/usr/include/mlpack/methods/rann/ra_search_rules_impl.hpp is in libmlpack-dev 1.0.10-1.
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* @file ra_search_rules_impl.hpp
* @author Parikshit Ram
*
* Implementation of RASearchRules.
*
* This file is part of MLPACK 1.0.10.
*
* MLPACK is free software: you can redistribute it and/or modify it under the
* terms of the GNU Lesser General Public License as published by the Free
* Software Foundation, either version 3 of the License, or (at your option) any
* later version.
*
* MLPACK is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
* A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
* details (LICENSE.txt).
*
* You should have received a copy of the GNU General Public License along with
* MLPACK. If not, see <http://www.gnu.org/licenses/>.
*/
#ifndef __MLPACK_METHODS_RANN_RA_SEARCH_RULES_IMPL_HPP
#define __MLPACK_METHODS_RANN_RA_SEARCH_RULES_IMPL_HPP
// In case it hasn't been included yet.
#include "ra_search_rules.hpp"
namespace mlpack {
namespace neighbor {
template<typename SortPolicy, typename MetricType, typename TreeType>
RASearchRules<SortPolicy, MetricType, TreeType>::
RASearchRules(const arma::mat& referenceSet,
const arma::mat& querySet,
arma::Mat<size_t>& neighbors,
arma::mat& distances,
MetricType& metric,
const double tau,
const double alpha,
const bool naive,
const bool sampleAtLeaves,
const bool firstLeafExact,
const size_t singleSampleLimit) :
referenceSet(referenceSet),
querySet(querySet),
neighbors(neighbors),
distances(distances),
metric(metric),
sampleAtLeaves(sampleAtLeaves),
firstLeafExact(firstLeafExact),
singleSampleLimit(singleSampleLimit)
{
// Validate tau to make sure that the rank approximation is greater than the
// number of neighbors requested.
// The rank approximation.
const size_t n = referenceSet.n_cols;
const size_t k = neighbors.n_rows;
const size_t t = (size_t) std::ceil(tau * (double) n / 100.0);
if (t < k)
{
Log::Warn << "Rank-approximation percentile " << tau << " corresponds to "
<< t << " points, which is less than k (" << k << ").";
Log::Fatal << "Cannot return " << k << " approximate nearest neighbors "
<< "from the nearest " << t << " points. Increase tau!" << std::endl;
}
else if (t == k)
Log::Warn << "Rank-approximation percentile " << tau << " corresponds to "
<< t << " points; because k = " << k << ", this is exact search!"
<< std::endl;
Timer::Start("computing_number_of_samples_reqd");
numSamplesReqd = MinimumSamplesReqd(n, k, tau, alpha);
Timer::Stop("computing_number_of_samples_reqd");
// Initialize some statistics to be collected during the search.
numSamplesMade = arma::zeros<arma::Col<size_t> >(querySet.n_cols);
numDistComputations = 0;
samplingRatio = (double) numSamplesReqd / (double) n;
Log::Info << "Minimum samples required per query: " << numSamplesReqd <<
", sampling ratio: " << samplingRatio << std::endl;
if (naive) // No tree traversal; just do naive sampling here.
{
// Sample enough points.
for (size_t i = 0; i < querySet.n_cols; ++i)
{
arma::uvec distinctSamples;
ObtainDistinctSamples(numSamplesReqd, n, distinctSamples);
for (size_t j = 0; j < distinctSamples.n_elem; j++)
BaseCase(i, (size_t) distinctSamples[j]);
}
}
}
template<typename SortPolicy, typename MetricType, typename TreeType>
inline force_inline
void RASearchRules<SortPolicy, MetricType, TreeType>::
ObtainDistinctSamples(const size_t numSamples,
const size_t rangeUpperBound,
arma::uvec& distinctSamples) const
{
// Keep track of the points that are sampled.
arma::Col<size_t> sampledPoints;
sampledPoints.zeros(rangeUpperBound);
for (size_t i = 0; i < numSamples; i++)
sampledPoints[(size_t) math::RandInt(rangeUpperBound)]++;
distinctSamples = arma::find(sampledPoints > 0);
return;
}
template<typename SortPolicy, typename MetricType, typename TreeType>
size_t RASearchRules<SortPolicy, MetricType, TreeType>::
MinimumSamplesReqd(const size_t n,
const size_t k,
const double tau,
const double alpha) const
{
size_t ub = n; // The upper bound on the binary search.
size_t lb = k; // The lower bound on the binary search.
size_t m = lb; // The minimum number of random samples.
