参考https://stats.stackexchange.com/questions/15798/how-to-calculate-a-gaussian-kernel-effectively-in-numpy,提供了计算预计算内核矩阵的解决方案。
from scipy.spatial.distance import pdist, squareform
X = loaddata() # this is an NxD matrix, where N is number of items and D its dimensions
pairwise_dists = squareform(pdist(X, 'euclidean'))
K = scip.exp(pairwise_dists / s**2)
如果输入是有向图的加权邻接矩阵,如何实现上述 Guassin 内核?