def test_cube():
XX = []
size = 10
for i in range(0, size):
for j in range(0, size):
for k in range(0, size):
XX.append([i, j, k])
XX = np.array(XX)
# for i, x in enumerate(XX):
# print (i, x)
dr = DRUHG(algorithm='slow', max_ranking=200, verbose=False)
dr.fit(XX)
s = 2 * len(XX) - 2
print(dr.mst_)
# assert (0==1)
labels = dr.labels_
n_clusters = len(set(labels)) - int(-1 in labels)
print('n_clusters', n_clusters)
print('labels', labels)
assert (n_clusters == 1 + 6)
# labels = dr.relabel(limit1=1)
labels = dr.relabel(limit1=1, limit2=len(XX))
print('out')
n_clusters = len(set(labels)) - int(-1 in labels)
print('n_clusters2', n_clusters)
print('labels2', labels)
assert (n_clusters == 1 + 6 + 12)
def test_iris():
iris = datasets.load_iris()
XX = iris['data']
# print (XX, type(XX))
dr = DRUHG(max_ranking=50, verbose=False)
dr.fit(XX)
labels = dr.labels_
ari = adjusted_rand_score(iris['target'], labels)
print('iris ari', ari)
assert (ari >= 0.50)
# breaking biggest cluster
labels = dr.relabel(limit1=0, limit2=int(len(XX) / 2), fix_outliers=1)
ari = adjusted_rand_score(iris['target'], labels)
print('iris ari', ari)
assert (ari >= 0.85)