[mlpack-git] master: Update documentation for changed names. (6be725a)

[gitdub at mlpack.org]

Repository : https://github.com/mlpack/mlpack
On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/56b53a09e2d46b65f4d80560964487f1e193d345...64c049efa2df2661ce5b321a60c4178c3439d025

>---------------------------------------------------------------

commit 6be725a3fa9b1efbca48c0cac66b7644b95b6b26
Author: Ryan Curtin <ryan at ratml.org>
Date:   Mon Apr 18 13:08:14 2016 -0700

    Update documentation for changed names.


>---------------------------------------------------------------

6be725a3fa9b1efbca48c0cac66b7644b95b6b26
 doc/guide/build.hpp                               |  2 +-
 doc/guide/formats.hpp                             |  2 +-
 doc/guide/sample.hpp                              |  4 +--
 doc/guide/timer.hpp                               |  2 +-
 doc/tutorials/fastmks/fastmks.txt                 |  2 +-
 doc/tutorials/neighbor_search/neighbor_search.txt | 38 +++++++++++------------
 doc/tutorials/range_search/range_search.txt       |  9 ------
 7 files changed, 25 insertions(+), 34 deletions(-)

diff --git a/doc/guide/build.hpp b/doc/guide/build.hpp
index 3260baa..390fe5a 100644
--- a/doc/guide/build.hpp
+++ b/doc/guide/build.hpp
@@ -111,7 +111,7 @@ You can specify individual components which you want to build, if you do not
 want to build everything in the library:
 
 @code
-$ make mlpack_pca mlpack_allknn mlpack_allkfn
+$ make mlpack_pca mlpack_knn mlpack_kfn
 @endcode
 
 If the build fails and you cannot figure out why, register an account on Trac
diff --git a/doc/guide/formats.hpp b/doc/guide/formats.hpp
index 846ef59..4b2d737 100644
--- a/doc/guide/formats.hpp
+++ b/doc/guide/formats.hpp
@@ -300,7 +300,7 @@ example files that may be useful to this end:
 
  - src/mlpack/methods/logistic_regression/logistic_regression_main.cpp
  - src/mlpack/methods/hoeffding_trees/hoeffding_tree_main.cpp
- - src/mlpack/methods/neighbor_search/allknn_main.cpp
+ - src/mlpack/methods/neighbor_search/knn_main.cpp
 
 If you are interested in adding support for more data types to mlpack, it would
 be preferable to add the support upstream to Armadillo instead, so that may be a
diff --git a/doc/guide/sample.hpp b/doc/guide/sample.hpp
index e5c1aa7..c54f52b 100644
--- a/doc/guide/sample.hpp
+++ b/doc/guide/sample.hpp
@@ -79,8 +79,8 @@ int main()
 
 For more complex examples, it is useful to refer to the main executables:
 
- - methods/neighbor_search/allknn_main.cpp
- - methods/neighbor_search/allkfn_main.cpp
+ - methods/neighbor_search/knn_main.cpp
+ - methods/neighbor_search/kfn_main.cpp
  - methods/emst/emst_main.cpp
  - methods/radical/radical_main.cpp
  - methods/nca/nca_main.cpp
diff --git a/doc/guide/timer.hpp b/doc/guide/timer.hpp
index 3e6f940..cff5001 100644
--- a/doc/guide/timer.hpp
+++ b/doc/guide/timer.hpp
@@ -7,7 +7,7 @@ methods.  The results of any timers used during the program are displayed at
 output by the mlpack::CLI object, when --verbose is given:
 
 @code
-$ allknn -r dataset.csv -n neighbors_out.csv -d distances_out.csv -k 5 -v
+$ mlpack_knn -r dataset.csv -n neighbors_out.csv -d distances_out.csv -k 5 -v
 <...>
 [INFO ] Program timers:
 [INFO ]   computing_neighbors: 0.010650s
diff --git a/doc/tutorials/fastmks/fastmks.txt b/doc/tutorials/fastmks/fastmks.txt
index 618cf5b..f1fc228 100644
--- a/doc/tutorials/fastmks/fastmks.txt
+++ b/doc/tutorials/fastmks/fastmks.txt
@@ -105,7 +105,7 @@ different types of kernels:
  - \ref mlpack::kernel::HyperbolicTangentKernel "hyperbolic tangent kernel"
 
