b996639740adde1bf4b39b278e83c4c295ad65ca,grakel/kernels/random_walk.py,RandomWalk,pairwise_operation,#RandomWalk#Any#Any#,200

Before Change

                S = np.diagflat(S)
            else:
                if self.kernel_type == "geometric":
                    S = np.diagflat(1/(1-self.lamda*Dij))
                elif self.kernel_type == "exponential":
                    S = np.diagflat(np.exp(self.lamda*Dij))

            return ff.dot(S).dot(ff.T)

After Change

            // Conjugate Gradient Method as presented in
            // [Vishwanathan et al., 2006] p.12, s.4.2
            Ax, Ay = X, Y
            xs, ys = Ax.shape[0], Ay.shape[0]
            mn = xs*ys

            def lsf(x, lamda):
                xm = x.reshape((xs, ys), order="F")

Instances