clustering.py

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf

num_vectors = 1000
num_clusters = 3
num_steps = 100
vector_values = []
for i in range(num_vectors):
    if np.random.random() > 0.5:
        vector_values.append([np.random.normal(0.5, 0.6),
                              np.random.normal(0.3, 0.9)])
    else:
        vector_values.append([np.random.normal(2.5, 0.4),
                              np.random.normal(0.8, 0.5)])
df = pd.DataFrame({"x": [v[0] for v in vector_values],
                   "y": [v[1] for v in vector_values]})
sns.lmplot("x", "y", data=df, fit_reg=False, size=7)
plt.show()
vectors = tf.constant(vector_values)
centroids = tf.Variable(tf.slice(tf.random_shuffle(vectors),
                                 [0, 0], [num_clusters, -1]))
expanded_vectors = tf.expand_dims(vectors, 0)
expanded_centroids = tf.expand_dims(centroids, 1)

print(expanded_vectors.get_shape())
print(expanded_centroids.get_shape())

distances = tf.reduce_sum(
    tf.square(tf.subtract(expanded_vectors, expanded_centroids)), 2)
assignments = tf.argmin(distances, 0)

means = tf.concat(axis=0, values=[
    tf.reduce_mean(
        tf.gather(vectors,
                  tf.reshape(
                      tf.where(
                          tf.equal(assignments, c)
                      ), [1, -1])
                  ), axis=[1])
    for c in range(num_clusters)])

update_centroids = tf.assign(centroids, means)
init_op = tf.global_variables_initializer()

# with tf.Session('local') as sess:
sess = tf.Session()
sess.run(init_op)

for step in range(num_steps):
    _, centroid_values, assignment_values = sess.run([update_centroids,
                                                      centroids,
                                                      assignments])
print("centroids")
print(centroid_values)

data = {"x": [], "y": [], "cluster": []}
for i in range(len(assignment_values)):
    data["x"].append(vector_values[i][0])
    data["y"].append(vector_values[i][1])
    data["cluster"].append(assignment_values[i])
df = pd.DataFrame(data)
sns.lmplot("x", "y", data=df,
           fit_reg=False, size=7,
           hue="cluster", legend=False)
plt.show()