LOF算法的实现代码
时间: 2023-08-04 11:08:13 浏览: 73
以下是Python实现的LOF算法代码:
```python
from sklearn.neighbors import NearestNeighbors
import numpy as np
def LOF(X, k):
n = X.shape[0]
nbrs = NearestNeighbors(n_neighbors=k+1).fit(X)
distances, indices = nbrs.kneighbors(X)
# compute reachability distance
RD = np.zeros((n, k))
for i in range(n):
for j in range(1, k+1):
RD[i, j-1] = max(distances[indices[i], j], distances[indices[indices[i, j]], 1])
# compute local reachability density
LRD = np.zeros(n)
for i in range(n):
LRD[i] = k / np.sum(RD[indices[i], :])
# compute LOF
LOF = np.zeros(n)
for i in range(n):
LOF[i] = np.sum(LRD[indices[i]]) / LRD[i] / k
return LOF
```
其中,`X`为输入数据,`k`为邻居数。函数返回每个样本的LOF值。算法分为三个步骤:
1. 计算每个点与其k个最近邻之间的可达距离(reachability distance)。
2. 计算每个点的局部可达密度(local reachability density)。
3. 计算每个点的LOF值。
参考资料:
[1] Breunig M M, Kriegel H P, Ng R T, et al. LOF: identifying density-based local outliers[J]. ACM sigmod record, 2000, 29(2): 93-104.