kmeans_plot_multiple_init.py

n_iter = 9
fig, ax = plt.subplots(3, 3, figsize=(16, 16))
ax = np.ravel(ax)
centers = []
for i in range(n_iter):
    # Run local implementation of kmeans
    km = Kmeans(n_clusters=2,
                max_iter=3,
                random_state=np.random.randint(0, 1000, size=1))
    km.fit(X_std)
    centroids = km.centroids
    centers.append(centroids)
    ax[i].scatter(X_std[km.labels == 0, 0], X_std[km.labels == 0, 1],
                  c='green', label='cluster 1')
    ax[i].scatter(X_std[km.labels == 1, 0], X_std[km.labels == 1, 1],
                  c='blue', label='cluster 2')
    ax[i].scatter(centroids[:, 0], centroids[:, 1],
                  c='r', marker='*', s=300, label='centroid')
    ax[i].set_xlim([-2, 2])
    ax[i].set_ylim([-2, 2])
    ax[i].legend(loc='lower right')
    ax[i].set_title(f'{km.error:.4f}')
    ax[i].set_aspect('equal')
plt.tight_layout();