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<span class="title_page">How to start </span>
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<div id="tab_space_lateral"><a href="#sub_1" class="lnk_text">Data structure</a></div>
<div id="tab_space_lateral"><a href="#sub_1_bis" class="lnk_text">Data scaling</a></div>
<div id="tab_space_lateral"><a href="#sub_2" class="lnk_text">How to prepeare the MOLMAP settings</a></div> </div>
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<span class="title_paragraph">_ Data structure </span>
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Three-way datasets are defined as samples x variables on the second mode x variables on the third mode (IxJxK). Type:<BR>
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load simulated_data </div>
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on the MATLAB command window to see an example of data structure. Class vectors are not required as input since you can not directly develop classification models with this toolbox. You can use the <strong>MOLMAP multiway toolbox</strong> to calculate MOLMAP scores and then apply what you prefer on the MOLMAP score matrix.<BR>
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<span class="title_paragraph">_ Data scaling </span>
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<span class="style1">PAY ATTENTION</span>: multiway data are always range scaled inbetween 0 and 1, in order to make them comparable with the net weights.
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<span class="title_paragraph">_ How to prepeare the MOLMAP settings</span>
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Some settings must be defined in order to run MOLMAP modeling; in order to create a default setting structure, type on the MATLAB command window:
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settings = som_settings('multiway')
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A default structure with the following fields will be built:
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<B>settings.nsize</B><BR>
Kohonen map net size (default value is NaN). This is the number of neurons for each side of the map. Taking into consideration that the map is a square, if you enter:
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settings.nsize = 7;
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you'lle get a total number of neurons equal to 7*7 = 49.
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<B>settings.epochs</B><BR>
number of epochs (default value is NaN) for training the Kohonen map. This defines the number of times the objects will be introduced in the net. For example, in order to train a net with 100 epochs, type:
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settings.epochs = 100;
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<span class="style1">PAY ATTENTION</span>: since both settings.nsize and settings.epochs have no default values, these settings must be always defined by the user before running a model. <BR>
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<B>settings.bound</B><BR>
boundary condition ('toroidal' or 'normal', defualt is 'toroidal') for Kohonen map. Toroidal means that each edge of the map has to be seen as connected with the opposite one. <BR>
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<B>settings.a_max</B> and <B>settings.a_min</B><BR>
are the initial learning rate (defualt value equal to 0.5) and the final learning rate (defualt value equal to 0.01) for training a Kohonen map, as suggested by Zupan, Novic and Ruis醤chez in "Kohonen and counterpropagation artificial neural networks in analytical chemistry", Chemometrics and Intelligent Laboratory Systems (1997) 38 1-23. <BR>
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