CLC clear all close all addpath(genpath(pwd)) % use fishiris dataset load fishiris inds = ~strcmp(species， 'setosa');data__ = meas(inds, 3:4);label_ =物种(第1);cvIndices = crossvalind('HoldOut'， length(data_)， 0.3);trainData = data_(cvIndices，:);trainLabel = label_(cvIndices，:);testData = data_(~cvIndices，:); / /数据testLabel = label_(~cvIndices，:);% kernel = kernel ('type'， '高斯'，'gamma'， 0.2);%参数参数= struct('display'， 'on'，… 'type', 'RVC',... 'kernelFunc', kernel); rvm = BaseRVM(parameter); % RVM model training, testing, and visualization rvm.train(trainData, trainLabel); results = rvm.test(testData, testLabel); rvm.draw(results)

* * * RVM模型(分类)列车运行时间完成* * * = 0.1604秒迭代= 20 = 70数量的样本数量的旅游房车= 2旅游房车的比例= 2.8571%的准确率= 94.2857% * * * RVM模型(分类)测试完成* * *运行时间= 0.0197秒的样本数量= 30精度= 96.6667%

clc clear all close all addpath(genpath(pwd)) % sinc function load sinc_data trainData = x;trainLabel = y;testData = xt;testLabel =欧美;% kernel函数kernel = kernel ('type'， '高斯'，'gamma'， 0.1);%参数参数= struct('display'， 'on'，…“类型”、“RVR’,……kernelFunc,内核);rvm = BaseRVM(参数);％RVM模型培训，测试和可视化RVM.Train（TrainData，TrainLabel）; results = rvm.test(testData, testLabel); rvm.draw(results)

*** RVM模型（回归）列车完成了***运行时间= 0.1757秒迭代= 76样品数量= 100个RVS = 6比率RVS = 6.0000％RMSE = 0.1260 R2 = 0.8821 MAE = 0.0999 *** RVM模型（回归）测试完成***运行时间= 0.0026秒样品数= 50 RMSE = 0.1424 R2 = 0.8553 MAE = 0.1106

%{类型-线性:k (x, y) = x ' * y多项式:k (x, y) =(γ* x ' * y + c) ^ d高斯:k (x, y) = exp(-γ* | | x - y | | ^ 2)乙状结肠:k (x, y) =双曲正切(γ* x ' * y + c)拉普拉斯算子:k (x, y) = exp(-γ* | | x - y | |)学位- d抵消cγ-γ%}内核=内核(“类型”、“高斯”、“伽马”,值);kernel = kernel ('type'， '多项式'，'degree'， value);kernel = kernel ('type'， 'linear');kernel = kernel ('type'， 'sigmoid'， 'gamma'， value);kernel = kernel ('type'， 'laplacian'， 'gamma'， value);

X = rand(5, 2);Y = rand(3,2);kernel = kernel ('type'， '高斯'，'gamma'， 2);kernelMatrix = kernel.computeMatrix(X, Y);>> kernelMatrix kernelMatrix = 0.5684 0.5607 0.4007 0.4651 0.8383 0.5091 0.8392 0.7116 0.9834 0.4731 0.8816 0.8052 0.5034 0.9807 0.7274

clc clear all close all addpath(genpath(pwd)) % sinc function load sinc_data trainData = x;trainLabel = y;testData = xt;testLabel =欧美;％内核函数kernel_1 =内核（'类型'，'高斯'，'gamma'，0.3）;kernel_2 = Kernel('type'， '多项式'，'degree'， 2);Kernelweight = [0.5,0.5];％参数参数= struct（'display'，'on'，...'type'，'rvr'，...'kernelfunc'，[kernelfunc'，[kernel_1，kernel_2]，...'kernelweight'，Kernelwight）;rvm = BaseRVM(参数);％RVM模型培训，测试和可视化RVM.Train（TrainData，TrainLabel）; results = rvm.test(testData, testLabel); rvm.draw(results)

