[mlpack-svn] r14100 - in mlpack/trunk/src/mlpack: methods methods/rann tests tests/data
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Wed Jan 9 17:12:17 EST 2013
Author: pram
Date: 2013-01-09 17:12:16 -0500 (Wed, 09 Jan 2013)
New Revision: 14100
Added:
mlpack/trunk/src/mlpack/methods/rann/
mlpack/trunk/src/mlpack/methods/rann/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/rann/allkrann_main.cpp
mlpack/trunk/src/mlpack/methods/rann/ra_search.hpp
mlpack/trunk/src/mlpack/methods/rann/ra_search_impl.hpp
mlpack/trunk/src/mlpack/methods/rann/ra_search_rules.hpp
mlpack/trunk/src/mlpack/methods/rann/ra_search_rules_impl.hpp
mlpack/trunk/src/mlpack/methods/rann/ra_typedef.hpp
mlpack/trunk/src/mlpack/tests/allkrann_search_test.cpp
mlpack/trunk/src/mlpack/tests/data/rann_test_q_3_100.csv
mlpack/trunk/src/mlpack/tests/data/rann_test_qr_ranks.csv
mlpack/trunk/src/mlpack/tests/data/rann_test_r_3_900.csv
Modified:
mlpack/trunk/src/mlpack/methods/CMakeLists.txt
mlpack/trunk/src/mlpack/tests/CMakeLists.txt
Log:
RANN moved into trunk from contrib/pram. Test added to the mlpack/tests/ directory along with relevant datasets
Modified: mlpack/trunk/src/mlpack/methods/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/CMakeLists.txt 2013-01-09 22:10:49 UTC (rev 14099)
+++ mlpack/trunk/src/mlpack/methods/CMakeLists.txt 2013-01-09 22:12:16 UTC (rev 14100)
@@ -19,6 +19,7 @@
pca
radical
range_search
+ rann
sparse_coding
)
Added: mlpack/trunk/src/mlpack/methods/rann/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/rann/CMakeLists.txt (rev 0)
+++ mlpack/trunk/src/mlpack/methods/rann/CMakeLists.txt 2013-01-09 22:12:16 UTC (rev 14100)
@@ -0,0 +1,36 @@
+# Define the files we need to compile
+# Anything not in this list will not be compiled into the output library
+# Do not include test programs here
+set(SOURCES
+
+ # rank-approximate search files
+ ra_search.hpp
+ ra_search_impl.hpp
+
+ # The rank-approximate search rules
+ ra_search_rules.hpp
+ ra_search_rules_impl.hpp
+
+ # The typedefs
+ ra_typedef.hpp
+)
+
+# add directory name to sources
+set(DIR_SRCS)
+foreach(file ${SOURCES})
+ set(DIR_SRCS ${DIR_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/${file})
+endforeach()
+# append sources (with directory name) to list of all MLPACK sources (used at the parent scope)
+set(MLPACK_CONTRIB_SRCS ${MLPACK_CONTRIB_SRCS} ${DIR_SRCS} PARENT_SCOPE)
+
+
+# The code to compute the rank-approximate neighbor
+# for the given query and reference sets
+add_executable(allkrann
+ allkrann_main.cpp
+)
+target_link_libraries(allkrann
+ mlpack
+)
+
+install(TARGETS allkrann RUNTIME DESTINATION bin)
\ No newline at end of file
Added: mlpack/trunk/src/mlpack/methods/rann/allkrann_main.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/rann/allkrann_main.cpp (rev 0)
+++ mlpack/trunk/src/mlpack/methods/rann/allkrann_main.cpp 2013-01-09 22:12:16 UTC (rev 14100)
@@ -0,0 +1,293 @@
+/**
+ * @file allkrann_main.cpp
+ * @author Parikshit Ram
+ *
+ * Implementation of the AllkRANN executable. Allows some number of standard
+ * options.
+ */
+#include <time.h>
+
+#include <mlpack/core.hpp>
+#include <mlpack/core/tree/cover_tree.hpp>
+
+#include <string>
+#include <fstream>
+#include <iostream>
+
+#include "ra_search.hpp"
+
+using namespace std;
+using namespace mlpack;
+using namespace mlpack::neighbor;
+using namespace mlpack::tree;
+
+// Information about the program itself.
+PROGRAM_INFO("All K-Rank-Approximate-Nearest-Neighbors",
+ "This program will calculate the k rank-approximate-nearest-neighbors "
+ "of a set of points. You may specify a separate set of reference "
+ "points and query points, or just a reference set which will be "
+ "used as both the reference and query set. You must specify the "
+ "rank approximation (in \%) (and maybe the success probability)."
+ "\n\n"
+ "For example, the following will return 5 neighbors from the top 0.1\% "
+ "of the data (with probability 0.95) for each point in 'input.csv' "
+ "and store the distances in 'distances.csv' and the neighbors in the "
+ "file 'neighbors.csv':"
+ "\n\n"
+ "$ allkrann -k 5 -r input.csv -d distances.csv -n neighbors.csv --tau=0.1"
+ "\n\n"
+ "The output files are organized such that row i and column j in the "
+ "neighbors output file corresponds to the index of the point in the "
+ "reference set which is the i'th nearest neighbor from the point in the "
+ "query set with index j. Row i and column j in the distances output file "
+ "corresponds to the distance between those two points.");
+
+// Define our input parameters that this program will take.
+PARAM_STRING_REQ("reference_file", "File containing the reference dataset.",
+ "r");
+PARAM_STRING("distances_file", "File to output distances into.", "d", "");
+PARAM_STRING("neighbors_file", "File to output neighbors into.", "n", "");
+
+PARAM_INT_REQ("k", "Number of furthest neighbors to find.", "k");
+
+PARAM_STRING("query_file", "File containing query points (optional).",
+ "q", "");
+
+PARAM_DOUBLE("tau", "The allowed rank-error in terms of the percentile of "
+ "the data.", "t", 0.1);
+PARAM_DOUBLE("alpha", "The desired success probability.", "a", 0.95);
+
+PARAM_INT("leaf_size", "Leaf size for tree building.", "l", 20);
+PARAM_FLAG("naive", "If true, sampling will be done without using a tree.",
+ "N");
+PARAM_FLAG("single_mode", "If true, single-tree search is used (as opposed to "
+ "dual-tree search.", "s");
+PARAM_FLAG("cover_tree", "If true, use cover trees to perform the search.",
+ "c");
+
+PARAM_FLAG("sample_at_leaves", "The flag to trigger sampling at leaves.", "L");
+PARAM_FLAG("first_leaf_exact", "The flag to trigger sampling only after "
+ "exactly exploring the first leaf.", "X");
+PARAM_INT("single_sample_limit", "The limit on the maximum number of "
+ "samples (and hence the largest node you can approximate).", "S", 20);
+
+int main(int argc, char *argv[])
+{
+ // Give CLI the command line parameters the user passed in.
+ CLI::ParseCommandLine(argc, argv);
+ math::RandomSeed(time(NULL));
+
+ // Get all the parameters.
+ string referenceFile = CLI::GetParam<string>("reference_file");
+ string distancesFile = CLI::GetParam<string>("distances_file");
+ string neighborsFile = CLI::GetParam<string>("neighbors_file");
+
+ int lsInt = CLI::GetParam<int>("leaf_size");
+ size_t singleSampleLimit = CLI::GetParam<int>("single_sample_limit");
+
+ size_t k = CLI::GetParam<int>("k");
+
+ double tau = CLI::GetParam<double>("tau");
+ double alpha = CLI::GetParam<double>("alpha");
+
+ bool naive = CLI::HasParam("naive");
+ bool singleMode = CLI::HasParam("single_mode");
+ bool sampleAtLeaves = CLI::HasParam("sample_at_leaves");
+ bool firstLeafExact = CLI::HasParam("first_leaf_exact");
+
+ arma::mat referenceData;
+ arma::mat queryData; // So it doesn't go out of scope.
+ data::Load(referenceFile.c_str(), referenceData, true);
+
+ Log::Info << "Loaded reference data from '" << referenceFile << "' ("
+ << referenceData.n_rows << " x " << referenceData.n_cols << ")." << endl;
+
+ // Sanity check on k value: must be greater than 0, must be less than the
+ // number of reference points.
+ if (k > referenceData.n_cols)
+ {
+ Log::Fatal << "Invalid k: " << k << "; must be greater than 0 and less ";
+ Log::Fatal << "than or equal to the number of reference points (";
+ Log::Fatal << referenceData.n_cols << ")." << endl;
+ }
+
+ // Sanity check on the value of 'tau' with respect to 'k' so that
+ // 'k' neighbors are not requested from the top-'rank_error' neighbors
+ // where 'rank_error' <= 'k'.
+ size_t rank_error
+ = (size_t) ceil(tau * (double) referenceData.n_cols / 100.0);
+ if (rank_error <= k)
+ Log::Fatal << "Invalid 'tau' (" << tau << ") - k (" << k << ") " <<
+ "combination. Increase 'tau' or decrease 'k'." << endl;
+
+ // Sanity check on leaf size.
+ if (lsInt < 0)
+ Log::Fatal << "Invalid leaf size: " << lsInt << ". Must be greater "
+ "than or equal to 0." << endl;
+ size_t leafSize = lsInt;
+
+ // Naive mode overrides single mode.
+ if (singleMode && naive)
+ Log::Warn << "--single_mode ignored because --naive is present." << endl;
+
+ // The actual output after the remapping
+ arma::Mat<size_t> neighbors;
+ arma::mat distances;
+
+ if (naive)
+ {
+ AllkRANN* allkrann;
+ if (CLI::GetParam<string>("query_file") != "")
+ {
+ string queryFile = CLI::GetParam<string>("query_file");
+
+ data::Load(queryFile.c_str(), queryData, true);
+
+ Log::Info << "Loaded query data from '" << queryFile << "' (" <<
+ queryData.n_rows << " x " << queryData.n_cols << ")." << endl;
+
+ allkrann = new AllkRANN(referenceData, queryData, naive);
+ }
+ else
+ allkrann = new AllkRANN(referenceData, naive);
+
+ Log::Info << "Computing " << k << " nearest neighbors " << "with " <<
+ tau << "% rank approximation..." << endl;
+
+ allkrann->Search(k, neighbors, distances, tau, alpha);
+
+ Log::Info << "Neighbors computed." << endl;
+
+ delete allkrann;
+ }
+ else
+ {
+ // The results output by the AllkRANN class
+ // shuffled because the tree construction shuffles the point sets.
