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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 gaussianSimilarity</title> <meta name="keywords" content="gaussianSimilarity"> <meta name="description" content="Evaluation of a PDF to see if it is close to 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> > gaussianSimilarity.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>gaussianSimilarity</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>Evaluation of a PDF to see if it is close to 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 similarity = gaussianSimilarity(x, binNum, 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"> Evaluation of a PDF to see if it is close to Gaussian distribution</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><li><a href="gaussianMle.html" class="code" title="function gaussianParam = gaussianMle(feature, plotOpt)">gaussianMle</a> mleGaussian: Maximum likelihood estimator for Gaussian distribution</li></ul>This function is called by:<ul style="list-style-image:url(../matlabicon.gif)"></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 similarity = gaussianSimilarity(x, binNum, plotOpt)</a>0002 <span class="comment">% Evaluation of a PDF to see if it is close to Gaussian distribution</span>0003 0004 <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>0005 dataNum = length(x);0006 <span class="keyword">if</span> nargin<2, binNum = dataNum/50; <span class="keyword">end</span>0007 <span class="keyword">if</span> nargin<3, plotOpt = 0; <span class="keyword">end</span>0008 0009 gPrm = <a href="gaussianMle.html" class="code" title="function gaussianParam = gaussianMle(feature, plotOpt)">gaussianMle</a>(x);0010 x = (x-gPrm.mu)/gPrm.sigma; <span class="comment">% 0 mean, unity variance</span>0011 [N, X] = hist(x, binNum);0012 0013 desired = <a href="gaussian.html" class="code" title="function out = gaussian(data, gParam);">gaussian</a>(X, gPrm);0014 k = dataNum*(max(x)-min(x))/binNum;0015 diff = mean(abs(desired-N/k));0016 similarity = bellmf(diff, [0.1 1 0]);0017 <span class="comment">% 0.28 ---> 0.9</span>0018 <span class="comment">% 0.03 ---> 0.1</span>0019 <span class="comment">%a = 4*(0.9-0.1);</span>0020 <span class="comment">%b = 0.9-a*0.28;</span>0021 <span class="comment">%similarity = a*similarity+b;</span>0022 0023 <span class="keyword">if</span> plotOpt,0024 bar(X, N/k, 1);0025 range = max(x)-min(x);0026 xi = linspace(min(x)-range/2, max(x)+range/2);0027 yi = <a href="gaussian.html" class="code" title="function out = gaussian(data, gParam);">gaussian</a>(xi, gPrm);0028 hold on0029 h = plot(xi, yi);0030 hold off0031 set(h, <span class="string">'linewidth'</span>, 2, <span class="string">'color'</span>, <span class="string">'r'</span>);0032 fprintf(<span class="string">'Average abs. difference = %g, similarity to Gaussian = %g\n'</span>, diff, similarity);0033 <span class="keyword">end</span>0034 0035 <span class="comment">% ======= Self demo</span>0036 <a name="_sub1" href="#_subfunctions" class="code">function selfdemo</a>0037 dataNum = 1000;0038 x = randn(dataNum, 1);0039 subplot(2,1,1);0040 feval(mfilename, x, 20, 1);0041 x = rand(dataNum, 1);0042 subplot(2,1,2);0043 feval(mfilename, x, 20, 1);</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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