K-Means.ipynb

Thank you!!
In python 3 , I think it works!

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

num_points = 2000
vectors_set = []

for i in range(num_points):
  if np.random.random() > 0.5:
    vectors_set.append([np.random.normal(0.0, 0.9), np.random.normal(0.0, 0.9)])
  else :
    vectors_set.append([np.random.normal(3.0, 0.5), np.random.normal(1.0, 0.5)])
    
df = pd.DataFrame({"x": [v[0] for v in vectors_set], "y": [v[1] for v in vectors_set]})
sns.lmplot("x","y", data=df, fit_reg=False, size=6)
plt.show()

# k-means algorithm
vectors = tf.constant(vectors_set)
num_clusters = 4
centroides = tf.Variable(tf.slice(tf.random_shuffle(vectors),[0,0],[k,-1]))

expanded_vectors = tf.expand_dims(vectors, 0)
expanded_centroides = tf.expand_dims(centroides, 1)

assignments = tf.argmin(tf.reduce_sum(tf.square(tf.subtract(expanded_vectors,expanded_centroides)), 2), 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_centroides = tf.assign(centroides, means)

init_op = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init_op)

for step in range(100):
  _, centroid_values, assignment_values = sess.run([update_centroides, centroides, assignments])
  
data = {"x": [], "y": [], "cluster": []}

for i in range(len(assignment_values)):
  data["x"].append(vectors_set[i][0])
  data["y"].append(vectors_set[i][1])
  data["cluster"].append(assignment_values[i])
  
df = pd.DataFrame(data)
sns.lmplot("x","y",data=df,fit_reg=False, size=6, hue="cluster", legend=False)
plt.show()

In python 3.6, it still works well. Thank You :D

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

num_points = 2000
vectors_set = []

for i in range(num_points):
    if np.random.random() > 0.5:
        vectors_set.append([np.random.normal(0.0, 0.9), np.random.normal(0.0, 0.9)])
    else:
        vectors_set.append([np.random.normal(3.0, 0.5), np.random.normal(1.0, 0.5)])

df = pd.DataFrame({"x": [v[0] for v in vectors_set], "y": [v[1] for v in vectors_set]})
sns.lmplot("x", "y", data=df, fit_reg=False, height=6)
plt.show()

# k-means algorithm
vectors = tf.constant(vectors_set)
num_clusters = 4
centroides = tf.Variable(tf.slice(tf.random_shuffle(vectors), [0, 0], [num_clusters, -1]))

expanded_vectors = tf.expand_dims(vectors, 0)
expanded_centroides = tf.expand_dims(centroides, 1)

assignments = tf.argmin(tf.reduce_sum(tf.square(tf.subtract(expanded_vectors, expanded_centroides)), 2), 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_centroides = tf.assign(centroides, means)

init_op = tf.global_variables_initializer()

sess = tf.Session()
sess.run(init_op)

for step in range(100):
    _, centroid_values, assignment_values = sess.run([update_centroides, centroides, assignments])

data = {"x": [], "y": [], "cluster": []}

for i in range(len(assignment_values)):
    data["x"].append(vectors_set[i][0])
    data["y"].append(vectors_set[i][1])
    data["cluster"].append(assignment_values[i])

df = pd.DataFrame(data)
sns.lmplot("x", "y", data=df, fit_reg=False, height=6, hue="cluster", legend=False)
plt.show()