# Clustering
kmeans = KMeans(k=n_centers, iterations=max_iterations, random_state=random_state, track_history=True)
kmeans.fit(X)
# Extract centroids
centroids = kmeans.history_centroids
# Create decision boundary data
h = .1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
area_data = np.c_[xx.ravel(), yy.ravel()]
# Prepare predictions
predicted_labels = []
predicted_area = []
for iteration in range(max_iterations):
kmeans.centroids = centroids[iteration]
area = np.array(kmeans.predict(area_data))
area = area.reshape(xx.shape)
predicted_labels.append(kmeans.predict(X))
predicted_area.append(area)
# Plotting and showing the animation.
fig, ax = plt.subplots(figsize=(15, 6), dpi=80)
animation = FuncAnimation(fig, update, frames=max_iterations, interval=800, repeat=False)
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.show()
