1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
|
# Trees
mlpack includes a number of space partitioning trees and other trees for its
geometric techniques. These trees are built on [data matrices](../matrices.md)
where each column in the matrix is a point in the tree. Trees are organized
such that "nearby" points (with respect to a given distance metric) are
generally grouped in the same node or branch of the tree.
All trees in mlpack implement
the [same API](../../developer/trees.md), allowing easy plug-and-play usage of
different trees. The following tree types are available in mlpack:
* [`KDTree`](trees/kdtree.md)
* [`MeanSplitKDTree`](trees/mean_split_kdtree.md)
* [`BallTree`](trees/ball_tree.md)
* [`MeanSplitBallTree`](trees/mean_split_ball_tree.md)
* [`RPTree`](trees/rp_tree.md)
* [`MaxRPTree`](trees/max_rp_tree.md)
* [`UBTree`](trees/ub_tree.md)
* [`BinarySpaceTree`](trees/binary_space_tree.md)
* [`CoverTree`](trees/cover_tree.md)
* [`Octree`](trees/octree.md)
* [`RTree`](trees/r_tree.md)
* [`RStarTree`](trees/r_star_tree.md)
* [`XTree`](trees/x_tree.md)
* [`RPlusTree`](trees/r_plus_tree.md)
* [`RPlusPlusTree`](trees/r_plus_plus_tree.md)
* [`HilbertRTree`](trees/hilbert_r_tree.md)
* [`RectangleTree`](trees/rectangle_tree.md)
* [`SPTree`](trees/sp_tree.md)
* [`MeanSPTree`](trees/mean_sp_tree.md)
* [`NonOrtSPTree`](trees/non_ort_sp_tree.md)
* [`NonOrtMeanSPTree`](trees/non_ort_mean_sp_tree.md)
* [`SpillTree`](trees/spill_tree.md)
---
In general, it is not necessary to create an mlpack tree directly, but instead
to simply specify the type of tree a particular algorithm should use via a
template parameter. For instance, all of the algorithms below use mlpack trees
and can have the type of tree specified via template parameters:
<!-- TODO: document these! -->
* [`NeighborSearch`](/src/mlpack/methods/neighbor_search/neighbor_search.hpp)
(for k-nearest-neighbor and k-furthest-neighbor)
* [`RangeSearch`](/src/mlpack/methods/range_search/range_search.hpp)
* [`KDE`](/src/mlpack/methods/kde/kde.hpp)
* [`FastMKS`](/src/mlpack/methods/fastmks/fastmks.hpp)
* [`DTB`](/src/mlpack/methods/emst/dtb.hpp) (for computing Euclidean minimum
spanning trees)
* [`KRANN`](/src/mlpack/methods/rann/rann.hpp)
---
***Note:*** if you are looking for documentation on **decision trees**, see the
documentation for the [`DecisionTree`](../methods/decision_tree.md) class.
|
