C++ TreeType::ParentDistance方法代码示例

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));
  }
}

double FastMKSRules<KernelType, TreeType>::Score(TreeType& queryNode,
        TreeType& referenceNode)
{
    // Update and get the query node's bound.
    queryNode.Stat().Bound() = CalculateBound(queryNode);
    const double bestKernel = queryNode.Stat().Bound();

    // First, see if we can make a parent-child or parent-parent prune.  These
    // four bounds on the maximum kernel value are looser than the bound normally
    // used, but they can prevent a base case from needing to be calculated.

    // Convenience caching so lines are shorter.
    const double queryParentDist = queryNode.ParentDistance();
    const double queryDescDist = queryNode.FurthestDescendantDistance();
    const double refParentDist = referenceNode.ParentDistance();
    const double refDescDist = referenceNode.FurthestDescendantDistance();
    double adjustedScore = traversalInfo.LastBaseCase();

    const double queryDistBound = (queryParentDist + queryDescDist);
    const double refDistBound = (refParentDist + refDescDist);
    double dualQueryTerm;
    double dualRefTerm;

    // The parent-child and parent-parent prunes work by applying the same pruning
    // condition as when the parent node was used, except they are tighter because
    //    queryDistBound < queryNode.Parent()->FurthestDescendantDistance()
    // and
    //    refDistBound < referenceNode.Parent()->FurthestDescendantDistance()
    // so we construct the same bounds that were used when Score() was called with
    // the parents, except with the tighter distance bounds.  Sometimes this
    // allows us to prune nodes without evaluating the base cases between them.
    if (traversalInfo.LastQueryNode() == queryNode.Parent())
    {
        // We can assume that queryNode.Parent() != NULL, because at the root node
        // combination, the traversalInfo.LastQueryNode() pointer will _not_ be
        // NULL.  We also should be guaranteed that
        // traversalInfo.LastReferenceNode() is either the reference node or the
        // parent of the reference node.
        adjustedScore += queryDistBound *
                         traversalInfo.LastReferenceNode()->Stat().SelfKernel();
        dualQueryTerm = queryDistBound;
    }
    else
    {
        // The query parent could be NULL, which does weird things and we have to
        // consider.
        if (traversalInfo.LastReferenceNode() != NULL)
        {
            adjustedScore += queryDescDist *
                             traversalInfo.LastReferenceNode()->Stat().SelfKernel();
            dualQueryTerm = queryDescDist;
        }
        else
        {
            // This makes it so a child-parent (or parent-parent) prune is not
            // possible.
            dualQueryTerm = 0.0;
            adjustedScore = bestKernel;
        }
    }

    if (traversalInfo.LastReferenceNode() == referenceNode.Parent())
    {
        // We can assume that referenceNode.Parent() != NULL, because at the root
        // node combination, the traversalInfo.LastReferenceNode() pointer will
        // _not_ be NULL.
        adjustedScore += refDistBound *
                         traversalInfo.LastQueryNode()->Stat().SelfKernel();
        dualRefTerm = refDistBound;
    }
    else
    {
        // The reference parent could be NULL, which does weird things and we have
        // to consider.
        if (traversalInfo.LastQueryNode() != NULL)
        {
            adjustedScore += refDescDist *
                             traversalInfo.LastQueryNode()->Stat().SelfKernel();
            dualRefTerm = refDescDist;
        }
        else
        {
            // This makes it so a child-parent (or parent-parent) prune is not
            // possible.
            dualRefTerm = 0.0;
            adjustedScore = bestKernel;
        }
    }

    // Now add the dual term.
    adjustedScore += (dualQueryTerm * dualRefTerm);

    if (adjustedScore < bestKernel)
    {
        // It is not possible that this node combination can contain a point
        // combination with kernel value better than the minimum kernel value to
        // improve any of the results, so we can prune it.
        return DBL_MAX;
    }

//.........这里部分代码省略.........

inline double NeighborSearchRules<SortPolicy, MetricType, TreeType>::Score(
    TreeType& queryNode,
    TreeType& referenceNode)
{
  ++scores; // Count number of Score() calls.

  // Update our bound.
  const double bestDistance = CalculateBound(queryNode);

  // Use the traversal info to see if a parent-child or parent-parent prune is
  // possible.  This is a looser bound than we could make, but it might be
  // sufficient.
  const double queryParentDist = queryNode.ParentDistance();
  const double queryDescDist = queryNode.FurthestDescendantDistance();
  const double refParentDist = referenceNode.ParentDistance();
  const double refDescDist = referenceNode.FurthestDescendantDistance();
  const double score = traversalInfo.LastScore();
  double adjustedScore;

  // We want to set adjustedScore to be the distance between the centroid of the
  // last query node and last reference node.  We will do this by adjusting the
  // last score.  In some cases, we can just use the last base case.
  if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
  {
    adjustedScore = traversalInfo.LastBaseCase();
  }
  else if (score == 0.0) // Nothing we can do here.
  {
    adjustedScore = 0.0;
  }
  else
  {
    // The last score is equal to the distance between the centroids minus the
    // radii of the query and reference bounds along the axis of the line
    // between the two centroids.  In the best case, these radii are the
    // furthest descendant distances, but that is not always true.  It would
    // take too long to calculate the exact radii, so we are forced to use
    // MinimumBoundDistance() as a lower-bound approximation.
    const double lastQueryDescDist =
        traversalInfo.LastQueryNode()->MinimumBoundDistance();
    const double lastRefDescDist =
        traversalInfo.LastReferenceNode()->MinimumBoundDistance();
    adjustedScore = SortPolicy::CombineWorst(score, lastQueryDescDist);
    adjustedScore = SortPolicy::CombineWorst(score, lastRefDescDist);
  }

