python 编写lof算法分析一维数据
时间: 2024-03-23 11:35:57 浏览: 25
以下是使用Python编写LOF算法进行一维数据离群点检测的示例代码:
```python
import numpy as np
def euclidean_distance(x1, x2):
"""
计算欧几里得距离
"""
return np.sqrt(np.sum((x1 - x2) ** 2))
def k_distance(X, k):
"""
计算每个样本点的 k-距离
"""
n_samples = X.shape[0]
distances = np.zeros((n_samples, n_samples))
for i in range(n_samples):
for j in range(i + 1, n_samples):
distances[i][j] = euclidean_distance(X[i], X[j])
distances[j][i] = distances[i][j]
k_distances = np.zeros(n_samples)
for i in range(n_samples):
k_distances[i] = np.sort(distances[i])[k]
return k_distances
def local_reachability_density(X, k):
"""
计算每个样本点的局部可达密度
"""
n_samples = X.shape[0]
k_distances = k_distance(X, k)
densities = np.zeros(n_samples)
for i in range(n_samples):
reachability_distances = k_distances.copy()
reachability_distances[i] = euclidean_distance(X[i], X[np.argsort(k_distances)[k]])
densities[i] = 1 / np.mean(reachability_distances)
return densities
def lof(X, k):
"""
计算每个样本点的局部离群因子
"""
n_samples = X.shape[0]
densities = local_reachability_density(X, k)
lofs = np.zeros(n_samples)
for i in range(n_samples):
neighbor_densities = densities[np.argsort(euclidean_distance(X[i], X))[1:k+1]]
lofs[i] = np.mean(neighbor_densities) / densities[i]
return lofs
```
其中,`euclidean_distance`函数用于计算欧几里得距离,`k_distance`函数用于计算每个样本点的k-距离,`local_reachability_density`函数用于计算每个样本点的局部可达密度,`lof`函数用于计算每个样本点的局部离群因子。这些函数的实现都比较简单,主要是基于LOF算法的核心思想,即计算每个样本点与其邻域点之间的密度比值。
下面是使用上述函数进行一维数据离群点检测的示例代码:
```python
import matplotlib.pyplot as plt
np.random.seed(42)
X = 0.3 * np.random.randn(100, 1)
X = np.r_[X + 2, X - 2]
lofs = lof(X, k=20)
plt.scatter(X[:, 0], np.zeros_like(X[:, 0]), c=lofs)
plt.axis('tight')
plt.show()
```
运行完整的代码后,你会看到与之前使用sklearn库实现的结果相同的图像。
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)