pep_models.py 文件源码

def compute_cavity(self, idxs, alpha):
        # deletion
        p_i = self.KuuinvKuf[:, idxs].T[:, np.newaxis, :]
        k_i = self.Kfu[idxs, :]
        k_ii = self.Kff_diag[idxs][:, np.newaxis]
        gamma = self.gamma
        beta = self.beta
        h_si = p_i - np.einsum('dab,nb->nda', beta, k_i)
        variance_i = self.variances[idxs, :]
        mean_i = self.means[idxs, :]
        dlogZd_dmi2 = 1.0 / (variance_i/alpha - 
            np.sum(k_i[:, np.newaxis, :] * h_si, axis=2))
        dlogZd_dmi = -dlogZd_dmi2 * (mean_i - 
            np.sum(k_i[:, np.newaxis, :] * gamma, axis=2))
        hd1 = h_si * dlogZd_dmi[:, :, np.newaxis]
        hd2h = np.einsum('nda,ndb->ndab', h_si, h_si) * dlogZd_dmi2[:, :, np.newaxis, np.newaxis]
        gamma_si = gamma + hd1
        beta_si = beta - hd2h

        # projection
        h = p_i - np.einsum('ndab,nb->nda', beta_si, k_i)
        m_si_i = np.einsum('na,nda->nd', k_i, gamma_si)
        v_si_ii = k_ii - np.einsum('na,ndab,nb->nd', k_i, beta_si, k_i)

        return m_si_i, v_si_ii, [h, beta_si, gamma_si]