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<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"><html><head><title>SOM Toolbox / som_quality </title></head><body bgcolor=#f0f0f0><table border=0 width="100%" cellpadding=0 cellspacing=0><tr><td valign=baseline><font size=+2>SOM Toolbox</font></td><td valign=baseline align=center><a href="somtoolbox.html">Online documentation</td><td valign=baseline align=right><a href="http://www.cis.hut.fi/projects/somtoolbox/" target="_top">http://www.cis.hut.fi/projects/somtoolbox/</a></td></tr></table><hr><H1> som_quality </H1><H3> Purpose </H3><PRE> Calculates two quality measures for the given map.</PRE><H3> Syntax </H3><UL><PRE> qe = som_quality(sM,sD); qe = som_quality(sM,D); [qe,te] = som_quality(...);</PRE></UL><H3> Description </H3><PRE> This function measures the quality of the given map. The measures are data-dependent: they measure the map in terms of the given data. Typically, the quality of the map is measured in terms of the training data. The returned quality measures are average quantization error and topographic error. The issue of SOM quality is a complicated one. Typically two evaluation criterias are used: resolution and topology preservation. There are many ways to measure them. The ones implemented here were chosen for their simplicity. qe : Average distance between each data vector and its BMU. Measures map resolution. te : Topographic error, the proportion of all data vectors for which first and second BMUs are not adjacent units. Measures topology preservation. NOTE: when calculating BMUs of data vectors, the mask of the given map is used. The mask affects the quantization errors, too. If you want the quantization errors without the weighting given by the mask, you can use the following code: bmus = som_bmus(sMap,D); % this uses the mask in finding the BMUs for i=1:length(bmus), dx = sMap.codebook(bmus(i),:)-D(i,:); % m - x dx(isnan(dx)) = 0; % remove NaNs qerr(i) = sqrt(sum(dx.^2)); % euclidian distance end qe = mean(qerr); % average quantization error Please note that you should _not_ trust the measures blindly. Generally, both measures give the best results when the map has overfitted the data. This may happen when the number of map units is as large or larger than the number of training samples. Beware when you have such a case.</PRE><H3> References </H3><PRE> Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, Berlin, 1995, pp. 113. Kiviluoto, K., "Topology Preservation in Self-Organizing Maps", in the proceeding of International Conference on Neural Networks (ICNN), 1996, pp. 294-299.</PRE><H3> Input arguments </H3><PRE> sMap (struct) Map struct. D The data to be used. (matrix) A data matrix, size dlen x dim. (struct) A data struct.</PRE><H3> Output arguments </H3><PRE> qe (scalar) mean quantization error te (scalar) topographic error</PRE><H3> Examples </H3><PRE> qe = som_quality(sMap,D); [qe,te] = som_quality(sMap,sD);</PRE><H3> See also </H3><TABLE NOBORDER WIDTH=80%><TR><TD><a href="som_bmus.html"><B>som_bmus</B></a><TD> Find BMUs for the given set of data vectors.</TABLE><p><hr><br><br><!-- Last updated: May 30 2002 --></body></html>?? 快捷鍵說明
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