+ arma::Mat<size_t> neighborsOut;
+ arma::mat distancesOut;
+
+ if (!CLI::HasParam("cover_tree"))
+ {
+ // Because we may construct it differently, we need a pointer.
+ AllkRANN* allkrann = NULL;
+
+ // Mappings for when we build the tree.
+ std::vector<size_t> oldFromNewRefs;
+
+ // Build trees by hand, so we can save memory: if we pass a tree to
+ // NeighborSearch, it does not copy the matrix.
+ Log::Info << "Building reference tree..." << endl;
+ Timer::Start("tree_building");
+
+ BinarySpaceTree<bound::HRectBound<2>, RAQueryStat<NearestNeighborSort> >
+ refTree(referenceData, oldFromNewRefs, leafSize);
+ BinarySpaceTree<bound::HRectBound<2>, RAQueryStat<NearestNeighborSort> >*
+ queryTree = NULL; // Empty for now.
+
+ Timer::Stop("tree_building");
+
+ std::vector<size_t> oldFromNewQueries;
+
+ if (CLI::GetParam<string>("query_file") != "")
+ {
+ string queryFile = CLI::GetParam<string>("query_file");
+
+ data::Load(queryFile.c_str(), queryData, true);
+
+ if (naive && leafSize < queryData.n_cols)
+ leafSize = queryData.n_cols;
+
+ Log::Info << "Loaded query data from '" << queryFile << "' (" <<
+ queryData.n_rows << " x " << queryData.n_cols << ")." << endl;
+
+ Log::Info << "Building query tree..." << endl;
+
+ // Build trees by hand, so we can save memory: if we pass a tree to
+ // NeighborSearch, it does not copy the matrix.
+ Timer::Start("tree_building");
+
+ queryTree = new BinarySpaceTree<bound::HRectBound<2>,
+ RAQueryStat<NearestNeighborSort> >
+ (queryData, oldFromNewQueries, leafSize);
+ Timer::Stop("tree_building");
+
+ allkrann = new AllkRANN(&refTree, queryTree, referenceData, queryData,
+ singleMode);
+
+ Log::Info << "Tree built." << endl;
+ }
+ else
+ {
+ allkrann = new AllkRANN(&refTree, referenceData, singleMode);
+ Log::Info << "Trees built." << endl;
+ }
+
+ Log::Info << "Computing " << k << " nearest neighbors " << "with " <<
+ tau << "% rank approximation..." << endl;
+ allkrann->Search(k, neighborsOut, distancesOut,
+ tau, alpha, sampleAtLeaves,
+ firstLeafExact, singleSampleLimit);
+
+ Log::Info << "Neighbors computed." << endl;
+
+ // We have to map back to the original indices from before the tree
+ // construction.
+ Log::Info << "Re-mapping indices..." << endl;
+
+ neighbors.set_size(neighborsOut.n_rows, neighborsOut.n_cols);
+ distances.set_size(distancesOut.n_rows, distancesOut.n_cols);
+
+ // Do the actual remapping.
+ if (CLI::GetParam<string>("query_file") != "")
+ {
+ for (size_t i = 0; i < distancesOut.n_cols; ++i)
+ {
+ // Map distances (copy a column).
+ distances.col(oldFromNewQueries[i]) = distancesOut.col(i);
+
+ // Map indices of neighbors.
+ for (size_t j = 0; j < distancesOut.n_rows; ++j)
+ {
+ neighbors(j, oldFromNewQueries[i])
+ = oldFromNewRefs[neighborsOut(j, i)];
+ }
+ }
+ }
+ else
+ {
+ for (size_t i = 0; i < distancesOut.n_cols; ++i)
+ {
+ // Map distances (copy a column).
+ distances.col(oldFromNewRefs[i]) = distancesOut.col(i);
+
+ // Map indices of neighbors.
+ for (size_t j = 0; j < distancesOut.n_rows; ++j)
+ {
+ neighbors(j, oldFromNewRefs[i])
+ = oldFromNewRefs[neighborsOut(j, i)];
+ }
+ }
+ }
+
+ // Clean up.
+ if (queryTree)
+ delete queryTree;
+
+ delete allkrann;
+ }
+ else // Cover trees.
+ {
+ Log::Fatal << "Cover tree case not implemented yet..." << endl;
+ }
+ }
+
+ // Save output.
+ if (distancesFile != "")
+ data::Save(distancesFile, distances);
+
+ if (neighborsFile != "")
+ data::Save(neighborsFile, neighbors);
+
+ return 0;
+}
Added: mlpack/trunk/src/mlpack/methods/rann/ra_search.hpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/rann/ra_search.hpp (rev 0)
+++ mlpack/trunk/src/mlpack/methods/rann/ra_search.hpp 2013-01-09 22:12:16 UTC (rev 14100)
@@ -0,0 +1,345 @@
+/**
+ * @file ra_search.hpp
+ * @author Parikshit Ram
+ *
+ * Defines the RASearch class, which performs an abstract
+ * rank-approximate nearest/farthest neighbor query on two datasets.
+ *
+ * The details of this method can be found in the following paper:
+ *
+ * @inproceedings{ram2009rank,
+ * title={{Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions}},
+ * author={{Ram, P. and Lee, D. and Ouyang, H. and Gray, A. G.}},
+ * booktitle={{Advances of Neural Information Processing Systems}},
+ * year={2009}
+ * }
+ *
+ */
+#ifndef __MLPACK_METHODS_NEIGHBOR_SEARCH_RA_SEARCH_HPP
+#define __MLPACK_METHODS_NEIGHBOR_SEARCH_RA_SEARCH_HPP
+
+#include <mlpack/core.hpp>
+#include <vector>
+#include <string>
+
+#include <mlpack/core/tree/binary_space_tree.hpp>
+
+#include <mlpack/core/metrics/lmetric.hpp>
+#include <mlpack/methods/neighbor_search/sort_policies/nearest_neighbor_sort.hpp>
+
+namespace mlpack {
+namespace neighbor /** Neighbor-search routines. These include
+ * all-nearest-neighbors and all-furthest-neighbors
+ * searches. */ {
+
+/**
+ * Extra data for each node in the tree. For neighbor searches, each node only
+ * needs to store a bound on neighbor distances.
+ *
+ * Every query is required to make a minimum number of samples to guarantee
+ * the desired approximation error. The 'numSamplesMade' keeps track of
+ * the minimum number of samples made by all queries in the node in question.
+ */
+template<typename SortPolicy>
+class RAQueryStat
+{
+ private:
+ //! The bound on the node's neighbor distances.
+ double bound;
+
+ //! The minimum number of samples made by any query in this node
+ size_t numSamplesMade;
+
+ public:
+ /**
+ * Initialize the statistic with the worst possible distance according to
+ * our sorting policy.
+ */
+ RAQueryStat() : bound(SortPolicy::WorstDistance()), numSamplesMade(0) { }
+
+ /**
+ * Initialization for a leaf, required by the StatisticType policy.
+ */
+ template<typename MatType>
+ RAQueryStat(const MatType& /* dataset */,
+ const size_t /* begin */,
+ const size_t /* count */) :
+ bound(SortPolicy::WorstDistance()),
+ numSamplesMade(0)
+ { }
+
+ /**
+ * Initialization for a node, required by the StatisticType policy.
+ */
+ template<typename MatType>
+ RAQueryStat(const MatType& /* dataset */,
+ const size_t /* begin */,
+ const size_t /* count */,
+ const RAQueryStat& /* leftStat */,
+ const RAQueryStat& /* rightStat */) :
+ bound(SortPolicy::WorstDistance()),
+ numSamplesMade(0)
+ { }
+
+ //! Get the bound.
+ double Bound() const { return bound; }
+ //! Modify the bound.
+ double& Bound() { return bound; }
+
+ //! Get the number of samples made
+ size_t NumSamplesMade() const { return numSamplesMade; }
+ //! Modify the number of samples made
+ size_t& NumSamplesMade() { return numSamplesMade; }
+};
+
+/**
+ * The RASearch class: This class provides a generic manner to perform
+ * rank-approximate search via random-sampling. If the 'naive' option
+ * is chosen, this rank-approximate search will be done by randomly
+ * sampled from the whole set. If the 'naive' option is not chosen,
+ * the sampling is done in a stratified manner in the tree as
+ * mentioned in the algorithms in Figure 2 of the aforementioned paper.
+ *
+ * @tparam SortPolicy The sort policy for distances; see NearestNeighborSort.
+ * @tparam MetricType The metric to use for computation.
+ * @tparam TreeType The tree type to use.
+ */
+template<typename SortPolicy = NearestNeighborSort,
+ typename MetricType = mlpack::metric::SquaredEuclideanDistance,
+ typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>,
+ RAQueryStat<SortPolicy> > >
+class RASearch
+{
+ public:
+ /**
+ * Initialize the RASearch object, passing both a query and reference
+ * dataset. Optionally, perform the computation in naive mode or single-tree
+ * mode, and set the leaf size used for tree-building. An initialized
+ * distance metric can be given, for cases where the metric has internal data
+ * (i.e. the distance::MahalanobisDistance class).
+ *
+ * This method will copy the matrices to internal copies,
+ * which are rearranged during tree-building. You can avoid this
+ * extra copy by pre-constructing the trees and passing them
+ * using a diferent constructor.
+ *
+ * @param referenceSet Set of reference points.
+ * @param querySet Set of query points.
+ * @param naive If true, the rank-approximate search will be performed
+ * by directly sampling the whole set instead of using the stratified
+ * sampling on the tree.
+ * @param singleMode If true, single-tree search will be used (as opposed to
+ * dual-tree search).
+ * @param leafSize Leaf size for tree construction (ignored if tree is given).
+ * @param metric An optional instance of the MetricType class.
+ */
+ RASearch(const typename TreeType::Mat& referenceSet,
+ const typename TreeType::Mat& querySet,
+ const bool naive = false,
+ const bool singleMode = false,
+ const size_t leafSize = 20,
+ const MetricType metric = MetricType());
+
+ /**
+ * Initialize the RASearch object, passing only one dataset, which is
+ * used as both the query and the reference dataset. Optionally, perform the
+ * computation in naive mode or single-tree mode, and set the leaf size used
+ * for tree-building. An initialized distance metric can be given, for cases
+ * where the metric has internal data (i.e. the distance::MahalanobisDistance
+ * class).
