from __future__ import print_function
import numpy as np
import random
# calculate Euclidean distance
def eucl_dist(vec1, vec2):
return np.sqrt(np.sum(np.power(vec2 - vec1, 2)))
# init centroids with random samples
def init_centroids(dataset, k):
num_samples, dim = dataset.shape
centroids = np.zeros((k, dim))
init_indexes = random.sample(range(num_samples), k)
for i in range(k):
index = init_indexes[i]
centroids[i, :] = dataset[index, :]
return centroids
# k-means cluster
def kmeans(dataset, k):
num_samples = dataset.shape[0]
# first column stores which cluster this sample belongs to,
# second column stores the error between this sample and its centroid
cluster_assignment = np.zeros((num_samples, 2))
cluster_updated = True
## step 1: init centroids
centroids = init_centroids(dataset, k)
while cluster_updated:
cluster_updated = False
# for each sample
for i in range(num_samples): #range
min_dist = np.finfo(np.float32).max
min_index = 0
# for each centroid
# step 2: find the centroid who is closest
for j in range(k):
distance = eucl_dist(centroids[j, :], dataset[i, :])
if distance < min_dist:
min_dist, min_index = distance, j
# step 3: update its cluster
if cluster_assignment[i, 0] != min_index:
cluster_updated = True
cluster_assignment[i, :] = min_index, min_dist**2
# step 4: update centroids
for j in range(k):
pts_in_cluster = dataset[np.nonzero(cluster_assignment[:, 0] == j)[0]]
centroids[j, :] = np.mean(pts_in_cluster, axis=0)
return centroids, cluster_assignment
if __name__ == '__main__':
points = [
[1, 2],
[2, 1],
[3, 1],
[5, 4],
[5, 5],
[6, 5],
[10, 8],
[7, 9],
[11, 5],
[14, 9],
[14, 14],
]
dataset = np.array(points, np.float32)
centroids, _ = kmeans(dataset, 3)
print(centroids)
