使用Python编写DBSCAN算法代码
时间: 2023-11-05 09:59:24 浏览: 47
DBSCAN(Density-Based Spatial Clustering of Applications with Noise)是一种基于密度的聚类算法,它可以自动确定簇的数量。下面是Python实现DBSCAN算法的代码:
```python
import numpy as np
class DBSCAN:
def __init__(self, eps=0.5, min_samples=5):
self.eps = eps
self.min_samples = min_samples
def fit(self, X):
self.labels_ = np.zeros(len(X), dtype=int)
cluster_id = 0
for i in range(len(X)):
if self.labels_[i] != 0:
continue
neighbors = self.region_query(X, i)
if len(neighbors) < self.min_samples:
self.labels_[i] = -1
else:
cluster_id += 1
self.expand_cluster(X, i, neighbors, cluster_id)
def expand_cluster(self, X, i, neighbors, cluster_id):
self.labels_[i] = cluster_id
j = 0
while j < len(neighbors):
n = neighbors[j]
if self.labels_[n] == -1:
self.labels_[n] = cluster_id
elif self.labels_[n] == 0:
self.labels_[n] = cluster_id
new_neighbors = self.region_query(X, n)
if len(new_neighbors) >= self.min_samples:
neighbors = np.concatenate((neighbors, new_neighbors))
j += 1
def region_query(self, X, i):
neighbors = []
for j in range(len(X)):
if np.linalg.norm(X[i] - X[j]) <= self.eps:
neighbors.append(j)
return np.array(neighbors)
```
该代码中,`__init__`函数初始化了DBSCAN算法的两个参数:`eps`和`min_samples`。`eps`是指定领域半径的参数,`min_samples`是指定一个点要成为核心点所需要的最小邻居数。
`fit`函数是DBSCAN算法的核心函数,它首先初始化每个点的标签为0,然后遍历每个点。如果该点已经被标记为所属簇,就跳过。否则,找到该点的所有邻居点,如果邻居点的个数小于最小邻居数,就将该点标记为噪声点,否则,将该点标记为当前簇的编号,然后扩展该簇,继续找到该点的邻居点,如果邻居点的个数大于等于最小邻居数,就将邻居点加入该簇。
`expand_cluster`函数是扩展簇的函数,它从一个核心点开始,逐步扩展该簇,直到没有新的点可以加入为止。
`region_query`函数是查找邻居点的函数,它根据领域半径eps找到所有距离该点小于等于eps的点。