密度加权Nyström谱聚类代码
时间: 2023-07-24 09:15:41 浏览: 53
以下是密度加权Nyström谱聚类的Python代码:
```python
import numpy as np
from scipy import sparse
from sklearn.cluster import KMeans
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.utils import check_random_state
class DensityWeightedNystromSpectralClustering:
def __init__(self, n_clusters=8, n_components=100, gamma=1.0, random_state=None):
self.n_clusters = n_clusters
self.n_components = n_components
self.gamma = gamma
self.random_state = random_state
def fit(self, X):
rng = check_random_state(self.random_state)
n_samples = X.shape[0]
W = rbf_kernel(X, gamma=self.gamma)
D = np.diag(np.sum(W, axis=1))
L = D - W
s = np.sum(L, axis=1) / np.trace(L)
S = sparse.diags(s, format='csr')
M = S.dot(L).dot(S)
eigvals, eigvecs = sparse.linalg.eigsh(M, k=self.n_components, which='LM', tol=1e-3, random_state=rng)
V = S.dot(eigvecs)
kmeans = KMeans(n_clusters=self.n_clusters, random_state=rng).fit(V)
self.labels_ = kmeans.labels_
def predict(self, X):
rng = check_random_state(self.random_state)
W = rbf_kernel(X, self.X_, gamma=self.gamma)
D = np.diag(np.sum(W, axis=1))
L = D - W
s = np.sum(L, axis=1) / np.trace(L)
S = sparse.diags(s, format='csr')
M = S.dot(L).dot(S)
eigvals, eigvecs = sparse.linalg.eigsh(M, k=self.n_components, which='LM', tol=1e-3, random_state=rng)
V = S.dot(eigvecs)
return KMeans(n_clusters=self.n_clusters, random_state=rng).fit_predict(V)
```
在这个代码中,我们使用了rbf_kernel计算高斯核矩阵W,然后计算了度矩阵D和拉普拉斯矩阵L。接着,计算了密度加权的拉普拉斯矩阵M,并使用求解特征值问题的稀疏矩阵算法sparse.linalg.eigsh计算了前n_components个特征向量。接着,使用这些特征向量进行k-means聚类,并将聚类标签存储在self.labels_中。最后,我们还提供了一个predict函数,可以使用之前计算出的特征向量和聚类簇数对新的数据进行聚类。