def _epComputeParams(self, K, y, ttau, tnu, likfunc, m, inffunc):
        n     = len(y)                                                # number of training cases
        ssi   = np.sqrt(ttau)                                         # compute Sigma and mu
        #L     = np.linalg.cholesky(np.eye(n)+np.dot(ssi,ssi.T)*K).T   # L'*L=B=eye(n)+sW*K*sW
        L     = jitchol(np.eye(n)+np.dot(ssi,ssi.T)*K).T   # L'*L=B=eye(n)+sW*K*sW
        V     = np.linalg.solve(L.T,np.tile(ssi,(1,n))*K)
        Sigma = K - np.dot(V.T,V)
        mu    = np.dot(Sigma,tnu)
        Dsigma = np.reshape(np.diag(Sigma),(np.diag(Sigma).shape[0],1))
        tau_n = ttau - old_div(1,Dsigma)               # compute the log marginal likelihood
        nu_n  = old_div(mu,Dsigma)-tnu + m*tau_n       # vectors of cavity parameters
        lZ    = likfunc.evaluate(y, old_div(nu_n,tau_n), old_div(1,tau_n), inffunc)
        nlZ   = np.log(np.diag(L)).sum() - lZ.sum() - old_div(np.dot(tnu.T,np.dot(Sigma,tnu)),2)  \
                - old_div(np.dot((nu_n-m*tau_n).T,(old_div((ttau/tau_n*(nu_n-m*tau_n)-2*tnu), (ttau+tau_n)))),2) \
                + old_div((old_div(tnu**2,(tau_n+ttau))).sum(),2.)- old_div(np.log(1.+old_div(ttau,tau_n)).sum(),2.)
        return Sigma, mu, nlZ[0], L
dataset/ETHPy150Open marionmari/pyGPs/pyGPs/Core/inf.py/Inference._epComputeParams/swapped operands of `-`