📄 linctrainmre.html

📁 一个关于数据聚类和模式识别的程序,在生物化学,化学中因该都可以用到.希望对大家有用,谢谢支持
💻 HTML
字号:
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"                "http://www.w3.org/TR/REC-html40/loose.dtd"><html><head>  <title>Description of lincTrainMre</title>  <meta name="keywords" content="lincTrainMre">  <meta name="description" content="lincTrainMre: Linear classifier training for min. regression error">  <meta http-equiv="Content-Type" content="text/html; charset=big5">  <meta name="generator" content="m2html &copy; 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> &gt;  <a href="index.html">dcpr</a> &gt; lincTrainMre.m</div><!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td><td align="right"><a href="index.html">Index for dcpr&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>--><h1>lincTrainMre</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>lincTrainMre: Linear classifier training for min. regression error</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, coef, regError]=lincTrainMre(DS, CParam, 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"> lincTrainMre: Linear classifier training for min. regression error
    Usage: [recogRate, coef, regError]=lincTrainMre(DS, CParam, 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="lincEval.html" class="code" title="function [lincOutput, recogRate, errorIndex1, errorIndex2, regOutput, regError]=lincEval(DS, coef)">lincEval</a>	lincEval: Evaluation of linear classifier</li></ul>This function is called by:<ul style="list-style-image:url(../matlabicon.gif)"><li><a href="crosscTrain.html" class="code" title="function [recogRate, coef]=crosscTrain(DS, CParam, plotOpt)">crosscTrain</a>	crosscTrain: Cross classifier training</li><li><a href="lincTrainWidro.html" class="code" title="function [recogRate, coef, allRecogRate, allCoef]=lincTrainWidro(DS, trainParam, plotOpt)">lincTrainWidro</a>	lincTrainWidro: Linear classifier (Perceptron) training using (revised) Widro-Hoff method</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, coef, regError]=lincTrainMre(DS, CParam, plotOpt)</a>0002 <span class="comment">% lincTrainMre: Linear classifier training for min. regression error</span>0003 <span class="comment">%    Usage: [recogRate, coef, regError]=lincTrainMre(DS, CParam, plotOpt)</span>0004 0005 <span class="comment">% Roger Jang, 20040926</span>0006 0007 <span class="keyword">if</span> nargin&lt;1, <a href="#_sub1" class="code" title="subfunction selfdemo">selfdemo</a>; <span class="keyword">return</span>; <span class="keyword">end</span>0008 <span class="keyword">if</span> nargin&lt;2, CParam=[]; <span class="keyword">end</span>0009 <span class="keyword">if</span> nargin&lt;3, plotOpt=0; <span class="keyword">end</span>0010 0011 [dim, dataNum]=size(DS.input);0012 <span class="comment">% Preapre A matrix for A*x=desired</span>0013 A=[DS.input', ones(dataNum,1)];0014 uniqueOutput=unique(DS.output);0015 <span class="keyword">if</span> length(uniqueOutput)~=2, error(<span class="string">'Must be 2-class problem!'</span>); <span class="keyword">end</span>0016 <span class="comment">% ∮N DS.output ”??? -1 ?? 1</span>0017 index=find(DS.output==max(uniqueOutput));0018 DS.output=-ones(1, size(DS.output,2));0019 DS.output(index)=1;0020 <span class="comment">% Prepare desired vector to be -1 or 1</span>0021 desired=-ones(dataNum, 1);0022 desired(DS.output&gt;0)=1;0023 coef=A\desired;0024 0025 0026 [lincOutput, recogRate, regOutput, regError]=<a href="lincEval.html" class="code" title="function [lincOutput, recogRate, errorIndex1, errorIndex2, regOutput, regError]=lincEval(DS, coef)">lincEval</a>(DS, coef);0027 0028 <span class="comment">%computed=A*coef;                    % Computed regression output</span>0029 <span class="comment">%regError=sum((computed-desired).^2);        % Regression MSE</span>0030 <span class="comment">%computed2=-ones(dataNum, 1);            % Computed classification output</span>0031 <span class="comment">%computed2(computed&gt;0)=1;</span>0032 <span class="comment">%recogRate=sum(computed2==desired)/dataNum;    % Classification recognition rate</span>0033 0034 <span class="keyword">if</span> plotOpt &amp; dim==20035     <a href="dcprDataPlot.html" class="code" title="function dcprDataPlot(DS, plotTitle, displayAnnotation)">dcprDataPlot</a>(DS); axis image0036     xMin=min(DS.input(1,:));0037     xMax=max(DS.input(1,:));0038     yMin=min(DS.input(2,:));0039     yMax=max(DS.input(2,:));0040     a=coef(1); b=coef(2); c=coef(3);0041     x1=xMin;0042     y1=(-c-a*x1)/b;0043     x2=xMax;0044     y2=(-c-a*x2)/b;0045     line([x1, x2], [y1, y2], <span class="string">'color'</span>, <span class="string">'k'</span>);0046 <span class="keyword">end</span>0047 0048 <span class="comment">% ====== Self demo</span>0049 <a name="_sub1" href="#_subfunctions" class="code">function selfdemo</a>0050 dataNum=100;0051 DS.input=2*rand(2, dataNum)-1;0052 DS.output=DS.input(1,:)+DS.input(2,:)&gt;0;0053 plotOpt=1;0054 [recogRate, coef, regError]=feval(mfilename, DS, [], plotOpt);0055 fprintf(<span class="string">'Regression MSE = %f\n'</span>, regError);0056 fprintf(<span class="string">'Recognition rate = %f%%\n'</span>, recogRate*100);0057 fprintf(<span class="string">'Coef = %s\n'</span>, mat2str(coef));</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> &copy; 2003</address></body></html>
⌨️ 快捷键说明

复制代码 Ctrl + C
搜索代码 Ctrl + F
全屏模式 F11
切换主题 Ctrl + Shift + D
显示快捷键 ?
增大字号 Ctrl + =
减小字号 Ctrl + -