def test_clustering():
from ultranest.mlfriends import update_clusters
for i in range(5):
np.random.seed(i * 100)
points = np.random.uniform(size=(100, 2))
nclusters, clusteridxs, overlapped_points = update_clusters(
points, points, 0.1**2)
for i in np.unique(clusteridxs):
x, y = points[clusteridxs == i].transpose()
plt.scatter(x, y)
plt.savefig('testclustering_0p1.pdf', bbox_inches='tight')
plt.close()
assert 1 < nclusters < 30
nclusters, clusteridxs, overlapped_points = update_clusters(
points, points, 0.2**2)
for i in np.unique(clusteridxs):
x, y = points[clusteridxs == i].transpose()
plt.scatter(x, y)
plt.savefig('testclustering_0p2.pdf', bbox_inches='tight')
plt.close()
assert 1 <= nclusters < 2
def test_overclustering_eggbox_txt():
from ultranest.mlfriends import update_clusters, ScalingLayer, MLFriends
np.random.seed(1)
for i in [20, 23, 24, 27, 49]:
print()
print("==== TEST CASE %d =====================" % i)
print()
points = np.loadtxt(os.path.join(here, "overclustered_u_%d.txt" % i))
for k in range(3):
transformLayer = ScalingLayer(wrapped_dims=[])
transformLayer.optimize(points, points)
region = MLFriends(points, transformLayer)
maxr = region.compute_maxradiussq(nbootstraps=30)
region.maxradiussq = maxr
nclusters = transformLayer.nclusters
print("manual: r=%e nc=%d" % (region.maxradiussq, nclusters))
# assert 1e-10 < maxr < 5e-10
nclusters, clusteridxs, overlapped_points = update_clusters(
points, points, maxr)
print("reclustered: nc=%d" % (nclusters))
if False:
plt.title('nclusters: %d' % nclusters)
for k in np.unique(clusteridxs):
x, y = points[clusteridxs == k].transpose()
plt.scatter(x, y)
plt.savefig('testoverclustering_eggbox_%d.pdf' % i,
bbox_inches='tight')
plt.close()
assert 14 < nclusters < 20, (nclusters, i)
for j in range(3):
nclusters, clusteridxs, overlapped_points = update_clusters(
points, points, maxr)
assert 14 < nclusters < 20, (nclusters, i)
def test_clusteringcase_eggbox():
from ultranest.mlfriends import update_clusters, ScalingLayer, MLFriends
points = np.loadtxt(os.path.join(here, "eggboxregion.txt"))
transformLayer = ScalingLayer()
transformLayer.optimize(points, points)
region = MLFriends(points, transformLayer)
maxr = region.compute_maxradiussq(nbootstraps=30)
assert 1e-10 < maxr < 5e-10
print('maxradius:', maxr)
nclusters, clusteridxs, overlapped_points = update_clusters(
points, points, maxr)
# plt.title('nclusters: %d' % nclusters)
# for i in np.unique(clusteridxs):
# x, y = points[clusteridxs == i].transpose()
# plt.scatter(x, y)
# plt.savefig('testclustering_eggbox.pdf', bbox_inches='tight')
# plt.close()
assert 14 < nclusters < 20, nclusters
def test_clusteringcase():
from ultranest.mlfriends import update_clusters
here = os.path.dirname(__file__)
points = np.loadtxt(os.path.join(here, "clusters2.txt"))
maxr = np.loadtxt(os.path.join(here, "clusters2_radius.txt"))
#transformLayer = ScalingLayer()
#transformLayer.optimize(points)
#region = MLFriends(points, transformLayer)
#maxr = region.compute_maxradiussq(nbootstraps=30)
print('maxradius:', maxr)
nclusters, clusteridxs, overlapped_points = update_clusters(
points, points, maxr)
plt.title('nclusters: %d' % nclusters)
for i in np.unique(clusteridxs):
x, y = points[clusteridxs == i].transpose()
plt.scatter(x, y)
plt.savefig('testclustering_2.pdf', bbox_inches='tight')
plt.close()
