<gh_stars>0
"""K-means algorithm from sci-kit learn
"""

import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
from sklearn.cluster import KMeans

style.use('ggplot')

x = np.array([[1, 2],
              [1.5, 1.8],
              [5, 8],
              [8, 8],
              [1, 0.6],
              [9, 11]])

# plt.scatter(x[:, 0], x[:, 1], s=150)
# plt.show()

clf = KMeans(n_clusters=2)
clf.fit(x)

centroids = clf.cluster_centers_
labels = clf.labels_

colors = ["g.", "r.", "c.", "b.", "k."]

for i in range(len(x)):
    plt.plot(x[i][0], x[i][1], colors[labels[i]], markersize=25)

plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', s=150, linewidths=5)
plt.show()

"""K-means algorithm from sci-kit learn
"""

import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
from sklearn.cluster import KMeans

style.use('ggplot')

x = np.array([[1, 2],
              [1.5, 1.8],
              [5, 8],
              [8, 8],
              [1, 0.6],
              [9, 11]])

# plt.scatter(x[:, 0], x[:, 1], s=150)
# plt.show()

clf = KMeans(n_clusters=2)
clf.fit(x)

centroids = clf.cluster_centers_
labels = clf.labels_

colors = ["g.", "r.", "c.", "b.", "k."]

for i in range(len(x)):
    plt.plot(x[i][0], x[i][1], colors[labels[i]], markersize=25)

plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', s=150, linewidths=5)
plt.show()
K-means algorithm from sci-kit learn # plt.scatter(x[:, 0], x[:, 1], s=150) # plt.show()