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<h1>compute_WMW</h1>
<introduction>
<p>Computes the Generalized Wilcoxon-Mann-Whitney Statistic.</p>
</introduction>
<h2>Contents</h2>
<div>
<ul>
<li><a href="#1">Syntax</a></li>
<li><a href="#2">Description</a></li>
<li><a href="#3">Input</a></li>
<li><a href="#11">Ouput</a></li>
<li><a href="#13">Signature</a></li>
<li><a href="#15">See also</a></li>
</ul>
</div>
<h2>Syntax<a name="1"></a></h2><pre>[WMW]=compute_WMW(data,o,enough_memory)</pre><h2>Description<a name="2"></a></h2>
<h2>Input<a name="3"></a></h2>
<div>
<ul>
<li>data ... structure containing the data regarding the ranking task at hand [See convert_data_to_ranking_format.m]</li>
<li>o ... vector containing the output of the ranking function</li>
<li>enough_memory .. if 1 uses the matrix version of the code else resorts to for loops</li>
</ul>
</div>
<p>Basic information---</p>
<div>
<ul>
<li>data.N ... number of data points</li>
<li>data.d ... data dimensionality</li>
<li>data.S ... number of classes</li>
<li>data.m ... number of inputs in each class</li>
</ul>
</div>
<p>Actual data---</p>
<div>
<ul>
<li>data.labels ... vector of class labels</li>
<li>data.X ... cell array where each cell contains the data belonging to one class</li>
<li>data.index ... index of the data belonging to one class</li>
<li>data.X_raw ... d x N original data matrix</li>
<li>data.y_raw ... 1 x N vector of the class labels</li>
</ul>
</div>
<p>Preference graph---</p>
<div>
<ul>
<li>data.graph_type ... graph type</li>
<li>data.C ... number of edges in the preference graph</li>
<li>data.G ... data.C x 2 matrix encoding the preference relations. The class in the second column is preferred over that in the
first column.
</li>
<li>data.num_of_pairs ... total number of pairwise preference realtions</li>
</ul>
</div>
<h2>Ouput<a name="11"></a></h2>
<div>
<ul>
<li>WMW ... Generalized Wilcoxon-Mann-Whitney Statistic</li>
</ul>
</div>
<h2>Signature<a name="13"></a></h2>
<div>
<ul>
<li><b>Author:</b> Vikas Chandrakant Raykar
</li>
<li><b>E-Mail:</b> <a href="mailto:vikas@cs.umd.edu">vikas@cs.umd.edu</a> Date: September 20, 2006
</li>
</ul>
</div>
<h2>See also<a name="15"></a></h2>
<p><a href="convert_data_to_ranking_format.html">convert_data_to_ranking_format</a></p>
<p class="footer"><br>
Published with wg_publish; V1.0<br></p>
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<!--
##### SOURCE BEGIN #####
%% compute_WMW
% Computes the Generalized Wilcoxon-Mann-Whitney Statistic.
%% Syntax
% [WMW]=compute_WMW(data,o,enough_memory)
%% Description
%%
%% Input
%%
%%
% * data ... structure containing the data regarding the ranking task at hand [See convert_data_to_ranking_format.m]
% * o ... vector containing the output of the ranking function
% * enough_memory .. if 1 uses the matrix version of the code else resorts to for loops
%%
% Basic informationREPLACE_WITH_DASH_DASH-
%%
%%
% * data.N ... number of data points
% * data.d ... data dimensionality
% * data.S ... number of classes
% * data.m ... number of inputs in each class
%%
% Actual dataREPLACE_WITH_DASH_DASH-
%%
%%
% * data.labels ... vector of class labels
% * data.X ... cell array where each cell contains the data belonging to one class
% * data.index ... index of the data belonging to one class
% * data.X_raw ... d x N original data matrix
% * data.y_raw ... 1 x N vector of the class labels
%%
% Preference graphREPLACE_WITH_DASH_DASH-
%%
%%
% * data.graph_type ... graph type
% * data.C ... number of edges in the preference graph
% * data.G ... data.C x 2 matrix encoding the preference relations. The class in the second column is preferred over that in the first column.
% * data.num_of_pairs ... total number of pairwise preference realtions
%%
%%
%% Ouput
%%
%%
% * WMW ... Generalized Wilcoxon-Mann-Whitney Statistic
%%
%% Signature
%%
%%
% * *Author:* Vikas Chandrakant Raykar
% * *E-Mail:* vikas@cs.umd.edu
% Date: September 20, 2006
%%
%% See also
%%
% convert_data_to_ranking_format
%%
%%
%
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