点密度加权模糊C均值聚类 Python代码
时间: 2023-10-24 19:05:38 浏览: 39
以下是点密度加权模糊C均值聚类的Python代码实现:
```python
import numpy as np
def distance(p1, p2):
return np.sqrt(np.sum(np.square(p1 - p2)))
def update_centers(X, U, m):
C = []
for j in range(m):
numerator = 0
denominator = 0
for i in range(len(X)):
numerator += (U[i][j] ** 2) * X[i]
denominator += U[i][j] ** 2
C.append(numerator / denominator)
return C
def update_memberships(X, C, m, p):
U = np.zeros((len(X), m))
for i in range(len(X)):
for j in range(m):
dist = distance(X[i], C[j])
if dist == 0:
U[i][j] = 1
else:
numerator = dist ** (2 / (p - 1))
denominator = 0
for k in range(m):
denominator += (distance(X[i], C[k]) ** (2 / (p - 1)))
U[i][j] = 1 / (numerator / denominator)
return U
def density_weights(X):
D = []
for i in range(len(X)):
density = 0
for j in range(len(X)):
if i != j:
density += 1 / distance(X[i], X[j])
D.append(density)
return D
def pdw_fcm(X, m, p, max_iter=100, error=1e-5):
D = density_weights(X)
U = np.random.rand(len(X), m)
U = U / np.sum(U, axis=1)[:, None]
err = 1e10
iter = 0
while err > error and iter < max_iter:
C = update_centers(X, U, m)
U_new = update_memberships(X, C, m, p)
D_new = density_weights(X)
U_new = U_new * (D_new / D)[:, None]
U_new = U_new / np.sum(U_new, axis=1)[:, None]
err = np.linalg.norm(U_new - U)
U = U_new
iter += 1
return U, C
```
其中,`X`是数据点的集合,`m`是聚类的个数,`p`是模糊指数,`max_iter`是最大迭代次数,`error`是聚类误差。函数返回聚类结果的隶属度矩阵`U`和聚类中心`C`。