// The rank-approximation.
const size_t t = (size_t) std::ceil(tau * (double) n / 100.0);
double prob;
Log::Assert(alpha <= 1.0);
// going through all values of sample sizes
// to find the minimum samples required to satisfy the
// desired bound
bool done = false;
// This performs a binary search on the integer values between 'lb = k'
// and 'ub = n' to find the minimum number of samples 'm' required to obtain
// the desired success probability 'alpha'.
do
{
prob = SuccessProbability(n, k, m, t);
if (prob > alpha)
{
if (prob - alpha < 0.001 || ub < lb + 2) {
done = true;
break;
}
else
ub = m;
}
else
{
if (prob < alpha)
{
if (m == lb)
{
m++;
continue;
}
else
lb = m;
}
else
{
done = true;
break;
}
}
m = (ub + lb) / 2;
} while (!done);
return (std::min(m + 1, n));
}
template<typename SortPolicy, typename MetricType, typename TreeType>
double RASearchRules<SortPolicy, MetricType, TreeType>::SuccessProbability(
const size_t n,
const size_t k,
const size_t m,
const size_t t) const
{
if (k == 1)
{
if (m > n - t)
return 1.0;
double eps = (double) t / (double) n;
return 1.0 - std::pow(1.0 - eps, (double) m);
} // Faster implementation for topK = 1.
else
{
if (m < k)
return 0.0;
if (m > n - t + k - 1)
return 1.0;
double eps = (double) t / (double) n;
double sum = 0.0;
// The probability that 'k' of the 'm' samples lie within the top 't'
// of the neighbors is given by:
// sum_{j = k}^m Choose(m, j) (t/n)^j (1 - t/n)^{m - j}
// which is also equal to
// 1 - sum_{j = 0}^{k - 1} Choose(m, j) (t/n)^j (1 - t/n)^{m - j}
//
// So this is a m - k term summation or a k term summation. So if
// m > 2k, do the k term summation, otherwise do the m term summation.
size_t lb;
size_t ub;
bool topHalf;
if (2 * k < m)
{
// Compute 1 - sum_{j = 0}^{k - 1} Choose(m, j) eps^j (1 - eps)^{m - j}
// eps = t/n.
//
// Choosing 'lb' as 1 and 'ub' as k so as to sum from 1 to (k - 1), and
// add the term (1 - eps)^m term separately.
lb = 1;
ub = k;
topHalf = true;
sum = std::pow(1 - eps, (double) m);
}
else
{
// Compute sum_{j = k}^m Choose(m, j) eps^j (1 - eps)^{m - j}
// eps = t/n.
//
// Choosing 'lb' as k and 'ub' as m so as to sum from k to (m - 1), and
// add the term eps^m term separately.
lb = k;
ub = m;
topHalf = false;
sum = std::pow(eps, (double) m);
}
for (size_t j = lb; j < ub; j++)
{
// Compute Choose(m, j).
double mCj = (double) m;
size_t jTrans;
// If j < m - j, compute Choose(m, j).
// If j > m - j, compute Choose(m, m - j).
if (topHalf)
jTrans = j;
else
jTrans = m - j;
for(size_t i = 2; i <= jTrans; i++)
{
mCj *= (double) (m - (i - 1));
mCj /= (double) i;
}
sum += (mCj * std::pow(eps, (double) j)
* std::pow(1.0 - eps, (double) (m - j)));
}
if (topHalf)
sum = 1.0 - sum;
return sum;
} // For k > 1.
