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matlab葡萄酒分类数据归一化问题% 选定训练集和测试集
% 将第一类的1-30,第二类的60-95,第三类的131-153做为训练集
train_wine = [wine(1:30,:);wine(60:95,:);wine(131:153,:)];
% 相应的训练集的标签也要分离出来
train_wine_labels = [wine_labels(1:30);wine_labels(60:95);wine_labels(131:153)];
% 将第一类的31-59,第二类的96-130,第三类的154-178做为测试集
test_wine = [wine(31:59,:);wine(96:130,:);wine(154:178,:)];
% 相应的测试集的标签也要分离出来
test_wine_labels = [wine_labels(31:59);wine_labels(96:130);wine_labels(154:178)];
[mtrain,ntrain] = size(train_wine);
[mtest,ntest] = size(test_wine);
dataset = [train_wine;test_wine];
% mapminmax为MATLAB自带的归一化函数
[dataset_scale,ps] = mapminmax(dataset',0,1);
dataset_scale = dataset_scale';
train_wine = dataset_scale(1:mtrain,:);
test_wine = dataset_scale( (mtrain+1):(mtrain+mtest),: );
为什么不可以改写为
[dataset_scale,ps] = mapminmax(wine,0,1);
train_wine = dataset_scale([wine(1:30,:);wine(60:95,:);wine(131:153,:)]);
test_wine = dataset_scale([wine(31:59,:);wine(96:130,:);wine(154:178,:)]);
本文探讨了如何在MATLAB中正确处理葡萄酒分类数据,作者指出原始代码中的归一化步骤可以简化,并解释了为何不能直接对整个数据集应用归一化函数。通过实例说明了如何针对训练集和测试集分别进行归一化处理。
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