Kmeans 用matlab写代码

以下是Kmeans的Matlab代码示例：

% 生成测试数据
data = [randn(100,2)*0.75+ones(100,2);
        randn(100,2)*0.5-ones(100,2)];

% 设定聚类数量
k = 2;

% 设定迭代次数
max_iters = 100;

% 随机初始化聚类中心
initial_centroids = kMeansInitCentroids(data, k);

% 运行 K-Means 算法
[centroids, idx] = runkMeans(data, initial_centroids, max_iters);

% 可视化聚类结果
plotKMeansData(data, idx, centroids, k);

其中 `kMeansInitCentroids` 和 `runkMeans` 是Kmeans算法的主要代码，下面给出这两个函数的代码实现：

% 随机初始化聚类中心
function centroids = kMeansInitCentroids(X, K)
  centroids = zeros(K, size(X, 2));
  randidx = randperm(size(X, 1));
  centroids = X(randidx(1:K), :);
end

% 运行 K-Means 算法
function [centroids, idx] = runkMeans(X, initial_centroids, max_iters)
  % 初始化一些变量
  [m, n] = size(X);
  K = size(initial_centroids, 1);
  centroids = initial_centroids;
  previous_centroids = centroids;
  idx = zeros(m, 1);

  % 迭代计算聚类中心
  for i=1:max_iters
      fprintf('K-Means iteration %d/%d...\n', i, max_iters);

      % 计算每个样本点离哪个聚类中心最近
      idx = findClosestCentroids(X, centroids);

      % 更新聚类中心
      centroids = computeCentroids(X, idx, K);

      % 判断聚类中心是否收敛
      if isequal(centroids, previous_centroids)
          fprintf('K-Means algorithm converged after %d iterations.\n', i);
          break;
      end

      previous_centroids = centroids;
  end
end

% 计算每个样本点离哪个聚类中心最近
function idx = findClosestCentroids(X, centroids)
  K = size(centroids, 1);
  idx = zeros(size(X,1), 1);
  m = size(X,1);
  for i=1:m
      min_dist = Inf;
      for j=1:K
          dist = sum((X(i,:) - centroids(j,:)).^2);
          if dist < min_dist
              idx(i) = j;
              min_dist = dist;
          end
      end
  end
end

% 更新聚类中心
function centroids = computeCentroids(X, idx, K)
  [m, n] = size(X);
  centroids = zeros(K, n);
  for i=1:K
      indices = find(idx == i);
      Ck = length(indices);
      centroids(i, :) = (1/Ck) * sum(X(indices,:));
  end
end

最后，还需要一个函数来可视化聚类结果：

% 可视化聚类结果
function plotKMeansData(X, idx, centroids, K)
  colors = hsv(K);
  figure;
  hold on;
  for i=1:K
      data_points = X(idx==i,:);
      scatter(data_points(:,1), data_points(:,2), 10, colors(i,:), 'filled');
  end
  scatter(centroids(:,1), centroids(:,2), 100, colors, 'x', 'LineWidth', 3);
  hold off;
end

以上就是Kmeans在Matlab中的实现示例。