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<span class="title_page">Matlab routines</span>
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<div id="tab_space_lateral"><a href="#sub_1" class="lnk_text">som_settings</a></div>
<div id="tab_space_lateral"><a href="#sub_2" class="lnk_text">model_kohonen</a></div>
<div id="tab_space_lateral"><a href="#sub_3" class="lnk_text">model_cpann</a></div>
<div id="tab_space_lateral"><a href="#sub_4" class="lnk_text">pred_kohonen</a></div>
<div id="tab_space_lateral"><a href="#sub_5" class="lnk_text">pred_cpann</a></div>
<div id="tab_space_lateral"><a href="#sub_6" class="lnk_text">cv_cpann</a></div>
<div id="tab_space_lateral"><a href="#sub_7" class="lnk_text">visualize_model</a></div>
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This is a list of the main MATLAB routines you can use to develop Kohonen and CPANNs models<BR><BR>
<span class="title_paragraph">_ som_settings</span>
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default <a href="start.htm" class="lnk_text">setting structure</a> for Kohonen maps and counterpropagation artificial neural networks (CPANNs); som_settings build a default structure with all the parameter needed to
perform Kohonen maps and CPANNs
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<strong>settings = som_settings(type);</strong><BR>
<BR>type "help som_settings" on the MATLAB command window for further informations
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<span class="title_paragraph">_ model_kohonen</span>
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Kohonen maps;
model_kohonen builds <a href="kohonen.htm" class="lnk_text">Kohonen maps</a> (self organising maps, SOM)<br>
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<strong>model = model_kohonen(X,settings);</strong><br>
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type "help model_kohonen" on the MATLAB command window for further informations <BR>
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<span class="title_paragraph">_ model_cpann</span>
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Counterpropagation Artificial Neural Networks (CPANNs);
model_cpann builds a classification model based on <a href="cpann.htm" class="lnk_text">CPANNs</a><br>
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model = model_cpann(X,class,settings);</strong><br>
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type "help model_cpann" on the MATLAB command window for further informations <BR>
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<span class="title_paragraph">_ pred_kohonen</span>
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prediction of unknown samples with <a href="kohonen.htm" class="lnk_text">Kohonen maps</a>;
pred_kohonen projects new samples by using a previuos model
built by means of Kohonen maps (model_kohonen)<br>
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<strong>pred = pred_kohonen(X,model);</strong><br>
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type "help pred_kohonen" on the MATLAB command window for further informations<BR>
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<span class="title_paragraph">_ pred_cpann</span>
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prediction of unknown samples with counterpropagation artificial neural networks (<a href="cpann.htm" class="lnk_text">CPANNs</a>);
pred_cpann predicts classes of unknown samples by using a previuos model built by means of CPANNs (model_cpann)<br>
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<strong>pred = pred_cpann(X,model);</strong><br>
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type "help pred_cpann" on the MATLAB command window for further informations<BR>
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<span class="title_paragraph">_ cv_cpann</span>
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cross validation for Counterpropagation Artificial Neural Networks (<a href="cpann.htm" class="lnk_text">CPANNs</a>); cross validation is perfomred with venetian blinds, i.e. with 3 cv groups
the split of the first group will be [1,0,0,1,0,0,....,1,0,0] and so on.<br>
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<strong>cv = cv_cpann(X,class,settings,cv_groups);</strong><br>
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type "help cv_cpann" on the MATLAB command window for further informations<BR>
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<span class="title_paragraph">_ visualize_model</span>
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visualisation of the <a href="kohonen.htm" class="lnk_text">Kohonen map</a> or <a href="cpann.htm" class="lnk_text">CPANNs</a> results;
visualize_model opens a GUI figure for exploring the results<br>
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<strong>visualize_model(model,labels);</strong><br>
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type "help visualize_model" on the MATLAB command window for further informations<BR>
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