C++ CoverTree::Children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(
    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;
}