}
template<typename SortPolicy, typename MetricType, typename TreeType>
inline force_inline
double RASearchRules<SortPolicy, MetricType, TreeType>::BaseCase(
const size_t queryIndex,
const size_t referenceIndex)
{
// If the datasets are the same, then this search is only using one dataset
// and we should not return identical points.
if ((&querySet == &referenceSet) && (queryIndex == referenceIndex))
return 0.0;
double distance = metric.Evaluate(querySet.unsafe_col(queryIndex),
referenceSet.unsafe_col(referenceIndex));
// If this distance is better than any of the current candidates, the
// SortDistance() function will give us the position to insert it into.
arma::vec queryDist = distances.unsafe_col(queryIndex);
arma::Col<size_t> queryIndices = neighbors.unsafe_col(queryIndex);
size_t insertPosition = SortPolicy::SortDistance(queryDist, queryIndices,
distance);
// SortDistance() returns (size_t() - 1) if we shouldn't add it.
if (insertPosition != (size_t() - 1))
InsertNeighbor(queryIndex, insertPosition, referenceIndex, distance);
numSamplesMade[queryIndex]++;
// TO REMOVE
numDistComputations++;
return distance;
}
template<typename SortPolicy, typename MetricType, typename TreeType>
inline double RASearchRules<SortPolicy, MetricType, TreeType>::Score(
const size_t queryIndex,
TreeType& referenceNode)
{
const arma::vec queryPoint = querySet.unsafe_col(queryIndex);
const double distance = SortPolicy::BestPointToNodeDistance(queryPoint,
&referenceNode);
const double bestDistance = distances(distances.n_rows - 1, queryIndex);
return Score(queryIndex, referenceNode, distance, bestDistance);
}
template<typename SortPolicy, typename MetricType, typename TreeType>
inline double RASearchRules<SortPolicy, MetricType, TreeType>::Score(
const size_t queryIndex,
TreeType& referenceNode,
const double baseCaseResult)
{
const arma::vec queryPoint = querySet.unsafe_col(queryIndex);
const double distance = SortPolicy::BestPointToNodeDistance(queryPoint,
&referenceNode, baseCaseResult);
const double bestDistance = distances(distances.n_rows - 1, queryIndex);
return Score(queryIndex, referenceNode, distance, bestDistance);
}
template<typename SortPolicy, typename MetricType, typename TreeType>
inline double RASearchRules<SortPolicy, MetricType, TreeType>::Score(
const size_t queryIndex,
TreeType& referenceNode,
const double distance,
const double bestDistance)
{
// If this is better than the best distance we've seen so far, maybe there
// will be something down this node. Also check if enough samples are already
// made for this query.
if (SortPolicy::IsBetter(distance, bestDistance)
&& numSamplesMade[queryIndex] < numSamplesReqd)
{
// We cannot prune this node; try approximating it by sampling.
// If we are required to visit the first leaf (to find possible duplicates),
// make sure we do not approximate.
if (numSamplesMade[queryIndex] > 0 || !firstLeafExact)
{
// Check if this node can be approximated by sampling.
size_t samplesReqd = (size_t) std::ceil(samplingRatio *
(double) referenceNode.NumDescendants());
samplesReqd = std::min(samplesReqd,
numSamplesReqd - numSamplesMade[queryIndex]);
if (samplesReqd > singleSampleLimit && !referenceNode.IsLeaf())
{
// If too many samples required and not at a leaf, then can't prune.
return distance;
}
else
{
if (!referenceNode.IsLeaf())
{
// Then samplesReqd <= singleSampleLimit.
// Hence, approximate the node by sampling enough number of points.
arma::uvec distinctSamples;
ObtainDistinctSamples(samplesReqd, referenceNode.NumDescendants(),
distinctSamples);
for (size_t i = 0; i < distinctSamples.n_elem; i++)
// The counting of the samples are done in the 'BaseCase' function
// so no book-keeping is required here.
BaseCase(queryIndex, referenceNode.Descendant(distinctSamples[i]));
// Node approximated, so we can prune it.
return DBL_MAX;
}
else // We are at a leaf.
{
if (sampleAtLeaves) // If allowed to sample at leaves.
{
// Approximate node by sampling enough number of points.
arma::uvec distinctSamples;
ObtainDistinctSamples(samplesReqd, referenceNode.NumDescendants(),
distinctSamples);
for (size_t i = 0; i < distinctSamples.n_elem; i++)
// The counting of the samples are done in the 'BaseCase' function
// so no book-keeping is required here.