+ *
+ * If naive mode is being used and a pre-built tree is given, it may not work:
+ * naive mode operates by building a one-node tree (the root node holds all
+ * the points). If that condition is not satisfied with the pre-built tree,
+ * then naive mode will not work.
+ *
+ * @param referenceSet Set of reference points.
+ * @param naive If true, the rank-approximate search will be performed
+ * by directly sampling the whole set instead of using the stratified
+ * sampling on the tree.
+ * @param singleMode If true, single-tree search will be used (as opposed to
+ * dual-tree search).
+ * @param leafSize Leaf size for tree construction (ignored if tree is given).
+ * @param metric An optional instance of the MetricType class.
+ */
+ RASearch(const typename TreeType::Mat& referenceSet,
+ const bool naive = false,
+ const bool singleMode = false,
+ const size_t leafSize = 20,
+ const MetricType metric = MetricType());
+
+ /**
+ * Initialize the RASearch object with the given datasets and
+ * pre-constructed trees. It is assumed that the points in referenceSet and
+ * querySet correspond to the points in referenceTree and queryTree,
+ * respectively. Optionally, choose to use single-tree mode. Naive mode is
+ * not available as an option for this constructor; instead, to run naive
+ * computation, construct a tree with all of the points in one leaf (i.e.
+ * leafSize = number of points). Additionally, an instantiated distance
+ * metric can be given, for cases where the distance metric holds data.
+ *
+ * There is no copying of the data matrices in this constructor (because
+ * tree-building is not necessary), so this is the constructor to use when
+ * copies absolutely must be avoided.
+ *
+ * @note
+ * Because tree-building (at least with BinarySpaceTree) modifies the ordering
+ * of a matrix, be sure you pass the modified matrix to this object! In
+ * addition, mapping the points of the matrix back to their original indices
+ * is not done when this constructor is used.
+ * @endnote
+ *
+ * @param referenceTree Pre-built tree for reference points.
+ * @param queryTree Pre-built tree for query points.
+ * @param referenceSet Set of reference points corresponding to referenceTree.
+ * @param querySet Set of query points corresponding to queryTree.
+ * @param singleMode Whether single-tree computation should be used (as
+ * opposed to dual-tree computation).
+ * @param metric Instantiated distance metric.
+ */
+ RASearch(TreeType* referenceTree,
+ TreeType* queryTree,
+ const typename TreeType::Mat& referenceSet,
+ const typename TreeType::Mat& querySet,
+ const bool singleMode = false,
+ const MetricType metric = MetricType());
+
+ /**
+ * Initialize the RASearch object with the given reference dataset and
+ * pre-constructed tree. It is assumed that the points in referenceSet
+ * correspond to the points in referenceTree. Optionally, choose to use
+ * single-tree mode. Naive mode is not available as an option for this
+ * constructor; instead, to run naive computation, construct a tree with all
+ * the points in one leaf (i.e. leafSize = number of points). Additionally,
+ * an instantiated distance metric can be given, for the case where the
+ * distance metric holds data.
+ *
+ * There is no copying of the data matrices in this constructor (because
+ * tree-building is not necessary), so this is the constructor to use when
+ * copies absolutely must be avoided.
+ *
+ * @note
+ * Because tree-building (at least with BinarySpaceTree) modifies the ordering
+ * of a matrix, be sure you pass the modified matrix to this object! In
+ * addition, mapping the points of the matrix back to their original indices
+ * is not done when this constructor is used.
+ * @endnote
+ *
+ * @param referenceTree Pre-built tree for reference points.
+ * @param referenceSet Set of reference points corresponding to referenceTree.
+ * @param singleMode Whether single-tree computation should be used (as
+ * opposed to dual-tree computation).
+ * @param metric Instantiated distance metric.
+ */
+ RASearch(TreeType* referenceTree,
+ const typename TreeType::Mat& referenceSet,
+ const bool singleMode = false,
+ const MetricType metric = MetricType());
+
+
+ /**
+ * Delete the RASearch object. The tree is the only member we are
+ * responsible for deleting. The others will take care of themselves.
+ */
+ ~RASearch();
+
+ /**
+ * Compute the rank approximate nearest neighbors and store the
+ * output in the given matrices. The matrices will be set to the size
+ * of n columns by k rows, where n is the number of points in the
+ * query dataset and k is the number of neighbors being searched for.
+ *
+ * @param k Number of neighbors to search for.
+ * @param resultingNeighbors Matrix storing lists of neighbors for each query
+ * point.
+ * @param distances Matrix storing distances of neighbors for each query
+ * point.
+ * @param tau The rank-approximation in percentile of the data. The
+ * default value is 0.1%.
+ * @param alpha The desired success probability. The default value is 0.95.
+ * @param sampleAtLeaves The flag to trigger sampling at leaves for
+ * faster yet less accurate computation. This defaults to 'false'.
+ * @param firstLeafExact The flag to trigger the traversal of the algorithm
+ * to the first leaf without approximation. This can ensure that the
+ * query definitely finds its (near) duplicate if there exists one.
+ * This defaults to 'false' for now.
+ * @param singleSampleLimit The limit on the largest node that can be
+ * approximated by sampling. This defaults to 20.
+ */
+ void Search(const size_t k,
+ arma::Mat<size_t>& resultingNeighbors,
+ arma::mat& distances,
+ const double tau = 0.1,
+ const double alpha = 0.95,
+ const bool sampleAtLeaves = false,
+ const bool firstLeafExact = false,
+ const size_t singleSampleLimit = 20);
+
+ /**
+ * This function recursively resets the RAQueryStat of the queryTree to set
+ * 'bound' to WorstDistance and the 'numSamplesMade' to 0. This allows
+ * a user to perform multiple searches on the same pair of trees,
+ * possibly with different levels of approximation without requiring
+ * to build a new pair of trees for every new (approximate) search.
+ */
+ void ResetQueryTree();
+
+
+
+ private:
+ //! Copy of reference dataset (if we need it, because tree building modifies
+ //! it).
+ arma::mat referenceCopy;
+ //! Copy of query dataset (if we need it, because tree building modifies it).
+ arma::mat queryCopy;
+
+ //! Reference dataset.
+ const arma::mat& referenceSet;
+ //! Query dataset (may not be given).
+ const arma::mat& querySet;
+
+ //! Pointer to the root of the reference tree.
+ TreeType* referenceTree;
+ //! Pointer to the root of the query tree (might not exist).
+ TreeType* queryTree;
+
+ //! Indicates if we should free the reference tree at deletion time.
+ bool ownReferenceTree;
+ //! Indicates if we should free the query tree at deletion time.
+ bool ownQueryTree;
+
+ //! Indicates if naive random sampling on the set is being used.
+ bool naive;
+ //! Indicates if single-tree search is being used (opposed to dual-tree).
+ bool singleMode;
+
+ //! Instantiation of kernel.
+ MetricType metric;
+
+ //! Permutations of reference points during tree building.
+ std::vector<size_t> oldFromNewReferences;
+ //! Permutations of query points during tree building.
+ std::vector<size_t> oldFromNewQueries;
+
+ //! Total number of pruned nodes during the neighbor search.
+ size_t numberOfPrunes;
+
+ /**
+ *
+ * @param treeNode The node of the tree whose RAQueryStat is reset
+ * and whose children are to be explored recursively.
+ */
+ void ResetRAQueryStat(TreeType* treeNode);
+
+}; // class RASearch
+
+}; // namespace neighbor
+}; // namespace mlpack
+
+// Include implementation.
+#include "ra_search_impl.hpp"
+
+// Include convenient typedefs.
+#include "ra_typedef.hpp"
+
+#endif
Added: mlpack/trunk/src/mlpack/methods/rann/ra_search_impl.hpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/rann/ra_search_impl.hpp (rev 0)
+++ mlpack/trunk/src/mlpack/methods/rann/ra_search_impl.hpp 2013-01-09 22:12:16 UTC (rev 14100)
@@ -0,0 +1,358 @@
+/**
+ * @file ra_search_impl.hpp
+ * @author Parikshit Ram
+ *
+ * Implementation of RASearch class to perform rank-approximate
+ * all-nearest-neighbors on two specified data sets.
+ */
+#ifndef __MLPACK_METHODS_NEIGHBOR_SEARCH_RA_SEARCH_IMPL_HPP
+#define __MLPACK_METHODS_NEIGHBOR_SEARCH_RA_SEARCH_IMPL_HPP
+
+#include <mlpack/core.hpp>
+
+#include "ra_search_rules.hpp"
+
+using namespace mlpack::neighbor;
+
+// Construct the object.
+template<typename SortPolicy, typename MetricType, typename TreeType>
+RASearch<SortPolicy, MetricType, TreeType>::
+RASearch(const typename TreeType::Mat& referenceSet,
+ const typename TreeType::Mat& querySet,
+ const bool naive,
+ const bool singleMode,
+ const size_t leafSize,
+ const MetricType metric) :
+ referenceCopy(referenceSet),
+ queryCopy(querySet),
+ referenceSet(referenceCopy),
+ querySet(queryCopy),
+ referenceTree(NULL),
+ queryTree(NULL),
+ ownReferenceTree(true), // False if a tree was passed.
+ ownQueryTree(true), // False if a tree was passed.
+ naive(naive),
+ singleMode(!naive && singleMode), // No single mode if naive.
+ metric(metric),
+ numberOfPrunes(0)
+{
+ // We'll time tree building, but only if we are building trees.
+ Timer::Start("tree_building");
+
+ // Construct as a naive object if we need to.
+ referenceTree = new TreeType(referenceCopy, oldFromNewReferences,
+ (naive ? referenceCopy.n_cols : leafSize));
+
+ queryTree = new TreeType(queryCopy, oldFromNewQueries,
+ (naive ? querySet.n_cols : leafSize));
+
+ // Stop the timer we started above (if we need to).
+ Timer::Stop("tree_building");
+}
+
+// Construct the object.
