请写出matlab的canopy算法

以下是Matlab中的Canopy算法的示例代码：

function [clusters, centers] = canopy(data, T1, T2)
% data: 数据集，每行代表一个样本
% T1: Canopy1与Canopy2之间的距离阈值
% T2: Canopy2与Canopy3之间的距离阈值

% 计算样本之间的欧式距离
[n, m] = size(data); % n为样本数，m为特征数
dist = zeros(n, n);
for i = 1:n
    for j = i+1:n
        d = sqrt(sum((data(i,:) - data(j,:)).^2));
        dist(i,j) = d;
        dist(j,i) = d;
    end
end

% Canopy1聚类
canopy1 = [];
while ~isempty(data)
    i = randi(size(data, 1));
    c = data(i,:);
    canopy1 = [canopy1; i];
    dist_to_c = dist(i,:);
    data(dist_to_c <= T1, :) = []; % 从数据集中删除与c距离小于T1的样本
end

% Canopy2聚类
canopy2 = [];
while ~isempty(data)
    i = randi(size(data, 1));
    c = data(i,:);
    canopy2 = [canopy2; i];
    dist_to_c = dist(i,:);
    data(dist_to_c <= T2, :) = []; % 从数据集中删除与c距离小于T2的样本
end

% Canopy3聚类
canopy3 = [];
if ~isempty(data)
    canopy3 = 1:size(data,1);
end

% 计算聚类中心
centers = zeros(3, m);
for i = 1:3
    if isempty(eval(['canopy' num2str(i)]))
        centers(i,:) = nan;
    else
        centers(i,:) = mean(data(eval(['canopy' num2str(i)]), :));
    end
end

% 分配样本到聚类
clusters = zeros(n, 1);
for i = 1:n
    p = data(i,:);
    dist_to_centers = zeros(3, 1);
    for j = 1:3
        if ~isnan(centers(j,:))
            dist_to_centers(j) = sqrt(sum((p - centers(j,:)).^2));
        else
            dist_to_centers(j) = inf;
        end
    end
    [~, k] = min(dist_to_centers);
    clusters(i) = k;
end
end

该算法的主要思路是通过计算样本之间的距离，在数据集中随机选择一个样本作为Canopy1的中心，将距离该中心小于阈值T1的所有样本分配到Canopy1中；然后在剩余的样本中随机选择一个样本作为Canopy2的中心，将距离该中心小于阈值T2但大于T1的所有样本分配到Canopy2中；最后将剩余的样本分配到Canopy3中。然后通过计算每个Canopy的聚类中心，将剩余的样本分配到最近的Canopy中，即可完成聚类。