📄 som_seqtrain.html
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<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"><html><head><title>SOM Toolbox / som_seqtrain </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_seqtrain </H1><H3> Purpose </H3><PRE> Trains a Self-Organizing Map using the sequential algorithm. </PRE><H3> Syntax </H3><UL><PRE> sM = som_seqtrain(sM,D); sM = som_seqtrain(sM,sD); sM = som_seqtrain(...,'argID',value,...); sM = som_seqtrain(...,value,...); [sM,sT] = som_seqtrain(M,D,...);</PRE></UL><H3> Description </H3><PRE> Trains the given SOM (sM or M above) with the given training data (sD or D) using sequential SOM training algorithm. If no optional arguments (argID, value) are given, a default training is done, the parameters are obtained from SOM_TRAIN_STRUCT function. Using optional arguments the training parameters can be specified. Returns the trained and updated SOM and a train struct which contains information on the training.</PRE><H3> References </H3><PRE> Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, Berlin, 1995, pp. 78-82. Kohonen, T., "Clustering, Taxonomy, and Topological Maps of Patterns", International Conference on Pattern Recognition (ICPR), 1982, pp. 114-128. Kohonen, T., "Self-Organized formation of topologically correct feature maps", Biological Cybernetics 43, 1982, pp. 59-69.</PRE><H3> Required input arguments </H3><PRE> sM The map to be trained. (struct) map struct (matrix) codebook matrix (field .data of map struct) Size is either [munits dim], in which case the map grid dimensions (msize) should be specified with optional arguments, or [msize(1) ... msize(k) dim] in which case the map grid dimensions are taken from the size of the matrix. Lattice, by default, is 'rect' and shape 'sheet'. D Training data. (struct) data struct (matrix) data matrix, size [dlen dim]</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. 'mask' (vector) BMU search mask, size dim x 1. Default is the one in sM (field '.mask') or a vector of ones if only a codebook matrix was given. 'msize' (vector) map grid dimensions. Default is the one in sM (field sM.topol.msize) or 'si = size(sM); msize = si(1:end-1);' if only a codebook matrix was given. 'radius' (vector) neighborhood radius length = 1: radius_ini = radius length = 2: [radius_ini radius_fin] = radius length > 2: the vector given neighborhood radius for each step separately trainlen = length(radius) 'radius_ini' (scalar) initial training radius 'radius_fin' (scalar) final training radius 'alpha' (vector) learning rate length = 1: alpha_ini = alpha length > 1: the vector gives learning rate for each step separately trainlen is set to length(alpha) alpha_type is set to 'user defined' 'alpha_ini' (scalar) initial learning rate 'tracking' (scalar) tracking level: 0, 1 (default), 2 or 3 0 - estimate time 1 - track time and quantization error 2 - plot quantization error 3 - plot quantization error and two first components 'trainlen' (scalar) training length (see also 'tlen_type') 'trainlen_type' *(string) is the trainlen argument given in 'epochs' or in 'samples'. Default is 'epochs'. 'sample_order'*(string) is the sample order 'random' (which is the the default) or 'ordered' in which case samples are taken in the order in which they appear in the data set 'train' *(struct) train struct, parameters for training. Default parameters, unless specified, are acquired using SOM_TRAIN_STRUCT (this also applies for 'trainlen', 'alpha_type', 'alpha_ini', 'radius_ini' and 'radius_fin'). 'sTrain', 'som_train' (struct) = 'train' 'neigh' *(string) The used neighborhood function. Default is the one in sM (field '.neigh') or 'gaussian' if only a codebook matrix was given. Other possible values is 'cutgauss', 'ep' and 'bubble'. 'topol' *(struct) topology of the map. Default is the one in sM (field '.topol'). 'sTopol', 'som_topol' (struct) = 'topol' 'alpha_type'*(string) learning rate function, 'inv', 'linear' or 'power' 'lattice' *(string) map lattice. Default is the one in sM (field sM.topol.lattice) or 'rect' if only a codebook matrix was given. 'shape' *(string) map shape. Default is the one in sM (field sM.topol.shape) or 'sheet' if only a codebook matrix was given. </PRE><H3> Output arguments </H3><PRE> sM the trained map (struct) if a map struct was given as input argument, a map struct is also returned. The current training is added to the training history (sM.trainhist). The 'neigh' and 'mask' fields of the map struct are updated to match those of the training. (matrix) if a matrix was given as input argument, a matrix is also returned with the same size as the input argument. sT (struct) train struct; information of the accomplished training</PRE><H3> Examples </H3><PRE> Simplest case: sM = som_seqtrain(sM,D); sM = som_seqtrain(sM,sD); To change the tracking level, 'tracking' argument is specified: sM = som_seqtrain(sM,D,'tracking',3); The change training parameters, the optional arguments 'train', 'neigh','mask','trainlen','radius','radius_ini', 'radius_fin', 'alpha', 'alpha_type' and 'alpha_ini' are used. sM = som_seqtrain(sM,D,'neigh','cutgauss','trainlen',10,'radius_fin',0); Another way to specify training parameters is to create a train struct: sTrain = som_train_struct(sM,'dlen',size(D,1),'algorithm','seq'); sTrain = som_set(sTrain,'neigh','cutgauss'); sM = som_seqtrain(sM,D,sTrain); By default the neighborhood radius goes linearly from radius_ini to radius_fin. If you want to change this, you can use the 'radius' argument to specify the neighborhood radius for each step separately: sM = som_seqtrain(sM,D,'radius',[5 3 1 1 1 1 0.5 0.5 0.5]); By default the learning rate (alpha) goes from the alpha_ini to 0 along the function defined by alpha_type. If you want to change this, you can use the 'alpha' argument to specify the learning rate for each step separately: alpha = 0.2*(1 - log([1:100])); sM = som_seqtrain(sM,D,'alpha',alpha); You don't necessarily have to use the map struct, but you can operate directly with codebook matrices. However, in this case you have to specify the topology of the map in the optional arguments. The following commads are identical (M is originally a 200 x dim sized matrix): M = som_seqtrain(M,D,'msize',[20 10],'lattice','hexa','shape','cyl'); M = som_seqtrain(M,D,'msize',[20 10],'hexa','cyl'); sT= som_set('som_topol','msize',[20 10],'lattice','hexa','shape','cyl'); M = som_seqtrain(M,D,sT); M = reshape(M,[20 10 dim]); M = som_seqtrain(M,D,'hexa','cyl'); The som_seqtrain also returns a train struct with information on the accomplished training. This is the same one as is added to the end of the trainhist field of map struct, in case a map struct is given. [M,sTrain] = som_seqtrain(M,D,'msize',[20 10]); [sM,sTrain] = som_seqtrain(sM,D); % sM.trainhist{end}==sTrain</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 SOM using default parameters.<TR><TD><a href="som_batchtrain.html"><B>som_batchtrain</B></a><TD> Train SOM with batch algorithm.<TR><TD><a href="som_train_struct.html"><B>som_train_struct</B></a><TD> Determine default training parameters.</TABLE><p><hr><br><br><!-- Last updated: May 30 2002 --></body></html>⌨️ 快捷键说明
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