+template<typename SortPolicy, typename MetricType, typename TreeType>
+RASearch<SortPolicy, MetricType, TreeType>::
+RASearch(const typename TreeType::Mat& referenceSet,
+ const bool naive,
+ const bool singleMode,
+ const size_t leafSize,
+ const MetricType metric) :
+ referenceCopy(referenceSet),
+ referenceSet(referenceCopy),
+ querySet(referenceCopy),
+ referenceTree(NULL),
+ queryTree(NULL),
+ ownReferenceTree(true),
+ ownQueryTree(false), // Since it will be the same as referenceTree.
+ naive(naive),
+ singleMode(!naive && singleMode), // No single mode if naive.
+ metric(metric),
+ numberOfPrunes(0)
+{
+ // We'll time tree building, but only if we are building trees.
+ Timer::Start("tree_building");
+
+ // Construct as a naive object if we need to.
+ referenceTree = new TreeType(referenceCopy, oldFromNewReferences,
+ (naive ? referenceSet.n_cols : leafSize));
+
+ // Stop the timer we started above.
+ Timer::Stop("tree_building");
+}
+
+// Construct the object.
+template<typename SortPolicy, typename MetricType, typename TreeType>
+RASearch<SortPolicy, MetricType, TreeType>::
+RASearch(TreeType* referenceTree,
+ TreeType* queryTree,
+ const typename TreeType::Mat& referenceSet,
+ const typename TreeType::Mat& querySet,
+ const bool singleMode,
+ const MetricType metric) :
+ referenceSet(referenceSet),
+ querySet(querySet),
+ referenceTree(referenceTree),
+ queryTree(queryTree),
+ ownReferenceTree(false),
+ ownQueryTree(false),
+ naive(false),
+ singleMode(singleMode),
+ metric(metric),
+ numberOfPrunes(0)
+// Nothing else to initialize.
+{ }
+
+// Construct the object.
+template<typename SortPolicy, typename MetricType, typename TreeType>
+RASearch<SortPolicy, MetricType, TreeType>::
+RASearch(TreeType* referenceTree,
+ const typename TreeType::Mat& referenceSet,
+ const bool singleMode,
+ const MetricType metric) :
+ referenceSet(referenceSet),
+ querySet(referenceSet),
+ referenceTree(referenceTree),
+ queryTree(NULL),
+ ownReferenceTree(false),
+ ownQueryTree(false),
+ naive(false),
+ singleMode(singleMode),
+ metric(metric),
+ numberOfPrunes(0)
+// Nothing else to initialize.
+{ }
+
+/**
+ * The tree is the only member we may be responsible for deleting. The others
+ * will take care of themselves.
+ */
+template<typename SortPolicy, typename MetricType, typename TreeType>
+RASearch<SortPolicy, MetricType, TreeType>::
+~RASearch()
+{
+ if (ownReferenceTree)
+ delete referenceTree;
+ if (ownQueryTree)
+ delete queryTree;
+}
+
+/**
+ * Computes the best neighbors and stores them in resultingNeighbors and
+ * distances.
+ */
+template<typename SortPolicy, typename MetricType, typename TreeType>
+void RASearch<SortPolicy, MetricType, TreeType>::
+Search(const size_t k,
+ arma::Mat<size_t>& resultingNeighbors,
+ arma::mat& distances,
+ const double tau,
+ const double alpha,
+ const bool sampleAtLeaves,
+ const bool firstLeafExact,
+ const size_t singleSampleLimit)
+{
+ Timer::Start("computing_neighbors");
+ // If we have built the trees ourselves, then we will have to map all the
+ // indices back to their original indices when this computation is finished.
+ // To avoid an extra copy, we will store the neighbors and distances in a
+ // separate matrix.
+ arma::Mat<size_t>* neighborPtr = &resultingNeighbors;
+ arma::mat* distancePtr = &distances;
+
+ if (!naive) // if naive, no re-mapping required since points are not mapped
+ {
+ if (ownQueryTree || (ownReferenceTree && !queryTree))
+ distancePtr = new arma::mat; // Query indices need to be mapped.
+ if (ownReferenceTree || ownQueryTree)
+ neighborPtr = new arma::Mat<size_t>; // All indices need mapping.
+ }
+
+ // Set the size of the neighbor and distance matrices.
+ neighborPtr->set_size(k, querySet.n_cols);
+ distancePtr->set_size(k, querySet.n_cols);
+ distancePtr->fill(SortPolicy::WorstDistance());
+
+ size_t numPrunes = 0;
+
+ if (singleMode || naive)
+ {
+ // Create the helper object for the tree traversal.
+ typedef RASearchRules<SortPolicy, MetricType, TreeType> RuleType;
+ RuleType rules(referenceSet, querySet, *neighborPtr, *distancePtr,
+ metric, tau, alpha, naive, sampleAtLeaves, firstLeafExact,
+ singleSampleLimit);
+
+ if (!referenceTree->IsLeaf())
+ {
+ Log::Info << "Running single-tree..." << std::endl;
+
+ // Create the traverser.
+ typename TreeType::template SingleTreeTraverser<RuleType>
+ traverser(rules);
+
+ // Now have it traverse for each point.
+ for (size_t i = 0; i < querySet.n_cols; ++i)
+ traverser.Traverse(i, *referenceTree);
+
+ numPrunes = traverser.NumPrunes();
+ }
+ else
+ {
+ assert(naive);
+ Log::Info << "Naive sampling already done!" << std::endl;
+ }
+
+ Log::Info << "ST: NDC: " << rules.NumDistComputations()
+ / querySet.n_cols << std::endl;
+ }
+ else // Dual-tree recursion.
+ {
+ Log::Info << "Running dual-tree ..." << std::endl;
+
+ typedef RASearchRules<SortPolicy, MetricType, TreeType> RuleType;
+ RuleType rules(referenceSet, querySet, *neighborPtr, *distancePtr,
+ metric, tau, alpha, sampleAtLeaves, firstLeafExact,
+ singleSampleLimit);
+
+ typename TreeType::template DualTreeTraverser<RuleType> traverser(rules);
+
+ Log::Info << "DT: QS Pre-search: " << queryTree->Stat().NumSamplesMade() <<
+ std::endl;
+
+ if (queryTree)
+ traverser.Traverse(*queryTree, *referenceTree);
+ else
+ traverser.Traverse(*referenceTree, *referenceTree);
+
+ numPrunes = traverser.NumPrunes();
+
+ Log::Info << "DT: NDC: " << rules.NumDistComputations()
+ / querySet.n_cols << std::endl;
+ }
+
+ Timer::Stop("computing_neighbors");
+ Log::Info << "Pruned " << numPrunes << " nodes." << std::endl;
+
+
+ // Now, do we need to do mapping of indices?
+ if ((!ownReferenceTree && !ownQueryTree) || naive)
+ {
+ // No mapping needed if we do not own the trees or if
+ // we are doing naive sampling. We are done.
+ return;
+ }
+ else if (ownReferenceTree && ownQueryTree) // Map references and queries.
+ {
+ // Set size of output matrices correctly.
+ resultingNeighbors.set_size(k, querySet.n_cols);
+ distances.set_size(k, querySet.n_cols);
+
+ for (size_t i = 0; i < distances.n_cols; i++)
+ {
+ // Map distances (copy a column).
+ distances.col(oldFromNewQueries[i]) = distancePtr->col(i);
+
+ // Map indices of neighbors.
+ for (size_t j = 0; j < distances.n_rows; j++)
+ {
+ resultingNeighbors(j, oldFromNewQueries[i]) =
+ oldFromNewReferences[(*neighborPtr)(j, i)];
+ }
+ }
+
+ // Finished with temporary matrices.
+ delete neighborPtr;
+ delete distancePtr;
+ }
+ else if (ownReferenceTree)
+ {
+ if (!queryTree) // No query tree -- map both references and queries.
+ {
+ resultingNeighbors.set_size(k, querySet.n_cols);
+ distances.set_size(k, querySet.n_cols);
+
+ for (size_t i = 0; i < distances.n_cols; i++)
+ {
+ // Map distances (copy a column).
+ distances.col(oldFromNewReferences[i]) = distancePtr->col(i);
+
+ // Map indices of neighbors.
+ for (size_t j = 0; j < distances.n_rows; j++)
+ {
+ resultingNeighbors(j, oldFromNewReferences[i]) =
+ oldFromNewReferences[(*neighborPtr)(j, i)];
+ }
+ }
+ }
+ else // Map only references.
+ {
+ // Set size of neighbor indices matrix correctly.
+ resultingNeighbors.set_size(k, querySet.n_cols);
+
+ // Map indices of neighbors.
+ for (size_t i = 0; i < resultingNeighbors.n_cols; i++)
+ {
+ for (size_t j = 0; j < resultingNeighbors.n_rows; j++)
+ {
+ resultingNeighbors(j, i) = oldFromNewReferences[(*neighborPtr)(j, i)];
+ }
+ }
+ }
+
+ // Finished with temporary matrix.
+ delete neighborPtr;
+ }
+ else if (ownQueryTree)
+ {
+ // Set size of matrices correctly.
+ resultingNeighbors.set_size(k, querySet.n_cols);
+ distances.set_size(k, querySet.n_cols);
+
+ for (size_t i = 0; i < distances.n_cols; i++)
+ {
+ // Map distances (copy a column).
+ distances.col(oldFromNewQueries[i]) = distancePtr->col(i);
+
+ // Map indices of neighbors.
+ resultingNeighbors.col(oldFromNewQueries[i]) = neighborPtr->col(i);
+ }
+
+ // Finished with temporary matrices.
+ delete neighborPtr;
+ delete distancePtr;
+ }
+} // Search
+
+
+template<typename SortPolicy, typename MetricType, typename TreeType>
+void RASearch<SortPolicy, MetricType, TreeType>::
+ResetQueryTree()
+{
+ if (!singleMode)
+ {
+ if (queryTree)
+ ResetRAQueryStat(queryTree);
+ else
+ ResetRAQueryStat(referenceTree);
+ }
+
+ return;
+}
+
+
+template<typename SortPolicy, typename MetricType, typename TreeType>
+void RASearch<SortPolicy, MetricType, TreeType>::
+ResetRAQueryStat(TreeType* treeNode)
+{
+ treeNode->Stat().Bound() = SortPolicy::WorstDistance();
+ treeNode->Stat().NumSamplesMade() = 0;
+
+ if (!treeNode->IsLeaf())
+ for (size_t i = 0; i < treeNode->NumChildren(); i++)
+ ResetRAQueryStat(&treeNode->Child(i));
+
+ return;
+} // ResetRAQueryStat
+
+#endif
Added: mlpack/trunk/src/mlpack/methods/rann/ra_search_rules.hpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/rann/ra_search_rules.hpp (rev 0)
+++ mlpack/trunk/src/mlpack/methods/rann/ra_search_rules.hpp 2013-01-09 22:12:16 UTC (rev 14100)
@@ -0,0 +1,310 @@
+/**
+ * @file ra_search_rules.hpp
+ * @author Parikshit Ram
+ *
+ * Defines the pruning rules and base case rules necessary to perform a
+ * tree-based rank-approximate search (with an arbitrary tree)
+ * for the RASearch class.
