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<span class="title_page">MOLMAP modeling</span>
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<div id="tab_space_lateral"><a href="#sub_1" class="lnk_text">Starting the model</a></div>
<div id="tab_space_lateral"><a href="#sub_2" class="lnk_text">How to read the results</a></div>
<div id="tab_space_lateral"><a href="#sub_3" class="lnk_text">How to plot the results</a></div>
<div id="tab_space_lateral"><a href="#sub_4" class="lnk_text">Prediction of new samples</a></div>
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<span class="title_paragraph">_ Starting the model</span>
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Once <a href="start.htm#sub_1" class="lnk_text">data</a> have been prepeared and <a href="start.htm#sub_2" class="lnk_text">settings</a> have been defined, you can calculate MOLMAP scores by typing the following code in the MATLAB command window:
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model = model_multiway(X,settings);
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where X is the <a href="start.htm#sub_1" class="lnk_text">three-way data matrix</a> (with dimension [IxJxK]) and settings is the <a href="start.htm#sub_2" class="lnk_text">setting structure</a>. Depending on the size and epochs used to train the model, MATLAB could take some minutes to calculate it. Anyway, in the MATLAB command window the number of processed epochs will be displayed.<BR>
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[<a href="#top" class="lnk_text">-> top</a>]
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<span class="title_paragraph">_ How to read the results</span>
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The given output (model) is a structure, with several fields containing all the results.
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<B>model.net.W</B><BR>
contains the kohonen weights with dimension [size x size x K]
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<B>model.net.settings</B><BR>
contains the used settings (epochs, size, etc...)
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<B>model.scal</B><BR>
is a structure containing all the scaling parameters (minimum and maximum)
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<strong>model.res.score</strong> <BR>
contains the calculated <span class="style1">MOLMAP scores</span>, with dimensions [I x size*size]. <span class="style1">This is the score matrix you can use to build a subsequent classification model</span>.<BR>
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<B>model.res.top_map</B><BR>
contains the input vector positions (coordinates) in the Kohonen Map, with dimensions [I*J x 2]. The top map coordinates are coded in the following way:
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<center><img src="kohonen_top_map.gif" width="393" height="311" border="1"></center>
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where, for example, the red point represents a generic input vector placed in the neuron with coordinates [3,2];
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<strong>model.label</strong><BR>
contains the input vector labels that will be used to display the results (see below) <BR>
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<B>model.label.label_sample</B><BR>
contains the input-vector labels on the basis of original multiway samples<br>
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<B>model.label.label_profile</B><BR>
contains the input-vector labels on the basis of second mode variables<BR>
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[<a href="#top" class="lnk_text">-> top</a>]
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<BR><a name="sub_3"></a>
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<span class="title_paragraph">_ How to plot the results</span>
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You can open a MATLAB GUI to visualize the results. To do so, type:
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visualize_model(model);
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where <a href="#sub_1" class="lnk_text">model</a> is the previously described model structure.
The follwoing GUI will appear:
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<img src="kohonen_visualize_small.gif" width="500" height="409"> <BR><BR>
<a href="kohonen_visualize.gif" target="_blank" class="lnk_text">enlarge figure</a>
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this plot represent the Kohonen top map, where input vectors and variable weights can be displayed. "Display labels" and "Display weights" set the input vector labels and variable weights respectivly. Neurons can be coloured from white (weight equal to zero, minimum value) to black (weight equal to 1, maximum value). "Update" is the button for updating the plot. You can move the map ("up", "down", "right" and "left"), while "get neuron weights" opens a new plot displaying all the weights of a selected neuron and "get neuron labels" opens a new plot with the list of all the input vector labels of a selected neuron. <BR>
In order to better understand how to read the results, have a look to <a href="example.htm" class="lnk_text">this example</a>. <BR>
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[<a href="#top" class="lnk_text">-> top</a>]
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<BR><a name="sub_4"></a>
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<span class="title_paragraph">_ Prediction of new samples</span>
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In order to predict MOLMAP scores of new samples with an existing MOLMAP model, type:
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pred = pred_multiway(Xnew,model);
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where Xnew is the data matrix (Inew x J x K) of the samples to be predicted
and <a href="#sub_1" class="lnk_text">model</a> is the previously described model structure.
Pred is a structure, containing the following field:
<BR><BR>
<B>pred.top_map</B><BR>
contains the positions (coordinates) of the input vectors of the predicted samples in the Kohonen Map.
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<B>pred.score</B><BR>
contains the predicted <span class="style1">MOLMAP scores</span> (Inew x size*size).<BR>
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[<a href="#top" class="lnk_text">-> top</a>]
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