[mlpack-svn] r11645 - mlpack/trunk/doc/tutorials/neighbor_search
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Wed Feb 29 09:46:50 EST 2012
Author: rcurtin
Date: 2012-02-29 09:46:50 -0500 (Wed, 29 Feb 2012)
New Revision: 11645
Modified:
mlpack/trunk/doc/tutorials/neighbor_search/neighbor_search.txt
Log:
Fix references.
Modified: mlpack/trunk/doc/tutorials/neighbor_search/neighbor_search.txt
===================================================================
--- mlpack/trunk/doc/tutorials/neighbor_search/neighbor_search.txt 2012-02-29 14:42:21 UTC (rev 11644)
+++ mlpack/trunk/doc/tutorials/neighbor_search/neighbor_search.txt 2012-02-29 14:46:50 UTC (rev 11645)
@@ -6,7 +6,7 @@
@page nstutorial NeighborSearch tutorial (k-nearest-neighbors)
- at section intro Introduction
+ at section intro_nstut Introduction
Nearest-neighbors search is a common machine learning task. In this setting, we
have a \b query and a \b reference dataset. For each point in the \b query
@@ -19,33 +19,33 @@
\b mlpack provides:
- - a \ref cli "simple command-line executable" to run nearest-neighbors search
+ - a \ref cli_nstut "simple command-line executable" to run nearest-neighbors search
(and furthest-neighbors search)
- - a \ref allknn "simple C++ interface" to perform nearest-neighbors search (and
+ - a \ref allknn_nstut "simple C++ interface" to perform nearest-neighbors search (and
furthest-neighbors search)
- - a \ref neighborsearch "generic, extensible, and powerful C++ class (NeighborSearch)" for complex usage
+ - a \ref neighborsearch_nstut "generic, extensible, and powerful C++ class (NeighborSearch)" for complex usage
- at section toc Table of Contents
+ at section toc_nstut Table of Contents
A list of all the sections this tutorial contains.
- - \ref intro
- - \ref toc
- - \ref cli
- - \ref cli_ex1
- - \ref cli_ex2
- - \ref cli_ex3
- - \ref allknn
- - \ref allknn_ex1
- - \ref allknn_ex2
- - \ref allknn_ex3
- - \ref neighborsearch
- - \ref sort_policy_doc
- - \ref metric_type_doc
- - \ref tree_type_doc
- - \ref further_doc
+ - \ref intro_nstut
+ - \ref toc_nstut
+ - \ref cli_nstut
+ - \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 neighborsearch_nstut
+ - \ref sort_policy_doc_nstut
+ - \ref metric_type_doc_nstut
+ - \ref tree_type_doc_nstut
+ - \ref further_doc_nstut
- at section cli Command-Line 'allknn'
+ at section cli_nstut Command-Line 'allknn'
The simplest way to perform nearest-neighbors search in \b mlpack is to use the
allknn executable. This program will perform nearest-neighbors search and place
@@ -62,7 +62,7 @@
$ allknn --help
@endcode
- at subsection cli_ex1 One dataset, 5 nearest neighbors
+ at subsection cli_ex1_nstut One dataset, 5 nearest neighbors
@code
$ allknn -r dataset.csv -n neighbors_out.csv -d distances_out.csv -k 5 -v
@@ -130,7 +130,7 @@
2.059402e-3. The third nearest neighbor to point 6 is point 16, with a distance
of 9.9748395e-3.
- at subsection cli_ex2 Query and reference dataset, 10 nearest neighbors
+ at subsection cli_ex2_nstut Query and reference dataset, 10 nearest neighbors
@code
$ allknn -q query_dataset.csv -r reference_dataset.csv -n neighbors_out.csv \
@@ -167,7 +167,7 @@
[INFO ] tree_building: 0.004949s
@endcode
- at subsection cli_ex3 One dataset, 3 nearest neighbors, leaf size of 15 points
+ at 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
@@ -202,7 +202,7 @@
Further documentation on options should be found by using the --help option.
- at section allknn The 'AllkNN' class
+ at section allknn_nstut The 'AllkNN' class
The 'AllkNN' class is, specifically, a typedef of the more extensible
NeighborSearch class, querying for nearest neighbors using the squared Euclidean
@@ -221,7 +221,7 @@
above). A handful of examples of simple usage of the AllkNN class are given
below.
- at subsection allknn_ex1 5 nearest neighbors on a single dataset
+ at subsection allknn_ex1_nstut 5 nearest neighbors on a single dataset
@code
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
@@ -242,7 +242,7 @@
The output of the search is stored in resultingNeighbors and resultingDistances.
- at subsection allknn_ex2 10 nearest neighbors on a query and reference dataset
+ at subsection allknn_ex2_nstut 10 nearest neighbors on a query and reference dataset
@code
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
@@ -261,7 +261,7 @@
a.Search(10, resultingNeighbors, resultingDistances);
@endcode
- at subsection allknn_ex3 Naive (exhaustive) search for 6 nearest neighbors on one dataset
+ at subsection allknn_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).
@@ -284,7 +284,7 @@
Needless to say, naive search can be very slow...
- at section neighborsearch The extensible 'NeighborSearch' class
+ at section neighborsearch_nstut The extensible 'NeighborSearch' class
The NeighborSearch class is very extensible, having the following template
arguments:
@@ -302,7 +302,7 @@
By choosing different components for each of these template classes, a very
arbitrary neighbor searching object can be constructed.
- at subsection sort_policy_doc SortPolicy policy class
+ at subsection sort_policy_doc_nstut SortPolicy policy class
The SortPolicy template parameter allows specification of how the NeighborSearch
object will decide which points are to be searched for. The
@@ -332,7 +332,7 @@
which is used to create the 'AllkFN' typedef class, which finds the furthest
neighbors, as opposed to the nearest neighbors.
- at subsection metric_type_doc MetricType policy class
+ at subsection metric_type_doc_nstut MetricType policy class
The MetricType policy class allows the neighbor search to take place in any
arbitrary metric space. The mlpack::metric::LMetric class is a good example
@@ -353,7 +353,7 @@
state (the covariance matrix). Therefore, you can write a non-static MetricType
class and use it seamlessly with NeighborSearch.
- at subsection tree_type_doc TreeType policy class
+ at subsection tree_type_doc_nstut TreeType policy class
The NeighborSearch class also allows a custom tree to be used. The standard
MLPACK tree, mlpack::tree::BinarySpaceTree, is also highly extensible in its own
@@ -382,7 +382,7 @@
mlpack::tree::BinarySpaceTree documentation for more information on tree
statistics.
- at subsection further_doc Further documentation
+ at subsection further_doc_nstut Further documentation
For further documentation on the NeighborSearch class, consult the
\ref mlpack::neighbor::NeighborSearch "complete API documentation".
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