stackoverflow question

kmeans.py

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf

points_n = 200
clusters_n = 3
iteration_n = 100

x =  tf.cast(tf.convert_to_tensor(np.random.rand(points_n,2),dtype=tf.float32),tf.float32)
points =  tf.placeholder(tf.float32, [points_n,2])

#points = tf.constant(np.random.uniform(0, 10, (points_n, 2)))
centroids = tf.Variable(tf.slice(tf.random_shuffle(points), [0, 0], [clusters_n, -1]))

points_expanded = tf.expand_dims(points, 0)
centroids_expanded = tf.expand_dims(centroids, 1)

distances = tf.reduce_sum(tf.square(tf.sub(points_expanded, centroids_expanded)), 2)
assignments = tf.argmin(distances, 0)

means = []
for c in xrange(clusters_n):
    means.append(tf.reduce_mean(
      tf.gather(points, 
                tf.reshape(
                  tf.where(
                    tf.equal(assignments, c)
                  ),[1,-1])
               ),reduction_indices=[1]))

new_centroids = tf.concat(0, means)

update_centroids = tf.assign(centroids, new_centroids)
init = tf.initialize_all_variables()


sess = tf.InteractiveSession()
sess.run(init)
for step in xrange(iteration_n):
    [_, centroid_values, points_values, assignment_values] = sess.run([update_centroids, centroids, points, assignments],feed_dict={
     points : x
    })
    print "centroids" + "\n", centroid_values