import matplotlib.pyplot as plt

import numpy as np

import tensorflow as tf

points_n = 200

clusters_n = 12

iteration_n = 100

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.subtract(points_expanded, centroids_expanded)), 2)

assignments = tf.argmin(distances, 0)

means = []

for c in range(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(means, 0)

update_centroids = tf.assign(centroids, new_centroids)

init = tf.global_variables_initializer()

with tf.Session() as sess:

sess.run(init)

for step in range(iteration_n):

[_, centroid_values, points_values, assignment_values] = sess.run(

[update_centroids, centroids, points, assignments])

print("centroids" + "\n", centroid_values)

plt.scatter(

points_values[:, 0],

points_values[:, 1],

c=assignment_values,

s=50,

alpha=0.5)

plt.plot(centroid_values[:, 0], centroid_values[:, 1], 'kx', markersize=15)

plt.show()