AI_Niewiarowska_lab5

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs

X, temp = make_blobs(n_samples=200, centers=30)

km = KMeans(n_clusters=3)

km.fit(X)
y_km = km.predict(X)

plt.scatter(km.cluster_centers_[0, 0], km.cluster_centers_[
            0, 1], s=250, marker='*', c='green', edgecolor='black', label='Centroid 1')
plt.scatter(X[y_km == 0, 0], X[y_km == 0, 1], s=50, c='blue',
            marker='.', edgecolor='black', label='Klaster 1')

plt.scatter(km.cluster_centers_[1, 0], km.cluster_centers_[
            1, 1], s=250, marker='*', c='red', edgecolor='black', label='Centroid 2')
plt.scatter(X[y_km == 1, 0], X[y_km == 1, 1], s=50, c='black',
            marker='.', edgecolor='black', label='Klaster 2')

plt.scatter(km.cluster_centers_[2, 0], km.cluster_centers_[
            2, 1], s=250, marker='*', c='yellow', edgecolor='black', label='Centroid 3')
plt.scatter(X[y_km == 2, 0], X[y_km == 2, 1], s=50, c='violet',
            marker='.', edgecolor='black', label='Klaster 3')


plt.legend(scatterpoints=1)
plt.grid()
plt.show()