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<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"><html><head><title>SOM Toolbox / som_train_struct </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_train_struct </H1><H3> Purpose </H3><PRE> Default values for SOM training parameters.</PRE><H3> Syntax </H3><UL><PRE> sT = som_train_struct('argID',value,...); sT = som_train_struct(value,...);</PRE></UL><H3> Description </H3><PRE> This function is used to give sensible values for SOM training parameters and returns a training struct. Often, the parameters depend on the properties of the map and the training data. These are given as optional arguments to the function. If a partially filled train struct is given, its empty fields (field value is [] or '' or NaN) are supplimented with default values. The training struct has a number of fields which depend on each other and the optional arguments in complex ways. The most important argument is 'phase' which can be either 'init', 'train', 'rough' or 'finetune'. 'init' Map initialization. 'train' Map training in a onepass operation, as opposed to the rough-finetune combination. 'rough' Rough organization of the map: large neighborhood, big initial value for learning coefficient. Short training. 'finetune' Finetuning the map after rough organization phase. Small neighborhood, learning coefficient is small already at the beginning. Long training. The fields of training struct set by this function are listed below. '.mask' Basically, a column vector of ones. But if a previous train or map struct is given, it is copied from there. '.neigh' Default value is 'gaussian' but if a previous train or map struct is given, it is copied from there. '.alpha_type' Default value is 'inv' but if a previous training struct is given, it is copied from there. '.alpha_ini' For 'train' and 'rough' phases, this is 0.5, for 'finetune' it is 0.05. '.radius_ini' Depends on the previous training operation and the maximum sidelength of the map ms = max(msize). if there isn't one, or it is 'randinit', rad_ini = max(1,ms/2) if it is 'lininit', rad_ini = max(1,ms/8) otherwise, rad_ini = rad_fin of the previous training '.radius_fin' Default value is 1, but if the training phase is 'rough', rad_fin = max(1,rad_ini/4). '.trainlen' For 'train' phase this is 20 x mpd epochs, for 'rough' phase 4 x mpd epochs and for 'finetune' 16 x mpd epochs, where mpd = munits/dlen. If mpd cannot be calculated, it is set to be = 0.5. In any case, trainlen is at least one epoch. '.algorithm' Default training algorithm is 'batch' and default initialization algorithm is 'lininit'.</PRE><H3> Optional input arguments </H3><PRE> argID (string) Argument identifier string (see below). value (varies) Value for the argument (see below). The optional arguments can be given as 'argID',value -pairs. If an argument is given value multiple times, the last one is used. The valid IDs and corresponding values are listed below. The values which are unambiguous (marked with '*') can be given without the preceeding argID. 'dim' (scalar) input space dimension 'dlen' (scalar) length of the training data 'data' (matrix / struct) the training data 'munits' (scalar) number of map units 'msize' (vector) map size 'previous' (struct) previous training struct can be given in conjunction with 'finetune' phase. 'phase' *(string) training phase: 'init', 'train', 'rough' or 'finetune' 'algorithm' *(string) algorithm to use: 'lininit', 'randinit', 'batch' or 'seq' 'map' *(struct) If a map struc is given, the last training struct in '.trainhist' field is used as the previous training struct. The map size and input space dimension are extracted from the map struct. 'sTrain' *(struct) a train struct, the empty fields of which are filled with sensible values</PRE><H3> Output arguments </H3><PRE> sT (struct) The training struct.</PRE><H3> Examples </H3><PRE> The most important optional argument for the training parameters is 'phase'. The second most important are 'previous' and/or 'map'. To get default initialization parameters, use: sTrain = som_train_struct('phase','init'); or sTrain = som_train_struct('init'); To get default training parameters, use: sTrain = som_train_struct('phase','train','data',D,'map',sMap); or sTrain = som_train_struct('train','data',D,sMap); or sTrain = som_train_struct('train','dlen',dlen, ... 'msize',sMap.topol.msize,'dim',dim); If you want to first rough train and then finetune, do like this: sT1 = som_train_struct('rough','dlen',length(D),sMap); % rough training sT2 = som_train_struct('finetune','previous',sT1); % finetuning</PRE><H3> See also </H3><TABLE NOBORDER WIDTH=80%><TR><TD><a href="som_make.html"><B>som_make</B></a><TD> Initialize and train a map using default parameters.<TR><TD><a href="som_topol_struct.html"><B>som_topol_struct</B></a><TD> Default map topology.<TR><TD><a href="som_randinint.html"><B>som_randinint</B></a><TD> Random initialization algorithm.<TR><TD><a href="som_lininit.html"><B>som_lininit</B></a><TD> Linear initialization algorithm.<TR><TD><a href="som_seqtrain.html"><B>som_seqtrain</B></a><TD> Sequential training algorithm.<TR><TD><a href="som_batchtrain.html"><B>som_batchtrain</B></a><TD> Batch training algorithm.</TABLE><p><hr><br><br><!-- Last updated: May 30 2002 --></body></html>⌨️ 快捷键说明
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