[GRASS-SVN] r67108 - grass-addons/grass7/vector/v.mrmr

[svn_grass at osgeo.org]

Author: spawley
Date: 2015-12-13 20:12:04 -0800 (Sun, 13 Dec 2015)
New Revision: 67108

Modified:
   grass-addons/grass7/vector/v.mrmr/v.mrmr.html
Log:


Modified: grass-addons/grass7/vector/v.mrmr/v.mrmr.html
===================================================================
--- grass-addons/grass7/vector/v.mrmr/v.mrmr.html	2015-12-13 21:30:40 UTC (rev 67107)
+++ grass-addons/grass7/vector/v.mrmr/v.mrmr.html	2015-12-14 04:12:04 UTC (rev 67108)
@@ -1,26 +1,26 @@
-<h2>NAME</h2>
+<h2>DESCRIPTION</h2>
 
 <em><b>v.mrmr</b></em> is a simple GUI for exporting data to the Minimum
 Redundancy Maximum Relevance (mRMR) feature selection command line tool
-(Peng et al., 2005)
-
-<h2>DESCRIPTION</h2> mRMR is designed to select features that have the
+(Peng et al., 2005). mRMR is designed to select features that have the
 maximal statistical "dependency" on the classification variable, while
 simultaneously minimizing the redundancy among the selected features.
 
-<br><br> The command line tool needs to be installed separately in a
+<h2>NOTES</h2>
+
+<p>The command line tool needs to be installed separately in a
 location that is recognized by the system or in the PATH. The command
 line tool can be installed on windows (binaries available), linux and OS
 X (needs compilation). Installation instructions are provided on <a
 href="http://penglab.janelia.org/proj/mRMR/">Peng's Website</a>.
 
-<br><br> The module requires data within a vector attribute table to be
+<p>The module requires data within a vector attribute table to be
 arranged in a specific order. The classification variable (i.e., class
 labels) need to be in the first column, except for the cat attribute
 which is not exported. The class label also needs to be in numerical
 form, i.e., 1, 2, 3.... rather than 'forest' or 'urban'.
-
-<br><br>The algorithm outputs a tab-separated list of attributes, ranked
+	
+<p>The algorithm outputs a tab-separated list of attributes, ranked
 by the most important feature first. The <i> method </i> parameter
 allows a choice between the Maximum Information Difference (MID) and
 Mutual Information Quotient (MIQ) feature evaluation criteria, which
@@ -36,13 +36,18 @@
 standard deviation. <i> layer </i> is the attribute layer to be used in
 the feature selection process.
 
-<h2>EXAMPLE</h2> v.mrmr.py vector=vector_layer layer=1 thres=1.0
+<h2>EXAMPLE</h2>
+
+v.mrmr.py vector=vector_layer layer=1 thres=1.0
 nfeatures=50 nsamples=10000 maxvar=10000 method=MID
 
-<h2>REFERENCES</h2> Peng, H.; Fulmi Long; Ding, C., "Feature selection
+<h2>REFERENCES</h2>
+
+Peng, H.; Fulmi Long; Ding, C., "Feature selection
 based on mutual information criteria of max-dependency, max-relevance,
 and min-redundancy," in Pattern Analysis and Machine Intelligence, IEEE
 Transactions on , vol.27, no.8, pp.1226-1238, Aug. 2005
 
-<h2>AUTHOR</h2> Steven Pawley <br><i>Last changed: Saturday 12 December
-2015</i>
\ No newline at end of file
+<h2>AUTHOR</h2>
+
+Steven Pawley <br><i>Last changed: Saturday 12 December 2015</i>
\ No newline at end of file