labelpropagation.py 文件源码

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

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

        #self.graph = self.graph + 3000 * sparse.eye(n,n)

        Di = sparse.diags([np.sqrt(1 / (self.graph.sum(axis=0) + nf * np.ones(n))).getA1()], [0])
        S = Di.dot(self.graph.dot(Di))
        S_iter = (sparse.eye(n) - alpha * S).tocsc()

        F = np.zeros((n,c))
        for i in range(c):
            F[:, i], info = slin.cg(S_iter, self.Y[:, i], tol=1e-12)
        toc = time.time()

        #print np.where(F > 0)
        self.ElapsedTime = toc - tick
        self.PredictedProbs = F