Kmeans.py

# coding: utf-8
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf

 

 

def display_partition(x_values,y_values,assignment_values):

    labels = []
    colors = ["red","blue","green","yellow"]
    for i in range(len(assignment_values)):
        labels.append(colors[(assignment_values[i])])
    color = labels
    df = pd.DataFrame(dict(x =x_values,y = y_values ,color = labels ))
    fig, ax = plt.subplots()
    ax.scatter(df['x'], df['y'], c=df['color'])
    plt.show()

 

 

num_vectors = 2000
num_clusters = 4
n_samples_per_cluster = 500

num_steps = 1000
x_values = []
y_values = []
vector_values = []

 

#CREATE RANDOM DATA

for i in range(num_vectors):
    if np.random.random() > 0.5:
        x_values.append(np.random.normal(0.4, 0.7))  #평균, 분산
        y_values.append(np.random.normal(0.2, 0.8))
    
    else:
        x_values.append(np.random.normal(0.6, 0.4))
        y_values.append(np.random.normal(0.8, 0.5))

vector_values = list(zip(x_values,y_values))
vectors = tf.constant(vector_values)

n_samples = tf.shape(vector_values)[0]

random_indices = tf.random_shuffle(tf.range(0, n_samples))

begin = [0,]
size = [num_clusters,]
size[0] = num_clusters
centroid_indices = tf.slice(random_indices, begin, size)
centroids = tf.Variable(tf.gather(vector_values, centroid_indices))
expanded_vectors = tf.expand_dims(vectors, 0)
expanded_centroids = tf.expand_dims(centroids, 1)

vectors_subtration = tf.subtract(expanded_vectors,expanded_centroids)
euclidean_distances = tf.reduce_sum(tf.square(vectors_subtration), 2)

assignments = tf.to_int32(tf.argmin(euclidean_distances, 0))


#partitions = [0, 0, 1, 1, 0]
#num_partitions = 2
#data = [10, 20, 30, 40, 50]
#outputs[0] = [10, 20, 50]
#outputs[1] = [30, 40]
partitions = tf.dynamic_partition(vectors, assignments, num_clusters)
update_centroids = tf.concat([tf.expand_dims(tf.reduce_mean(partition, 0), 0) for partition in partitions],0)


sess = tf.Session()
sess.run(tf.global_variables_initializer())
for step in range(num_steps):
   iii,xxx, centroid_values, assignment_values = sess.run([centroid_indices,update_centroids,centroids,assignments])
#   print(step, iii,"\n")
#   print(centroid_values,"\n")


display_partition(x_values,y_values,assignment_values)

plt.plot(x_values,y_values, 'o', label='Input Data')
plt.legend()
plt.show()