C++ TreeType::NumChildren方法代码示例

  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 CleanTree(TreeType& node)
{
  node.Stat().LastDistance() = 0.0;

  for (size_t i = 0; i < node.NumChildren(); ++i)
    CleanTree(node.Child(i));
}

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;
}

  FastMKSStat(const TreeType& node) :
      bound(-DBL_MAX),
      lastKernel(0.0),
      lastKernelNode(NULL)
  {
    // Do we have to calculate the centroid?
    if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
    {
      // If this type of tree has self-children, then maybe the evaluation is
      // already done.  These statistics are built bottom-up, so the child stat
      // should already be done.
      if ((tree::TreeTraits<TreeType>::HasSelfChildren) &&
          (node.NumChildren() > 0) &&
          (node.Point(0) == node.Child(0).Point(0)))
      {
        selfKernel = node.Child(0).Stat().SelfKernel();
      }
      else
      {
        selfKernel = sqrt(node.Metric().Kernel().Evaluate(
            node.Dataset().col(node.Point(0)),
            node.Dataset().col(node.Point(0))));
      }
    }
    else
    {
      // Calculate the centroid.
      arma::vec center;
      node.Center(center);

      selfKernel = sqrt(node.Metric().Kernel().Evaluate(center, center));
    }
  }

 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 CheckHierarchy(const TreeType& tree)
{
  for (size_t i = 0; i < tree.NumChildren(); i++)
  {
    BOOST_REQUIRE_EQUAL(&tree, tree.Child(i).Parent());
    CheckHierarchy(tree.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]);
    }
  }
}

void CheckContainment(const TreeType& tree)
{
  if (tree.NumChildren() == 0)
  {
    for (size_t i = 0; i < tree.Count(); i++)
      BOOST_REQUIRE(tree.Bound().Contains(
          tree.Dataset().unsafe_col(tree.Points()[i])));
  }
  else
  {
    for (size_t i = 0; i < tree.NumChildren(); i++)
    {
      for (size_t j = 0; j < tree.Bound().Dim(); j++)
        BOOST_REQUIRE(tree.Bound()[j].Contains(tree.Children()[i]->Bound()[j]));

      CheckContainment(*(tree.Children()[i]));
    }
  }
}

void CheckExactContainment(const TreeType& tree)
{
  if (tree.NumChildren() == 0)
  {
    for (size_t i = 0; i < tree.Bound().Dim(); i++)
    {
      double min = DBL_MAX;
      double max = -1.0 * DBL_MAX;
      for(size_t j = 0; j < tree.Count(); j++)
      {
        if (tree.LocalDataset().col(j)[i] < min)
          min = tree.LocalDataset().col(j)[i];
        if (tree.LocalDataset().col(j)[i] > max)
          max = tree.LocalDataset().col(j)[i];
      }
      BOOST_REQUIRE_EQUAL(max, tree.Bound()[i].Hi());
      BOOST_REQUIRE_EQUAL(min, tree.Bound()[i].Lo());
    }
  }
  else
  {
    for (size_t i = 0; i < tree.Bound().Dim(); i++)
    {
      double min = DBL_MAX;
      double max = -1.0 * DBL_MAX;
      for (size_t j = 0; j < tree.NumChildren(); j++)
      {
        if(tree.Child(j).Bound()[i].Lo() < min)
          min = tree.Child(j).Bound()[i].Lo();
        if(tree.Child(j).Bound()[i].Hi() > max)
          max = tree.Child(j).Bound()[i].Hi();
      }

      BOOST_REQUIRE_EQUAL(max, tree.Bound()[i].Hi());
      BOOST_REQUIRE_EQUAL(min, tree.Bound()[i].Lo());
    }

    for (size_t i = 0; i < tree.NumChildren(); i++)
      CheckExactContainment(tree.Child(i));
  }
}

std::vector<arma::vec*> GetAllPointsInTree(const TreeType& tree)
{
  std::vector<arma::vec*> vec;
  if (tree.NumChildren() > 0)
  {
    for (size_t i = 0; i < tree.NumChildren(); i++)
    {
      std::vector<arma::vec*> tmp = GetAllPointsInTree(*(tree.Children()[i]));
      vec.insert(vec.begin(), tmp.begin(), tmp.end());
    }
  }
  else
  {
    for (size_t i = 0; i < tree.Count(); i++)
    {
      arma::vec* c = new arma::vec(tree.Dataset().col(tree.Points()[i]));
      vec.push_back(c);
    }
  }
  return vec;
}

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();
}

int GetMinLevel(const TreeType& tree)
{
  int min = 1;
  if (!tree.IsLeaf())
  {
    int m = INT_MAX;
    for (size_t i = 0; i < tree.NumChildren(); i++)
    {
      int n = GetMinLevel(*tree.Children()[i]);
      if (n < m)
        m = n;
    }
    min += m;
  }

  return min;
}

int GetMaxLevel(const TreeType& tree)
{
  int max = 1;
  if (!tree.IsLeaf())
  {
    int m = 0;
    for (size_t i = 0; i < tree.NumChildren(); i++)
    {
      int n = GetMaxLevel(*tree.Children()[i]);
      if (n > m)
        m = n;
    }
    max += m;
  }

  return max;
}

  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();
  }

void CheckSync(const TreeType& tree)
{
  if (tree.IsLeaf())
  {
    for (size_t i = 0; i < tree.Count(); i++)
    {
      for (size_t j = 0; j < tree.LocalDataset().n_rows; j++)
      {
        BOOST_REQUIRE_EQUAL(tree.LocalDataset().col(i)[j],
                            tree.Dataset().col(tree.Points()[i])[j]);
      }
    }
  }
  else
  {
    for (size_t i = 0; i < tree.NumChildren(); i++)
      CheckSync(*tree.Children()[i]);
  }
}