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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 knnrLoo</title> <meta name="keywords" content="knnrLoo"> <meta name="description" content="knnrLoo: Leave-one-out recognition rate of KNNR"> <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> > knnrLoo.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>knnrLoo</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>knnrLoo: Leave-one-out recognition rate of KNNR</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 [recogRate, computed, nearestIndex] = knnrLoo(DS, k, 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">knnrLoo: Leave-one-out recognition rate of KNNR
Usage: [recogRate, computed, nearestIndex] = knnrLoo(DS, k, plotOpt)
recogRate: recognition rate
computed: Computed output
nearestIndex: Nearest sample index of all data points
DS: Design set
DS.input: Input data (each column is a feature vector)
DS.output: Output class (ranging from 1 to N)
k: The "k" in k-nearest neighbor rule
plotOpt: 1 for ploting data (2D only)
For example:
DS=prData('random2');
k=1;
plotOpt=1;
[recogRate, computed, nearestIndex] = knnrLoo(DS, k, 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="dcprDataPlot.html" class="code" title="function dcprDataPlot(DS, plotTitle, displayAnnotation)">dcprDataPlot</a> dcprDataPlot: Plot of 2D data for data clustering or pattern recognition</li><li><a href="knnr.html" class="code" title="function [computedOutput, combinedComputedOutput, nearestIndex, knnrMat] = knnr(DS, TS, k)">knnr</a> knnr: K-nearest neighbor rule for classification</li><li><a href="prData.html" class="code" title="function [DS, TS]=prData(dataName)">prData</a> prData: Various data set for PR</li></ul>This function is called by:<ul style="list-style-image:url(../matlabicon.gif)"><li><a href="knnrLooWrtK.html" class="code" title="function [misclassify, elapsed_time] = knnrLooWrtK(DS, kMax, plotOpt)">knnrLooWrtK</a> knnrWrtK: Try various values of K in leave-one-out K-NNR.</li><li><a href="lda.html" class="code" title="function [DS2, discrimVec, eigValues] = lda(DS, discrimVecNum)">lda</a> lda: Linear discriminant analysis</li><li><a href="ldaKnnrLoo.html" class="code" title="function recogRate=ldaKnnrLoo(DS, maxDim, plotOpt)">ldaKnnrLoo</a> ldaKnnrLoo: LDA analysis using KNNR and LOO</li><li><a href="pcaKnnrLoo.html" class="code" title="function recogRate=pcaKnnrLoo(DS, plotOpt)">pcaKnnrLoo</a> ldaKnnrLoo: PCA analysis using KNNR and LOO</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 [recogRate, computed, nearestIndex] = knnrLoo(DS, k, plotOpt)</a>0002 <span class="comment">%knnrLoo: Leave-one-out recognition rate of KNNR</span>0003 <span class="comment">% Usage: [recogRate, computed, nearestIndex] = knnrLoo(DS, k, plotOpt)</span>0004 <span class="comment">% recogRate: recognition rate</span>0005 <span class="comment">% computed: Computed output</span>0006 <span class="comment">% nearestIndex: Nearest sample index of all data points</span>0007 <span class="comment">% DS: Design set</span>0008 <span class="comment">% DS.input: Input data (each column is a feature vector)</span>0009 <span class="comment">% DS.output: Output class (ranging from 1 to N)</span>0010 <span class="comment">% k: The "k" in k-nearest neighbor rule</span>0011 <span class="comment">% plotOpt: 1 for ploting data (2D only)</span>0012 <span class="comment">%</span>0013 <span class="comment">% For example:</span>0014 <span class="comment">% DS=prData('random2');</span>0015 <span class="comment">% k=1;</span>0016 <span class="comment">% plotOpt=1;</span>0017 <span class="comment">% [recogRate, computed, nearestIndex] = knnrLoo(DS, k, plotOpt);</span>0018 0019 <span class="comment">% Roger Jang, 19970628, 20040928</span>0020 0021 <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>0022 <span class="keyword">if</span> nargin<2, k=1; <span class="keyword">end</span>0023 <span class="keyword">if</span> nargin<3, plotOpt=0; <span class="keyword">end</span>0024 0025 output=unique(DS.output);0026 <span class="keyword">if</span> ~isequal(1:length(output), output)0027 error(<span class="string">'DS.output has wrong format! (It should have a value from 1 to no. of classes.)\n'</span>);0028 <span class="keyword">end</span>0029 0030 [dim, dataNum] = size(DS.input);0031 nearestIndex = zeros(1, dataNum);0032 computed = zeros(size(DS.output));0033 <span class="keyword">for</span> i=1:dataNum0034 <span class="comment">% if rem(i, 100)==0, fprintf('%d/%d\n', i, dataNum); end</span>0035 looData = DS;0036 looData.input(:,i) = [];0037 looData.output(:,i) = [];0038 TS.input=DS.input(:,i);0039 TS.output=DS.output(:,i);0040 [computed(i), junk, tmp] = <a href="knnr.html" class="code" title="function [computedOutput, combinedComputedOutput, nearestIndex, knnrMat] = knnr(DS, TS, k)">knnr</a>(looData, TS, k);0041 nearestIndex(i) = tmp(1);0042 <span class="keyword">if</span> nearestIndex(i)>=i,0043 nearestIndex(i)=nearestIndex(i)+1;0044 <span class="keyword">end</span>0045 <span class="keyword">end</span>0046 hitIndex = find(DS.output==computed);0047 recogRate = length(hitIndex)/dataNum;0048 0049 <span class="keyword">if</span> plotOpt & dim==20050 <a href="dcprDataPlot.html" class="code" title="function dcprDataPlot(DS, plotTitle, displayAnnotation)">dcprDataPlot</a>(DS);0051 axis image; box on0052 missIndex=1:dataNum;0053 missIndex(hitIndex)=[];0054 <span class="comment">% display these points</span>0055 <span class="keyword">for</span> i=1:length(missIndex),0056 line(DS.input(1,missIndex(i)), DS.input(2,missIndex(i)), <span class="string">'marker'</span>, <span class="string">'x'</span>, <span class="string">'color'</span>, <span class="string">'k'</span>);0057 <span class="keyword">end</span>0058 titleString = sprintf(<span class="string">'%d leave-one-out error points denoted by "x".'</span>, length(missIndex));0059 title(titleString);0060 <span class="keyword">end</span>0061 0062 <span class="comment">% ====== Self demo ======</span>0063 <a name="_sub1" href="#_subfunctions" class="code">function selfdemo</a>0064 DS=<a href="prData.html" class="code" title="function [DS, TS]=prData(dataName)">prData</a>(<span class="string">'random2'</span>);0065 k=1;0066 plotOpt=1;0067 [recogRate, hitIndex, nearestIndex] = feval(mfilename, DS, k, plotOpt);</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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