支持向量机分类识别的MATLAB别代码(2)

[Yc,codebook,old_codebook] = code(Y,codefct);

%---------------------------------------------------

% 交叉验证优化参数

%[gam,sig2] = tunelssvm({X,Yc,type,gam,sig2,kernel_type,preprocess})

%---------------------------------------------------

% 训练与测试

[alpha,b] = trainlssvm({X,Yc,type,gam,sig2,kernel_type,preprocess}); % 训练 Yd0 = simlssvm({X,Yc,type,gam,sig2,kernel_type,preprocess},{alpha,b},Xt); % 分类

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% 解码

Yd = code(Yd0,old_codebook,[],codebook);

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% 结果统计

Result = ~abs(Yd-Yt) % 正确分类显示为1

Percent = sum(Result)/length(Result) % 正确分类率

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% 注意：以这两种写法等价

% -- 1 --

% [Yc,codebook,old_codebook] = code(Y, codefct)

% [alpha, b] = trainlssvm({X,Yc,type,gam,sig2,kernel,preprocess}) % Yd0 = simlssvm({X,Yc,type,gam,sig2,kernel}, {alpha,b}, Xt)

% Yd = code(Yd0,old_codebook,[],codebook)

% -- 2 --

% model = initlssvm(X,Y,type,gam,sig2,kernel,preprocess)

% model = changelssvm(model,'codetype',codefct)

% model = trainlssvm(model)

% Yd = simlssvm(model, Xt)