ML|迷你批量K均值聚类算法

Given a dataset D = {d1, d2, d3, .....dn},
no. of iterations t,
batch size b,
no. of clusters k.
k clusters C = {c1, c2, c3, ......ck}
initialize k cluster centers O = {o1, o2, .......ok}
# _initialize each cluster
Ci = Φ (1=< i =< k)
# _initialize no. of data in each cluster
Nc<sub>isub> = 0 (1=< i =< k)
for j=1 to t do:
# M is the batch dataset and xm
# is the sample randomly chosen from D
M = {xm | 1 =< m =< b}
# catch cluster center for each
# sample in the batch data set
for m=1 to b do:
oi(xm) = sum(xm)/|c|i (xm ε M and xm ε ci)
end for
# update the cluster center with each batch set
for m=1 to b do:
# get the cluster center for xm
oi = oi(xm)
# update number of data for each cluster center
Nc<sub>isub> = Nc<sub>isub> + 1
#calculate learning rate for each cluster center
lr=1/Nc<sub>isub>
# take gradient step to update cluster center
oi = (1-lr)oi + lr*xm
end for
end for


from sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn.metrics.pairwise import pairwise_distances_argmin
from sklearn.datasets.samples_generator import make_blobs
# Load data in X 
batch_size = 45
centers = [[1, 1], [-2, -1], [1, -2], [1, 9]]
n_clusters = len(centers)
X, labels_true = make_blobs(n_samples = 3000,
                            centers = centers,
                            cluster_std = 0.9)
# perform the mini batch K-means
mbk = MiniBatchKMeans(init ='k-means++', n_clusters = 4,
                      batch_size = batch_size, n_init = 10,
                      max_no_improvement = 10, verbose = 0)
mbk.fit(X)
mbk_means_cluster_centers = np.sort(mbk.cluster_centers_, axis = 0)
mbk_means_labels = pairwise_distances_argmin(X, mbk_means_cluster_centers)
# print the labels of each data
print(mbk_means_labels)