swh:1:snp:00bd25be6ef59fb8210ef6a4c842086902e04451
Tip revision: f3c511060ce62072a34a50319237d28b82d10448 authored by Vincent Dutordoir on 27 August 2020, 11:22:49 UTC
Release 2.1.0 (#1552)
Release 2.1.0 (#1552)
Tip revision: f3c5110
products.py
from functools import reduce
import tensorflow as tf
from .. import kernels
from ..inducing_variables import InducingPoints
from ..probability_distributions import DiagonalGaussian
from . import dispatch
from .expectations import expectation
NoneType = type(None)
@dispatch.expectation.register(DiagonalGaussian, kernels.Product, NoneType, NoneType, NoneType)
def _E(p, kernel, _, __, ___, nghp=None):
r"""
Compute the expectation:
<\HadamardProd_i diag(Ki_{X[:, active_dims_i], X[:, active_dims_i]})>_p(X)
- \HadamardProd_i Ki_{.,.} :: Product kernel
- p :: DiagonalGaussian distribution (p.cov NxD)
:return: N
"""
if not kernel.on_separate_dimensions:
raise NotImplementedError(
"Product currently needs to be defined on separate dimensions."
) # pragma: no cover
exps = [expectation(p, k, nghp=nghp) for k in kernel.kernels]
return reduce(tf.multiply, exps)
@dispatch.expectation.register(
DiagonalGaussian, kernels.Product, InducingPoints, NoneType, NoneType
)
def _E(p, kernel, inducing_variable, __, ___, nghp=None):
r"""
Compute the expectation:
<\HadamardProd_i Ki_{X[:, active_dims_i], Z[:, active_dims_i]}>_p(X)
- \HadamardProd_i Ki_{.,.} :: Product kernel
- p :: DiagonalGaussian distribution (p.cov NxD)
:return: NxM
"""
if not kernel.on_separate_dimensions:
raise NotImplementedError(
"Product currently needs to be defined on separate dimensions."
) # pragma: no cover
exps = [expectation(p, (k, inducing_variable), nghp=nghp) for k in kernel.kernels]
return reduce(tf.multiply, exps)
@dispatch.expectation.register(
DiagonalGaussian, kernels.Product, InducingPoints, kernels.Product, InducingPoints
)
def _E(p, kern1, feat1, kern2, feat2, nghp=None):
r"""
Compute the expectation:
expectation[n] = < prodK_{Z, x_n} prodK_{x_n, Z} >_p(x_n)
= < (\HadamardProd_i Ki_{Z[:, active_dims_i], x[n, active_dims_i]}) <-- Mx1
1xM --> (\HadamardProd_j Kj_{x[n, active_dims_j], Z[:, active_dims_j]}) >_p(x_n) (MxM)
- \HadamardProd_i Ki_{.,.}, \HadamardProd_j Kj_{.,.} :: Product kernels
- p :: DiagonalGaussian distribution (p.cov NxD)
:return: NxMxM
"""
if feat1 != feat2:
raise NotImplementedError("Different inducing variables are not supported.")
if kern1 != kern2:
raise NotImplementedError(
"Calculating the expectation over two " "different Product kernels is not supported."
)
kernel = kern1
inducing_variable = feat1
if not kernel.on_separate_dimensions:
raise NotImplementedError(
"Product currently needs to be defined on separate dimensions."
) # pragma: no cover
exps = [
expectation(p, (k, inducing_variable), (k, inducing_variable), nghp=nghp)
for k in kernel.kernels
]
return reduce(tf.multiply, exps)