K-means

k-means.py

# import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

X = -2 * np.random.rand(100, 2)
X1 = 1 + 2 * np.random.rand(50, 2)
X[50:100, :] = X1

plt.scatter(X[:,0], X[:,1], s = 50, c = 'b')
plt.show()

from sklearn.cluster import KMeans

Kmean = KMeans(n_clusters=2)
Kmean.fit(X)

# Kmean.cluster_centers_

plt.scatter(X[:,0], X[:,1], s = 50, c = 'b')
plt.scatter(-0.94665068, -0.97138368, s = 200, c = 'g', marker = 's')
plt.scatter(2.01559419, 2.02597093, s = 200, c = 'r', marker = 's')
plt.show()

# Kmean.labels_

sample_test=np.array([-3.0,-3.0])
second_test=sample_test.reshape(1, -1)

Kmean.predict(second_test)