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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 prData</title> <meta name="keywords" content="prData"> <meta name="description" content="prData: Various data set for PR"> <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> > prData.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>prData</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>prData: Various data set for PR</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 [DS, TS]=prData(dataName) </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"> prData: Various data set for PR Usage: [DS, TS]=prData(dataName) dataName: 'irir', 'wine', 'abalone', 'random2', 'random6'</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></ul>This function is called by:<ul style="list-style-image:url(../matlabicon.gif)"><li><a href="classSize.html" class="code" title="function [class, count] = classSize(DS, plotOpt)">classSize</a> classSize: Class sizes for a sample data set</li><li><a href="dcprDataPlot3.html" class="code" title="function dcprDataPlot3(DS, plotTitle, displayAnnotation)">dcprDataPlot3</a> dcprDataPlot: Plot of 3D data for data clustering or pattern recognition</li><li><a href="decisionBoundaryPlot.html" class="code" title="function out=decisionBoundaryPlot(surfObj)">decisionBoundaryPlot</a> decisionBoundaryPlot: Plot of the decision boundary of a classification problem</li><li><a href="getCvData.html" class="code" title="function cvData=getCvData(DS, m)">getCvData</a> getcvData: Get m-fold cross validation data</li><li><a href="gmmTrainEvalWrtGaussianNum.html" class="code" title="function [gmmData, recogRate1, recogRate2, validMixNumIndex]=gmmTrainEvalWrtGaussianNum(DS, TS, vecOfMixNum, covType, gmmTrainParam)">gmmTrainEvalWrtGaussianNum</a> gmmTrainEvalWrtMixNum: GMM training and test, w.r.t. varying number of mixtures</li><li><a href="inputSelectExhaustive.html" class="code" title="function [bestSelectedInput, bestRecogRate, allSelectedInput, allRecogRate, elapsedTime] = inputSelectExhaustive(DS, inputNum, classifier, param, plotOpt)">inputSelectExhaustive</a> inputSelectExhaustive: Input selection via sequential forward selection using leave-one-out</li><li><a href="inputSelectSequential.html" class="code" title="function [bestSelectedInput, bestRecogRate, allSelectedInput, allRecogRate, elapsedTime] = inputSelectSequential(DS, inputNum, classifier, param, plotOpt)">inputSelectSequential</a> inputSelectSequential: Input selection via sequential forward selection using leave-one-out</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="knnrLoo.html" class="code" title="function [recogRate, computed, nearestIndex] = knnrLoo(DS, k, plotOpt)">knnrLoo</a> knnrLoo: Leave-one-out recognition rate of KNNR</li><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="knnrWrtK.html" class="code" title="function [misclassify, elapsed_time] = knnrWrtK(DS, TS, kMax, plotOpt)">knnrWrtK</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="pca.html" class="code" title="function [DS2, eigVec, eigValue] = pca(DS, eigVecNum)">pca</a> pca: Principal component analysis</li><li><a href="pcaKnnrLoo.html" class="code" title="function recogRate=pcaKnnrLoo(DS, plotOpt)">pcaKnnrLoo</a> ldaKnnrLoo: PCA analysis using KNNR and LOO</li><li><a href="plot2dProj.html" class="code" title="function plot2dProj(DS)">plot2dProj</a> plotClassVsFeature: Plot of class vs. feature</li><li><a href="plot3dProj.html" class="code" title="function plot2dProj(DS)">plot3dProj</a> plotClassVsFeature: Plot of class vs. feature</li><li><a href="plotClassVsFeature.html" class="code" title="function plotClassVsFeature(DS)">plotClassVsFeature</a> plotClassVsFeature: Plot of class vs. feature</li><li><a href="plotFeatureVsIndex.html" class="code" title="function out = plotFeatureVsIndex(DS)">plotFeatureVsIndex</a> plotFeatureVsIndex: Plot of feature vs. data index</li><li><a href="rangePlot.html" class="code" title="function rangeVec = rangePlot(DS)">rangePlot</a> rangePlot: range plot of a data set</li><li><a href="sgcEval.html" class="code" title="function [computedClass, recogRate, hitIndex]=sgcEval(DS, classParam, plotOpt)">sgcEval</a> sgcTrain: Evaluation for single Gaussian classifier</li><li><a href="sgcTrain.html" class="code" title="function [classParam, recogRate, hitIndex]=sgcTrain(DS, prior, plotOpt)">sgcTrain</a> sgcTrain: Training for single Gaussian classifier training</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 [DS, TS]=prData(dataName)</a>0002 <span class="comment">% prData: Various data set for PR</span>0003 <span class="comment">% Usage: [DS, TS]=prData(dataName)</span>0004 <span class="comment">% dataName: 'irir', 'wine', 'abalone', 'random2', 'random6'</span>0005 0006 <span class="comment">% Roger Jang</span>0007 0008 <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>0009 0010 <span class="keyword">switch</span> lower(dataName)0011 <span class="keyword">case</span> <span class="string">'iris'</span>0012 load iris.dat0013 DS.dataName=<span class="string">'iris'</span>;0014 inputName={<span class="string">'sepal length'</span>, <span class="string">'sepal width'</span>, <span class="string">'petal length'</span>, <span class="string">'petal width'</span>};0015 [DS.inputName]=deal(inputName);0016 outputName={<span class="string">'Iris Setosa'</span>, <span class="string">'Iris Versicolour'</span>, <span class="string">'Iris Virginica'</span>};0017 outputName={<span class="string">'Setosa'</span>, <span class="string">'Versicolour'</span>, <span class="string">'Virginica'</span>};0018 [DS.outputName]=deal(outputName);0019 DS.input=iris(:, 1:end-1)';0020 DS.output=iris(:, end)';0021 <span class="keyword">if</span> nargout==20022 dataNum=size(DS.input, 2);0023 TS=DS;0024 DS.input= DS.input(:, 1:2:dataNum);0025 DS.output=DS.output(:, 1:2:dataNum);0026 TS.input= TS.input(:, 2:2:dataNum);0027 TS.output=TS.output(:, 2:2:dataNum);0028 <span class="keyword">end</span>0029 <span class="keyword">case</span> <span class="string">'wine'</span>0030 load wine.dat0031 DS.dataName=<span class="string">'wine'</span>;0032 inputName={<span class="string">'Alcohol'</span>, <span class="string">'Malic acid'</span>, <span class="string">'Ash'</span>, <span class="string">'Alcalinity of ash'</span>, <span class="string">'Magnesium'</span>, <span class="string">'Total phenols'</span>, <span class="string">'Flavanoids'</span>, <span class="string">'Nonflavanoid phenols'</span>, <span class="string">'Proanthocyanins'</span>, <span class="string">'Color intensity'</span>, <span class="string">'Hue'</span>, <span class="string">'OD280/OD315 of diluted wines'</span>, <span class="string">'Proline'</span>};0033 [DS.inputName]=deal(inputName);0034 DS.input=wine(:, 2:end)';0035 DS.output=wine(:, 1)';0036 <span class="keyword">if</span> nargout==20037 dataNum=size(DS.input, 2);0038 TS=DS;0039 DS.input= DS.input(:, 1:2:dataNum);0040 DS.output=DS.output(:, 1:2:dataNum);0041 TS.input= TS.input(:, 2:2:dataNum);0042 TS.output=TS.output(:, 2:2:dataNum);0043 <span class="keyword">end</span>0044 <span class="keyword">case</span> <span class="string">'abalone'</span>0045 load abalone.dat⌨️ 快捷键说明
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