用python写出基于密度的聚类方法代码
时间: 2023-03-06 22:39:37 浏览: 68
以下是基于密度的聚类方法(Density-Based Spatial Clustering of Applications with Noise, DBSCAN)的Python代码实现:
```python
import numpy as np
from sklearn.neighbors import NearestNeighbors
class DBSCAN:
def __init__(self, eps, min_samples):
self.eps = eps
self.min_samples = min_samples
def fit(self, X):
self.X = X
self.visited = set()
self.labels = np.zeros(X.shape[0], dtype=int)
self.cluster_id = 0
nbrs = NearestNeighbors(n_neighbors=self.min_samples, metric='euclidean').fit(X)
for i in range(X.shape[0]):
if i in self.visited:
continue
self.visited.add(i)
neighbors = nbrs.radius_neighbors([X[i]], radius=self.eps, return_distance=False)[0]
if len(neighbors) < self.min_samples:
self.labels[i] = -1 # 标记为噪声点
else:
self._expand_cluster(i, neighbors)
self.cluster_id += 1
def _expand_cluster(self, point_idx, neighbors):
self.labels[point_idx] = self.cluster_id
i = 0
while i < len(neighbors):
n = neighbors[i]
if n not in self.visited:
self.visited.add(n)
new_neighbors = nbrs.radius_neighbors([self.X[n]], radius=self.eps, return_distance=False)[0]
if len(new_neighbors) >= self.min_samples:
neighbors = np.concatenate((neighbors, new_neighbors))
if self.labels[n] == 0:
self.labels[n] = self.cluster_id
i += 1
```
其中,`eps`是半径参数,用于确定邻域的大小;`min_samples`是最小样本数参数,用于确定核心对象。算法通过`fit()`方法对数据进行聚类,最终返回`labels`数组,表示每个样本所属的簇的编号,如果某个样本被标记为-1,表示它是噪声点。
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