neighbor_search.hpp
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1 
13 #ifndef MLPACK_METHODS_NEIGHBOR_SEARCH_NEIGHBOR_SEARCH_HPP
14 #define MLPACK_METHODS_NEIGHBOR_SEARCH_NEIGHBOR_SEARCH_HPP
15 
16 #include <mlpack/prereqs.hpp>
17 #include <vector>
18 #include <string>
19 
23 
24 #include "neighbor_search_stat.hpp"
27 
28 namespace mlpack {
29 // Neighbor-search routines. These include all-nearest-neighbors and
30 // all-furthest-neighbors searches.
31 namespace neighbor {
32 
33 // Forward declaration.
34 template<typename SortPolicy>
36 
39 {
44 };
45 
69 template<typename SortPolicy = NearestNeighborSort,
70  typename MetricType = mlpack::metric::EuclideanDistance,
71  typename MatType = arma::mat,
72  template<typename TreeMetricType,
73  typename TreeStatType,
74  typename TreeMatType> class TreeType = tree::KDTree,
75  template<typename RuleType> class DualTreeTraversalType =
76  TreeType<MetricType,
78  MatType>::template DualTreeTraverser,
79  template<typename RuleType> class SingleTreeTraversalType =
80  TreeType<MetricType,
81  NeighborSearchStat<SortPolicy>,
82  MatType>::template SingleTreeTraverser>
84 {
85  public:
87  typedef TreeType<MetricType, NeighborSearchStat<SortPolicy>, MatType> Tree;
88 
105  NeighborSearch(MatType referenceSet,
106  const NeighborSearchMode mode = DUAL_TREE_MODE,
107  const double epsilon = 0,
108  const MetricType metric = MetricType());
109 
133  NeighborSearch(Tree referenceTree,
134  const NeighborSearchMode mode = DUAL_TREE_MODE,
135  const double epsilon = 0,
136  const MetricType metric = MetricType());
137 
148  const double epsilon = 0,
149  const MetricType metric = MetricType());
150 
157  NeighborSearch(const NeighborSearch& other);
158 
166 
172  NeighborSearch& operator=(const NeighborSearch& other);
173 
180 
185  ~NeighborSearch();
186 
196  void Train(MatType referenceSet);
197 
207  void Train(Tree referenceTree);
208 
226  void Search(const MatType& querySet,
227  const size_t k,
228  arma::Mat<size_t>& neighbors,
229  arma::mat& distances);
230 
251  void Search(Tree& queryTree,
252  const size_t k,
253  arma::Mat<size_t>& neighbors,
254  arma::mat& distances,
255  bool sameSet = false);
256 
271  void Search(const size_t k,
272  arma::Mat<size_t>& neighbors,
273  arma::mat& distances);
274 
290  static double EffectiveError(arma::mat& foundDistances,
291  arma::mat& realDistances);
292 
304  static double Recall(arma::Mat<size_t>& foundNeighbors,
305  arma::Mat<size_t>& realNeighbors);
306 
309  size_t BaseCases() const { return baseCases; }
310 
312  size_t Scores() const { return scores; }
313 
315  NeighborSearchMode SearchMode() const { return searchMode; }
317  NeighborSearchMode& SearchMode() { return searchMode; }
318 
320  double Epsilon() const { return epsilon; }
322  double& Epsilon() { return epsilon; }
323 
325  const MatType& ReferenceSet() const { return *referenceSet; }
326 
328  const Tree& ReferenceTree() const { return *referenceTree; }
330  Tree& ReferenceTree() { return *referenceTree; }
331 
333  template<typename Archive>
334  void serialize(Archive& ar, const unsigned int /* version */);
335 
336  private:
338  std::vector<size_t> oldFromNewReferences;
340  Tree* referenceTree;
342  const MatType* referenceSet;
343 
345  NeighborSearchMode searchMode;
347  double epsilon;
348 
350  MetricType metric;
351 
353  size_t baseCases;
355  size_t scores;
356 
359  bool treeNeedsReset;
360 
362  template<typename SortPol>
363  friend class TrainVisitor;
364 }; // class NeighborSearch
365 
366 } // namespace neighbor
367 } // namespace mlpack
368 
369 // Include implementation.
370 #include "neighbor_search_impl.hpp"
371 
372 // Include convenience typedefs.
373 #include "typedef.hpp"
374 
375 #endif
const MatType & ReferenceSet() const
Access the reference dataset.
double Epsilon() const
Access the relative error to be considered in approximate search.
size_t Scores() const
Return the number of node combination scores during the last search.
const Tree & ReferenceTree() const
Access the reference tree.
Linear algebra utility functions, generally performed on matrices or vectors.
Extra data for each node in the tree.
The core includes that mlpack expects; standard C++ includes and Armadillo.
NeighborSearch(MatType referenceSet, const NeighborSearchMode mode=DUAL_TREE_MODE, const double epsilon=0, const MetricType metric=MetricType())
Initialize the NeighborSearch object, passing a reference dataset (this is the dataset which is searc...
The NeighborSearch class is a template class for performing distance-based neighbor searches...
static double EffectiveError(arma::mat &foundDistances, arma::mat &realDistances)
Calculate the average relative error (effective error) between the distances calculated and the true ...
double & Epsilon()
Modify the relative error to be considered in approximate search.
Tree & ReferenceTree()
Modify the reference tree.
static double Recall(arma::Mat< size_t > &foundNeighbors, arma::Mat< size_t > &realNeighbors)
Calculate the recall (% of neighbors found) given the list of found neighbors and the true set of nei...
NeighborSearchMode & SearchMode()
Modify the search mode.
NeighborSearchMode SearchMode() const
Access the search mode.
NeighborSearch & operator=(const NeighborSearch &other)
Copy the given NeighborSearch object.
TreeType< MetricType, NeighborSearchStat< SortPolicy >, MatType > Tree
Convenience typedef.
TrainVisitor sets the reference set to a new reference set on the given NSType.
Definition of generalized binary space partitioning tree (BinarySpaceTree).
void Search(const MatType &querySet, const size_t k, arma::Mat< size_t > &neighbors, arma::mat &distances)
For each point in the query set, compute the nearest neighbors and store the output in the given matr...
size_t BaseCases() const
Return the total number of base case evaluations performed during the last search.
void Train(MatType referenceSet)
Set the reference set to a new reference set, and build a tree if necessary.
void serialize(Archive &ar, const unsigned int)
Serialize the NeighborSearch model.
BinarySpaceTree< MetricType, StatisticType, MatType, bound::HRectBound, MidpointSplit > KDTree
The standard midpoint-split kd-tree.
Definition: typedef.hpp:63
LMetric< 2, true > EuclideanDistance
The Euclidean (L2) distance.
Definition: lmetric.hpp:112
NeighborSearchMode
NeighborSearchMode represents the different neighbor search modes available.
~NeighborSearch()
Delete the NeighborSearch object.