  // Assemble an adjusted score.  For nearest neighbor search, this adjusted
  // score is a lower bound on MinDistance(queryNode, referenceNode) that is
  // assembled without actually calculating MinDistance().  For furthest
  // neighbor search, it is an upper bound on
  // MaxDistance(queryNode, referenceNode).  If the traversalInfo isn't usable
  // then the node should not be pruned by this.
  if (traversalInfo.LastQueryNode() == queryNode.Parent())
  {
    const double queryAdjust = queryParentDist + queryDescDist;
    adjustedScore = SortPolicy::CombineBest(adjustedScore, queryAdjust);
  }
  else if (traversalInfo.LastQueryNode() == &queryNode)
  {
    adjustedScore = SortPolicy::CombineBest(adjustedScore, queryDescDist);
  }
  else
  {
    // The last query node wasn't this query node or its parent.  So we force
    // the adjustedScore to be such that this combination can't be pruned here,
    // because we don't really know anything about it.

    // It would be possible to modify this section to try and make a prune based
    // on the query descendant distance and the distance between the query node
    // and last traversal query node, but this case doesn't actually happen for
    // kd-trees or cover trees.
    adjustedScore = SortPolicy::BestDistance();
  }

  if (traversalInfo.LastReferenceNode() == referenceNode.Parent())
  {
    const double refAdjust = refParentDist + refDescDist;
    adjustedScore = SortPolicy::CombineBest(adjustedScore, refAdjust);
  }
  else if (traversalInfo.LastReferenceNode() == &referenceNode)
  {
    adjustedScore = SortPolicy::CombineBest(adjustedScore, refDescDist);
  }
  else
  {
    // The last reference node wasn't this reference node or its parent.  So we
    // force the adjustedScore to be such that this combination can't be pruned
    // here, because we don't really know anything about it.

    // It would be possible to modify this section to try and make a prune based
    // on the reference descendant distance and the distance between the
    // reference node and last traversal reference node, but this case doesn't
    // actually happen for kd-trees or cover trees.
    adjustedScore = SortPolicy::BestDistance();
  }

  // Can we prune?
  if (SortPolicy::IsBetter(bestDistance, adjustedScore))
  {
    if (!(tree::TreeTraits<TreeType>::FirstPointIsCentroid && score == 0.0))
//.........这里部分代码省略.........

double FastMKSRules<KernelType, TreeType>::Score(const size_t queryIndex,
        TreeType& referenceNode)
{
    // Compare with the current best.
    const double bestKernel = products(products.n_rows - 1, queryIndex);

    // See if we can perform a parent-child prune.
    const double furthestDist = referenceNode.FurthestDescendantDistance();
    if (referenceNode.Parent() != NULL)
    {
        double maxKernelBound;
        const double parentDist = referenceNode.ParentDistance();
        const double combinedDistBound = parentDist + furthestDist;
        const double lastKernel = referenceNode.Parent()->Stat().LastKernel();
        if (kernel::KernelTraits<KernelType>::IsNormalized)
        {
            const double squaredDist = std::pow(combinedDistBound, 2.0);
            const double delta = (1 - 0.5 * squaredDist);
            if (lastKernel <= delta)
            {
                const double gamma = combinedDistBound * sqrt(1 - 0.25 * squaredDist);
                maxKernelBound = lastKernel * delta +
                                 gamma * sqrt(1 - std::pow(lastKernel, 2.0));
            }
            else
            {
                maxKernelBound = 1.0;
            }
        }
        else
        {
            maxKernelBound = lastKernel +
                             combinedDistBound * queryKernels[queryIndex];
        }

        if (maxKernelBound < bestKernel)
            return DBL_MAX;
    }

    // Calculate the maximum possible kernel value, either by calculating the
    // centroid or, if the centroid is a point, use that.
    ++scores;
    double kernelEval;
    if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
    {
        // Could it be that this kernel evaluation has already been calculated?
        if (tree::TreeTraits<TreeType>::HasSelfChildren &&
                referenceNode.Parent() != NULL &&
                referenceNode.Point(0) == referenceNode.Parent()->Point(0))
        {
            kernelEval = referenceNode.Parent()->Stat().LastKernel();
        }
        else
        {
            kernelEval = BaseCase(queryIndex, referenceNode.Point(0));
        }
    }
    else
    {
        const arma::vec queryPoint = querySet.unsafe_col(queryIndex);
        arma::vec refCentroid;
        referenceNode.Centroid(refCentroid);

        kernelEval = kernel.Evaluate(queryPoint, refCentroid);
    }

    referenceNode.Stat().LastKernel() = kernelEval;

    double maxKernel;
    if (kernel::KernelTraits<KernelType>::IsNormalized)
    {
        const double squaredDist = std::pow(furthestDist, 2.0);
        const double delta = (1 - 0.5 * squaredDist);
        if (kernelEval <= delta)
        {
            const double gamma = furthestDist * sqrt(1 - 0.25 * squaredDist);
            maxKernel = kernelEval * delta +
                        gamma * sqrt(1 - std::pow(kernelEval, 2.0));
        }
        else
        {
            maxKernel = 1.0;
        }
    }
    else
    {
        maxKernel = kernelEval + furthestDist * queryKernels[queryIndex];
    }

    // We return the inverse of the maximum kernel so that larger kernels are
    // recursed into first.
    return (maxKernel > bestKernel) ? (1.0 / maxKernel) : DBL_MAX;
}