+ */
+#ifndef __MLPACK_METHODS_NEIGHBOR_SEARCH_RA_SEARCH_RULES_HPP
+#define __MLPACK_METHODS_NEIGHBOR_SEARCH_RA_SEARCH_RULES_HPP
+
+namespace mlpack {
+namespace neighbor {
+
+template<typename SortPolicy, typename MetricType, typename TreeType>
+class RASearchRules
+{
+ public:
+ RASearchRules(const arma::mat& referenceSet,
+ const arma::mat& querySet,
+ arma::Mat<size_t>& neighbors,
+ arma::mat& distances,
+ MetricType& metric,
+ const double tau = 0.1,
+ const double alpha = 0.95,
+ const bool naive = false,
+ const bool sampleAtLeaves = false,
+ const bool firstLeafExact = false,
+ const size_t singleSampleLimit = 20);
+
+
+
+ double BaseCase(const size_t queryIndex, const size_t referenceIndex);
+
+ /**
+ * TOFIX: This function is specified for the cover tree (usually) so
+ * I need to think about it more algorithmically and keep its
+ * implementation mostly empty.
+ * Also, since the access to the points in the subtree of a cover tree
+ * is non-trivial, we might have to re-work this.
+ * FOR NOW: I am just using as for a BSP-tree, I will fix it when
+ * we figure out cover trees.
+ *
+ */
+
+ double Prescore(TreeType& queryNode,
+ TreeType& referenceNode,
+ TreeType& referenceChildNode,
+ const double baseCaseResult) const;
+ double PrescoreQ(TreeType& queryNode,
+ TreeType& queryChildNode,
+ TreeType& referenceNode,
+ const double baseCaseResult) const;
+
+
+
+ /**
+ * Get the score for recursion order. A low score indicates priority for
+ * recursion, while DBL_MAX indicates that the node should not be recursed
+ * into at all (it should be pruned).
+ *
+ * For rank-approximation, the scoring function first checks if pruning
+ * by distance is possible.
+ * If yes, then the node is given the score of
+ * 'DBL_MAX' and the expected number of samples from that node are
+ * added to the number of samples made for the query.
+ *
+ * If no, then the function tries to see if the node can be pruned by
+ * approximation. If number of samples required from this node is small
+ * enough, then that number of samples are acquired from this node
+ * and the score is set to be 'DBL_MAX'.
+ *
+ * If the pruning by approximation is not possible either, the algorithm
+ * continues with the usual tree-traversal.
+ *
+ * @param queryIndex Index of query point.
+ * @param referenceNode Candidate node to be recursed into.
+ */
+ double Score(const size_t queryIndex, TreeType& referenceNode);
+
+ /**
+ * Get the score for recursion order. A low score indicates priority for
+ * recursion, while DBL_MAX indicates that the node should not be recursed
+ * into at all (it should be pruned).
+ *
+ * For rank-approximation, the scoring function first checks if pruning
+ * by distance is possible.
+ * If yes, then the node is given the score of
+ * 'DBL_MAX' and the expected number of samples from that node are
+ * added to the number of samples made for the query.
+ *
+ * If no, then the function tries to see if the node can be pruned by
+ * approximation. If number of samples required from this node is small
+ * enough, then that number of samples are acquired from this node
+ * and the score is set to be 'DBL_MAX'.
+ *
+ * If the pruning by approximation is not possible either, the algorithm
+ * continues with the usual tree-traversal.
+ *
+ * @param queryIndex Index of query point.
+ * @param referenceNode Candidate node to be recursed into.
+ * @param baseCaseResult Result of BaseCase(queryIndex, referenceNode).
+ */
+ double Score(const size_t queryIndex,
+ TreeType& referenceNode,
+ const double baseCaseResult);
+
+ /**
+ * Re-evaluate the score for recursion order. A low score indicates priority
+ * for recursion, while DBL_MAX indicates that the node should not be
+ * recursed into at all (it should be pruned). This is used when the score
+ * has already been calculated, but another recursion may have modified the
+ * bounds for pruning. So the old score is checked against the new pruning
+ * bound.
+ *
+ * For rank-approximation, it also checks if the number of samples left
+ * for a query to satisfy the rank constraint is small enough at this
+ * point of the algorithm, then this node is approximated by sampling
+ * and given a new score of 'DBL_MAX'.
+ *
+ * @param queryIndex Index of query point.
+ * @param referenceNode Candidate node to be recursed into.
+ * @param oldScore Old score produced by Score() (or Rescore()).
+ */
+ double Rescore(const size_t queryIndex,
+ TreeType& referenceNode,
+ const double oldScore);
+
+ /**
+ * Get the score for recursion order. A low score indicates priority for
+ * recursionm while DBL_MAX indicates that the node should not be recursed
+ * into at all (it should be pruned).
+ *
+ * For the rank-approximation, we check if the referenceNode can be
+ * approximated by sampling. If it can be, enough samples are made for
+ * every query in the queryNode. No further query-tree traversal is
+ * performed.
+ *
+ * The 'NumSamplesMade' query stat is propagated up the tree. And then
+ * if pruning occurs (by distance or by sampling), the 'NumSamplesMade'
+ * stat is not propagated down the tree. If no pruning occurs, the
+ * stat is propagated down the tree.
+ *
+ * @param queryNode Candidate query node to recurse into.
+ * @param referenceNode Candidate reference node to recurse into.
+ */
+ double Score(TreeType& queryNode, TreeType& referenceNode);
+
+ /**
+ * Get the score for recursion order, passing the base case result (in the
+ * situation where it may be needed to calculate the recursion order). A low
+ * score indicates priority for recursion, while DBL_MAX indicates that the
+ * node should not be recursed into at all (it should be pruned).
+ *
+ * For the rank-approximation, we check if the referenceNode can be
+ * approximated by sampling. If it can be, enough samples are made for
+ * every query in the queryNode. No further query-tree traversal is
+ * performed.
+ *
+ * The 'NumSamplesMade' query stat is propagated up the tree. And then
+ * if pruning occurs (by distance or by sampling), the 'NumSamplesMade'
+ * stat is not propagated down the tree. If no pruning occurs, the
+ * stat is propagated down the tree.
+ *
+ * @param queryNode Candidate query node to recurse into.
+ * @param referenceNode Candidate reference node to recurse into.
+ * @param baseCaseResult Result of BaseCase(queryIndex, referenceNode).
+ */
+ double Score(TreeType& queryNode,
+ TreeType& referenceNode,
+ const double baseCaseResult);
+
+ /**
+ * Re-evaluate the score for recursion order. A low score indicates priority
+ * for recursion, while DBL_MAX indicates that the node should not be
+ * recursed into at all (it should be pruned). This is used when the score
+ * has already been calculated, but another recursion may have modified the
+ * bounds for pruning. So the old score is checked against the new pruning
+ * bound.
+ *
+ * For the rank-approximation, we check if the referenceNode can be
+ * approximated by sampling. If it can be, enough samples are made for
+ * every query in the queryNode. No further query-tree traversal is
+ * performed.
+ *
+ * The 'NumSamplesMade' query stat is propagated up the tree. And then
+ * if pruning occurs (by distance or by sampling), the 'NumSamplesMade'
+ * stat is not propagated down the tree. If no pruning occurs, the
+ * stat is propagated down the tree.
+ *
+ * @param queryNode Candidate query node to recurse into.
+ * @param referenceNode Candidate reference node to recurse into.
+ * @param oldScore Old score produced by Socre() (or Rescore()).
+ */
+ double Rescore(TreeType& queryNode,
+ TreeType& referenceNode,
+ const double oldScore);
+
+
+ size_t NumDistComputations() { return numDistComputations; }
+ size_t NumEffectiveSamples()
+ {
+ if (numSamplesMade.n_elem == 0)
+ return 0;
+ else
+ return arma::sum(numSamplesMade);
+ }
+
+ private:
+ //! The reference set.
+ const arma::mat& referenceSet;
+
+ //! The query set.
+ const arma::mat& querySet;
+
+ //! The matrix the resultant neighbor indices should be stored in.
+ arma::Mat<size_t>& neighbors;
+
+ //! The matrix the resultant neighbor distances should be stored in.
+ arma::mat& distances;
+
+ //! The instantiated metric.
+ MetricType& metric;
+
+ //! Whether to sample at leaves or just use all of it
+ bool sampleAtLeaves;
+
+ //! Whether to do exact computation on the first leaf before any sampling
+ bool firstLeafExact;
+
+ //! The limit on the largest node that can be approximated by sampling
+ size_t singleSampleLimit;
+
+ //! The minimum number of samples required per query
+ size_t numSamplesReqd;
+
+ //! The number of samples made for every query
+ arma::Col<size_t> numSamplesMade;
+
+ //! The sampling ratio
+ double samplingRatio;
+
+ // TO REMOVE: just for testing
+ size_t numDistComputations;
+
+
+ /**
+ * Insert a point into the neighbors and distances matrices; this is a helper
+ * function.
+ *
+ * @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.
+ */
+ void InsertNeighbor(const size_t queryIndex,
+ const size_t pos,
+ const size_t neighbor,
+ const double distance);
+
+ /**
+ * Compute the minimum number of samples required to guarantee
+ * the given rank-approximation and success probability.
+ *
+ * @param n Size of the set to be sampled from.
+ * @param k The number of neighbors required within the rank-approximation.
+ * @param tau The rank-approximation in percentile of the data.
+ * @param alpha The success probability desired.
+ */
+ size_t MinimumSamplesReqd(const size_t n,
+ const size_t k,
+ const double tau,
+ const double alpha) const;
+
+ /**
+ * Compute the success probability of obtaining 'k'-neighbors from a
+ * set of size 'n' within the top 't' neighbors if 'm' samples are made.
+ *
+ * @param n Size of the set being sampled from.
