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function [recogRate, computed, nearestIndex] = knnrLoo(DS, param, plotOpt) %knnrLoo: Leave-one-out recognition rate of KNNR % Usage: [recogRate, computed, nearestIndex] = knnrLoo(DS, param, plotOpt)

% DCPR Toolbox: Data Clustering and Pattern Recognition Toolbox % % Test Data Generation: % randn2 - Generate 2D Gaussian-distributed data % dcdata - Generate data for data clustering algorithms

function dcprDataPlot3(DS, plotTitle, displayAnnotation) % dcprDataPlot: Plot of 3D data for data clustering or pattern recognition % Usage: dcprDataPlot3(DS, plotTitle, inputName, pointLabel) % D

% This script demonstrate the use of Picard iteration on finding the optimal pitch shift for DTW in melody recognition % inputPitch: input pitch vector inputPitch=[48.044247 48.917323 49.836778 50

function [recogRate, computed, nearestIndex] = knnrLoo(DS, k, plotOpt) %knnrLoo: Leave-one-out recognition rate of KNNR % Usage: [recogRate, computed, nearestIndex] = knnrLoo(DS, k, plotOpt) % rec

title="DCPR (Data Clustering & Pattern Recognition) Toolbox"; D

function dcprDataPlot(DS, plotTitle, displayAnnotation) % dcprDataPlot: Plot of 2D data for data clustering or pattern recognition % Usage: dcprDataPlot(DS, plotTitle, inputName, pointLabel) % DS:

function confMat = confMatGet(desiredOutput, computedOutput) % confMatGet: Get confusion matrix from recognition result % Usage: confMat = confMatGet(desiredOutput, computedOutput) % % For example