CLC clear all close all addpath(genpath(pwd)) % use fishiris dataset load fishiris inds = ~strcmp(species， 'setosa');data__ = meas(inds, 3:4);label_ =物种(第1);cvIndices = crossvalind('HoldOut'， length(data_)， 0.3);trainData = data_(cvIndices，:);trainLabel = label_(cvIndices，:);testData = data_(~cvIndices，:); / /数据testLabel = label_(~cvIndices，:);% kernel = kernel ('type'， '高斯'，'gamma'， 5);%参数优化option .method = 'bayes'; % bayes, ga, pso opt.display = 'on'; opt.iteration = 20; % parameter parameter = struct( 'display', 'on',... 'type', 'RVC',... 'kernelFunc', kernel,... 'optimization', opt); rvm = BaseRVM(parameter); % RVM model training, testing, and visualization rvm.train(trainData, trainLabel); results = rvm.test(trainData, trainLabel); rvm.draw(results)

* * * RVM模型(分类)训练完成* * *迭代运行时间= 13.3356秒= 88的样品数量= 70旅游房车的数量= 4旅游房车的比例= 5.7143%的准确率= 97.1429% gaussian_gamma优化参数表  ______________ 7.8261 * * * RVM模型(分类)测试完成* * *运行时间样本的数量= 70 = 0.0195秒精度= 97.1429%

%{一个带有参数优化的混合内核RVM模型演示%}clc clear all close all addpath(genpath(pwd)) % data load UCI_data trainData = x;trainLabel = y;testData = xt;testLabel =欧美;% kernel函数kernel_1 = kernel ('type'， '高斯'，'gamma'， 0.5);kernel_2 = Kernel('type'， '多项式'，'degree'， 2);%参数优化option .method = 'bayes';% bayes, ga, pso opt.display = 'on';opt.iteration = 30;%参数参数= struct('display'， 'on'，… 'type', 'RVR',... 'kernelFunc', [kernel_1, kernel_2],... 'optimization', opt); rvm = BaseRVM(parameter); % RVM model training, testing, and visualization rvm.train(trainData, trainLabel); results = rvm.test(testData, testLabel); rvm.draw(results)

* * * RVM模型(回归)训练完成* * *迭代运行时间= 24.4042秒= 377的样品数量= 264旅游房车的数量= 22旅游房车的比例= 8.3333% RMSE R2 = 0.4864 = 0.7719美= 0.3736优化参数表1×6 gaussian_gamma polynomial_gamma polynomial_offset polynomial_degree gaussian_weight polynomial_weight  ______________________________ _________________ _________________ _______________ _________________ 22.315 13.595 44.83 0.042058 - 0.95794 6 * * * RVM模型(回归)测试完成* * *运行时间样本的数量= 112 = 0.0008秒RMSE R2 = 0.7400 = 0.6668美= 0.4867

struct('display'， 'on'，…“类型”、“RVC’,……kernelFunc,内核,…“KFold”,5);

参数= struct（'display'，'on'，...'type'，'rvc'，...'kernelfunc'，内核，...'holdout'，0.3）;

%%自定义优化选项％{opt.method ='贝叶斯';% bayes, ga, pso opt.display = 'on';opt.iteration = 20;opt.point = 10;％高斯内核函数opt.gaussian.parametername = {'gamma'};opt.gaussian.parametertype = {'真实'};opt.gaussian.lowerbound = 2 ^ -6;opt.gaussian.upperbound = 2 ^ 6;％laplacian内核函数opt.laplacian.parametername = {'gamma'};opt.laplacian.parametertype = {'真实'}; opt.laplacian.lowerBound = 2^-6; opt.laplacian.upperBound = 2^6; % polynomial kernel function opt.polynomial.parameterName = {'gamma'; 'offset'; 'degree'}; opt.polynomial.parameterType = {'real'; 'real'; 'integer'}; opt.polynomial.lowerBound = [2^-6; 2^-6; 1]; opt.polynomial.upperBound = [2^6; 2^6; 7]; % sigmoid kernel function opt.sigmoid.parameterName = {'gamma'; 'offset'}; opt.sigmoid.parameterType = {'real'; 'real'}; opt.sigmoid.lowerBound = [2^-6; 2^-6]; opt.sigmoid.upperBound = [2^6; 2^6]; %} %% RVM model parameter %{ 'display' : 'on', 'off' 'type' : 'RVR', 'RVC' 'kernelFunc' : kernel function 'KFolds' : cross validation, for example, 5 'HoldOut' : cross validation, for example, 0.3 'freeBasis' : 'on', 'off' 'maxIter' : max iteration, for example, 1000 %}