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setting.m
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setting.m
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function netset=setting(method)
% setting initializes a default structure for DBGSOM parameters
% input: method type of settings
% 'dbgsom' for an unsupervised kohonen network.
% output: netset
% netset.method method name ('dbgsom')
% netset.epch number of epochs (default = NaN)
% netset.amax max. learning rate (default = 0.5)
% netset.amin min. learning rate (default = 0.01)
% netset.vis training visualization (for 2D datasets) ('y', 'n') (default = 'n')
% netset.pmax initial neighborhood function width (a number between 0 and 1) (default = 2)
% netset.pmin final neighborhood function width (a number between 0 and pmax)(default = 0.7)
% netset.sf spread-out factor ( 0 > sf > 1) (default = NaN)
% Directed Batch Growing Self Organizing Map (DBGSOM)
% version 1.0 - July 2017
% Mahdi Vasighi
% Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
% Department of Computer Science and Information Technology
% www.iasbs.ac.ir/~vasighi/
% Directed Batch Growing Self Organizing Map (DBGSOM), version 1.0
% Copyright (C) 2017 Mahdi Vasighi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
netset.method = method; % method name ('dbgsom')
netset.mod = 'batch'; % learning mode: 'batch' or 'sequential'
netset.epch = NaN; % number of epochs
netset.amax = 0.5; % max. learning rate
netset.amin = 0.01; % min. learning rate
netset.vis = 'n'; % training visualization (yes, no)
netset.stnr = 4; % number of starting neurons (4: square shape , 5:plus shape)
netset.inw = 'eg'; % weight of starting neurons (eg: eigenvector, rn: random)
netset.pmax = 2;
netset.pmin = 0.7;
netset.sf = NaN; % spread-out factor