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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>kdist.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>d</span>=<span class=defun_name>kdist</span>(<span class=defun_in>X,model</span>)<br><span class=h1>% KDIST Computes distance between vectors in kernel space.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Synopsis:</span></span><br><span class=help>% d = kdist(X,model)</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Description:</span></span><br><span class=help>% It computes distance between vectors mapped into the feature </span><br><span class=help>% space induced by the kernel function (model.options.ker,</span><br><span class=help>% model.options.arg). The distance is computed between images</span><br><span class=help>% of vectors X [dim x num_data] mapped into feature space</span><br><span class=help>% and a point in the feature space given by model:</span><br><span class=help>%</span><br><span class=help>% d(i) = kernel(X(:,i),X(:,i)) </span><br><span class=help>% - 2*kernel(X(:,i),models.sv.X)*model.Alpha + b,</span><br><span class=help>%</span><br><span class=help>% where b [1x1] is assumed to be equal to </span><br><span class=help>% model.b = model.Alpha'*kernel(model.sv.X)*model.Alpha.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Input:</span></span><br><span class=help>% X [dim x num_data] Input vectors.</span><br><span class=help>% model [struct] Deternines a point of the feature space:</span><br><span class=help>% .Alpha [nsv x 1] Multipliers.</span><br><span class=help>% .sv.X [dim x nsv] Vectors.</span><br><span class=help>% .b [1x1] Bias.</span><br><span class=help>% .options.ker [string] Kernel identifier (see 'help kernel').</span><br><span class=help>% .options.arg [1 x nargs] Kernel argument(s).</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Output:</span></span><br><span class=help>% d [num_data x 1] Distance between vectors in the feature space.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Example:</span></span><br><span class=help>% data = load('riply_trn');</span><br><span class=help>% model.Alpha = dualmean(size(data.X,2));</span><br><span class=help>% model.sv.X = data.X;</span><br><span class=help>% model.options.ker = 'rbf';</span><br><span class=help>% model.options.arg = 0.25;</span><br><span class=help>% model.b = model.Alpha'*kernel(data.X,'rbf',0.25)*model.Alpha;</span><br><span class=help>% [Ax,Ay] = meshgrid(linspace(-5,5,100), linspace(-5,5,100));</span><br><span class=help>% dist = kdist([Ax(:)';Ay(:)'],model);</span><br><span class=help>% figure; hold on; </span><br><span class=help>% ppatterns(data.X); contour( Ax, Ay, reshape(dist,100,100));</span><br><span class=help>% </span><br><span class=help>% See also </span><br><span class=help>% MINBALL.</span><br><span class=help>%</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>% 25-aug-2004, VF, MINBALL added to See also </span><br><span class=help1>% 16-may-2004, VF</span><br><span class=help1>% 26-feb-2003, VF</span><br><span class=help1>% 13-sep-2002, VF</span><br><span class=help1>% 15-jun-2002, VF</span><br><br><hr>[dim,num_data]=size(X);<br><br>x2 = diagker( X, model.options.ker, model.options.arg);<br><br>Ksvx = kernel( X, model.sv.X, model.options.ker, model.options.arg);<br><br>d = sqrt( x2 - 2*Ksvx*model.Alpha(:) + model.b*ones(num_data,1) );<br><br><span class=jump>return</span>;<br><span class=comment>% EOF</span><br></code>?? 快捷鍵說明
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