DPK-means算法 python
时间: 2023-11-07 14:12:30 浏览: 36
以下是DPK-means算法的Python代码实现:
```python
import numpy as np
def dist(a, b):
return np.linalg.norm(a - b)
def DPKMeans(data, k, p, q, max_iter=100):
n = len(data)
# 初始化聚类中心
centers = np.zeros((k, data.shape[1]))
centers[0] = data[np.random.choice(n)]
for i in range(1, k):
distances = np.zeros(n)
for j in range(n):
distances[j] = np.min([dist(data[j], centers[m]) for m in range(i)])
prob = distances**p / np.sum(distances**p)
idx = np.random.choice(n, p=prob)
centers[i] = data[idx]
# 迭代聚类
labels = np.zeros(n)
for iter in range(max_iter):
for i in range(n):
distances = [dist(data[i], centers[j])**q for j in range(k)]
labels[i] = np.argmin(distances)
for j in range(k):
members = data[labels == j]
if len(members) > 0:
new_center = np.mean(members, axis=0)
distances = [dist(members[i], new_center)**q for i in range(len(members))]
sse = np.sum(distances)
if sse < p:
centers[j] = new_center
return labels
```
其中,`data`是输入的数据集,`k`是需要聚类的簇的数量,`p`和`q`是算法的参数,`max_iter`是最大迭代次数。函数返回每个数据点的聚类标签。
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