Method predict_log_density

gpflow/models/cglb.py:275–293

Compute the log density of the data at the new data points.

(
        self,
        data: RegressionData,
        full_cov: bool = False,
        full_output_cov: bool = False,
        cg_tolerance: Optional[float] = 1e-3,
    )

    @inherit_check_shapes
    def predict_log_density(
        self,
        data: RegressionData,
        full_cov: bool = False,
        full_output_cov: bool = False,
        cg_tolerance: Optional[float] = 1e-3,
    ) -> tf.Tensor:
        """
        Compute the log density of the data at the new data points.
        """
        assert_params_false(
            self.predict_log_density, full_cov=full_cov, full_output_cov=full_output_cov
        )

        x, y = data
        f_mean, f_var = self.predict_f(
            x, full_cov=full_cov, full_output_cov=full_output_cov, cg_tolerance=cg_tolerance
        )
        return self.likelihood.predict_log_density(x, f_mean, f_var, y)


class NystromPreconditioner: