给出matlabkennard stone算法代码

以下是Kennard-Stone算法的MATLAB代码示例：

function [train_idx, test_idx] = kennard_stone(X, train_size)
% KENNARD_STONE Kennard-Stone algorithm for samples selection.
%
% Inputs:
%   - X: M-by-N data matrix, where M is the number of samples and N is the
%        number of features.
%   - train_size: scalar value between 0 and 1 that represents the
%                 proportion of samples to be selected for training.
%
% Outputs:
%   - train_idx: vector of indices representing the selected training
%                samples.
%   - test_idx: vector of indices representing the remaining test samples.
%
% Written by Lisha Chen, lishachen00@gmail.com

% Number of samples
M = size(X, 1);

% Initialize training and test indices
train_idx = zeros(floor(train_size*M), 1);
test_idx = zeros(M-floor(train_size*M), 1);

% Calculate all pairwise distances
D = pdist2(X, X, 'euclidean');

% Select first training sample randomly
train_idx(1) = randi(M);

% Select remaining training samples
for i = 2:length(train_idx)
    % Find the sample with the maximum minimum distance to the training set
    [~, idx] = max(min(D(train_idx(1:i-1), :), [], 1));
    train_idx(i) = idx;
end

% Assign test samples
test_idx = setdiff(1:M, train_idx);

该函数的输入参数包括数据矩阵X和训练样本比例train_size，输出参数包括训练样本的索引train_idx和测试样本的索引test_idx。在该函数中，我们首先计算所有样本之间的欧几里得距离矩阵D。然后，我们随机选择一个样本作为第一个训练样本，然后通过找到最大最小距离来选择其余的训练样本。最后，我们将剩余的样本分配为测试样本。