 Note that when a shift-invariant kernel is used, the results will be the same as
-nearest neighbor search, so @ref nstutorial "allknn" may be a better option.  A
+nearest neighbor search, so @ref nstutorial "KNN" may be a better option.  A
 shift-invariant kernel is a kernel that depends only on the distance between the
 two input points.  The \ref mlpack::kernel::GaussianKernel "Gaussian kernel",
 \ref mlpack::kernel::EpanechnikovKernel "Epanechnikov kernel", and \ref
diff --git a/doc/tutorials/neighbor_search/neighbor_search.txt b/doc/tutorials/neighbor_search/neighbor_search.txt
index e744e9c..6710580 100644
--- a/doc/tutorials/neighbor_search/neighbor_search.txt
+++ b/doc/tutorials/neighbor_search/neighbor_search.txt
@@ -21,7 +21,7 @@ be stated more simply: for each point in the dataset, we wish to know the
 
  - a \ref cli_nstut "simple command-line executable" to run nearest-neighbors search
    (and furthest-neighbors search)
- - a \ref allknn_nstut "simple C++ interface" to perform nearest-neighbors search (and
+ - a \ref knn_nstut "simple C++ interface" to perform nearest-neighbors search (and
    furthest-neighbors search)
  - a \ref neighborsearch_nstut "generic, extensible, and powerful C++ class (NeighborSearch)" for complex usage
 
@@ -35,10 +35,10 @@ A list of all the sections this tutorial contains.
    - \ref cli_ex1_nstut
    - \ref cli_ex2_nstut
    - \ref cli_ex3_nstut
- - \ref allknn_nstut
-   - \ref allknn_ex1_nstut
-   - \ref allknn_ex2_nstut
-   - \ref allknn_ex3_nstut
+ - \ref knn_nstut
+   - \ref knn_ex1_nstut
+   - \ref knn_ex2_nstut
+   - \ref knn_ex3_nstut
  - \ref neighborsearch_nstut
    - \ref sort_policy_doc_nstut
    - \ref metric_type_doc_nstut
@@ -47,7 +47,7 @@ A list of all the sections this tutorial contains.
    - \ref traverser_type_doc_nstut
  - \ref further_doc_nstut
 
- at section cli_nstut Command-Line 'allknn'
+ at section cli_nstut Command-Line 'mlpack_knn'
 
 The simplest way to perform nearest-neighbors search in \b mlpack is to use the
 \c mlpack_knn executable.  This program will perform nearest-neighbors search
@@ -189,7 +189,7 @@ $ mlpack_knn -q query_dataset.csv -r reference_dataset.csv \
 @subsection cli_ex3_nstut One dataset, 3 nearest neighbors, leaf size of 15 points
 
 @code
-$ allknn -r dataset.csv -n neighbors_out.csv -d distances_out.csv -k 3 -l 15 -v
+$ mlpack_knn -r dataset.csv -n neighbors_out.csv -d distances_out.csv -k 3 -l 15 -v
 [INFO ] Loading 'dataset.csv' as CSV data.  Size is 3 x 1000.
 [INFO ] Loaded reference data from 'dataset.csv' (3 x 1000).
 [INFO ] Building reference tree...
@@ -230,26 +230,26 @@ $ allknn -r dataset.csv -n neighbors_out.csv -d distances_out.csv -k 3 -l 15 -v
 
 Further documentation on options should be found by using the --help option.
 
- at section allknn_nstut The 'AllkNN' class
+ at section knn_nstut The 'KNN' class
 
-The 'AllkNN' class is, specifically, a typedef of the more extensible
+The 'KNN' class is, specifically, a typedef of the more extensible
 NeighborSearch class, querying for nearest neighbors using the Euclidean
 distance.
 