+ * @param k The number of neighbors required within the rank-approximation.
+ * @param m The number of random samples.
+ * @param t The desired rank-approximation.
+ */
+ double SuccessProbability(const size_t n,
+ const size_t k,
+ const size_t m,
+ const size_t t) const;
+
+ /**
+ * Pick up desired number of samples (with replacement) from a given range
+ * of integers so that only the distinct samples are returned from
+ * the range [0 - specified upper bound)
+ *
+ * @param numSamples Number of random samples.
+ * @param rangeUpperBound The upper bound on the range of integers.
+ * @param distinctSamples The list of the distinct samples.
+ */
+ void ObtainDistinctSamples(const size_t numSamples,
+ const size_t rangeUpperBound,
+ arma::uvec& distinctSamples) const;
+
+}; // class RASearchRules
+
+}; // namespace neighbor
+}; // namespace mlpack
+
+// Include implementation.
+#include "ra_search_rules_impl.hpp"
+
+#endif // __MLPACK_METHODS_NEIGHBOR_SEARCH_RA_SEARCH_RULES_HPP
Added: mlpack/trunk/src/mlpack/methods/rann/ra_search_rules_impl.hpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/rann/ra_search_rules_impl.hpp (rev 0)
+++ mlpack/trunk/src/mlpack/methods/rann/ra_search_rules_impl.hpp 2013-01-09 22:12:16 UTC (rev 14100)
@@ -0,0 +1,1298 @@
+/**
+ * @file ra_search_rules_impl.hpp
+ * @author Parikshit Ram
+ *
+ * Implementation of RASearchRules.
+ */
+#ifndef __MLPACK_METHODS_NEIGHBOR_SEARCH_RA_SEARCH_RULES_IMPL_HPP
+#define __MLPACK_METHODS_NEIGHBOR_SEARCH_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)
+{
+ Timer::Start("computing_number_of_samples_reqd");
+ numSamplesReqd = MinimumSamplesReqd(referenceSet.n_cols, neighbors.n_rows,
+ tau, alpha);
+ Timer::Stop("computing_number_of_samples_reqd");
+
+ // initializing some stats to be collected during the search
+ numSamplesMade = arma::zeros<arma::Col<size_t> >(querySet.n_cols);
+ numDistComputations = 0;
+ samplingRatio = (double) numSamplesReqd / (double) referenceSet.n_cols;
+
+ Log::Info << "Minimum Samples Required per-query: " << numSamplesReqd <<
+ ", Sampling Ratio: " << samplingRatio << std::endl;
+
+ if (naive) // no tree traversal going to happen, just do naive sampling here.
+ {
+ // sample enough number of points
+ for (size_t i = 0; i < querySet.n_cols; ++i)
+ {
+ arma::uvec distinctSamples;
+ ObtainDistinctSamples(numSamplesReqd, referenceSet.n_cols,
+ 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
+ size_t t = (size_t) std::ceil(tau * (double) n / 100.0);
+
+ double prob;
+ 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 (m + 1);
+}
+
+
+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, m);
+
+ } // faster implementation for topK = 1
+ else
+ {
+ if (m < k)
+ return 0.0;
+
+ if (m > n - t + k)
+ 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, 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, 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, j) * std::pow(1.0 - eps, m - j));
+ }
+
+ if (topHalf)
+ sum = 1.0 - sum;
+
+ return sum;
+ } // for k > 1
+} // FastComputeProb
+
+
+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);
+ size_t insertPosition = SortPolicy::SortDistance(queryDist, 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>::
+Prescore(TreeType& queryNode,
+ TreeType& referenceNode,
+ TreeType& referenceChildNode,
+ const double baseCaseResult) const
+{
+ return 0.0;
+}
+
+
+template<typename SortPolicy, typename MetricType, typename TreeType>
+inline double RASearchRules<SortPolicy, MetricType, TreeType>::
+PrescoreQ(TreeType& queryNode,
+ TreeType& queryChildNode,
+ TreeType& referenceNode,
+ const double baseCaseResult) const
+{
+ return 0.0;
+}
+
+
+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);
+
+
+ // 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 this node by sampling
+
+ // If you are required to visit the first leaf (to find possible
+ // duplicates), make sure you 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.Count());
+ 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()) // if not a leaf
+ {
+ // Then samplesReqd <= singleSampleLimit.
+ // Hence approximate node by sampling enough number of points
+ arma::uvec distinctSamples;
+ ObtainDistinctSamples(samplesReqd, referenceNode.Count(),
+ 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.Begin() + (size_t) 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.Count(),
+ 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.Begin() + (size_t) distinctSamples[i]);
+
+ // (Leaf) Node approximated so can prune it
+ return DBL_MAX;
+ }
+ else
+ {
+ // not allowed to sample from leaves, so cannot prune.
+ return distance;
+ } // sample at leaves
+ } // if not-leaf
+ } // if cannot-approximate by sampling
+ }
+ else
+ {
+ // try first to visit your first leaf to boost your accuracy
+ // and find your (near) duplicates if they exist
+ return distance;
+ } // if first-leaf exact visit required
+ }
+ 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 already made, this step does not change the
+ // result of the search.
+ numSamplesMade[queryIndex] +=
+ (size_t) std::floor(samplingRatio * (double) referenceNode.Count());
+
+ return DBL_MAX;
+ } // if can-prune
+} // Score(point, node)
+
+
+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);
+
+ // Hereon, this 'Score' function is exactly same as the previous
+ // 'Score' function.
+
+ // 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 this node by sampling
+
+ // If you are required to visit the first leaf (to find possible
+ // duplicates), make sure you do not approximate.
+ if (numSamplesMade[queryIndex] > 0 || !firstLeafExact)
+ {
+ // Check if this node can be approximated by sampling:
+ // 'referenceNode.Count() should correspond to the number of points
+ // present in this subtree.
+ size_t samplesReqd =
+ (size_t) std::ceil(samplingRatio * (double) referenceNode.Count());
+ 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()) // if not a leaf
+ {
+ // Then samplesReqd <= singleSampleLimit.
+ // Hence approximate node by sampling enough number of points
+ // from this subtree.
+ arma::uvec distinctSamples;
+ ObtainDistinctSamples(samplesReqd, referenceNode.Count(),
+ 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.Begin() + (size_t) 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.Count(),
+ 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.Begin() + (size_t) distinctSamples[i]);
+
+ // (Leaf) Node approximated so can prune it
+ return DBL_MAX;
+ }
+ else
+ {
+ // not allowed to sample from leaves, so cannot prune.
+ return distance;
+ } // sample at leaves
+ } // if not-leaf
+ } // if cannot-approximate by sampling
+ }
+ else
+ {
+ // try first to visit your first leaf to boost your accuracy
+ return distance;
+ } // if first-leaf exact visit required
+ }
+ 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 already made, this step does not change the
+ // result of the search.
+ if (numSamplesMade[queryIndex] < numSamplesReqd)
+ // add 'fake' samples from this node; fake because the distances to
+ // these samples need not be computed.
+ numSamplesMade[queryIndex] +=
+ (size_t) std::floor(samplingRatio * (double) referenceNode.Count());
+
+ return DBL_MAX;
+ } // if can-prune
+
+ return (SortPolicy::IsBetter(distance, bestDistance)) ? distance : DBL_MAX;
+
+} // Score(point, node, point-node-point-distance)
+
+
+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
+ // 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 checks 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.Count());
+ 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 oldScore;
+ }
+ else
+ {
+ if (!referenceNode.IsLeaf()) // if not a leaf
+ {
+ // Then samplesReqd <= singleSampleLimit.
+ // Hence approximate node by sampling enough number of points
+ arma::uvec distinctSamples;
+ ObtainDistinctSamples(samplesReqd, referenceNode.Count(),
+ 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.Begin() + (size_t) 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.Count(),
+ 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.Begin() + (size_t) distinctSamples[i]);
+
+ // (Leaf) Node approximated so can prune it
+ return DBL_MAX;
+ }
+ else
+ {
+ // not allowed to sample from leaves, so cannot prune.
+ return oldScore;
+ } // sample at leaves
+ } // if not-leaf
+ } // if cannot-approximate by sampling
+ }
+ 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 already made, this step does not change the
+ // result of the search.
+ numSamplesMade[queryIndex] +=
+ (size_t) std::floor(samplingRatio * (double) referenceNode.Count());
+
+ return DBL_MAX;
+ } // if can-prune
+} // 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.
+
+ // finding 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();
+
+ // 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 to 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 you are required to visit the first leaf (to find possible
+ // duplicates), make sure you 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.Count());
+ samplesReqd
+ = std::min(samplesReqd,
+ numSamplesReqd - queryNode.Stat().NumSamplesMade());
+
+ if (samplesReqd > singleSampleLimit && !referenceNode.IsLeaf())
+ {
+ // if too many samples required and not at a leaf, then can't prune
+
+ // Since query tree descend 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()) // if not a leaf
+ {
+ // Then samplesReqd <= singleSampleLimit.
+ // Hence approximate node by sampling enough number of points
+ // for every query in the 'queryNode'
+ for (size_t queryIndex = queryNode.Begin();
+ queryIndex < queryNode.End(); queryIndex++)
+ {
+ arma::uvec distinctSamples;
+ ObtainDistinctSamples(samplesReqd, referenceNode.Count(),
+ 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.Begin() + (size_t) distinctSamples[i]);
+ }
+
+ // 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 you are not going to descend down the query tree for this
+ // referenceNode, there is no point updating the number of
+ // samples made for the child nodes of this queryNode.
+
+ // 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
+ // for every query in the 'queryNode'.
+ for (size_t queryIndex = queryNode.Begin();
+ queryIndex < queryNode.End(); queryIndex++)
+ {
+ arma::uvec distinctSamples;
+ ObtainDistinctSamples(samplesReqd, referenceNode.Count(),
+ 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.Begin() + (size_t) distinctSamples[i]);
+ }
+
+ // 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 you are not going to descend down the query tree for this
+ // referenceNode, there is no point updating the number of
+ // samples made for the child nodes of this queryNode.
+
+ // (Leaf) Node approximated so can prune it
+ return DBL_MAX;
+ }
+ else
+ {
+ // Not allowed to sample from leaves, so 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;
+ } // sample at leaves
+ } // if not-leaf
+ } // if cannot-approximate by sampling
+ }
+ else
+ {
+ // Have to first to visit your first leaf to boost your accuracy
+
+ // 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;
+ } // if first-leaf exact visit required
+ }
+ 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 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.Count());
+
+ // since you 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 queryNode.
