14 #ifndef MLPACK_METHODS_RANN_RA_SEARCH_RULES_HPP
15 #define MLPACK_METHODS_RANN_RA_SEARCH_RULES_HPP
30 template<
typename SortPolicy,
typename MetricType,
typename TreeType>
56 const arma::mat& querySet,
60 const double alpha = 0.95,
61 const bool naive =
false,
62 const bool sampleAtLeaves =
false,
63 const bool firstLeafExact =
false,
64 const size_t singleSampleLimit = 20,
65 const bool sameSet =
false);
74 void GetResults(arma::Mat<size_t>& neighbors, arma::mat& distances);
83 double BaseCase(
const size_t queryIndex,
const size_t referenceIndex);
107 double Score(
const size_t queryIndex, TreeType& referenceNode);
132 double Score(
const size_t queryIndex,
133 TreeType& referenceNode,
134 const double baseCaseResult);
153 double Rescore(
const size_t queryIndex,
154 TreeType& referenceNode,
155 const double oldScore);
175 double Score(TreeType& queryNode, TreeType& referenceNode);
197 double Score(TreeType& queryNode,
198 TreeType& referenceNode,
199 const double baseCaseResult);
223 double Rescore(TreeType& queryNode,
224 TreeType& referenceNode,
225 const double oldScore);
231 if (numSamplesMade.n_elem == 0)
234 return arma::sum(numSamplesMade);
244 const arma::mat& referenceSet;
247 const arma::mat& querySet;
250 typedef std::pair<double, size_t> Candidate;
253 struct CandidateCmp {
254 bool operator()(
const Candidate& c1,
const Candidate& c2)
256 return !SortPolicy::IsBetter(c2.first, c1.first);
261 typedef std::priority_queue<Candidate, std::vector<Candidate>, CandidateCmp>
265 std::vector<CandidateList> candidates;
280 size_t singleSampleLimit;
283 size_t numSamplesReqd;
286 arma::Col<size_t> numSamplesMade;
289 double samplingRatio;
292 size_t numDistComputations;
306 void InsertNeighbor(
const size_t queryIndex,
307 const size_t neighbor,
308 const double distance);
313 double Score(
const size_t queryIndex,
314 TreeType& referenceNode,
315 const double distance,
316 const double bestDistance);
321 double Score(TreeType& queryNode,
322 TreeType& referenceNode,
323 const double distance,
324 const double bestDistance);
326 static_assert(tree::TreeTraits<TreeType>::UniqueNumDescendants,
"TreeType "
327 "must provide a unique number of descendants points.");
334 #include "ra_search_rules_impl.hpp"
336 #endif // MLPACK_METHODS_RANN_RA_SEARCH_RULES_HPP
RASearchRules(const arma::mat &referenceSet, const arma::mat &querySet, const size_t k, MetricType &metric, const double tau=5, const double alpha=0.95, const bool naive=false, const bool sampleAtLeaves=false, const bool firstLeafExact=false, const size_t singleSampleLimit=20, const bool sameSet=false)
Construct the RASearchRules object.
The TraversalInfo class holds traversal information which is used in dual-tree (and single-tree) trav...
tree::TraversalInfo< TreeType > TraversalInfoType
const TraversalInfoType & TraversalInfo() const
double Rescore(const size_t queryIndex, TreeType &referenceNode, const double oldScore)
Re-evaluate the score for recursion order.
double Score(const size_t queryIndex, TreeType &referenceNode)
Get the score for recursion order.
size_t NumDistComputations()
size_t NumEffectiveSamples()
see subsection cli_alt_reg_tut Alternate DET regularization The usual regularized error f $R_ alpha(t)\f $of a node\f $t\f $is given by
TraversalInfoType & TraversalInfo()
double BaseCase(const size_t queryIndex, const size_t referenceIndex)
Get the distance from the query point to the reference point.
void GetResults(arma::Mat< size_t > &neighbors, arma::mat &distances)
Store the list of candidates for each query point in the given matrices.
The RASearchRules class is a template helper class used by RASearch class when performing rank-approx...