BaseCase(queryIndex,
referenceNode.Descendant(distinctSamples[i]));
// (Leaf) node approximated, so we can prune it.
return DBL_MAX;
}
else
{
// Not allowed to sample from leaves, so cannot prune.
return distance;
}
}
}
}
else
{
// Try first to visit the first leaf to boost your accuracy and find
// (near) duplicates if they exist.
return distance;
}
}
else
{
// Either there cannot be anything better in this node, or enough number of
// samples are already made. So prune it.
// Add 'fake' samples from this node; they are fake because the distances to
// these samples need not be computed.
// If enough samples are already made, this step does not change the result
// of the search.
numSamplesMade[queryIndex] += (size_t) std::floor(
samplingRatio * (double) referenceNode.NumDescendants());
return DBL_MAX;
}
}
template<typename SortPolicy, typename MetricType, typename TreeType>
inline double RASearchRules<SortPolicy, MetricType, TreeType>::
Rescore(const size_t queryIndex,
TreeType& referenceNode,
const double oldScore)
{
// If we are already pruning, still prune.
if (oldScore == DBL_MAX)
return oldScore;
// Just check the score again against the distances.
const double bestDistance = distances(distances.n_rows - 1, queryIndex);
// If this is better than the best distance we've seen so far,
// maybe there will be something down this node.
// Also check if enough samples are already made for this query.
if (SortPolicy::IsBetter(oldScore, bestDistance)
&& numSamplesMade[queryIndex] < numSamplesReqd)
{
// We cannot prune this node; thus, we try approximating this node by
// sampling.
// Here, we assume that since we are re-scoring, the algorithm has already
// sampled some candidates, and if specified, also traversed to the first
// leaf. So no check regarding that is made any more.
// Check if this node can be approximated by sampling.
size_t samplesReqd = (size_t) std::ceil(samplingRatio *
(double) referenceNode.NumDescendants());
samplesReqd = std::min(samplesReqd, numSamplesReqd -
numSamplesMade[queryIndex]);
if (samplesReqd > singleSampleLimit && !referenceNode.IsLeaf())
{
// If too many samples are required and we are not at a leaf, then we
// can't prune.
return oldScore;
}
else
{
if (!referenceNode.IsLeaf())
{
// Then, samplesReqd <= singleSampleLimit. Hence, approximate the node
// by sampling enough number of points.
arma::uvec distinctSamples;
ObtainDistinctSamples(samplesReqd, referenceNode.NumDescendants(),
distinctSamples);
for (size_t i = 0; i < distinctSamples.n_elem; i++)
// The counting of the samples are done in the 'BaseCase' function so
// no book-keeping is required here.
BaseCase(queryIndex, referenceNode.Descendant(distinctSamples[i]));
// Node approximated, so we can prune it.
return DBL_MAX;
}
else // We are at a leaf.
{
if (sampleAtLeaves)
{
// Approximate node by sampling enough points.
arma::uvec distinctSamples;
ObtainDistinctSamples(samplesReqd, referenceNode.NumDescendants(),
distinctSamples);
for (size_t i = 0; i < distinctSamples.n_elem; i++)
// The counting of the samples are done in the 'BaseCase' function
// so no book-keeping is required here.
BaseCase(queryIndex, referenceNode.Descendant(distinctSamples[i]));
// (Leaf) node approximated, so we can prune it.
return DBL_MAX;
}
else
{
// We cannot sample from leaves, so we cannot prune.
return oldScore;
}
}
}
}
else
{
// Either there cannot be anything better in this node, or enough number of
// samples are already made, so prune it.
// Add 'fake' samples from this node; they are fake because the distances to
// these samples need not be computed. If enough samples are already made,
// this step does not change the result of the search.
numSamplesMade[queryIndex] += (size_t) std::floor(samplingRatio *
(double) referenceNode.NumDescendants());
return DBL_MAX;
}
} // Rescore(point, node, oldScore)
template<typename SortPolicy, typename MetricType, typename TreeType>
inline double RASearchRules<SortPolicy, MetricType, TreeType>::Score(
TreeType& queryNode,
TreeType& referenceNode)
{
// First try to find the distance bound to check if we can prune by distance.
