code. compile. run. debug. share.
import numpy as np
from sklearn.datasets import load_iris
X = load_iris().data # selected data
def kmeans(X, K, i=10):
c = X[np.random.choice(len(X), K, 0)]
for _ in range(i):
l = ((X[:,None]-c)**2).sum(2).argmin(1)
c = np.array([X[l==k].mean(0) for k in range(K)])
return l, c
def sse(X, l, c):
return sum(np.linalg.norm(x-c[k]) for x,k in zip(X,l))
for K in range(1,6):
l,c = kmeans(X,K)
print(K, sse(X,l,c))
Compiling Program...
Standard Input:
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