SVM多分类代码

clc; clear all; [iris_label,iris_data] = libsvmread('iris.scale');%读取数据到matlab格式 % [~,~,labels] = unique(species); %# labels: 1/2/3 % data = zscore(meas); %# scale features numInst = size(iris_data,1);%个数 150 numLabels = max(iris_label);%个数3 %# split training/testing idx = randperm(numInst); %把150个数据进行随机打乱 numTrain = 100;%取前100个 numTest = numInst - numTrain; trainData = iris_data(idx(1:numTrain),:); testData = iris_data(idx(numTrain+1:end),:); trainLabel = iris_label(idx(1:numTrain)); testLabel = iris_label(idx(numTrain+1:end)); %# train one-against-all models model = cell(numLabels,1); %模型的个数 for k=1:numLabels model{k} = svmtrain(double(trainLabel==k), trainData, '-c 1 -g 0.2 -b 1'); end %# get probability estimates of test instances using each model prob = zeros(numTest,numLabels); for k=1:numLabels [~,~,p] = svmpredict(double(testLabel==k), testData, model{k}, '-b 1'); prob(:,k) = p(:,model{k}.Label==1); %# probability of class==k end %# predict the class with the highest probability [~,pred] = max(prob,[],2); acc =sum(pred == testLabel) ./ numel(testLabel) %# accuracy C = confusionmat(testLabel, pred) %# confusion matrix