def test_search_sub_tree():
capacity = 2
samples = np.array([[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]])
ls = np.array(list(range(6)))
kd_tree = KDTree.construct_kd_tree(samples, ls)
knn_heap = KDTree.KnnHeap(capacity)
KDTree.search_sub_tree((0, 0), kd_tree, knn_heap)
print(knn_heap.knns)
def test_knn_heap():
capacity = 9
knn_heap = KDTree.KnnHeap(capacity)
print(knn_heap.get_max_dist_sq())
knn_heap.update_value(1, None, None)
knn_heap.update_value(2, None, None)
knn_heap.update_value(9, None, None)
knn_heap.update_value(8, None, None)
print(knn_heap.get_max_dist_sq())
knn_heap.update_value(1, None, None)
knn_heap.update_value(2, None, None)
knn_heap.update_value(11, None, None)
knn_heap.update_value(20, None, None)
knn_heap.update_value(111, None, None)
knn_heap.update_value(22, None, None)
print(knn_heap.knns)
knn_heap.update_value(101, None, None)
print(knn_heap.knns)
knn_heap.update_value(181, None, None)
print(knn_heap.knns)
knn_heap.update_value(1, None, None)
print(knn_heap.knns)
print(knn_heap.get_max_dist_sq())
