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 check_shapes import Shape, check_shapes
from deprecated import deprecated

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


[docs]class InducingVariables(Module, abc.ABC): """ 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 @property @abc.abstractmethod def shape(self) -> Shape: """ Return the shape of these inducing variables. Shape should be some variation of ``[M, D, P]``, where: * ``M`` is the number of inducing variables. * ``D`` is the number of input dimensions. * ``P`` is the number of output dimensions (1 if this is not a multi-output inducing variable). """
[docs]class InducingPointsBase(InducingVariables): @check_shapes( "Z: [M, D]", ) def __init__(self, Z: TensorData, name: Optional[str] = None): """ :param Z: The initial positions of the inducing points. """ super().__init__(name=name) if not isinstance(Z, (tf.Variable, tfp.util.TransformedVariable)): Z = Parameter(Z) self.Z = Z @property # type: ignore[misc] # mypy doesn't like decorated properties. @check_shapes( "return: []", ) def num_inducing(self) -> Optional[tf.Tensor]: return tf.shape(self.Z)[0] @property def shape(self) -> Shape: shape = self.Z.shape if not shape: return None return tuple(shape) + (1,)
[docs]class InducingPoints(InducingPointsBase): """ Real-space inducing points """
[docs]class Multiscale(InducingPointsBase): r""" Multi-scale inducing variables Originally proposed in :cite:t:`NIPS2009_3876`. """ @check_shapes( "Z: [M, D]", "scales: [M, D]", ) 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()) @staticmethod @check_shapes( "A: [N, D]", "B: [M, D]", "sc: [broadcast N, broadcast M, D]", "return: [N, M]", ) 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. """ return tf.reduce_sum(tf.square((tf.expand_dims(A, 1) - tf.expand_dims(B, 0)) / sc), 2)