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📄 som_drmake.html

📁 Kohonen的SOM软件包
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<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"><html><head><title>SOM Toolbox / som_drmake </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_drmake </H1><P><B> [sR,best,sig,Cm] = som_drmake(D,inds1,inds2,sigmea,nanis)</B></P><PRE> SOM_DRMAKE Make descriptive rules for given group within the given data.  sR = som_drmake(D,[inds1],[inds2],[sigmea],[nanis])    D        (struct) map or data struct           (matrix) the data, of size [dlen x dim]  [inds1]  (vector) indeces belonging to the group                    (the whole data set by default)  [inds2]  (vector) indeces belonging to the contrast group                    (the rest of the data set by default)  [sigmea] (string) significance measure: 'accuracy',                     'mutuconf' (default), or 'accuracyI'.                    (See definitions below).  [nanis]  (scalar) value given for NaNs: 0 (=FALSE, default),                    1 (=TRUE) or NaN (=ignored)  sR      (struct array) best rule for each component. Each                    struct has the following fields:    .type     (string) 'som_rule'    .name     (string) name of the component    .low      (scalar) the low end of the rule range    .high     (scalar) the high end of the rule range    .nanis    (scalar) how NaNs are handled: NaN, 0 or 1  best    (vector) indeces of rules which make the best combined rule  sig     (vector) significance measure values for each rule, and for the combined rule  Cm      (matrix) A matrix of vectorized confusion matrices for each rule,                    and for the combined rule: [a, c, b, d] (see below).   For each rule, such rules sR.low <= x < sR.high are found  which optimize the given significance measure. The confusion matrix below between the given grouping (G: group - not G: contrast group)  and rule (R: true or false) is used to determine the significance values:          G    not G           ---------------    accuracy  = (a+d) / (a+b+c+d) true  |  a  |   b   |           |--------------    mutuconf  =  a*a  / ((a+b)(a+c))  false |  c  |   d   |        ---------------    accuracyI =   a   / (a+b+c) See also  SOM_DREVAL, SOM_DRTABLE.</PRE><p><hr><br><center>[ <a href="somtoolbox.html">SOM Toolbox online doc</a> ]</center><br><!-- Last updated: May 30 2002 --></body></html>

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