 @code
 typedef NeighborSearch<NearestNeighborSort, metric::EuclideanDistance>
-    AllkNN;
+    KNN;
 @endcode
 
-Using the AllkNN class is particularly simple; first, the object must be
+Using the KNN class is particularly simple; first, the object must be
 constructed and given a dataset.  Then, the method is run, and two matrices are
 returned: one which holds the indices of the nearest neighbors, and one which
 holds the distances of the nearest neighbors.  These are of the same structure
 as the output --neighbors_file and --distances_file for the CLI interface (see
-above).  A handful of examples of simple usage of the AllkNN class are given
+above).  A handful of examples of simple usage of the KNN class are given
 below.
 
- at subsection allknn_ex1_nstut 5 nearest neighbors on a single dataset
+ at subsection knn_ex1_nstut 5 nearest neighbors on a single dataset
 
 @code
 #include <mlpack/methods/neighbor_search/neighbor_search.hpp>
@@ -259,7 +259,7 @@ using namespace mlpack::neighbor;
 // Our dataset matrix, which is column-major.
 extern arma::mat data;
 
-AllkNN a(data);
+KNN a(data);
 
 // The matrices we will store output in.
 arma::Mat<size_t> resultingNeighbors;
@@ -270,7 +270,7 @@ a.Search(5, resultingNeighbors, resultingDistances);
 
 The output of the search is stored in resultingNeighbors and resultingDistances.
 
- at subsection allknn_ex2_nstut 10 nearest neighbors on a query and reference dataset
+ at subsection knn_ex2_nstut 10 nearest neighbors on a query and reference dataset
 
 @code
 #include <mlpack/methods/neighbor_search/neighbor_search.hpp>
@@ -280,7 +280,7 @@ using namespace mlpack::neighbor;
 // Our dataset matrices, which are column-major.
 extern arma::mat queryData, referenceData;
 
-AllkNN a(referenceData);
+KNN a(referenceData);
 
 // The matrices we will store output in.
 arma::Mat<size_t> resultingNeighbors;
@@ -289,7 +289,7 @@ arma::mat resultingDistances;
 a.Search(queryData, 10, resultingNeighbors, resultingDistances);
 @endcode
 
- at subsection allknn_ex3_nstut Naive (exhaustive) search for 6 nearest neighbors on one dataset
+ at subsection knn_ex3_nstut Naive (exhaustive) search for 6 nearest neighbors on one dataset
 
 This example uses the O(n^2) naive search (not the tree-based search).
 
@@ -301,7 +301,7 @@ using namespace mlpack::neighbor;
 // Our dataset matrix, which is column-major.
 extern arma::mat dataset;
 
-AllkNN a(dataset, true);
+KNN a(dataset, true);
 
 // The matrices we will store output in.
 arma::Mat<size_t> resultingNeighbors;
@@ -363,7 +363,7 @@ static const double BestDistance();
 @endcode
 
 The mlpack::neighbor::FurthestNeighborSort class is another implementation,
-which is used to create the 'AllkFN' typedef class, which finds the furthest
+which is used to create the 'KFN' typedef class, which finds the furthest
 neighbors, as opposed to the nearest neighbors.
 
 @subsection metric_type_doc_nstut MetricType policy class
diff --git a/doc/tutorials/range_search/range_search.txt b/doc/tutorials/range_search/range_search.txt
index 61ad710..a1a3a97 100644
--- a/doc/tutorials/range_search/range_search.txt
+++ b/doc/tutorials/range_search/range_search.txt
@@ -253,15 +253,6 @@ similar to the output files --neighbors_file and --distances_file for the CLI
 interface (see above).  A handful of examples of simple usage of the RangeSearch
 class are given below.
 
-
-Using the AllkNN class is particularly simple; first, the object must be
-constructed and given a dataset.  Then, the method is run, and two matrices are
-returned: one which holds the indices of the nearest neighbors, and one which
-holds the distances of the nearest neighbors.  These are of the same structure
-as the output --neighbors_file and --reference_file for the CLI interface (see
-above).  A handful of examples of simple usage of the AllkNN class are given
-below.
-
 @subsection rs_ex1_rstut Distance less than 2.0 on a single dataset
 
 @code