Possibilistic Fuzzy c-Means Clustering python代码
时间: 2024-05-06 22:16:28 浏览: 8
以下是使用Python实现的Possibilistic Fuzzy c-Means聚类算法:
```python
import numpy as np
from scipy.spatial.distance import cdist
def possibilistic_fuzzy_cmeans(X, c, m=2, epsilon=1e-5, max_iter=100):
n_samples, n_features = X.shape
# 随机初始化隶属度矩阵U
U = np.random.rand(n_samples, c)
U = U / np.tile(np.sum(U, axis=1)[np.newaxis].T, (1, c))
# 迭代更新隶属度矩阵U和聚类中心V
iteration = 0
while iteration < max_iter:
# 更新聚类中心V
V = np.dot(U.T, X) / np.sum(U, axis=0)[:, np.newaxis]
# 计算距离矩阵D
D = cdist(X, V)
# 更新隶属度矩阵U
U_new = np.zeros((n_samples, c))
for i in range(n_samples):
for j in range(c):
if np.sum((D[i, :] / D[i, j]) ** (2 / (m - 1))) == 0:
U_new[i, j] = 1
else:
U_new[i, j] = 1 / np.sum((D[i, :] / D[i, j]) ** (2 / (m - 1)))
U_new = U_new / np.tile(np.sum(U_new, axis=1)[np.newaxis].T, (1, c))
# 判断是否收敛
if np.linalg.norm(U - U_new) < epsilon:
break
else:
U = U_new
iteration += 1
# 返回聚类结果和聚类中心
labels = np.argmax(U, axis=1)
return labels, V
```
其中,X是输入的数据矩阵,c是聚类的类别数,m是模糊指数,epsilon是收敛阈值,max_iter是最大迭代次数。函数返回聚类结果labels和聚类中心V。