另外可以给我dbscan通用的可调参的python代码吗?
时间: 2024-02-11 16:07:47 浏览: 34
当然可以,以下是一个通用的可调参的 Python 实现 DBSCAN 算法的代码:
```python
import numpy as np
from sklearn.neighbors import NearestNeighbors
class DBSCAN:
def __init__(self, eps=0.5, min_samples=5):
self.eps = eps
self.min_samples = min_samples
self.n_clusters = 0
self.labels_ = None
def fit_predict(self, X):
"""
:param X: 数据集,一行代表一个样本
:return: 返回每个样本所属簇的标签
"""
n_samples = X.shape[0]
# 计算距离矩阵
nbrs = NearestNeighbors(n_neighbors=self.min_samples).fit(X)
distances, indices = nbrs.kneighbors(X)
# 初始化核心点集合
core_indices = np.zeros(n_samples, dtype=bool)
core_indices[np.where(distances[:, -1] <= self.eps)[0]] = True
# 初始化簇标签
self.labels_ = np.zeros(n_samples, dtype=int)
# 初始化未访问的样本集合
unvisited = set(range(n_samples))
# 开始聚类
while len(unvisited) > 0:
idx = unvisited.pop()
if not core_indices[idx]:
continue
self.n_clusters += 1
self.labels_[idx] = self.n_clusters
seed_set = set(indices[idx]).intersection(unvisited)
while len(seed_set) > 0:
current_idx = seed_set.pop()
if core_indices[current_idx]:
self.labels_[current_idx] = self.n_clusters
unvisited.discard(current_idx)
new_seeds = set(indices[current_idx]).intersection(unvisited)
seed_set.update(new_seeds)
else:
self.labels_[current_idx] = self.n_clusters
unvisited.discard(current_idx)
# 把未归类的样本标记为噪声点
self.labels_[self.labels_ == 0] = -1
return self.labels_
```
其中,`eps` 和 `min_samples` 是 DBSCAN 算法的两个核心参数,分别表示邻域半径和最小样本数。可以根据具体的数据集进行调整。
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