// Calculate the best node-to-node distance.
const double distance = SortPolicy::BestNodeToNodeDistance(&queryNode,
&referenceNode);
double pointBound = DBL_MAX;
double childBound = DBL_MAX;
const double maxDescendantDistance = queryNode.FurthestDescendantDistance();
for (size_t i = 0; i < queryNode.NumPoints(); i++)
{
const double bound = distances(distances.n_rows - 1, queryNode.Point(i))
+ maxDescendantDistance;
if (bound < pointBound)
pointBound = bound;
}
for (size_t i = 0; i < queryNode.NumChildren(); i++)
{
const double bound = queryNode.Child(i).Stat().Bound();
if (bound < childBound)
childBound = bound;
}
// Update the bound.
queryNode.Stat().Bound() = std::min(pointBound, childBound);
const double bestDistance = queryNode.Stat().Bound();
return Score(queryNode, referenceNode, distance, bestDistance);
}
template<typename SortPolicy, typename MetricType, typename TreeType>
inline double RASearchRules<SortPolicy, MetricType, TreeType>::Score(
TreeType& queryNode,
TreeType& referenceNode,
const double baseCaseResult)
{
// First try to find the distance bound to check if we can prune
// by distance.
// Find the best node-to-node distance.
const double distance = SortPolicy::BestNodeToNodeDistance(&queryNode,
&referenceNode, baseCaseResult);
double pointBound = DBL_MAX;
double childBound = DBL_MAX;
const double maxDescendantDistance = queryNode.FurthestDescendantDistance();
for (size_t i = 0; i < queryNode.NumPoints(); i++)
{
const double bound = distances(distances.n_rows - 1, queryNode.Point(i))
+ maxDescendantDistance;
if (bound < pointBound)
pointBound = bound;
}
for (size_t i = 0; i < queryNode.NumChildren(); i++)
{
const double bound = queryNode.Child(i).Stat().Bound();
if (bound < childBound)
childBound = bound;
}
// update the bound
queryNode.Stat().Bound() = std::min(pointBound, childBound);
const double bestDistance = queryNode.Stat().Bound();
return Score(queryNode, referenceNode, distance, bestDistance);
}
template<typename SortPolicy, typename MetricType, typename TreeType>
inline double RASearchRules<SortPolicy, MetricType, TreeType>::Score(
TreeType& queryNode,
TreeType& referenceNode,
const double distance,
const double bestDistance)
{
// Update the number of samples made for this node -- propagate up from child
// nodes if child nodes have made samples that the parent node is not aware
// of. Remember, we must propagate down samples made to the child nodes if
// 'queryNode' descend is deemed necessary.
// Only update from children if a non-leaf node, obviously.
if (!queryNode.IsLeaf())
{
size_t numSamplesMadeInChildNodes = std::numeric_limits<size_t>::max();
// Find the minimum number of samples made among all children.
for (size_t i = 0; i < queryNode.NumChildren(); i++)
{
const size_t numSamples = queryNode.Child(i).Stat().NumSamplesMade();
if (numSamples < numSamplesMadeInChildNodes)
numSamplesMadeInChildNodes = numSamples;
}
// The number of samples made for a node is propagated up from the child
// nodes if the child nodes have made samples that the parent (which is the
// current 'queryNode') is not aware of.
queryNode.Stat().NumSamplesMade() = std::max(
queryNode.Stat().NumSamplesMade(), numSamplesMadeInChildNodes);
}
// Now check if the node-pair interaction can be pruned.
// If this is better than the best distance we've seen so far, maybe there
// will be something down this node. Also check if enough samples are already
// made for this 'queryNode'.
if (SortPolicy::IsBetter(distance, bestDistance)
&& queryNode.Stat().NumSamplesMade() < numSamplesReqd)
{
// We cannot prune this node; try approximating this node by sampling.
