写一个dbscan的算法代码
时间: 2024-05-12 12:16:07 浏览: 14
好的,这是 dbscan 算法的 Python 代码:
```
def dbscan(points, eps, minpts):
# 初始化所有点为未分类
labels = [None] * len(points)
# 初始化核心点集合为空
core_samples = set()
# 找到所有核心点和相应的邻居
for i in range(len(points)):
point = points[i]
neighbors = get_neighbors(points, point, eps)
if len(neighbors) >= minpts:
labels[i] = 0
core_samples.add(i)
for j in neighbors:
if labels[j] == None:
labels[j] = 0
core_samples.add(j)
else:
labels[i] = None
# 开始聚类
cluster = 0
while core_samples:
# 任选一个核心点
current = next(iter(core_samples))
# 初始化一簇
cluster += 1
# 初始化一个队列,用来存储当前簇中的点
queue = [current]
core_samples.remove(current)
# 扩展当前簇中的所有点
while queue:
# 取出队列的首元素
current = queue.pop(0)
# 找到当前点的所有邻居
neighbors = get_neighbors(points, points[current], eps)
# 找到当前点的所有邻居中的核心点
core_neighbors = [i for i in neighbors if i in core_samples]
# 把邻居中的未分类点加入当前簇
for i in [n for n in neighbors if labels[n] == None]:
labels[i] = cluster
queue.append(i)
# 把邻居中的核心点加入当前簇
for i in core_neighbors:
if labels[i] == None:
labels[i] = cluster
queue.append(i)
if i in core_samples:
core_samples.remove(i)
return labels
def get_neighbors(points, point, eps):
neighbors = []
for i in range(len(points)):
if np.linalg.norm(point - points[i]) < eps:
neighbors.append(i)
return neighbors
```
这个代码实现了 dbscan 算法,其中 `points` 是数据点的集合,`eps` 是邻域半径的阈值,`minpts` 是核心点的阈值。算法将数据点聚成若干簇,并且将每个点分配到一个簇当中。