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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 fldainsel</title> <meta name="keywords" content="fldainsel"> <meta name="description" content="LDAINSEL LDA for input selection"> <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> > fldainsel.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>fldainsel</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>LDAINSEL LDA for input selection</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 recog = fldainsel(feature, class, dim, k1, k2); </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">LDAINSEL LDA for input selection
Usage: recog = fldainsel(feature, class, dim, k1, k2);
feature: Feature matrix
class: class of each feature vector
dim: No. of selected dimension
k1: Value of k as in k-nearest neighbor for leave-one-out estimate
k2: Value of k for fuzzification class label
Type "fldainsel" for self demo.</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="flda.html" class="code" title="function [newSampleIn, discrimVec] = lda(sampleIn, sampleOut, discrimVecNum)">flda</a> LDA Linear discriminant analysis</li><li><a href="initfknn.html" class="code" title="function fuz_class = initfknn(sampledata, k)">initfknn</a> INITfknn Initialize fuzzy membership grades of sample output for fuzzy KNN.</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 recog = fldainsel(feature, class, dim, k1, k2);</a>0002 <span class="comment">%LDAINSEL LDA for input selection</span>0003 <span class="comment">% Usage: recog = fldainsel(feature, class, dim, k1, k2);</span>0004 <span class="comment">% feature: Feature matrix</span>0005 <span class="comment">% class: class of each feature vector</span>0006 <span class="comment">% dim: No. of selected dimension</span>0007 <span class="comment">% k1: Value of k as in k-nearest neighbor for leave-one-out estimate</span>0008 <span class="comment">% k2: Value of k for fuzzification class label</span>0009 <span class="comment">%</span>0010 <span class="comment">% Type "fldainsel" for self demo.</span>0011 0012 <span class="comment">% Roger Jang, 20010226</span>0013 0014 <span class="keyword">if</span> nargin==0; <a href="#_sub1" class="code" title="subfunction selfdemo">selfdemo</a>; <span class="keyword">return</span>; <span class="keyword">end</span>0015 <span class="keyword">if</span> nargin<5, k2=3; <span class="keyword">end</span>0016 <span class="keyword">if</span> nargin<4, k1=1; <span class="keyword">end</span>0017 <span class="keyword">if</span> nargin<3, dim=size(feature,2); <span class="keyword">end</span>0018 0019 dataNum = size(feature,1);0020 dimNum = size(feature,2);0021 data = [feature, class];0022 0023 sampleIn = data(:, 1:end-1);0024 sampleOut = data(:, end);0025 sampleFuzzyOut = <a href="initfknn.html" class="code" title="function fuz_class = initfknn(sampledata, k)">initfknn</a>(data, k2);0026 0027 fprintf(<span class="string">'Leave-one-out analysis:\n'</span>);0028 fprintf(<span class="string">'\tFull data:\n'</span>);0029 wrong = looknn(data, k1); 0030 correct = size(data, 1) - wrong;0031 fprintf(<span class="string">'\t\tLOO error count = %g\n'</span>, wrong);0032 fprintf(<span class="string">'\t\tRecognition rate = %g/%g = %5.2f%%\n'</span>, correct, dataNum,<span class="keyword">...</span>0033 correct/dataNum*100);0034 0035 recog = zeros(dimNum, 1);0036 newSampleIn = <a href="flda.html" class="code" title="function [newSampleIn, discrimVec] = lda(sampleIn, sampleOut, discrimVecNum)">flda</a>(feature, sampleFuzzyOut);0037 <span class="keyword">for</span> i = 1:dimNum,0038 fprintf(<span class="string">'\tPartial data after LDA (dimension = %g):\n'</span>, i);0039 wrong = looknn([newSampleIn(:, 1:i) sampleOut], k1); 0040 correct = size(data, 1) - wrong;0041 recog(i) = correct/dataNum*100;0042 fprintf(<span class="string">'\t\tLOO error count = %g\n'</span>, wrong);0043 fprintf(<span class="string">'\t\tRecognition rate = %g/%g = %5.2f%%\n'</span>, correct, <span class="keyword">...</span>0044 dataNum, recog(i));0045 <span class="keyword">end</span>0046 0047 <span class="comment">% ====== Self demo</span>0048 <a name="_sub1" href="#_subfunctions" class="code">function selfdemo</a>0049 load wine.dat;0050 feature = normal(wine(:,2:end));0051 class = wine(:,1);0052 dim = size(feature,2);0053 recog = feval(mfilename, feature, class, dim);0054 figure0055 plot(1:dim, recog, <span class="string">'-o'</span>);0056 xlabel(<span class="string">'Dimensions used in LDA'</span>);0057 ylabel(<span class="string">'Leave-one-out recognition rates'</span>);</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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