// If we are required to visit the first leaf (to find possible duplicates),
// make sure we do not approximate.
if (queryNode.Stat().NumSamplesMade() > 0 || !firstLeafExact)
{
// Check if this node can be approximated by sampling.
size_t samplesReqd = (size_t) std::ceil(samplingRatio
* (double) referenceNode.NumDescendants());
samplesReqd = std::min(samplesReqd, numSamplesReqd -
queryNode.Stat().NumSamplesMade());
if (samplesReqd > singleSampleLimit && !referenceNode.IsLeaf())
{
// If too many samples are required and we are not at a leaf, then we
// can't prune. Since query tree descent is necessary now, propagate
// the number of samples made down to the children.
// Iterate through all children and propagate the number of samples made
// to the children. Only update if the parent node has made samples the
// children have not seen.
for (size_t i = 0; i < queryNode.NumChildren(); i++)
queryNode.Child(i).Stat().NumSamplesMade() = std::max(
queryNode.Stat().NumSamplesMade(),
queryNode.Child(i).Stat().NumSamplesMade());
return distance;
}
else
{
if (!referenceNode.IsLeaf())
{
// Then samplesReqd <= singleSampleLimit. Hence, approximate node by
// sampling enough number of points for every query in the query node.
for (size_t i = 0; i < queryNode.NumDescendants(); ++i)
{
const size_t queryIndex = queryNode.Descendant(i);
arma::uvec distinctSamples;
ObtainDistinctSamples(samplesReqd, referenceNode.NumDescendants(),
distinctSamples);
for (size_t j = 0; j < distinctSamples.n_elem; j++)
// The counting of the samples are done in the 'BaseCase' function
// so no book-keeping is required here.
BaseCase(queryIndex,
referenceNode.Descendant(distinctSamples[j]));
}
// Update the number of samples made for the queryNode and also update
// the number of sample made for the child nodes.
queryNode.Stat().NumSamplesMade() += samplesReqd;
// Since we are not going to descend down the query tree for this
// reference node, there is no point updating the number of samples
// made for the child nodes of this query node.
// Node is approximated, so we can prune it.
return DBL_MAX;
}
else
{
if (sampleAtLeaves)
{
// Approximate node by sampling enough number of points for every
// query in the query node.
for (size_t i = 0; i < queryNode.NumDescendants(); ++i)
{
const size_t queryIndex = queryNode.Descendant(i);
arma::uvec distinctSamples;
ObtainDistinctSamples(samplesReqd, referenceNode.NumDescendants(),
distinctSamples);
for (size_t j = 0; j < distinctSamples.n_elem; j++)
// The counting of the samples are done in the 'BaseCase'
// function so no book-keeping is required here.
BaseCase(queryIndex,
referenceNode.Descendant(distinctSamples[j]));
}
// Update the number of samples made for the queryNode and also
// update the number of sample made for the child nodes.
queryNode.Stat().NumSamplesMade() += samplesReqd;
// Since we are not going to descend down the query tree for this
// reference node, there is no point updating the number of samples
// made for the child nodes of this query node.
// (Leaf) node is approximated, so we can prune it.
return DBL_MAX;
}
else
{
// We cannot sample from leaves, so we cannot prune. Propagate the
// number of samples made down to the children.
// Go through all children and propagate the number of
// samples made to the children.
for (size_t i = 0; i < queryNode.NumChildren(); i++)
queryNode.Child(i).Stat().NumSamplesMade() = std::max(
queryNode.Stat().NumSamplesMade(),
queryNode.Child(i).Stat().NumSamplesMade());
return distance;
}
}
}
}
else
{
// We must first visit the first leaf to boost accuracy.
// Go through all children and propagate the number of
// samples made to the children.
for (size_t i = 0; i < queryNode.NumChildren(); i++)
queryNode.Child(i).Stat().NumSamplesMade() = std::max(
queryNode.Stat().NumSamplesMade(),
queryNode.Child(i).Stat().NumSamplesMade());
return distance;
}
}
else
{
// Either there cannot be anything better in this node, or enough number of
// samples are already made, so prune it.
