Source code for gpflow.inducing_variables.inducing_variables

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import abc
from typing import Optional

import tensorflow as tf
import tensorflow_probability as tfp
from deprecated import deprecated

from ..base import Module, Parameter, TensorData, TensorType
from ..utilities import positive


[docs]class InducingVariables(Module): """ Abstract base class for inducing variables. """ @property @abc.abstractmethod def num_inducing(self) -> tf.Tensor: """ Returns the number of inducing variables, relevant for example to determine the size of the variational distribution. """ raise NotImplementedError @deprecated( reason="len(iv) should return an `int`, but this actually returns a `tf.Tensor`." " Use `iv.num_inducing` instead." ) def __len__(self) -> tf.Tensor: return self.num_inducing
[docs]class InducingPointsBase(InducingVariables): def __init__(self, Z: TensorData, name: Optional[str] = None): """ :param Z: the initial positions of the inducing points, size [M, D] """ super().__init__(name=name) if not isinstance(Z, (tf.Variable, tfp.util.TransformedVariable)): Z = Parameter(Z) self.Z = Z @property def num_inducing(self) -> Optional[tf.Tensor]: return tf.shape(self.Z)[0]
[docs]class InducingPoints(InducingPointsBase): """ Real-space inducing points """
[docs]class Multiscale(InducingPointsBase): r""" Multi-scale inducing variables Originally proposed in :: @incollection{NIPS2009_3876, title = {Inter-domain Gaussian Processes for Sparse Inference using Inducing Features}, author = {Miguel L\'{a}zaro-Gredilla and An\'{\i}bal Figueiras-Vidal}, booktitle = {Advances in Neural Information Processing Systems 22}, year = {2009}, } """ def __init__(self, Z: TensorData, scales: TensorData): super().__init__(Z) # Multi-scale inducing_variable widths (std. dev. of Gaussian) self.scales = Parameter(scales, transform=positive()) if self.Z.shape != self.scales.shape: raise ValueError( "Input locations `Z` and `scales` must have the same shape." ) # pragma: no cover @staticmethod def _cust_square_dist(A: TensorType, B: TensorType, sc: TensorType) -> tf.Tensor: """ Custom version of _square_dist that allows sc to provide per-datapoint length scales. sc: [N, M, D]. """ return tf.reduce_sum(tf.square((tf.expand_dims(A, 1) - tf.expand_dims(B, 0)) / sc), 2)