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# Copyright (c) 2022, Manfred Moitzi
# License: MIT License
import pytest
from ezdxf.math import Vec3
from ezdxf.math.clustering import k_means, average_cluster_radius, average_intra_cluster_distance
POINTS = [Vec3.random(100) for _ in range(100)]
@pytest.mark.parametrize("k", [4, 5, 6])
def test_cluster_random_points(k):
clusters = k_means(POINTS, k, max_iter=5)
assert len(clusters) == k
assert sum(map(len, clusters)) == 100
def test_measure_average_cluster_radius():
clusters = k_means(POINTS, 5, max_iter=5)
assert average_cluster_radius(clusters) > 10.0
def test_measure_average_intra_cluster_distance():
clusters = k_means(POINTS, 5, max_iter=5)
assert average_intra_cluster_distance(clusters) > 10.0
if __name__ == "__main__":
pytest.main([__file__])
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