b9d42d6154f5f98696a67097cc93dac027398d76,GPflow/sgpr.py,SGPR,build_predict,#SGPR#,73

Before Change

        Kuu = self.kern.K(self.Z) + eye(num_inducing) * 1e-6
        Kus = self.kern.K(self.Z, Xnew)
        L = tf.cholesky(Kuu)
        A = tf.matrix_triangular_solve(L, Kuf, lower=True)*tf.sqrt(1./self.likelihood.variance)
        B = tf.matmul(A, tf.transpose(A)) + 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)
        tmp1 = tf.matrix_triangular_solve(L, Kus, lower=True)
        tmp2 = tf.matrix_triangular_solve(LB, tmp1, lower=True)
        mean = tf.matmul(tf.transpose(tmp2), c)
        if full_cov:
            var = self.kern.K(Xnew) + tf.matmul(tf.transpose(tmp2), tmp2) - tf.matmul(tf.transpose(tmp1), tmp1)
            var = tf.tile(tf.expand_dims(var, 2), tf.pack([1,1, tf.shape(self.Y)[1]]))
        else:
            var = self.kern.Kdiag(Xnew) + tf.reduce_sum(tf.square(tmp2), 0) - tf.reduce_sum(tf.square(tmp1), 0)

After Change

        Kuf = self.kern.K(self.Z, self.X)
        Kuu = self.kern.K(self.Z) + eye(num_inducing) * 1e-6
        Kus = self.kern.K(self.Z, Xnew)
        sigma = tf.sqrt(self.likelihood.variance)
        L = tf.cholesky(Kuu)
        A = tf.matrix_triangular_solve(L, Kuf, lower=True) / sigma
        B = tf.matmul(A, tf.transpose(A)) + eye(num_inducing)
        LB = tf.cholesky(B)
        Aerr = tf.matmul(A, err)
        c = tf.matrix_triangular_solve(LB, Aerr, lower=True) / sigma
        tmp1 = tf.matrix_triangular_solve(L, Kus, lower=True)
        tmp2 = tf.matrix_triangular_solve(LB, tmp1, lower=True)
        mean = tf.matmul(tf.transpose(tmp2), c)
        if full_cov:
            var = self.kern.K(Xnew) + tf.matmul(tf.transpose(tmp2), tmp2)\
                - tf.matmul(tf.transpose(tmp1), tmp1)
            shape = tf.pack([1, 1, tf.shape(self.Y)[1]])
            var = tf.tile(tf.expand_dims(var, 2), shape)

Instances