def make_clusters_KMeans(dat_to_cluster , nb_clust, n=15000):
estimator = KMeans(n_clusters=nb_clust)
if n < len(dat_to_cluster):
s=np.asarray(sample(list(dat_to_cluster), k = n), dtype=np.float_)
estimator= estimator.fit(s)
res = []
notinit = True
for i in range(0, len(dat_to_cluster) // n + 1):
d = np.asarray(list(dat_to_cluster)[n * i: n * (i + 1)])
if notinit:
res = list(estimator.predict(d))
notinit = False
else:
a =list(estimator.predict(d))
for j in a:
res.append(j)
else :
res = estimator.fit_predict(dat_to_cluster)
return res, estimator
def make_clusters(dat_to_cluster , nb_clust=4):
estimator = KMeans(n_clusters=nb_clust)
res = estimator.fit_predict(dat_to_cluster)
return res, estimator