From fa73dbb444fcbd4b2dc2dfc27f9670c43798e466 Mon Sep 17 00:00:00 2001 From: Adarsh Santoria <108261986+AdarshSantoria@users.noreply.github.com> Date: Wed, 1 Feb 2023 17:54:58 +0530 Subject: [PATCH] fixing links in approx_kfn.md --- doc/tutorials/approx_kfn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/tutorials/approx_kfn.md b/doc/tutorials/approx_kfn.md index c703aefa45..8d0f5eb581 100644 --- a/doc/tutorials/approx_kfn.md +++ b/doc/tutorials/approx_kfn.md @@ -114,8 +114,8 @@ search: These two programs allow a large number of algorithms to be used to find approximate furthest neighbors. Note that the `mlpack_kfn` program is also -documented in the [KNN tutorial](knn.md) page, as it shares options with the -`mlpack_knn` program. +documented in the [KNN tutorial](neighbor_search.md) page, as it shares options +with the `mlpack_knn` program. Below are several examples of how the `mlpack_approx_kfn` and `mlpack_kfn` programs might be used. The first examples focus on the `mlpack_approx_kfn` @@ -869,7 +869,7 @@ qdafn.Search(querySet, 3, neighbors, distances); The extensive `NeighborSearch` class also provides a way to search for approximate furthest neighbors using a different, tree-based technique. For full documentation on this class, see the [NeighborSearch -tutorial](nstutorial.md). The `KFN` class is a convenient typedef of the +tutorial](neighbor_search.md). The `KFN` class is a convenient typedef of the `NeighborSearch` class that can be used to perform the furthest neighbors task with `kd`-trees. @@ -982,6 +982,6 @@ kfn.Search(querySet, 2, neighbors, distances); ## Further documentation For further documentation on the approximate furthest neighbor facilities -offered by mlpack, see also [the NeighborSearch tutorial](nstutorial.md). Also, +offered by mlpack, see also [the NeighborSearch tutorial](neighbor_search.md). Also, each class (`QDAFN`, `DrusillaSelect`, `NeighborSelect`) are well-documented, and more details can be found in the source code documentation.