修正以下代码:def dbscan(SP, DP, r, minPoints): for i in range(len(SP)): neighbor = region_query(DP, SP, i, r) labels = expand_cluster(i, DP, neighbor, minPoints) return labels def region_query(DP, SP, point, r): distances = [] neighbor = [] a, b = SP[point][0], SP[point][1] for i in range(times): x = DP[i][0] y = DP[i][1] distances.append(((x - a) ** 2 + (y - b) ** 2)) for i in distances: if i <= r ** 2: neighbor.append(distances.index(i)) return neighbor def expand_cluster(SPindex, DP, neighbor, minPoints): set = [] while len(set) < minPoints: for i in range(neighbor): set.append([SPindex, DP[i]]) return set DP = [(1, 2), (2, 2), (2, 3), (8, 7), (8, 8), (25, 80)] # 供应点坐标 SP = [[3, 4], [0, 0], [6, 8]] # 设定半径和最小样本点数量 times = len(DP) radius = 3.0 min_points = 2 labels = dbscan(SP, radius, min_points, DP)
时间: 2024-04-12 09:32:18 浏览: 25
修正后的代码如下:
```python
def dbscan(SP, DP, r, minPoints):
labels = [-1] * len(DP)
for i in range(len(SP)):
neighbor = region_query(DP, SP, i, r)
labels = expand_cluster(i, DP, neighbor, minPoints, labels)
return labels
def region_query(DP, SP, point, r):
distances = []
neighbor = []
a, b = SP[point][0], SP[point][1]
for i in range(len(DP)):
x = DP[i][0]
y = DP[i][1]
distances.append(((x - a) ** 2 + (y - b) ** 2))
for i in range(len(distances)):
if distances[i] <= r ** 2:
neighbor.append(i)
return neighbor
def expand_cluster(SPindex, DP, neighbor, minPoints, labels):
if labels[SPindex] != -1:
return labels
labels[SPindex] = 0
cluster = [SPindex]
i = 0
while i < len(cluster):
point = cluster[i]
if len(neighbor[point]) >= minPoints:
for p in neighbor[point]:
if labels[p] == -1:
labels[p] = 0
cluster.append(p)
i += 1
return labels
DP = [(1, 2), (2, 2), (2, 3), (8, 7), (8, 8), (25, 80)]
# 供应点坐标
SP = [[3, 4], [0, 0], [6, 8]]
# 设定半径和最小样本点数量
radius = 3.0
min_points = 2
labels = dbscan(SP, DP, radius, min_points)
print(labels)
```
在修正后的代码中,主要进行了以下修改:
1. 在 `expand_cluster` 函数中,添加了对已经属于某个簇的点的判断,避免重复处理。
2. 在 `expand_cluster` 函数中,修改了簇的扩展方式,使用了一个 `cluster` 列表来记录当前簇中的点,并且通过迭代的方式将相邻的点加入簇中。
3. 在 `dbscan` 函数中,修正了参数传递的错误,将原来的参数顺序调整正确。
最后,我们通过打印 `labels` 变量来输出聚类结果。