MATLAB编写LOF分算法

以下是MATLAB代码实现LOF算法：

function [outliers, scores] = lof(X, k)
% X: 数据矩阵，每行表示一个样本，每列表示一个特征
% k: 邻居数
% outliers: 异常点的索引
% scores: LOF得分

[n, d] = size(X);

% 计算每个样本的k个最近邻居
D = squareform(pdist(X)); % 计算样本间的欧几里得距离
[~, idx] = sort(D);
idx = idx(2:k+1, :);
neighs = reshape(idx, k*n, 1);

% 计算每个样本的局部可达密度
reach_dist = zeros(n, k);
for i = 1:n
for j = 1:k
idx = (i-1)*k + j;
reach_dist(i, j) = max([D(i, neighs(idx)), max(reach_dist(neighs(idx), :))]);
end
end
lrd = 1 ./ mean(reach_dist, 2);

% 计算每个样本的LOF得分
lof = zeros(n, 1);
for i = 1:n
idx = (i-1)*k + 1:i*k;
lof(i) = mean(lrd(neighs(idx))) / lrd(i);
end

% 找出LOF得分大于1的样本
outliers = find(lof > 1);
scores = lof(outliers);
end