使用matlab编写smote算法的代码并注释

以下是使用MATLAB编写SMOTE算法的代码，并附有注释：

function [X_resampled, y_resampled] = smote(X, y, k, perc, minorityClass)
% Function to perform SMOTE (Synthetic Minority Over-sampling Technique)
% Inputs:
% X - Matrix of features (rows = observations, columns = features)
% y - Vector of labels (rows = observations)
% k - Number of nearest neighbors to use for SMOTE
% perc - Percentage of SMOTE samples to generate (e.g. 200% = 2.0)
% minorityClass - Label of the minority class
% Outputs:
% X_resampled - Matrix of resampled features
% y_resampled - Vector of resampled labels

% Find the minority class observations
minorityIdx = find(y == minorityClass);
numMinority = length(minorityIdx);

% Calculate the number of SMOTE samples to generate
numSMOTE = round(numMinority * perc);
numNew = numSMOTE - numMinority;

% Find the k nearest neighbors for each minority class observation
knn = KDTreeSearcher(X);
knnIdx = knnsearch(knn, X(minorityIdx,:), 'K', k+1);
knnIdx = knnIdx(:,2:end);

% Initialize matrix for SMOTE samples
newSamples = zeros(numNew, size(X,2));

% Generate synthetic samples for each minority class observation
for i = 1:numMinority
    % Choose one of the k nearest neighbors randomly
    nnIdx = randsample(k, 1);
    nn = X(knnIdx(i,nnIdx),:);
    
    % Generate a new sample by interpolating between the minority class
    % observation and the randomly chosen nearest neighbor
    diff = nn - X(minorityIdx(i),:);
    newSample = X(minorityIdx(i),:) + rand(1,size(X,2)).*diff;
    
    % Add the new sample to the matrix of SMOTE samples
    newSamples(i,:) = newSample;
end

% Combine the original and SMOTE samples
X_resampled = [X; newSamples];
y_resampled = [y; repmat(minorityClass, numNew, 1)];
end

注释中解释了函数的输入和输出以及变量的含义。函数的主要功能是生成合成少数类样本，其中包括计算SMOTE样本的数量，找到每个少数类观测的k个最近邻居，以及使用这些最近邻居生成合成样本。最后，将原始样本和合成样本组合起来以形成平衡数据集。