Method predict_f

gpflow/models/model.py:196–225

r""" Compute the mean and variance of the posterior latent function(s) at the input points. Given $x_i$ this computes $f_i$, for: .. math:: :nowrap: \begin{align} \theta & \sim p(\theta) \\ f & \sim \ma

(
        self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False
    )

        "return[1]: [batch..., N, P] if (not full_cov) and (not full_output_cov)",
    )
    def predict_f(
        self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False
    ) -> MeanAndVariance:
        r"""
        Compute the mean and variance of the posterior latent function(s) at the input points.

        Given $x_i$ this computes $f_i$, for:

        .. math::
           :nowrap:

           \begin{align}
               \theta        & \sim p(\theta) \\
               f             & \sim \mathcal{GP}(m(x), k(x, x'; \theta)) \\
               f_i           & = f(x_i) \\
           \end{align}

        For an example of how to use ``predict_f``, see
        :doc:`../../../../notebooks/getting_started/basic_usage`.

        :param Xnew:
            Input locations at which to compute mean and variance.
        :param full_cov:
            If ``True``, compute the full covariance between the inputs.
            If ``False``, only returns the point-wise variance.
        :param full_output_cov:
            If ``True``, compute the full covariance between the outputs.
            If ``False``, assumes outputs are independent.
        """
        raise NotImplementedError

    @check_shapes(
        "Xnew: [batch..., N, D]",