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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>Index for Directory dcpr</title> <meta name="keywords" content="dcpr"> <meta name="description" content="Index for Directory dcpr"> <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><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>Index for dcpr</h1><h2>Matlab files in this directory:</h2><table><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="aggclust.html">aggclust</a></td><td>aggClust: Hierarchical (agglomerative) clustering </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="classConvert.html">classConvert</a></td><td>classConvert: Convert class labels into integers from 1 to n </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="classSize.html">classSize</a></td><td>classSize: Class sizes for a sample data set </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="conddm.html">conddm</a></td><td>Demo of condensing technique for data reduction </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="confMatGet.html">confMatGet</a></td><td>confMatGet: Get confusion matrix from recognition result </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="confMatPlot.html">confMatPlot</a></td><td>confMatPlot: Display the confusion matrix </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="cosSimilarity.html">cosSimilarity</a></td><td>cosSimilarity: Cosine of the angles betweeen two set of vectors </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="crosscEval.html">crosscEval</a></td><td>crosscEval: Evaluation of cross classifier </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="crosscTrain.html">crosscTrain</a></td><td>crosscTrain: Cross classifier training </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dcData.html">dcData</a></td><td>DCDATA Test data sets for data clustering (no class label). </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dcprDataPlot.html">dcprDataPlot</a></td><td>dcprDataPlot: Plot of 2D data for data clustering or pattern recognition </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dcprDataPlot3.html">dcprDataPlot3</a></td><td>dcprDataPlot: Plot of 3D data for data clustering or pattern recognition </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dec2pos.html">dec2pos</a></td><td>DEC2POS Output data transformation </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="decisionBoundaryPlot.html">decisionBoundaryPlot</a></td><td>decisionBoundaryPlot: Plot of the decision boundary of a classification problem </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dendro.html">dendro</a></td><td>DENDRO Dendrogrma plot for the result from hierarchical clustering. </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dpPathPlot4strMatch.html">dpPathPlot4strMatch</a></td><td>dpPathPlot4strMatch: Plot the path of dynamic programming for string match. </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dtw1.html">dtw1</a></td><td>dtw1: Dynamic time warping with local paths of 27, 45, and 63 degrees </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dtw2.html">dtw2</a></td><td>dtw2: Dynamic time warping with local paths of 0, 45, and 90 degrees </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dtw3.html">dtw3</a></td><td>dtw2: Dynamic time warping with local paths of 0 and 45 degrees, presumably for the comparison of music mid vector to note vector </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dtwFixedPoint.html">dtwFixedPoint</a></td><td>dtwFixedPoint: Use of Picard iteration on finding the optimal pitch shift for DTW </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dtwPlot.html">dtwPlot</a></td><td>DTWPLOT Plot the result of DTW of two pitch/MFCC vectors </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dtwPlot2.html">dtwPlot2</a></td><td>dtwPlot2: Plot the result of DTW of two pitch/MFCC vectors </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="dtwPlot3.html">dtwPlot3</a></td><td>dtwPlot2: Plot the result of DTW of two pitch/MFCC vectors </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="editDistance - 狡籹.html">editDistance - 狡籹</a></td><td>lcs: Longest (maximum) common subsequence </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="editDistance.html">editDistance</a></td><td>editDistance: Edit distance (ED) via dynamic programming </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="editdm.html">editdm</a></td><td>Demo of editing technique for data reduction </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="fknn.html">fknn</a></td><td>FKNN Fuzzy k-nearest neighbor classification rule </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="flda.html">flda</a></td><td>LDA Linear discriminant analysis </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="fldainsel.html">fldainsel</a></td><td>LDAINSEL LDA for input selection </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gaussian.html">gaussian</a></td><td>gaussian: Multi-dimensional Gaussian propability density function </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gaussianLog.html">gaussianLog</a></td><td>gaussianLog: Multi-dimensional