PrologMethods/Geometry/knn.md

+# k-Nearest-Neighbors Search
+
+An implementation of k-nearest-neighbor search using single-tree and dual-tree algorithms. Given a set of reference points and query points, this can find the k nearest neighbors in the reference set of each query point using trees.
+
+# Available Predicates
+
+* [initAndBuildModel/10](/PrologMethods/Geometry/knn#initandbuildmodel10)
+* [searchWithQuery/10](/PrologMethods/Geometry/knn#searchwithquery10)
+* [searchNoQuery/7](/PrologMethods/Geometry/knn#searchnoquery7)
+
+---
+
+[links/resources](/PrologMethods/Geometry/knn#connected-linksresources)
+
+## **_initAndBuildModel/10_**
+
+Initialize the Model and build it.
+
+```prolog
+%% part of the predicate definition
+initAndBuildModel(  +string, +string,
+                    +integer, 
+                    +integer, +float32, +float32, +float32,
+                    +pointer(float_array), +integer, +integer).
+```
+
+### Parameters
+| Name | Type | Description | Default |
+|------|------|-------------|---------|
+| treeType | +string | Type of tree to use: "kd", "vp", "rp", "max-rp", "ub", "cover", "r", "r-star", "x", "ball", "hilbert-r", "r-plus", "r-plus-plus", "spill", "oct". | kd |
+| searchMode | +string | Type of neighbor search: "naive", "single_tree", "dual_tree", "greedy". | dual_tree |
+| randomBasis | +integer(bool) | Before tree-building, project the data onto a random orthogonal basis. | (0)false |
+| leafSize | +integer | Leaf size for tree building. Not used by cover trees | 20 |
+| tau | +float | Overlapping size (only valid for spill trees). | 0.7 |
+| rho | +float | Balance threshold (only valid for spill trees). | 0.0 |
+| epsilon | +float | If specified, will do approximate furthest neighbor search with given relative error. Must be in the range \[0,1). | 0.0 |
+| referenceSet | +matrix | Matrix containing the reference dataset. | - |
+
+---
+
+## **_searchWithQuery/10_**
+
+Perform neighbor search on the queryset.
+
+```prolog
+%% part of the predicate definition
+searchWithQuery(  +pointer(float_array), +integer, +integer,
+                  +integer,
+                  -pointer(float_array), -integer, -integer,
+                  -pointer(float_array), -integer, -integer)
+```
+
+### Parameters
+| Name | Type | Description | Default |
+|------|------|-------------|---------|
+| querySet | +matrix | Matrix containing query points (optional). | - |
+| k | +integer | Number of furthest neighbors to find. | 0 |
+| neighbors | -matrix | Matrix to output distances into. | - |
+| distances | -matrix | Matrix to output neighbors into. | - |
+
+---
+
+## **_searchNoQuery/7_**
+
+Perform monochromatic neighbor search.
+
+```prolog
+%% part of the predicate definition
+searchNoQuery(  +integer,
+                -pointer(float_array), -integer, -integer,
+                -pointer(float_array), -integer, -integer)
+```
+
+### Parameters
+| Name | Type | Description | Default |
+|------|------|-------------|---------|
+| k | +integer | Number of furthest neighbors to find. | 0 |
+| neighbors | -matrix | Matrix to output distances into. | - |
+| distances | -matrix | Matrix to output neighbors into. | - |
+
+---
+
+# Connected Links/Resources
+
+If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.
+
+* [MLpack::knn_C++\_documentation](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1neighbor_1_1NeighborSearch.html)
+* [MLpack::knn_Python_documentation](https://www.mlpack.org/doc/stable/python_documentation.html#knn)
+
+added some of the links from the python documentation
+
+* lsh
+* krann
+* kfn
+* [NeighborSearch tutorial (k-nearest-neighbors)](https://mlpack.org/doc/mlpack-git/doxygen/nstutorial.html)
+* [Tree-independent dual-tree algorithms (pdf)](http://proceedings.mlr.press/v28/curtin13.pdf)
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