please check this

and this is my code

```
from sklearn.datasets import make_blobs
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
centers = np.random.randint(1, 51, (3, 2))
data = make_blobs(n_samples=1000, centers=centers)
X, y = data
def get_initial_centroids(X, y):
classes = np.unique(y)
return np.random.randint(20, 60, size=(classes.shape[0], X.shape[1]))
def associate_centroids(X, centroids):
y = np.zeros((X.shape[0], 2))
for i in range(X.shape[0]):
x_i = X[i, :]
distances = np.zeros(centroids.shape[0])
for k in range(centroids.shape[0]):
c_k = centroids[k, :]
distance = np.sqrt(np.sum(np.power(c_k - x_i, 2)))
distances[k] = distance
cid = np.argmin(distances)
y[i] = [cid.astype(int), distances[cid]]
return y
def update_centroids(X, y, centroids):
for i in range(centroids.shape[0]):
centroids[i] = np.mean(X[np.where(y == i)], axis=0)
return centroids
def train(X, y, centroids):
for i in range(50):
y_pred = associate_centroids(X, centroids)
score = round((y_pred[:, 0] == y).mean() * 100, 3)
print(f"EPOCH: {i}, SCORE={score}%")
centroids = update_centroids(X, y, centroids.copy())
if score >= 95:
break
return centroids
def plot_clusters(centroids, X, y, title):
colors = [("gold", "black"), ("navy", "aqua"), ("green", "lime")]
for i in range(centroids.shape[0]):
blob = X[np.where(y == i)]
plt.scatter(blob[:, 0], blob[:,1], c=colors[i][0])
plt.scatter(centroids[i, 0], centroids[i, 1], label=f"Centroid {i+1}", c=colors[i][1])
plt.title(title)
plt.legend()
centroids = get_initial_centroids(X, y)
plot_clusters(centroids, X, y, "Before Training")
centroids = train(X, y, centroids)
plot_clusters(centroids, X, y, "After Train")
```