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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd"><html><head> <title>Description of gaussianMle</title> <meta name="keywords" content="gaussianMle"> <meta name="description" content="mleGaussian: Maximum likelihood estimator for Gaussian distribution"> <meta http-equiv="Content-Type" content="text/html; charset=big5"> <meta name="generator" content="m2html © 2003 Guillaume Flandin"> <meta name="robots" content="index, follow"> <link type="text/css" rel="stylesheet" href="../m2html.css"></head><body><a name="_top"></a><div><a href="../index.html">Home</a> > <a href="index.html">dcpr</a> > gaussianMle.m</div><!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png"> Master index</a></td><td align="right"><a href="index.html">Index for dcpr <img alt=">" border="0" src="../right.png"></a></td></tr></table>--><h1>gaussianMle</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>mleGaussian: Maximum likelihood estimator for Gaussian distribution</strong></div><h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>function gaussianParam = gaussianMle(feature, plotOpt) </strong></div><h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="fragment"><pre class="comment"> mleGaussian: Maximum likelihood estimator for Gaussian distribution
gaussianParam = mleGaussian(feature)
gaussianParam.mu: MLE of the mean
gaussianParam.sigma: MLE of the variance
feature: Feature matrix where each column corresponds to a vector
For example:
dataNum=1000;
x = randn(dataNum, 1);
plotOpt=1;
gaussianParam=gaussianMle(x, plotOpt);</pre></div><!-- crossreference --><h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>This function calls:<ul style="list-style-image:url(../matlabicon.gif)"><li><a href="gaussian.html" class="code" title="function out = gaussian(data, gParam);">gaussian</a> gaussian: Multi-dimensional Gaussian propability density function</li></ul>This function is called by:<ul style="list-style-image:url(../matlabicon.gif)"><li><a href="gaussianSimilarity.html" class="code" title="function similarity = gaussianSimilarity(x, binNum, plotOpt)">gaussianSimilarity</a> Evaluation of a PDF to see if it is close to Gaussian distribution</li><li><a href="roc.html" class="code" title="function [threshold, fp, fn, mu1, var1, mu2, var2, a, b]=roc(data1, data2, plotOpt);">roc</a> roc</li><li><a href="sgcTrain.html" class="code" title="function [classParam, recogRate, hitIndex]=sgcTrain(DS, prior, plotOpt)">sgcTrain</a> sgcTrain: Training for single Gaussian classifier training</li></ul><!-- crossreference --><h2><a name="_subfunctions"></a>SUBFUNCTIONS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><ul style="list-style-image:url(../matlabicon.gif)"><li><a href="#_sub1" class="code">function selfdemo</a></li></ul><h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function gaussianParam = gaussianMle(feature, plotOpt)</a>0002 <span class="comment">% mleGaussian: Maximum likelihood estimator for Gaussian distribution</span>0003 <span class="comment">% gaussianParam = mleGaussian(feature)</span>0004 <span class="comment">% gaussianParam.mu: MLE of the mean</span>0005 <span class="comment">% gaussianParam.sigma: MLE of the variance</span>0006 <span class="comment">% feature: Feature matrix where each column corresponds to a vector</span>0007 <span class="comment">%</span>0008 <span class="comment">% For example:</span>0009 <span class="comment">% dataNum=1000;</span>0010 <span class="comment">% x = randn(dataNum, 1);</span>0011 <span class="comment">% plotOpt=1;</span>0012 <span class="comment">% gaussianParam=gaussianMle(x, plotOpt);</span>0013 0014 <span class="comment">% Roger Jang, 20000428, 20080726</span>0015 0016 <span class="keyword">if</span> nargin<1, <a href="#_sub1" class="code" title="subfunction selfdemo">selfdemo</a>; <span class="keyword">return</span>; <span class="keyword">end</span>0017 <span class="keyword">if</span> nargin<2, plotOpt=0; <span class="keyword">end</span>0018 0019 <span class="keyword">if</span> size(feature, 2)==1, feature=feature'; <span class="keyword">end</span>0020 [dim, dataNum] = size(feature);0021 <span class="keyword">if</span> dataNum<=dim0022 fprintf(<span class="string">'Warning: dataNum<=dim, the resulting parameters are not trustworthy!\n'</span>);0023 <span class="keyword">end</span>0024 0025 gaussianParam.mu = mean(feature, 2);0026 gaussianParam.sigma = (feature*feature'-dataNum*gaussianParam.mu*gaussianParam.mu')/(dataNum-1);0027 0028 <span class="keyword">if</span> plotOpt0029 <span class="keyword">if</span> dim==10030 binNum = 20;0031 [N, X] = hist(feature, binNum);0032 maxX=max(feature);0033 minX=min(feature);0034 rangeX=maxX-minX;0035 k = dataNum*rangeX/binNum;0036 bar(X, N/k, 1);0037 xi = linspace(minX-rangeX/2, maxX+rangeX/2);0038 yi = <a href="gaussian.html" class="code" title="function out = gaussian(data, gParam);">gaussian</a>(xi, gaussianParam);0039 hold on0040 h = plot(xi, yi);0041 hold off0042 set(h, <span class="string">'linewidth'</span>, 2, <span class="string">'color'</span>, <span class="string">'r'</span>);0043 title(<span class="string">'Gaussian PDF'</span>);0044 <span class="keyword">end</span>0045 <span class="keyword">end</span>0046 0047 <span class="comment">% ====== Self demo</span>0048 <a name="_sub1" href="#_subfunctions" class="code">function selfdemo</a>0049 dataNum = 1000;0050 <span class="comment">% ====== Gaussian PDF</span>0051 x = randn(dataNum, 1);0052 plotOpt=1;0053 subplot(2,1,1);0054 feval(mfilename, x, plotOpt);0055 <span class="comment">% ====== Uniform PDF</span>0056 x = rand(dataNum, 1);0057 subplot(2,1,2);0058 feval(mfilename, x, plotOpt);0059 title(<span class="string">'Uniform PDF'</span>);</pre></div><hr><address>Generated on Thu 30-Oct-2008 12:53:56 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> © 2003</address></body></html>⌨️ 快捷键说明
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