[mlpack-svn] r11177 - mlpack/trunk/doc/tutorials/neighbor_search

[fastlab-svn at coffeetalk-1.cc.gatech.edu]

Author: rcurtin
Date: 2012-01-19 13:34:38 -0500 (Thu, 19 Jan 2012)
New Revision: 11177

Modified:
   mlpack/trunk/doc/tutorials/neighbor_search/neighbor_search.txt
Log:
Some formatting fixes.  We have lines longer than 80 characters but that is
unfortunately necessary to make Doxygen work right in some cases...


Modified: mlpack/trunk/doc/tutorials/neighbor_search/neighbor_search.txt
===================================================================
--- mlpack/trunk/doc/tutorials/neighbor_search/neighbor_search.txt	2012-01-19 18:27:34 UTC (rev 11176)
+++ mlpack/trunk/doc/tutorials/neighbor_search/neighbor_search.txt	2012-01-19 18:34:38 UTC (rev 11177)
@@ -23,8 +23,7 @@
    (and furthest-neighbors search)
  - a \ref allknn "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 "generic, extensible, and powerful C++ class (NeighborSearch)" for complex usage
 
 @section toc Table of Contents
 
@@ -114,16 +113,16 @@
 41,7,45,49,47
 
 $ head distances_out.csv
-7.0961442157e-04,2.0594017307e-03,4.0534606848e-03,4.6617527871e-03,1.0975766503e-02
-8.9219094854e-04,1.6944224237e-03,2.8275047594e-03,4.0659085081e-03,7.5416924388e-03
-5.9153940619e-03,6.8348261245e-03,8.0287780046e-03,9.0490742594e-03,1.6145844252e-02
-7.1565291364e-03,9.1822852410e-03,1.0054094126e-02,1.0754117196e-02,1.2889286480e-02
-5.3753598384e-03,9.0572140940e-03,9.8901718497e-03,1.0145773597e-02,1.1402159302e-02
-2.0594017307e-03,5.1443719285e-03,9.9748395427e-03,1.0246362715e-02,1.4435578329e-02
-4.2735541987e-03,6.3675054757e-03,6.7247857776e-03,8.7732353272e-03,1.0453054915e-02
-1.9993584709e-03,3.8824033162e-03,4.1911827369e-03,9.3069356843e-03,1.2123748134e-02
-2.1545427671e-03,8.1889521020e-03,1.1836045084e-02,1.2513545450e-02,1.2778332798e-02
-8.4308799684e-03,1.2294632518e-02,1.6047220935e-02,1.8866141375e-02,1.8972768693e-02
+7.09614421e-04,2.05940173e-03,4.05346068e-03,4.66175278e-03,1.09757665e-02
+8.92190948e-04,1.69442242e-03,2.82750475e-03,4.06590850e-03,7.54169243e-03
+5.91539406e-03,6.83482612e-03,8.02877800e-03,9.04907425e-03,1.61458442e-02
+7.15652913e-03,9.18228524e-03,1.00540941e-02,1.07541171e-02,1.28892864e-02
+5.37535983e-03,9.05721409e-03,9.89017184e-03,1.01457735e-02,1.14021593e-02
+2.05940173e-03,5.14437192e-03,9.97483954e-03,1.02463627e-02,1.44355783e-02
+4.27355419e-03,6.36750547e-03,6.72478577e-03,8.77323532e-03,1.04530549e-02
+1.99935847e-03,3.88240331e-03,4.19118273e-03,9.30693568e-03,1.21237481e-02
+2.15454276e-03,8.18895210e-03,1.18360450e-02,1.25135454e-02,1.27783327e-02
+8.43087996e-03,1.22946325e-02,1.60472209e-02,1.88661413e-02,1.89727686e-02
 @endcode
 
 So, the nearest neighbor to point 0 is point 14, with a distance of 7.096144e-4.
@@ -262,8 +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 Naive (exhaustive) search for 6 nearest neighbors on one dataset
 
 This example uses the O(n^2) naive search (not the tree-based search).