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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>greedykls.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>[model,Z]</span>=<span class=defun_name>greedykpca</span>(<span class=defun_in>X,y,options</span>)<br><span class=h1>% GREEDYKLS Greedy Regularized Kernel Least Squares.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Synopsis:</span></span><br><span class=help>% model = greedykls(X)</span><br><span class=help>% model = greedykls(X,options)</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Description:</span></span><br><span class=help>% This function approximates input vectors X in the feature</span><br><span class=help>% space using GREEDYKPCA. Then the regularized least squares</span><br><span class=help>% are applied on the approximated data. </span><br><span class=help>%</span><br><span class=help>% See help of KLS for more info about regularize least squares.</span><br><span class=help>% See help of GREEDYKPCA for more info on approximation of data</span><br><span class=help>% in the feature space.</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 column vectors.</span><br><span class=help>% y [num_data x 1] Output values.</span><br><span class=help>% </span><br><span class=help>% options [struct] Control parameters:</span><br><span class=help>% .ker [string] Kernel identifier. See HELP KERNEL for more info.</span><br><span class=help>% .arg [1 x narg] Kernel argument.</span><br><span class=help>% .m [1x1] Maximal number of base vectors (Default m=0.25*num_data).</span><br><span class=help>% .p [1x1] Depth of search for the best basis vector (Default p=m).</span><br><span class=help>% .mserr [1x1] Desired mean squared reconstruction errors of approximation.</span><br><span class=help>% .maxerr [1x1] Desired maximal reconstruction error of approximation.</span><br><span class=help>% See 'help greedyappx' for more info about the stopping conditions.</span><br><span class=help>% .verb [1x1] If 1 then some info is displayed (default 0).</span><br><span class=help>% </span><br><span class=help>% <span class=help_field>Output:</span></span><br><span class=help>% model [struct] Kernel projection:</span><br><span class=help>% .Alpha [nsv x new_dim] Multipliers defining kernel projection.</span><br><span class=help>% .sv.X [dim x num_data] Selected subset of the training vectors.</span><br><span class=help>% .nsv [1x1] Number of basis vectors.</span><br><span class=help>% .kercnt [1x1] Number of kernel evaluations.</span><br><span class=help>% .MaxErr [1 x nsv] Maximal reconstruction error for corresponding</span><br><span class=help>% number of base vectors.</span><br><span class=help>% .MsErr [1 x nsv] Mean square reconstruction error for corresponding</span><br><span class=help>% number of base vectors.</span><br><span class=help>% </span><br><span class=help>% <span class=help_field>Example:</span></span><br><span class=help>% x = [0:0.05:2*pi]; y = sin(x) + 0.1*randn(size(x));</span><br><span class=help>% model = greedykls(x,y(:),struct('ker','rbf','arg',1,'lambda',0.001));</span><br><span class=help>% y_est = kernelproj(x,model);</span><br><span class=help>% figure; hold on;</span><br><span class=help>% plot(x,y,'+k'); plot(x,y_est,'b'); </span><br><span class=help>% plot(x,sin(x),'r'); plot(x(model.sv.inx),y(model.sv.inx),'ob');</span><br><span class=help>%</span><br><span class=help>% See also </span><br><span class=help>% KERNELPROJ, KPCA, GREEDYKPCA.</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) 1999-2005, 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>% 01-mar-2005, VF</span><br><span class=help1>% 22-feb-2005, VF</span><br><br><hr>start_time = cputime;<br>[dim,num_data]=size(X);<br><br><span class=comment>% process input arguments</span><br><span class=comment>%------------------------------------</span><br><span class=keyword>if</span> <span class=stack>nargin</span> < 2, options = []; <span class=keyword>else</span> options=c2s(options); <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'ker'</span>), options.ker = <span class=quotes>'linear'</span>; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'arg'</span>), options.arg = 1; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'m'</span>), options.m = fix(0.25*num_data); <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'p'</span>), options.p = options.m; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'maxerr'</span>), options.maxerr = 1e-6; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'mserr'</span>), options.mserr = 1e-6; <span class=keyword>end</span>
<br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'verb'</span>), options.verb = 0; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'lambda'</span>), options.lambda = 0.001; <span class=keyword>end</span><br><br><span class=comment>% greedy algorithm to select subset of training data</span><br><span class=comment>%-------------------------------------------------------</span><br><br>[inx,Alpha,Z,kercnt,MsErr,MaxErr] = ...<br> greedyappx(X,options.ker,options.arg,...<br> options.m,options.p,options.mserr,options.maxerr,options.verb); <br> <br><span class=comment>% apply ordinary linear least squares</span><br><span class=comment>%------------------------------</span><br>w = inv( Z*Z' + options.lambda*num_data*eye(size(Z,1))) * Z*y;<br><br><span class=comment>% fill up the output model</span><br><span class=comment>%-------------------------------------</span><br>model.Alpha = Alpha'*w;<br>model.nsv = length(Alpha); <br>model.b = 0;<br>model.sv.X= X(:,inx);<br>model.sv.inx = inx;<br>model.kercnt = kercnt;<br>model.GreedyMaxErr = MaxErr;<br>model.GreedyMsErr = MsErr;<br>model.options = options;<br>model.cputime = cputime - start_time;<br>model.fun = <span class=quotes>'kernelproj'</span>;<br><br><span class=jump>return</span>;<br><span class=comment>% EOF</span><br></code>?? 快捷鍵說明
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