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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">DEMO_EMGMM</b><td valign="baseline" align="right" class="function"><a href="../demos/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Demo on Expectation-Maximization (EM) algorithm.</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> demo_emgmm</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This demo shows the Expectation-Maximization (EM) algorithm</span><br><span class=help> [<a href="../references.html#Schles68" title = "" >Schles68</a>][<a href="../references.html#DLR77" title = "" >DLR77</a>] for Gaussians mixture model (GMM). The EM </span><br><span class=help> fits the GMM to i.i.d. sample data (in this case only 2D) </span><br><span class=help> such that the likelihood is maximized. </span><br><span class=help></span><br><span class=help> The found model is described by ellipsoids (shape of </span><br><span class=help> covariances) and a crosses (mean value vectors). The value</span><br><span class=help> of the optimized log-likelihood function for the current estimate </span><br><span class=help> is displayed in the bottom part.</span><br><span class=help></span><br><span class=help> <span class=help_field>Control:</span></span><br><span class=help> Covariance - Determines type of the covariance matrix:</span><br><span class=help> Diagonal (independent features),</span><br><span class=help> Full (correlated features).</span><br><span class=help> Components - Number of components (Gaussians) in the mixture.</span><br><span class=help> </span><br><span class=help> Iterations - Number of iterations in one step.</span><br><span class=help> Random init - the initial model is randomly generated and/or </span><br><span class=help> first n training samples are taken as the</span><br><span class=help> mean vectors.</span><br><span class=help></span><br><span class=help> FIG2EPS - Export screen to the PostScript file.</span><br><span class=help> Save model - Save current model to file.</span><br><span class=help> Load data - Load input point sets from file.</span><br><span class=help> Create data - Invoke program for creating point sets.</span><br><span class=help> Reset - Set the tested algorithm to the initial state.</span><br><span class=help> Play - Run the tested algorithm.</span><br><span class=help> Stop - Stop the running algorithm.</span><br><span class=help> Step - Perform only one step.</span><br><span class=help> Info - Info box.</span><br><span class=help> Close - Close the program.</span><br><span class=help></span><br><span class=help> <span class=also_field>See also </span><span class=also><a href = "../probab/estimation/emgmm.html" target="mdsbody">EMGMM</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../demos/list/demo_emgmm.html">demo_emgmm.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> 19-sep-2003, VF<br> 11-june-2001, V.Franc, comments added.<br> 27.02.00 V. Franc<br> 5. 4.00 V. Franc<br> 23.06.00 V. Hlavac Comments polished. Message when no data loaded.<br> Export of the solution to global variables.<br> 27-mar-2001, V.Franc, Graph og log-likelihood function added<br></body></html>?? 快捷鍵說明
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