+
+ return DBL_MAX;
+ } // if can-prune
+} // Score(node, node)
+
+
+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();
+
+ // 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 to 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 you are required to visit the first leaf (to find possible
+ // duplicates), make sure you 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.Count());
+ samplesReqd
+ = std::min(samplesReqd,
+ numSamplesReqd - queryNode.Stat().NumSamplesMade());
+
+ if (samplesReqd > singleSampleLimit && !referenceNode.IsLeaf())
+ {
+ // if too many samples required and not at a leaf, then can't prune
+
+ // Since query tree descend 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()) // if not a leaf
+ {
+ // Then samplesReqd <= singleSampleLimit.
+ // Hence approximate node by sampling enough number of points
+ // for every query in the 'queryNode'
+ for (size_t queryIndex = queryNode.Begin();
+ queryIndex < queryNode.End(); queryIndex++)
+ {
+ arma::uvec distinctSamples;
+ ObtainDistinctSamples(samplesReqd, referenceNode.Count(),
+ 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.Begin() + (size_t) distinctSamples[i]);
+ }
+
+ // 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 you are not going to descend down the query tree for this
+ // referenceNode, there is no point updating the number of
+ // samples made for the child nodes of this queryNode.
+
+ // 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
+ // for every query in the 'queryNode'.
+ for (size_t queryIndex = queryNode.Begin();
+ queryIndex < queryNode.End(); queryIndex++)
+ {
+ arma::uvec distinctSamples;
+ ObtainDistinctSamples(samplesReqd, referenceNode.Count(),
+ 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.Begin() + (size_t) distinctSamples[i]);
+ }
+
+ // 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 you are not going to descend down the query tree for this
+ // referenceNode, there is no point updating the number of
+ // samples made for the child nodes of this queryNode.
+
+ // (Leaf) Node approximated so can prune it
+ return DBL_MAX;
+ }
+ else
+ {
+ // Not allowed to sample from leaves, so 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;
+ } // sample at leaves
+ } // if not-leaf
+ } // if cannot-approximate by sampling
+ }
+ else
+ {
+ // Have to first to visit your first leaf to boost your accuracy
+
+ // 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;
+ } // if first-leaf exact visit required
+ }
+ 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 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.Count());
+
+ // since you 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 queryNode.
+
+ return DBL_MAX;
+ } // if can-prune
+} // Score(node, node, baseCaseResult)
+
+
+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 to 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
+ // 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 checks 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.Count());
+ samplesReqd
+ = std::min(samplesReqd,
+ numSamplesReqd - queryNode.Stat().NumSamplesMade());
+
+ if (samplesReqd > singleSampleLimit && !referenceNode.IsLeaf())
+ {
+ // if too many samples required and not at a leaf, then can't prune
+
+ // Since query tree descend 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 node by sampling enough number of points
+ // for every query in the 'queryNode'
+ for (size_t queryIndex = queryNode.Begin();
+ queryIndex < queryNode.End(); queryIndex++)
+ {
+ arma::uvec distinctSamples;
+ ObtainDistinctSamples(samplesReqd, referenceNode.Count(),
+ 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.Begin() + (size_t) distinctSamples[i]);
+ }
+
+ // 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 you are not going to descend down the query tree for this
+ // referenceNode, there is no point updating the number of
+ // samples made for the child nodes of this queryNode.
+
+ // 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
+ // for every query in the 'queryNode'.
+ for (size_t queryIndex = queryNode.Begin();
+ queryIndex < queryNode.End(); queryIndex++)
+ {
+ arma::uvec distinctSamples;
+ ObtainDistinctSamples(samplesReqd, referenceNode.Count(),
+ 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.Begin() + (size_t) distinctSamples[i]);
+ }
+
+ // 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 you are not going to descend down the query tree for this
+ // referenceNode, there is no point updating the number of
+ // samples made for the child nodes of this queryNode.
+
+ // (Leaf) Node approximated so can prune it
+ return DBL_MAX;
+ }
+ else
+ {
+ // not allowed to sample from leaves, so cannot prune.
+ // propagate the number of samples made down to the children
+
+ // going 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 oldScore;
+ } // sample at leaves
+ } // if not-leaf
+ } // if cannot-approximate by sampling
+ }
+ 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 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.Count());
+
+ // since you 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 queryNode.
+
+ return DBL_MAX;
+ } // if can-prune
+} // 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_NEIGHBOR_SEARCH_NEAREST_NEIGHBOR_RULES_IMPL_HPP
Added: mlpack/trunk/src/mlpack/methods/rann/ra_typedef.hpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/rann/ra_typedef.hpp (rev 0)
+++ mlpack/trunk/src/mlpack/methods/rann/ra_typedef.hpp 2013-01-09 22:12:16 UTC (rev 14100)
@@ -0,0 +1,53 @@
+/**
+ * @file ra_typedef.hpp
+ * @author Parikshit Ram
+ *
+ * Simple typedefs describing template instantiations of the RASearch
+ * class which are commonly used.
+ */
+#ifndef __MLPACK_NEIGHBOR_SEARCH_RA_TYPEDEF_H
+#define __MLPACK_NEIGHBOR_SEARCH_RA_TYPEDEF_H
+
+// In case someone included this directly.
+#include "ra_search.hpp"
+
+#include <mlpack/core/metrics/lmetric.hpp>
+
+#include <mlpack/methods/neighbor_search/sort_policies/nearest_neighbor_sort.hpp>
+#include <mlpack/methods/neighbor_search/sort_policies/furthest_neighbor_sort.hpp>
+
+namespace mlpack {
+namespace neighbor {
+
+/**
+ * The AllkRANN class is the all-k-rank-approximate-nearest-neighbors method.
+ * It returns squared L2 distances (squared Euclidean distances) for each
+ * of the k rank-approximate nearest-neighbors. Squared distances are used
+ * because they are slightly faster than non-squared distances
+ * (they have one fewer call to sqrt()).
+ *
+ * The approximation is controlled with two parameters (see allkrann_main.cpp)
+ * which can be specified at search time. So the tree building is done
+ * only once while the search can be performed multiple times with
+ * different approximation levels.
+ */
+typedef RASearch<> AllkRANN;
+
+/**
+ * The AllkRAFN class is the all-k-rank-approximate-farthest-neighbors method.
+ * It returns squared L2 distances (squared Euclidean distances) for each
+ * of the k rank-approximate farthest-neighbors. Squared distances are used
+ * because they are slightly faster than non-squared distances
+ * (they have one fewer call to sqrt()).
+ *
+ * The approximation is controlled with two parameters (see allkrann_main.cpp)
+ * which can be specified at search time. So the tree building is done
+ * only once while the search can be performed multiple times with
+ * different approximation levels.
+ */
+typedef RASearch<FurthestNeighborSort> AllkRAFN;
+
+}; // namespace neighbor
+}; // namespace mlpack
+
+#endif
Modified: mlpack/trunk/src/mlpack/tests/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/tests/CMakeLists.txt 2013-01-09 22:10:49 UTC (rev 14099)
+++ mlpack/trunk/src/mlpack/tests/CMakeLists.txt 2013-01-09 22:12:16 UTC (rev 14100)
@@ -3,6 +3,7 @@
mlpack_test.cpp
allkfn_test.cpp
allknn_test.cpp
+ allkrann_search_test.cpp
arma_extend_test.cpp
aug_lagrangian_test.cpp
cli_test.cpp
Added: mlpack/trunk/src/mlpack/tests/allkrann_search_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/tests/allkrann_search_test.cpp (rev 0)
+++ mlpack/trunk/src/mlpack/tests/allkrann_search_test.cpp 2013-01-09 22:12:16 UTC (rev 14100)
@@ -0,0 +1,307 @@
+/**
+ * @file allkrann_search_test.cpp
+ *
+ * Unit tests for the 'RASearch' class and consequently the
+ * 'RASearchRules' class
+ */
+#include <time.h>
+#include <mlpack/core.hpp>
+#include <mlpack/core/metrics/lmetric.hpp>
+
+// So that we can test private members. This is hackish (for now).
+#define private public
+#include <mlpack/methods/rann/ra_search.hpp>
+#undef private
+
+#include <boost/test/unit_test.hpp>
+#include "old_boost_test_definitions.hpp"
+
+using namespace std;
+using namespace mlpack;
+using namespace mlpack::neighbor;
+
+
+BOOST_AUTO_TEST_SUITE(AllkRANNTest);
+
+BOOST_AUTO_TEST_CASE(AllkRANNNaiveSearch)
+{
+ // first testing on a small set.
+
+ arma::mat rdata(2, 10);
+ rdata << 3 << 2 << 4 << 3 << 5 << 6 << 0 << 8 << 3 << 1 << arma::endr <<
+ 0 << 3 << 4 << 7 << 8 << 4 << 1 << 0 << 4 << 3 << arma::endr;
+
+ arma::mat qdata(2, 3);
+ qdata << 3 << 2 << 0 << arma::endr << 5 << 3 << 4 << arma::endr;
+
+
+ metric::SquaredEuclideanDistance dMetric;
+ double rankApproximation = 30;
+ double successProb = 0.95;
+
+ // Search for 1 rank-approximate nearest-neighbors in the top 30%
+ // of the point (rank error of 3)
+ arma::Mat<size_t> neighbors;
+ arma::mat distances;
+
+
+ // Test naive rank-approximate search
+
+ // Predict what the actual RANN-RS result would be
+ math::RandomSeed(0);
+
+ size_t numSamples = (size_t) ceil( log (1.0 / (1.0 - successProb))
+ / log (1.0 / (1.0 - (rankApproximation / 100.0) ) ) );
+
+ arma::Mat<size_t> samples(qdata.n_cols, numSamples);
+ for (size_t j = 0; j < qdata.n_cols; j++)
+ for (size_t i = 0; i < numSamples; i++)
+ samples(j, i) = (size_t) math::RandInt(10);
+
+ arma::Col<size_t> rann(qdata.n_cols);
+ arma::vec rann_distance(qdata.n_cols);
+ rann_distance.fill(DBL_MAX);
+
+ for (size_t j = 0; j < qdata.n_cols; j++)
+ {
+ for (size_t i = 0; i < numSamples; i++)
+ {
+ double dist = dMetric.Evaluate(qdata.unsafe_col(j),
+ rdata.unsafe_col(samples(j, i)));
+ if (dist < rann_distance[j])
+ {
+ rann[j] = samples(j, i);
+ rann_distance[j] = dist;
+ }
+ }
+ }
+
+ // use RANN-RS implementation
+ math::RandomSeed(0);
+
+ arma::mat naive_rdata = rdata;
+ arma::mat naive_qdata = qdata;
+
+ RASearch<> *naive = new RASearch<>(naive_rdata, naive_qdata, true);
+ naive->Search(1, neighbors, distances, rankApproximation);
+
+ delete naive;
+ naive_rdata.reset();
+ naive_qdata.reset();
+
+ // Things to check:
+ //
+ // 1. (implicitly) The minimum number of required samples for
+ // guaranteed approximation
+ // 2. (implicitly) Check the samples obtained.
