在建立了一个2 节点的 Hadoop 集群，了解了Hadoop 和 Python并基于这个幼稚的实现之后，我最终得到了以下代码：

def kmeans(data, k, c=None):
    if c is not None:
        centroids = c
    else:
        centroids = []
        centroids = randomize_centroids(data, centroids, k)

    old_centroids = [[] for i in range(k)] 

    iterations = 0
    while not (has_converged(centroids, old_centroids, iterations)):
        iterations += 1

        clusters = [[] for i in range(k)]

        # assign data points to clusters
        clusters = euclidean_dist(data, centroids, clusters)

        # recalculate centroids
        index = 0
        for cluster in clusters:
            old_centroids[index] = centroids[index]
            centroids[index] = np.mean(cluster, axis=0).tolist()
            index += 1


    print("The total number of data instances is: " + str(len(data)))

我已经测试了它的串行执行，它是好的。如何使其分布在 Hadoop 中？换句话说，什么应该去reducer，什么应该去mapper？

请注意，如果可能的话，我想遵循教程的风格，因为这是我理解的。