a28284b9bd72fb5f180d489327e6e5571d9aa8d8,gpflow/models/gpr.py,GPR,predict_f,#GPR#Any#Any#Any#,67

Before Change

        X = self.X
        y = self.Y - self.mean_function(X)
        Kmn = self.kernel(X, Xnew)
        S = tf.eye(X.shape[0], dtype=X.dtype) * self.likelihood.variance
        Kmm = self.kernel(X)
        Knn = self.kernel(Xnew, full=full_cov)
        f_mean, f_var = base_conditional(Kmn, Kmm + S, Knn, y, full_cov=full_cov,
                                         white=False)  // [N, P], [N, P] or [P, N, N]
        return f_mean + self.mean_function(Xnew), f_var

After Change

        kmn = self.kernel(x_data, predict_at)

        num_data = x_data.shape[0]
        s = tf.linalg.diag(tf.fill([num_data], self.likelihood.variance))

        conditional = gpflow.conditionals.base_conditional
        f_mean_zero, f_var = conditional(kmn, kmm + s, knn, err, full_cov=full_cov,
                                         white=False)  // [N, P], [N, P] or [P, N, N]

Instances