b9d42d6154f5f98696a67097cc93dac027398d76,GPflow/sgpr.py,SGPR,build_likelihood,#SGPR#,39

Before Change

        AAT = tf.matmul(A, tf.transpose(A))
        B = AAT + eye(num_inducing)
        LB = tf.cholesky(B)
        c = tf.matrix_triangular_solve(LB, tf.matmul(A, err), lower=True) * tf.sqrt(1./self.likelihood.variance)

        //compute log marginal bound
        bound = -0.5*tf.cast(num_data*output_dim, tf.float64)*np.log(2*np.pi)

After Change

        AAT = tf.matmul(A, tf.transpose(A))
        B = AAT + eye(num_inducing)
        LB = tf.cholesky(B)
        Aerr = tf.matmul(A, err)
        c = tf.matrix_triangular_solve(LB, Aerr, lower=True) / sigma

        // compute log marginal bound
        bound = -0.5*tf.cast(num_data*output_dim, tf.float64)*np.log(2*np.pi)

Instances