三、实验原理及内容
- 4 维特征空间，随机取 100 样本训练，50 样本测试。 - 训练数据 trainData，测试数
据 testData，训练样本真实标签 trainLabel，测试样本真实标签 testLabel，测试样本
预测标 签 pred。 - 测试样本集 accuracy-acc，混淆矩阵-C。
clear;
addpath(genpath('.\libsvm-3.18'));
%# Fisher Iris dataset
load fisheriris
[~,~,labels] = unique(species); %# labels: 1/2/3
% data = zscore(meas); %# scale features
data = meas;
numInst = size(data,1);
numLabels = max(labels);
%# split training/testing
idx = randperm(numInst);
numTrain = 100; numTest = numInst - numTrain;
trainData = data(idx(1:numTrain),:); testData = data(idx(numTrain+1:end),:);
mean_train=mean(trainData);
std_train=std(trainData);
trainData=(trainData-ones(size(trainData,1),1)*mean_train)./(ones(size(trainData,1
),1)*std_train);
testData=(testData-ones(size(testData,1),1)*mean_train)./(ones(size(testData,1),1)
*std_train);
trainLabel = labels(idx(1:numTrain)); testLabel = labels(idx(numTrain+1:end));
%# train one-against-all models
model = cell(numLabels,1);
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);
