNode.NumChildren() - 1;
      // Recurse the best child.
      Traverse(queryIndex, referenceNode.Child(bestChild));
    }
    else
    {
      // Run the base case over first minBaseCases number of descendants.
      for (size_t i = 0; i <= minBaseCases; ++i)
        rule.BaseCase(queryIndex, referenceNode.Descendant(i));
    }
  }
}

inline double DTBRules<MetricType, TreeType>::CalculateBound(
    TreeType& queryNode) const
{
  double worstPointBound = -DBL_MAX;
  double bestPointBound = DBL_MAX;

  double worstChildBound = -DBL_MAX;
  double bestChildBound = DBL_MAX;

  // Now, find the best and worst point bounds.
  for (size_t i = 0; i < queryNode.NumPoints(); ++i)
  {
    const size_t pointComponent = connections.Find(queryNode.Point(i));
    const double bound = neighborsDistances[pointComponent];

    if (bound > worstPointBound)
      worstPointBound = bound;
    if (bound < bestPointBound)
      bestPointBound = bound;
  }

  // Find the best and worst child bounds.
  for (size_t i = 0; i < queryNode.NumChildren(); ++i)
  {
    const double maxBound = queryNode.Child(i).Stat().MaxNeighborDistance();
    if (maxBound > worstChildBound)
      worstChildBound = maxBound;

    const double minBound = queryNode.Child(i).Stat().MinNeighborDistance();
    if (minBound < bestChildBound)
      bestChildBound = minBound;
  }

  // Now calculate the actual bounds.
  const double worstBound = std::max(worstPointBound, worstChildBound);
  const double bestBound = std::min(bestPointBound, bestChildBound);
  // We must check that bestBound != DBL_MAX; otherwise, we risk overflow.
  const double bestAdjustedBound = (bestBound == DBL_MAX) ? DBL_MAX :
      bestBound + 2 * queryNode.FurthestDescendantDistance();

  // Update the relevant quantities in the node.
  queryNode.Stat().MaxNeighborDistance() = worstBound;
  queryNode.Stat().MinNeighborDistance() = bestBound;
  queryNode.Stat().Bound() = std::min(worstBound, bestAdjustedBound);

  return queryNode.Stat().Bound();
}

  DualTreeKMeansStatistic(TreeType& node) :
      closestQueryNode(NULL),
      minQueryNodeDistance(DBL_MAX),
      maxQueryNodeDistance(DBL_MAX),
      clustersPruned(0),
      iteration(size_t() - 1)
  {
    // Empirically calculate the centroid.
    centroid.zeros(node.Dataset().n_rows);
    for (size_t i = 0; i < node.NumPoints(); ++i)
      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();
  }

    PellegMooreKMeansStatistic(TreeType& node)
    {
        centroid.zeros(node.Dataset().n_rows);

        // Hope it's a depth-first build procedure.  Also, this won't work right for
        // trees that have self-children or stuff like that.
        for (size_t i = 0; i < node.NumChildren(); ++i)
        {
            centroid += node.Child(i).NumDescendants() *
                        node.Child(i).Stat().Centroid();
        }

        for (size_t i = 0; i < node.NumPoints(); ++i)
        {
            centroid += node.Dataset().col(node.Point(i));
        }

        if (node.NumDescendants() > 0)
            centroid /= node.NumDescendants();
        else
            centroid.fill(DBL_MAX); // Invalid centroid.  What else can we do?
    }

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.........

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));
  }
}

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.........

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;
}