def main():
# 生成模拟数据
db_size = 64
dim = 3
leaf_size = 4
k = 8
db_np = np.random.rand(db_size, dim)
root = kdtree_construction(db_np, leaf_size=leaf_size)
# 测试Kd-Tree遍历
depth = [0]
max_depth = [0]
traverse_kdtree(root, depth, max_depth)
print("Tree max depth: %d" % max_depth[0])
# 测试KNN search
query = np.asarray([0, 0, 0])
result_set = KNNResultSet(capacity=k)
kdtree_knn_search(root, db_np, result_set, query)
print(result_set)
# 测试brute-force法
diff = np.linalg.norm(np.expand_dims(query, 0) - db_np, axis=1)
nn_idx = np.argsort(diff)
nn_dist = diff[nn_idx]
print(nn_idx[0:k])
print(nn_dist[0:k])
# 测试Radius search
result_set = RadiusNNResultSet(radius=0.5)
kdtree_radius_search(root, db_np, result_set, query)
print(result_set)
def test_search():
# Data generation
db_size = 100
k = 5 #搜寻5个点
radius = 2.0
query_key = 6 # 查找点
data = np.random.permutation(db_size).tolist() #random.permutation 随机排列一个数组
root =None
for i,point in enumerate(data): #返回序号和数据
root = insert(root,point,i)
# result_set = KNNResultSet(capacity=k)
# knn_search(root, result_set, query_key)
# print('kNN Search:')
# print('index - distance')
# print(result_set)
result_set = RadiusNNResultSet(radius=radius)
radius_search(root, result_set, query_key)
print('Radius NN Search:')
print('index - distance')
print(result_set)
