# Untitled

unknown
python
a year ago
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Never
```def objective(X, y, cen):
n = len(X)
obj = 0
for i in range(n):
#obj += np.linalg.norm(X[i]-cen[y[i]])
obj += (X[i] - cen[y[i]])**2
return np.sum(obj)

def find_clusters(X, n_clusters, rseed=2):
# 1. Randomly choose clusters
rng = np.random.RandomState(rseed)
i = rng.permutation(X.shape[0])[:n_clusters]
centers = X[i]
#print(centers)
i = 0
while True:
print("Loop no: ", i)
i+=1
# 2a. Assign labels based on closest center
#labels = pairwise_distances_argmin(X, centers,metric='euclidean')
labels = pairwise_distances(X, centers, metric='euclidean').argmin(axis=1)
print("Pairwise cosine start")
t1 = datetime.now()
#labels = pairwise_distances(X, centers, metric='cosine').argmax(axis=1)
print("Pairwise cosine end")
t2 = datetime.now()
print(labels)

# 2b. Find new centers from means of points
new_centers = np.array([X[labels == i].mean(0)
for i in range(n_clusters)])
print(new_centers)

print(t2-t1)
# 2c. Check for convergence
if np.all(centers == new_centers):
break
centers = new_centers
ob = objective(X, labels, new_centers)
print(centers)
print(ob)

return centers, labels```