sklearn.cluster.MiniBatchKMeans — scikit-learn 0.20.4 documentation (original) (raw)

from sklearn.cluster import MiniBatchKMeans import numpy as np X = np.array([[1, 2], [1, 4], [1, 0], ... [4, 2], [4, 0], [4, 4], ... [4, 5], [0, 1], [2, 2], ... [3, 2], [5, 5], [1, -1]])

manually fit on batches

kmeans = MiniBatchKMeans(n_clusters=2, ... random_state=0, ... batch_size=6) kmeans = kmeans.partial_fit(X[0:6,:]) kmeans = kmeans.partial_fit(X[6:12,:]) kmeans.cluster_centers_ array([[1, 1], [3, 4]]) kmeans.predict([[0, 0], [4, 4]]) array([0, 1], dtype=int32)

fit on the whole data

kmeans = MiniBatchKMeans(n_clusters=2, ... random_state=0, ... batch_size=6, ... max_iter=10).fit(X) kmeans.cluster_centers_ array([[3.95918367, 2.40816327], [1.12195122, 1.3902439 ]]) kmeans.predict([[0, 0], [4, 4]]) array([1, 0], dtype=int32)