def test_cluster_sigmin_pruning(reach, n_child, members):
# Tests pruning left and right, insignificant splitpoints, empty nodelists
# Parameters chosen specifically for this task
# Case 1: Three pseudo clusters, 2 of which are too small
# Case 2: Two pseudo clusters, 1 of which are too small
# Normalize
reach = reach / np.max(reach[1:])
ordering = np.r_[0:20]
cluster_boundaries = _find_local_maxima(reach, 5)
root = _TreeNode(ordering, 0, 20, None)
# Build cluster tree inplace on root node
_cluster_tree(root, None, cluster_boundaries, reach, ordering, 5, .75, .7,
.4, .3)
assert_equal(root.split_point, cluster_boundaries[0])
assert_equal(n_child, len(root.children))
assert_array_equal(members, root.children[0].points)
def test_cluster_sigmin_pruning(reach, n_child, members):
# Tests pruning left and right, insignificant splitpoints, empty nodelists
# Parameters chosen specifically for this task
# Case 1: Three pseudo clusters, 2 of which are too small
# Case 2: Two pseudo clusters, 1 of which are too small
# Normalize
reach = reach / np.max(reach[1:])
ordering = np.r_[0:20]
cluster_boundaries = _find_local_maxima(reach, 5)
root = _TreeNode(ordering, 0, 20, None)
# Build cluster tree inplace on root node
_cluster_tree(root, None, cluster_boundaries, reach, ordering,
5, .75, .7, .4, .3)
assert_equal(root.split_point, cluster_boundaries[0])
assert_equal(n_child, len(root.children))
assert_array_equal(members, root.children[0].points)