56acbf4e0a09b2a4a3c35c787378c137961076a1,GPy/inference/latent_function_inference/var_gauss.py,VarGauss,inference,#VarGauss#Any#Any#Any#Any#Any#Any#Any#,28

Before Change

            dL_dthetaL = np.array([])
        dF_da = np.dot(K, dF_dm)
        SigmaB = Sigma*self.beta
        dF_db = -np.diag(Sigma.dot(np.diag(dF_dv.flatten())).dot(SigmaB))*2
        KL = 0.5*(Alogdet + np.trace(Ai) - num_data + np.sum(m*self.alpha))
        dKL_da = m
        A_A2 = Ai - Ai.dot(Ai)
        dKL_db = np.diag(np.dot(KB.T, A_A2))

After Change

        dF_da = np.dot(K, dF_dm)
        SigmaB = Sigma*self.beta
        //dF_db_ = -np.diag(Sigma.dot(np.diag(dF_dv.flatten())).dot(SigmaB))*2
        dF_db = -2*np.sum(Sigma**2 * (dF_dv * self.beta), 0)
        //assert np.allclose(dF_db, dF_db_)

        KL = 0.5*(Alogdet + np.trace(Ai) - num_data + np.sum(m*self.alpha))
        dKL_da = m

Instances