写一个DBSCAN函数，用MATLAB

function [C, L] = dbscan(X, eps, minPts)
% DBSCAN clustering algorithm implementation in MATLAB
% Inputs:
% X: input data (n x d matrix)
% eps: radius of the neighborhood
% minPts: minimum number of points required to form a dense region
% Outputs:
% C: cluster labels (n x 1 vector)
% L: cluster labels for core points (n x 1 vector)

n = size(X, 1); % number of data points
C = zeros(n, 1); % cluster labels
visited = zeros(n, 1); % visited flags
dist = pdist2(X,X); % pairwise distances
L = zeros(n, 1); % core point labels
C_idx = 0; % cluster index

for i = 1:n
if ~visited(i)
visited(i) = 1;
neighbors = find(dist(i,:) <= eps);
if length(neighbors) < minPts
C(i) = -1; % noise point
else
C_idx = C_idx + 1;
C(i) = C_idx;
L(i) = C_idx;
k = 1;
while k <= length(neighbors)
j = neighbors(k);
if ~visited(j)
visited(j) = 1;
neighbors_j = find(dist(j,:) <= eps);
if length(neighbors_j) >= minPts
neighbors = [neighbors, neighbors_j];
end
end
if C(j) == 0
C(j) = C_idx;
L(j) = C_idx;
end
k = k + 1;
end
end
end
end

% assign labels to non-core points
for i = 1:n
if C(i) == 0
neighbors = find(dist(i,:) <= eps);
if any(L(neighbors))
C(i) = mode(C(L(neighbors)));
else
C(i) = -1; % noise point
end
end
end

end