log Gaussian propability density function </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gaussianMle.html">gaussianMle</a></td><td>mleGaussian: Maximum likelihood estimator for Gaussian distribution </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gaussianSimilarity.html">gaussianSimilarity</a></td><td>Evaluation of a PDF to see if it is close to Gaussian distribution </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gdDemo.html">gdDemo</a></td><td>Interactive demo of Gradient descent paths on "peaks" surface. </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="genBBT.html">genBBT</a></td><td>GENBBT Generate BBT (Branch and Bound Tree) for nearest neighbor search </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="getClassDataCount.html">getClassDataCount</a></td><td>classDataCount: Get the data count for each class </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="getCvData.html">getCvData</a></td><td>getcvData: Get m-fold cross validation data </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmEval.html">gmmEval</a></td><td>gmmEval: Evaluation of a GMM (Gaussian mixture model) </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmGaussianNumEstimate.html">gmmGaussianNumEstimate</a></td><td>gmmMixNumEstimate: Estimate the number of mixture number of a GMM </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmGrow.html">gmmGrow</a></td><td>gmmGrow: Grow gaussians within a GMM </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmGrowDemo.html">gmmGrowDemo</a></td><td>Example of using gmmGrow.m for growing a GMM (gaussian mixture models). </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmInitParamSet.html">gmmInitParamSet</a></td><td>gmmParamSet: Set a set of initial parameters for GMM </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmMleWrtGaussianNum.html">gmmMleWrtGaussianNum</a></td><td> </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmRead.html">gmmRead</a></td><td>gmmRead: Read GMM from a file </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmTrain.html">gmmTrain</a></td><td>gmmTrain: Parameter training for gaussian mixture model (GMM) </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmTrainDemo1d.html">gmmTrainDemo1d</a></td><td>Example of using GMM (gaussian mixture model) for 1-D data </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmTrainDemo2dCovType01.html">gmmTrainDemo2dCovType01</a></td><td>Animation of GMM training with covType=1 (isotropic) for 2D data </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmTrainDemo2dCovType02.html">gmmTrainDemo2dCovType02</a></td><td>Animation of GMM training with covType=2 (diagonal cov. matrix) for 2D data </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmTrainDemo2dCovType03.html">gmmTrainDemo2dCovType03</a></td><td>Animation of GMM training with covType=3 (full cov. matrix) for 2D data </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmTrainEvalWrtGaussianNum.html">gmmTrainEvalWrtGaussianNum</a></td><td>gmmTrainEvalWrtMixNum: GMM training and test, w.r.t. varying number of mixtures </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmTrainParamSet.html">gmmTrainParamSet</a></td><td>The following parameters are used for gmmTrain() </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="gmmWrite.html">gmmWrite</a></td><td>gmmWrite: Write the parameters of a GMM to a file </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="hclusteringDemo.html">hclusteringDemo</a></td><td>LINKCLU Display the formation of hierarchical clustering step by step </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="initfcm2.html">initfcm2</a></td><td>INITFCM2 Generate initial CRISP partition matrix for fuzzy c-means clustering. </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="initfknn.html">initfknn</a></td><td>INITfknn Initialize fuzzy membership grades of sample output for fuzzy KNN. </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="initkm2.html">initkm2</a></td><td>INITKM2 Find the initial centers for a K-means clustering algorithm. </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="inputNameList.html">inputNameList</a></td><td> </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="inputNormalize.html">inputNormalize</a></td><td>inputNormalize: Input (feature) normalization to have zero mean and unity variance for each feature </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="inputSelectExhaustive.html">inputSelectExhaustive</a></td><td>inputSelectExhaustive: Input selection via sequential forward selection using leave-one-out </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="inputSelectPlot.html">inputSelectPlot</a></td><td>inputSelectPlot: Plot for input selection </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="inputSelectSequential.html">inputSelectSequential</a></td><td>inputSelectSequential: Input selection via sequential forward selection using leave-one-out </td></tr><tr><td><img src="../matlabicon.gif" alt="" border=""> <a href="interpDist.html">interpDist</a></td><td>DISTINTERP ?H?Z?∫???????⌨️ 快捷键说明
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