// Add 'fake' samples from this node; fake because the distances to
// these samples need not be computed. If enough samples are already made,
// this step does not change the result of the search since this queryNode
// will never be descended anymore.
queryNode.Stat().NumSamplesMade() += (size_t) std::floor(samplingRatio *
(double) referenceNode.NumDescendants());
// Since we are not going to descend down the query tree for this reference
// node, there is no point updating the number of samples made for the child
// nodes of this query node.
return DBL_MAX;
}
}
template<typename SortPolicy, typename MetricType, typename TreeType>
inline double RASearchRules<SortPolicy, MetricType, TreeType>::
Rescore(TreeType& queryNode,
TreeType& referenceNode,
const double oldScore)
{
if (oldScore == DBL_MAX)
return oldScore;
// First try to find the distance bound to check if we can prune by distance.
double pointBound = DBL_MAX;
double childBound = DBL_MAX;
const double maxDescendantDistance = queryNode.FurthestDescendantDistance();
for (size_t i = 0; i < queryNode.NumPoints(); i++)
{
const double bound = distances(distances.n_rows - 1, queryNode.Point(i))
+ maxDescendantDistance;
if (bound < pointBound)
pointBound = bound;
}
for (size_t i = 0; i < queryNode.NumChildren(); i++)
{
const double bound = queryNode.Child(i).Stat().Bound();
if (bound < childBound)
childBound = bound;
}
// Update the bound.
queryNode.Stat().Bound() = std::min(pointBound, childBound);
const double bestDistance = queryNode.Stat().Bound();
// Now check if the node-pair interaction can be pruned by sampling.
// Update the number of samples made for that node. Propagate up from child
// nodes if child nodes have made samples that the parent node is not aware
// of. Remember, we must propagate down samples made to the child nodes if
// the parent samples.
// Only update from children if a non-leaf node, obviously.
if (!queryNode.IsLeaf())
{
size_t numSamplesMadeInChildNodes = std::numeric_limits<size_t>::max();
// Find the minimum number of samples made among all children
for (size_t i = 0; i < queryNode.NumChildren(); i++)
{
const size_t numSamples = queryNode.Child(i).Stat().NumSamplesMade();
if (numSamples < numSamplesMadeInChildNodes)
numSamplesMadeInChildNodes = numSamples;
}
// The number of samples made for a node is propagated up from the child
// nodes if the child nodes have made samples that the parent (which is the
// current 'queryNode') is not aware of.
queryNode.Stat().NumSamplesMade() = std::max(
queryNode.Stat().NumSamplesMade(), numSamplesMadeInChildNodes);
}
// Now check if the node-pair interaction can be pruned by sampling.
// If this is better than the best distance we've seen so far, maybe there
// will be something down this node. Also check if enough samples are already
// made for this query.
if (SortPolicy::IsBetter(oldScore, bestDistance) &&
queryNode.Stat().NumSamplesMade() < numSamplesReqd)
{
// We cannot prune this node, so approximate by sampling.
// Here we assume that since we are re-scoring, the algorithm has already
// sampled some candidates, and if specified, also traversed to the first
// leaf. So no checks regarding that are made any more.
size_t samplesReqd = (size_t) std::ceil(
samplingRatio * (double) referenceNode.NumDescendants());
samplesReqd = std::min(samplesReqd,
numSamplesReqd - queryNode.Stat().NumSamplesMade());
if (samplesReqd > singleSampleLimit && !referenceNode.IsLeaf())
{
// If too many samples are required and we are not at a leaf, then we
// can't prune.
// Since query tree descent is necessary now, propagate the number of
// samples made down to the children.
// Go through all children and propagate the number of samples made to the
// children. Only update if the parent node has made samples the children
// have not seen.
for (size_t i = 0; i < queryNode.NumChildren(); i++)
queryNode.Child(i).Stat().NumSamplesMade() = std::max(
queryNode.Stat().NumSamplesMade(),
queryNode.Child(i).Stat().NumSamplesMade());
return oldScore;
}
else
{
if (!referenceNode.IsLeaf()) // If not a leaf,
{
// then samplesReqd <= singleSampleLimit. Hence, approximate the node
// by sampling enough points for every query in the query node.
for (size_t i = 0; i < queryNode.NumDescendants(); ++i)
{
const size_t queryIndex = queryNode.Descendant(i);
arma::uvec distinctSamples;
ObtainDistinctSamples(samplesReqd, referenceNode.NumDescendants(),
distinctSamples);
for (size_t j = 0; j < distinctSamples.n_elem; j++)
// The counting of the samples are done in the 'BaseCase'
// function so no book-keeping is required here.
