采MC-UVE写一个光谱特征选择matlab函数，带注释

好的，我可以回答这个问题。以下是采用MC-UVE算法编写的光谱特征选择的Matlab函数，带有注释：

function [selected_features, w] = MC_UVE(data, labels, alpha, beta, gamma)
% MC_UVE: 基于MC-UVE算法的光谱特征选择函数
% 输入参数:
% data: 数据矩阵，每行代表一个样本，每列代表一个特征
% labels: 标签向量，每个元素代表对应样本的类别
% alpha: MC-UVE算法中的参数alpha
% beta: MC-UVE算法中的参数beta
% gamma: MC-UVE算法中的参数gamma
% 输出参数:
% selected_features: 选择的特征下标
% w: 特征权重向量

% 计算类别数和特征数
num_classes = length(unique(labels));
num_features = size(data, 2);

% 计算类内散度矩阵Sw和类间散度矩阵Sb
Sw = zeros(num_features, num_features);
Sb = zeros(num_features, num_features);
for i = 1:num_classes
class_data = data(labels == i, :);
class_mean = mean(class_data);
class_size = size(class_data, 1);
class_Sw = zeros(num_features, num_features);
for j = 1:class_size
class_Sw = class_Sw + (class_data(j, :) - class_mean)' * (class_data(j, :) - class_mean);
end
Sw = Sw + class_Sw;
Sb = Sb + class_size * (class_mean - mean(data))' * (class_mean - mean(data));
end

% 计算特征权重向量w
w = alpha * Sw \ Sb;
w = w / norm(w);

% 计算每个特征的MC-UVE值
mc_uve = zeros(num_features, 1);
for i = 1:num_features
mc_uve(i) = beta * w(i)^2 + gamma * (Sw(i, i) / Sb(i, i));
end

% 选择特征
[~, sorted_indices] = sort(mc_uve, 'descend');
selected_features = sorted_indices(1:round(num_features/2)); % 选择前一半的特征

end