我需要DBSCAN原始代码
时间: 2024-02-21 16:59:04 浏览: 20
以下是 Python 代码实现 DBSCAN 算法的基本版本:
```python
import numpy as np
from sklearn.metrics.pairwise import euclidean_distances
class DBSCAN:
def __init__(self, eps, min_samples):
self.eps = eps
self.min_samples = min_samples
def fit(self, X):
self.labels_ = np.zeros(len(X))
self.cluster_id_ = 0
for i in range(len(X)):
if self.labels_[i] != 0:
continue
neighbors = self.get_neighbors(X, i)
if len(neighbors) < self.min_samples:
self.labels_[i] = -1
continue
self.cluster_id_ += 1
self.expand_cluster(X, i, neighbors)
return self.labels_
def expand_cluster(self, X, i, neighbors):
self.labels_[i] = self.cluster_id_
while len(neighbors) > 0:
j = neighbors.pop()
if self.labels_[j] == -1:
self.labels_[j] = self.cluster_id_
elif self.labels_[j] == 0:
self.labels_[j] = self.cluster_id_
new_neighbors = self.get_neighbors(X, j)
if len(new_neighbors) >= self.min_samples:
neighbors.extend(new_neighbors)
def get_neighbors(self, X, i):
neighbors = []
for j in range(len(X)):
if i == j:
continue
if euclidean_distances(X[i:i+1], X[j:j+1])[0][0] < self.eps:
neighbors.append(j)
return neighbors
```
使用方法:
```python
import pandas as pd
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
# 生成数据
X, y = make_blobs(n_samples=1000, centers=3, random_state=42)
# 数据可视化
plt.scatter(X[:, 0], X[:, 1], s=10)
plt.show()
# 使用 DBSCAN 聚类
dbscan = DBSCAN(eps=1, min_samples=5)
labels = dbscan.fit(X)
# 聚类结果可视化
plt.scatter(X[:, 0], X[:, 1], c=labels, s=10)
plt.show()
```
注:以上代码仅为基本版本,实际应用中可能需要对代码进行优化和改进。
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