Performs the mst calculation using the dual-tree boruvka algorithm, using any type of tree.
struct SortEdgesHelper
For sorting the edge list after the computation.
DualTreeBoruvka (const typename TreeType::Mat &dataset, const bool naive=false, const MetricType metric=MetricType())
Create the tree from the given dataset. DualTreeBoruvka (TreeType *tree, const typename TreeType::Mat &dataset, const MetricType metric=MetricType())
Create the DualTreeBoruvka object with an already initialized tree. ~DualTreeBoruvka ()
Delete the tree, if it was created inside the object. void ComputeMST (arma::mat &results)
Iteratively find the nearest neighbor of each component until the MST is complete. std::string ToString () const
Returns a string representation of this object.
void AddAllEdges ()
Adds all the edges found in one iteration to the list of neighbors. void AddEdge (const size_t e1, const size_t e2, const double distance)
Adds a single edge to the edge list. void Cleanup ()
The values stored in the tree must be reset on each iteration. void CleanupHelper (TreeType *tree)
This function resets the values in the nodes of the tree nearest neighbor distance, and checks for fully connected nodes. void EmitResults (arma::mat &results)
Unpermute the edge list and output it to results.
UnionFind connections
Connections. const TreeType::Mat & data
Reference to the data (this is what should be used for accessing data). TreeType::Mat dataCopy
Copy of the data (if necessary). std::vector< EdgePair > edges
Edges. MetricType metric
The instantiated metric. bool naive
Indicates whether or not O(n^2) naive mode will be used. arma::vec neighborsDistances
List of edge distances. arma::Col< size_t > neighborsInComponent
List of edge nodes. arma::Col< size_t > neighborsOutComponent
List of edge nodes. std::vector< size_t > oldFromNew
Permutations of points during tree building. bool ownTree
Indicates whether or not we 'own' the tree. struct
mlpack::emst::DualTreeBoruvka::SortEdgesHelper SortFun"
double totalDist
Total distance of the tree. TreeType * tree
Pointer to the root of the tree.
Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree.
For more information on the algorithm, see the following citation:
@inproceedings{ author = {March, W.B., Ram, P., and Gray, A.G.}, title = {{Fast Euclidean Minimum Spanning Tree: Algorithm, Analysis, Applications.}}, booktitle = {Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining} series = {KDD 2010}, year = {2010} }
General usage of this class might be like this:
extern arma::mat data; // We want to find the MST of this dataset. DualTreeBoruvka<> dtb(data); // Create the tree with default options. // Find the MST. arma::mat mstResults; dtb.ComputeMST(mstResults);
More advanced usage of the class can use different types of trees, pass in an already-built tree, or compute the MST using the O(n^2) naive algorithm.
Template Parameters:
MetricType The metric to use. IMPORTANT: this hasn't really been tested with anything other than the L2 metric, so user beware. Note that the tree type needs to compute bounds using the same metric as the type specified here.
TreeType Type of tree to use. Should use DTBStat as a statistic.
Definition at line 91 of file dtb.hpp.
Create the tree from the given dataset. This copies the dataset to an internal copy, because tree-building modifies the dataset.
Parameters:
data Dataset to build a tree for.
naive Whether the computation should be done in O(n^2) naive mode.
leafSize The leaf size to be used during tree construction.
Create the DualTreeBoruvka object with an already initialized tree. This will not copy the dataset, and can save a little processing power. 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).
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.
Parameters:
tree Pre-built tree.
dataset Dataset corresponding to the pre-built tree.
Delete the tree, if it was created inside the object.
Adds all the edges found in one iteration to the list of neighbors.
Adds a single edge to the edge list.
The values stored in the tree must be reset on each iteration.
This function resets the values in the nodes of the tree nearest neighbor distance, and checks for fully connected nodes.
Iteratively find the nearest neighbor of each component until the MST is complete. The results will be a 3xN matrix (with N equal to the number of edges in the minimum spanning tree). The first row will contain the lesser index of the edge; the second row will contain the greater index of the edge; and the third row will contain the distance between the two edges.
Parameters:
results Matrix which results will be stored in.
Unpermute the edge list and output it to results.
Returns a string representation of this object.
Connections.
Definition at line 111 of file dtb.hpp.
Reference to the data (this is what should be used for accessing data).
Definition at line 97 of file dtb.hpp.
Copy of the data (if necessary).
Definition at line 95 of file dtb.hpp.
Edges.
Definition at line 108 of file dtb.hpp.
The instantiated metric.
Definition at line 126 of file dtb.hpp.
Indicates whether or not O(n^2) naive mode will be used.
Definition at line 105 of file dtb.hpp.
List of edge distances.
Definition at line 120 of file dtb.hpp.
List of edge nodes.
Definition at line 116 of file dtb.hpp.
List of edge nodes.
Definition at line 118 of file dtb.hpp.
Permutations of points during tree building.
Definition at line 114 of file dtb.hpp.
Indicates whether or not we 'own' the tree.
Definition at line 102 of file dtb.hpp.
Total distance of the tree.
Definition at line 123 of file dtb.hpp.
Pointer to the root of the tree.
Definition at line 100 of file dtb.hpp.
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