BaseCase(queryIndex, referenceNode.Descendant(distinctSamples[j]));
}
// Update the number of samples made for the query node and also update
// the number of samples made for the child nodes.
queryNode.Stat().NumSamplesMade() += samplesReqd;
// Since we are not going to descend down the query tree for this
// reference node, there is no point updating the number of samples made
// for the child nodes of this query node.
// Node approximated, so we can prune it.
return DBL_MAX;
}
else // We are at a leaf.
{
if (sampleAtLeaves)
{
// Approximate node by sampling enough points for every query in the
// query node.
for (size_t i = 0; i < queryNode.NumDescendants(); ++i)
{
const size_t queryIndex = queryNode.Descendant(i);
arma::uvec distinctSamples;
ObtainDistinctSamples(samplesReqd, referenceNode.NumDescendants(),
distinctSamples);
for (size_t j = 0; j < distinctSamples.n_elem; j++)
// The counting of the samples are done in BaseCase() so no
// book-keeping is required here.
BaseCase(queryIndex,
referenceNode.Descendant(distinctSamples[j]));
}
// Update the number of samples made for the query node and also
// update the number of samples made for the child nodes.
queryNode.Stat().NumSamplesMade() += samplesReqd;
// Since we are not going to descend down the query tree for this
// reference node, there is no point updating the number of samples
// made for the child nodes of this query node.
// (Leaf) node approximated, so we can prune it.
return DBL_MAX;
}
else
{
// We cannot sample from leaves, so we cannot prune.
// Propagate the number of samples made down to the children.
for (size_t i = 0; i < queryNode.NumChildren(); i++)
queryNode.Child(i).Stat().NumSamplesMade() = std::max(
queryNode.Stat().NumSamplesMade(),
queryNode.Child(i).Stat().NumSamplesMade());
return oldScore;
}
}
}
}
else
{
// Either there cannot be anything better in this node, or enough samples
// are already made, so prune it.
// Add 'fake' samples from this node; fake because the distances to
// these samples need not be computed. If enough samples are already made,
// this step does not change the result of the search since this query node
// will never be descended anymore.
queryNode.Stat().NumSamplesMade() += (size_t) std::floor(samplingRatio *
(double) referenceNode.NumDescendants());
// Since we are not going to descend down the query tree for this reference
// node, there is no point updating the number of samples made for the child
// nodes of this query node.
return DBL_MAX;
}
} // Rescore(node, node, oldScore)
/**
* Helper function to insert a point into the neighbors and distances matrices.
*
* @param queryIndex Index of point whose neighbors we are inserting into.
* @param pos Position in list to insert into.
* @param neighbor Index of reference point which is being inserted.
* @param distance Distance from query point to reference point.
*/
template<typename SortPolicy, typename MetricType, typename TreeType>
void RASearchRules<SortPolicy, MetricType, TreeType>::InsertNeighbor(
const size_t queryIndex,
const size_t pos,
const size_t neighbor,
const double distance)
{
// We only memmove() if there is actually a need to shift something.
if (pos < (distances.n_rows - 1))
{
int len = (distances.n_rows - 1) - pos;
memmove(distances.colptr(queryIndex) + (pos + 1),
distances.colptr(queryIndex) + pos,
sizeof(double) * len);
memmove(neighbors.colptr(queryIndex) + (pos + 1),
neighbors.colptr(queryIndex) + pos,
sizeof(size_t) * len);
}
// Now put the new information in the right index.
distances(pos, queryIndex) = distance;
neighbors(pos, queryIndex) = neighbor;
}
}; // namespace neighbor
}; // namespace mlpack
#endif // __MLPACK_METHODS_RANN_RA_SEARCH_RULES_IMPL_HPP
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