Method predict_y

gpflow/models/model.py:290–325

r""" Compute the mean and variance of the held-out data at the input points. Given $x_i$ this computes $y_i$, for: .. math:: :nowrap: \begin{align} \theta & \sim p(\theta) \\ f & \sim \mathcal{GP}(m(x),

(
        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_y(
        self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False
    ) -> MeanAndVariance:
        r"""
        Compute the mean and variance of the held-out data at the input points.

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

        .. math::
           :nowrap:

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


        For an example of how to use ``predict_y``, 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.
        """
        # See https://github.com/GPflow/GPflow/issues/1461
        assert_params_false(self.predict_y, full_cov=full_cov, full_output_cov=full_output_cov)

        f_mean, f_var = self.predict_f(Xnew, full_cov=full_cov, full_output_cov=full_output_cov)
        return self.likelihood.predict_mean_and_var(Xnew, f_mean, f_var)

    @check_shapes(
        "data[0]: [batch..., N, D]",