将数据集拆分为matlab / octave中的两个子集

Question

将数据集拆分为两个子集，例如“train”和“test” 火车组包含80％的数据，测试集包含剩余的20％。

拆分意味着生成长度等于的逻辑索引 数据集中的观察数量，培训数量为1 样品，0为测试样品。

N =长度（data.x）

输出：名为idxTrain和idxTest的逻辑数组。

Answer 1

这应该可以解决问题：

% Generate sample data...
data = rand(32000,1);

% Calculate the number of training entries...
train_off = round(numel(data) * 0.8);

% Split data into training and test vectors...
train = data(1:train_off);
test = data(train_off+1:end);

但是，如果您真的想依赖逻辑索引，可以按以下步骤操作：

% Generate sample data...
data = rand(32000,1);
data_len = numel(data);

% Calculate the number of training entries...
train_count = round(data_len * 0.8);

% Create the logical indexing...
is_training = [true(train_count,1); false(data_len-train_count,1)];

% Split data into training and test vectors...
train = data(is_training);
test = data(~is_training);

您还可以使用randsample function来获取提取中的一些随机性，但是每次运行脚本时，这都不会为您提供测试和训练元素的确切绘制数量：

% Generate sample data...
data = rand(32000,1);

% Generate a random true/false indexing with unequally weighted probabilities...
is_training = logical(randsample([0 1],32000,true,[0.2 0.8]));

% Split data into training and test vectors...
train = data(is_training);
test = data(~is_training);

您可以通过生成正确数量的测试和培训索引，然后使用基于randperm的索引对其进行混洗来避免此问题：

% Generate sample data...
data = rand(32000,1);
data_len = numel(data);

% Calculate the number of training entries...
train_count = round(data_len * 0.8);

% Create the logical indexing...
is_training = [true(train_count,1); false(data_len-train_count,1)];

% Shuffle the logical indexing...
is_training = is_training(randperm(32000));

% Split data into training and test vectors...
train = data(is_training);
test = data(~is_training);