def cluster_kmeans(n_samples):
rng = np.random.RandomState(9)
kmeans = MiniBatchKMeans(n_clusters=2, random_state=rng, verbose=True, compute_labels=True)
i = 0;
batch_size = 10
while(i < n_samples):
#partial fit 100 authors and there subsequent comparisons
print "k_means partial fitting, i = ", str(i)
data = extract_data(start=i, end=i+batch_size)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
kmeans.partial_fit(data)
i+=batch_size
print "fitting of one-third data finished."
return kmeans
def cluster_kmeans(n_samples):
rng = np.random.RandomState(9)
kmeans = MiniBatchKMeans(n_clusters=2,
random_state=rng,
verbose=True,
compute_labels=True)
i = 0
batch_size = 10
while (i < n_samples):
#partial fit 100 authors and there subsequent comparisons
print "k_means partial fitting, i = ", str(i)
data = extract_data(start=i, end=i + batch_size)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
kmeans.partial_fit(data)
i += batch_size
print "fitting of one-third data finished."
return kmeans