labelpropagation.py 文件源码

def variantWalk(self):
        tick = time.time()

        alpha = self.param['alpha']
        n = self.graph.shape[0]
        c = self.Y.shape[1]
        nf = self.param['normalize_factor']

        data = (self.graph.sum(axis=0) + nf * np.ones(n)).ravel()
        Di = sparse.spdiags(data,0,n,n).tocsc()
        S_iter = (Di - alpha * self.graph).tocsc()

        F = np.zeros((n, c))
        for i in range(c):
            F[:, i], info = slin.cg(S_iter, self.Y[:, i], tol=1e-10)

        toc = time.time()

        self.ElapsedTime = toc - tick
        self.PredictedProbs = F