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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>gsamp.m</title><link rel="stylesheet" type="text/css" href="../../m-syntax.css"></head><body><code><span class=defun_kw>function</span> <span class=defun_out>X</span>=<span class=defun_name>gsamp</span>(<span class=defun_in>varargin</span>)<br><span class=h1>% GSAMP Generates sample from Gaussian distribution.</span><br><span class=help>% </span><br><span class=help>% <span class=help_field>Synopsis:</span></span><br><span class=help>% X = gsamp( Mean, Cov, num_data )</span><br><span class=help>% X = gsamp( model, num_data )</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Description:</span></span><br><span class=help>% X = gsamp(Mean,Cov,num_data) generates num_data samples from </span><br><span class=help>% a multi-variate Gassian distribution given by mean vector </span><br><span class=help>% Mean [dim x 1] and covariance matrix Cov [dim x dim]. </span><br><span class=help>%</span><br><span class=help>% X = gsamp(model,num_data) assumes that parameters of Gaussian</span><br><span class=help>% are given in structure with fields model.Mean a model.Cov.</span><br><span class=help>% </span><br><span class=help>% <span class=help_field>Example:</span></span><br><span class=help>% model = struct('Mean',1,'Cov',2);</span><br><span class=help>% figure; hold on;</span><br><span class=help>% plot([-4:0.1:5],pdfgauss([-4:0.1:5],model),'r');</span><br><span class=help>% [Y,X] = hist(gsamp(model,500),10);</span><br><span class=help>% bar(X,Y/500);</span><br><span class=help>%</span><br><span class=help>% See also </span><br><span class=help>% PDFGAUSS, GMMSAMP.</span><br><hr><span class=help1>% <span class=help1_field>About:</span> Statistical Pattern Recognition Toolbox</span><br><span class=help1>% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac</span><br><span class=help1>% <a href="http://www.cvut.cz">Czech Technical University Prague</a></span><br><span class=help1>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a></span><br><span class=help1>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a></span><br><br><span class=help1>% <span class=help1_field>Modifications:</span></span><br><span class=help1>% 28-apr-2004, VF, adopted from P.Krizek </span><br><br><hr><span class=keyword>if</span> <span class=stack>nargin</span> > 2,<br> Mean = <span class=stack>varargin</span>{1};<br> Cov = <span class=stack>varargin</span>{2};<br> num_data = <span class=stack>varargin</span>{3};<br><span class=keyword>else</span><br> Mean = <span class=stack>varargin</span>{1}.Mean;<br> Cov = <span class=stack>varargin</span>{1}.Cov;<br> num_data = <span class=stack>varargin</span>{2};<br><span class=keyword>end</span><br><br><span class=comment>% get dimension</span><br>dim = length(Mean);<br><br><span class=comment>% compute eigen values and vectors</span><br>[U,L] = eig(Cov);<br><br><span class=comment>% dewhitening transform</span><br>X = inv(U')*sqrt(L)*randn(dim,num_data)+repmat(Mean,1,num_data);<br><br><br><span class=jump>return</span>;<br><br></code>?? 快捷鍵說(shuō)明
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