22 lines
436 B
Python
22 lines
436 B
Python
import numpy as np
|
|
from sklearn.datasets import make_blobs
|
|
|
|
from mla.kmeans import KMeans
|
|
|
|
|
|
def kmeans_example(plot=False):
|
|
X, y = make_blobs(
|
|
centers=4, n_samples=500, n_features=2, shuffle=True, random_state=42
|
|
)
|
|
clusters = len(np.unique(y))
|
|
k = KMeans(K=clusters, max_iters=150, init="++")
|
|
k.fit(X)
|
|
k.predict()
|
|
|
|
if plot:
|
|
k.plot()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
kmeans_example(plot=True)
|