canopy算法matlab代码

Sure, here is a simple implementation of the Canopy algorithm in MATLAB:

function [clusters, centers = canopy(data, T1, T2)
    n = size(data, 1);
    distances = pdist2(data, data);
    canopies = [];

    while ~isempty(data)
        center = data(1, :);
        d = distances(1, :);
        T1_indices = d < T1;

        canopy = data(T1_indices, :);
        canopies = [canopies; canopy];

        data = data(~T1_indices, :);
        distances = distances(~T1_indices, :);
        distances = distances(:, ~T1_indices);
    end

    num_canopies = size(canopies, 1);
    centers = zeros(num_canopies, size(data, 2));
    clusters = cell(num_canopies, 1);

    for i = 1:num_canopies
        center = canopies(i, :);
        d = pdist2(center, canopies);
        indices = d < T2;
        clusters{i} = canopies(indices, :);
        centers(i, :) = mean(clusters{i}, 1);
    end
end

To use this code, you can pass your data as a matrix to the `canopy` function along with the threshold values `T1` and `T2`. It will return the clusters and the cluster centers.

Note that this implementation assumes that your data is represented as a matrix where each row represents a data point. You may need to adjust the code accordingly if your data has a different format.

Also, keep in mind that this is a basic implementation of the Canopy algorithm and may not be optimized for large datasets.