Source code for gpflow.utilities.model_utils

from typing import Any, Callable

import tensorflow as tf
from check_shapes import check_shapes

from ..base import TensorType
from ..likelihoods import Gaussian


[docs] def assert_params_false( called_method: Callable[..., Any], **kwargs: bool, ) -> None: """ Asserts that parameters are ``False``. :param called_method: The method or function that is calling this. Used for nice error messages. :param kwargs: Parameters that must be ``False``. :raises NotImplementedError: If any ``kwargs`` are ``True``. """ errors_str = ", ".join(f"{param}={value}" for param, value in kwargs.items() if value) if errors_str: raise NotImplementedError( f"{called_method.__qualname__} does not currently support: {errors_str}" )
[docs] @check_shapes( "K: [batch..., N, N]", "likelihood_variance: [broadcast batch..., broadcast N]", "return: [batch..., N, N]", ) def add_noise_cov(K: tf.Tensor, likelihood_variance: TensorType) -> tf.Tensor: """ Returns K + σ², where σ² is the diagonal likelihood noise variance. """ k_diag = tf.linalg.diag_part(K) return tf.linalg.set_diag(K, k_diag + likelihood_variance)
[docs] @check_shapes( "K: [batch..., N, N]", "X: [batch..., N, D]", "return: [batch..., N, N]", ) def add_likelihood_noise_cov(K: tf.Tensor, likelihood: Gaussian, X: TensorType) -> tf.Tensor: """ Returns K + σ², where σ² is the likelihood noise variance. """ return add_noise_cov(K, tf.squeeze(likelihood.variance_at(X), axis=-1))