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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>Contents.m</title><link rel="stylesheet" type="text/css" href="../stpr.css"></head><body><table border=0 width="100%" cellpadding=0 cellspacing=0><tr valign="baseline"><td valign="baseline" class="function"><b class="function">GENLSDATA</b><td valign="baseline" align="right" class="function"><a href="../data/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Generates linearly separable binary data.</b></p> <hr><div class='code'><code><span class=help></span><br><span class=help> <span class=help_field>Synopsis:</span></span><br><span class=help> data = genlsdata(dim,num_data,margin)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> It generates randomly binary labeled vectors which </span><br><span class=help> are linearly separable with prescribed margin. </span><br><span class=help> </span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> dim [1x1] Data dimension.</span><br><span class=help> num_data [1x1] Number of generated data.</span><br><span class=help> margin [1x1] Minimal ensured margin (distance of the closest</span><br><span class=help> vector to the separating hyperplane).</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> data [struct] Generated data:</span><br><span class=help> .X [dim x num_data] Sample data.</span><br><span class=help> .y [1 x num_data] Data labels (1 or 2).</span><br><span class=help></span><br><span class=help> model [struct] Ground truth linear classifier:</span><br><span class=help> .W [dim x 1] Normal vector of separating hyperplane.</span><br><span class=help> .b [1x1] Bias of the hyperplane.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> data = genlsdata(2,50,1);</span><br><span class=help> model = ekozinec( data );</span><br><span class=help> model.margin</span><br><span class=help> figure; ppatterns(data); pline(model);</span><br><span class=help></span><br><span class=help> <span class=also_field>See also </span><span class=also></span><br><span class=help><span class=also> <a href = "../linear/finite/perceptron.html" target="mdsbody">PERCEPTRON</a>, <a href = "../linear/finite/ekozinec.html" target="mdsbody">EKOZINEC</a>, <a href = "../linear/linclass.html" target="mdsbody">LINCLASS</a>, SVM.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../data/list/genlsdata.html">genlsdata.m</a> <p><b class="info_field">About: </b> Statistical Pattern Recognition Toolbox<br> (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac<br> <a href="http://www.cvut.cz">Czech Technical University Prague</a><br> <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a><br> <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a><br> <p><b class="info_field">Modifications: </b> <br> 3-may-2004, VF<br> 16-Feb-2003, VF<br> 26-feb-2001 V.Franc<br></body></html>⌨️ 快捷键说明
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