5
$\begingroup$

After setting up a 2-noded Hadoop cluster, understanding Hadoop and Python and based on this naive implementation, I ended up with this code:

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)))

I have tested it for serial execution and it is OK. How to make it distributed in Hadoop? In other words, what should go to the reducer and what to the mapper?

Please note that if possible, I would like to follow the tutorial's style, since it's something I have understood.

$\endgroup$
3
$\begingroup$

Unless you are trying to do this as a learning exercise, just use Spark which has ML libraries made for distributed computing. See http://spark.apache.org/docs/latest/mllib-clustering.html

$\endgroup$
  • $\begingroup$ Bob sorry, I want to learn, not just call a function, I shall update my question, but you deserve an upvote for throwing that info for future use! ;) $\endgroup$ – gsamaras Feb 6 '16 at 3:15

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.