+ // 3. Check the neighbor returned.
+
+ for (size_t i = 0; i < qdata.n_cols; i++)
+ {
+ BOOST_REQUIRE(neighbors(0, i) == rann[i]);
+ BOOST_REQUIRE_CLOSE(distances(0, i), rann_distance[i], 1e-5);
+ }
+
+ Log::Warn << "RANN-RS (no tree) works as expected on small set." << endl;
+
+ neighbors.reset();
+ distances.reset();
+
+ // now test the correctness & guarantees of this algorithm
+ math::RandomSeed(time(NULL));
+
+ arma::mat refData;
+ arma::mat queryData;
+
+ data::Load("./data/rann_test_r_3_900.csv", refData, true);
+ data::Load("./data/rann_test_q_3_100.csv", queryData, true);
+
+ RASearch<> *rann_rs = new RASearch<>(refData, queryData, true);
+
+ arma::mat qrRanks;
+ data::Load("./data/rann_test_qr_ranks.csv", qrRanks, true);
+ qrRanks = qrRanks.t();
+
+ size_t numRounds = 1000;
+ arma::Col<size_t> numSuccessRounds(queryData.n_cols);
+ numSuccessRounds.fill(0);
+
+ // 1% of 900 is 9, so the rank is expected to be less than 10
+ size_t expectedRankErrorUB = 10;
+
+ for (size_t rounds = 0; rounds < numRounds; rounds++)
+ {
+ rann_rs->Search(1, neighbors, distances, 1.0);
+
+ for (size_t i = 0; i < queryData.n_cols; i++)
+ if (qrRanks(i, neighbors(0, i)) < expectedRankErrorUB)
+ numSuccessRounds[i]++;
+
+ neighbors.reset();
+ distances.reset();
+ }
+
+ delete rann_rs;
+
+ // Finding the 95%-tile threshold so that 95% of the queries should
+ // pass this threshold
+ size_t threshold = floor(numRounds * (0.95 - (1.96 * sqrt(0.95 * 0.05
+ / numRounds))));
+ size_t numQueriesFail = 0;
+ for (size_t i = 0; i < queryData.n_cols; i++)
+ if (numSuccessRounds[i] < threshold)
+ numQueriesFail++;
+
+ Log::Warn << "RANN-RS: RANN guarantee fails on " << numQueriesFail <<
+ " queries." << endl;
+
+ // assert that at most 5% of the queries fall out of this threshold
+ // 5% of 100 queries is 5.
+ size_t maxNumQueriesFail = 6;
+
+ BOOST_REQUIRE(numQueriesFail < maxNumQueriesFail);
+ Log::Warn << "RANN-RS (no tree) guarantees desired rank-approximation." <<
+ endl;
+}
+
+
+BOOST_AUTO_TEST_CASE(AllkRANNSingleTreeSearch)
+{
+ // Test single-tree rank-approximate search (harder to test because of
+ // the randomness involved)
+
+ // Checking the correctness & guarantees of the algorithm
+ math::RandomSeed(time(NULL));
+
+ arma::mat refData;
+ arma::mat queryData;
+
+ data::Load("./data/rann_test_r_3_900.csv", refData, true);
+ data::Load("./data/rann_test_q_3_100.csv", queryData, true);
+
+ // Search for 1 rank-approximate nearest-neighbors in the top 30%
+ // of the point (rank error of 3)
+ arma::Mat<size_t> neighbors;
+ arma::mat distances;
+
+
+ RASearch<> *rann_tss = new RASearch<>(refData, queryData, false, true, 5);
+
+ // The relative ranks for the given query reference pair
+ arma::Mat<size_t> qrRanks;
+ data::Load("./data/rann_test_qr_ranks.csv", qrRanks, true);
+ qrRanks = qrRanks.t();
+
+ size_t numRounds = 1000;
+ arma::Col<size_t> numSuccessRounds(queryData.n_cols);
+ numSuccessRounds.fill(0);
+
+ // 1% of 900 is 9, so the rank is expected to be less than 10
+ size_t expectedRankErrorUB = 10;
+
+ for (size_t rounds = 0; rounds < numRounds; rounds++)
+ {
+ rann_tss->Search(1, neighbors, distances, 1.0, 0.95, false, false, 5);
+
+ for (size_t i = 0; i < queryData.n_cols; i++)
+ if (qrRanks(i, neighbors(0, i)) < expectedRankErrorUB)
+ numSuccessRounds[i]++;
+
+ neighbors.reset();
+ distances.reset();
+ }
+
+ delete rann_tss;
+
+ // Finding the 95%-tile threshold so that 95% of the queries should
+ // pass this threshold
+ size_t threshold = floor(numRounds * (0.95 - (1.96 * sqrt(0.95 * 0.05
+ / numRounds))));
+ size_t numQueriesFail = 0;
+ for (size_t i = 0; i < queryData.n_cols; i++)
+ if (numSuccessRounds[i] < threshold)
+ numQueriesFail++;
+
+ Log::Warn << "RANN-TSS: RANN guarantee fails on " << numQueriesFail <<
+ " queries." << endl;
+
+ // assert that at most 5% of the queries fall out of this threshold
+ // 5% of 100 queries is 5.
+ size_t maxNumQueriesFail = 6;
+
+ BOOST_REQUIRE(numQueriesFail < maxNumQueriesFail);
+ Log::Warn << "RANN-TSS (single tree) guarantees desired " <<
+ "rank-approximation." << endl;
+}
+
+BOOST_AUTO_TEST_CASE(AllkRANNDualTreeSearch)
+{
+ // Test dual-tree rank-approximate search (harder to test because of
+ // the randomness involved)
+ // Test dual-tree rank-approximate search
+
+ // Checking the correctness & guarantees of the algorithm
+ math::RandomSeed(time(NULL));
+
+ arma::mat refData;
+ arma::mat queryData;
+
+ data::Load("./data/rann_test_r_3_900.csv", refData, true);
+ data::Load("./data/rann_test_q_3_100.csv", queryData, true);
+
+ // Search for 1 rank-approximate nearest-neighbors in the top 30%
+ // of the point (rank error of 3)
+ arma::Mat<size_t> neighbors;
+ arma::mat distances;
+
+ RASearch<> *rann_tsd = new RASearch<>(refData, queryData, false, false, 5);
+
+ arma::Mat<size_t> qrRanks;
+ data::Load("./data/rann_test_qr_ranks.csv", qrRanks, true);
+ qrRanks = qrRanks.t();
+
+ size_t numRounds = 1000;
+ arma::Col<size_t> numSuccessRounds(queryData.n_cols);
+ numSuccessRounds.fill(0);
+
+ // 1% of 900 is 9, so the rank is expected to be less than 10
+ size_t expectedRankErrorUB = 10;
+
+ for (size_t rounds = 0; rounds < numRounds; rounds++)
+ {
+ rann_tsd->Search(1, neighbors, distances, 1.0, 0.95, false, false, 5);
+
+ for (size_t i = 0; i < queryData.n_cols; i++)
+ if (qrRanks(i, neighbors(0, i)) < expectedRankErrorUB)
+ numSuccessRounds[i]++;
+
+ neighbors.reset();
+ distances.reset();
+
+ rann_tsd->ResetQueryTree();
+ }
+
+ delete rann_tsd;
+
+ // Finding the 95%-tile threshold so that 95% of the queries should
+ // pass this threshold
+ size_t threshold = floor(numRounds * (0.95 - (1.96 * sqrt(0.95 * 0.05
+ / numRounds))));
+ size_t numQueriesFail = 0;
+ for (size_t i = 0; i < queryData.n_cols; i++)
+ if (numSuccessRounds[i] < threshold)
+ numQueriesFail++;
+
+ Log::Warn << "RANN-TSD: RANN guarantee fails on " << numQueriesFail <<
+ " queries." << endl;
+
+ // assert that at most 5% of the queries fall out of this threshold
+ // 5% of 100 queries is 5.
+ size_t maxNumQueriesFail = 6;
+
+ BOOST_REQUIRE(numQueriesFail < maxNumQueriesFail);
+ Log::Warn << "RANN-TSD (dual tree) guarantees desired " <<
+ "rank-approximation." << endl;
+}
+
+BOOST_AUTO_TEST_SUITE_END();
Added: mlpack/trunk/src/mlpack/tests/data/rann_test_q_3_100.csv
===================================================================
--- mlpack/trunk/src/mlpack/tests/data/rann_test_q_3_100.csv (rev 0)
+++ mlpack/trunk/src/mlpack/tests/data/rann_test_q_3_100.csv 2013-01-09 22:12:16 UTC (rev 14100)
@@ -0,0 +1,100 @@
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Added: mlpack/trunk/src/mlpack/tests/data/rann_test_qr_ranks.csv
===================================================================
--- mlpack/trunk/src/mlpack/tests/data/rann_test_qr_ranks.csv (rev 0)
+++ mlpack/trunk/src/mlpack/tests/data/rann_test_qr_ranks.csv 2013-01-09 22:12:16 UTC (rev 14100)
@@ -0,0 +1,100 @@
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Added: mlpack/trunk/src/mlpack/tests/data/rann_test_r_3_900.csv
===================================================================
--- mlpack/trunk/src/mlpack/tests/data/rann_test_r_3_900.csv (rev 0)
+++ mlpack/trunk/src/mlpack/tests/data/rann_test_r_3_900.csv 2013-01-09 22:12:16 UTC (rev 14100)
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