7d10d024cebd46a97b134d6902def43fba6d9324,pygsp/graphs/community.py,Community,__init__,#Community#Any#Any#Any#Any#Any#Any#Any#,42

Before Change

                                 sizes has to be equal to N")

        if not min_com:
            min_com = round(float(N) / Nc / 3.)

        if not min_deg:
            min_deg = round(min_com/2.)

        if not world_density:
            world_density = 1./N

        // Begining
        if np.shape(com_sizes)[0] == 0:
            x = N - (min_com - 1)*Nc - 1
            com_lims = np.sort(np.resize(np.random.permutation(int(x)), (Nc - 1.))) + 1
            com_lims += np.cumsum((min_com-1)*np.ones(np.shape(com_lims)))
            com_lims = np.concatenate((np.array([0]), com_lims, np.array([N])))
            com_sizes = np.diff(com_lims)

        if False:  // Verbose > 2 ?
            X = np.zeros((10000, Nc + 1))
            // pick randomly param.Nc-1 points to cut the rows in communtities:
            for i in range(10000):
                com_lims_tmp = np.sort(np.resize(np.random.permutation(int(x)),
                                                 (Nc - 1.))) + 1
                com_lims_tmp += np.cumsum((min_com - 1) *
                                          np.ones(np.shape(com_lims_tmp)))
                X[i, :] = np.concatenate((np.array([0]), com_lims_tmp,
                                          np.array([N])))
                // dX = np.diff(X.T).T

After Change

                                 sizes has to be equal to N")

        if not min_com:
            min_com = int(round(float(N) / Nc / 3.))

        if not min_deg:
            min_deg = round(min_com/2.)

        if not world_density:
            world_density = 1./N

        // Begining
        if np.shape(com_sizes)[0] == 0:
            x = N - (min_com - 1)*Nc - 1
            com_lims = np.sort(np.resize(np.random.permutation(int(x)), (Nc - 1.))) + 1
            com_lims += np.cumsum((min_com-1)*np.ones(np.shape(com_lims)))
            com_lims = np.concatenate((np.array([0]), com_lims, np.array([N])))
            com_sizes = np.diff(com_lims)

        if False:  // Verbose > 2 ?
            X = np.zeros((10000, Nc + 1))
            // pick randomly param.Nc-1 points to cut the rows in communtities:
            for i in range(10000):
                com_lims_tmp = np.sort(np.resize(np.random.permutation(int(x)),
                                                 (Nc - 1.))) + 1
                com_lims_tmp += np.cumsum((min_com - 1) *
                                          np.ones(np.shape(com_lims_tmp)))
                X[i, :] = np.concatenate((np.array([0]), com_lims_tmp,
                                          np.array([N])))
                // dX = np.diff(X.T).T

Instances