Revision

**291ae6c7dbfcbded27c604f136982a5067d14b8e**authored by thevincentadam on**20 January 2020, 12:17:20 UTC**, committed by thevincentadam on**20 January 2020, 12:17:20 UTC****1 parent**5dc31b8

cross_kernels.py

```
import tensorflow as tf
from . import dispatch
from .. import kernels
from ..inducing_variables import InducingPoints
from ..probability_distributions import DiagonalGaussian, Gaussian
from .expectations import expectation
@dispatch.expectation.register((Gaussian, DiagonalGaussian), kernels.SquaredExponential,
InducingPoints, kernels.Linear, InducingPoints)
def _E(p, sqexp_kern, feat1, lin_kern, feat2, nghp=None):
"""
Compute the expectation:
expectation[n] = <Ka_{Z1, x_n} Kb_{x_n, Z2}>_p(x_n)
- K_lin_{.,.} :: SqExp kernel
- K_sqexp_{.,.} :: Linear kernel
Different Z1 and Z2 are handled if p is diagonal and K_lin and K_sqexp have disjoint
active_dims, in which case the joint expectations simplify into a product of expectations
:return: NxM1xM2
"""
if sqexp_kern.on_separate_dims(lin_kern) and isinstance(
p, DiagonalGaussian): # no joint expectations required
eKxz1 = expectation(p, (sqexp_kern, feat1))
eKxz2 = expectation(p, (lin_kern, feat2))
return eKxz1[:, :, None] * eKxz2[:, None, :]
if feat1 != feat2:
raise NotImplementedError(
"inducing_variables have to be the same for both kernels.")
if sqexp_kern.active_dims != lin_kern.active_dims:
raise NotImplementedError(
"active_dims have to be the same for both kernels.")
# use only active dimensions
Xcov = sqexp_kern.slice_cov(
tf.linalg.diag(p.cov) if isinstance(p, DiagonalGaussian) else p.cov)
Z, Xmu = sqexp_kern.slice(feat1.Z, p.mu)
N = tf.shape(Xmu)[0]
D = tf.shape(Xmu)[1]
def take_with_ard(value):
if not sqexp_kern.ard:
return tf.zeros((D, ), dtype=value.dtype) + value
return value
lin_kern_variances = take_with_ard(lin_kern.variance)
sqexp_kern_lengthscale = take_with_ard(sqexp_kern.lengthscale)
chol_L_plus_Xcov = tf.linalg.cholesky(
tf.linalg.diag(sqexp_kern_lengthscale**2) + Xcov) # NxDxD
Z_transpose = tf.transpose(Z)
all_diffs = Z_transpose - tf.expand_dims(Xmu, 2) # NxDxM
exponent_mahalanobis = tf.linalg.triangular_solve(chol_L_plus_Xcov,
all_diffs,
lower=True) # NxDxM
exponent_mahalanobis = tf.reduce_sum(tf.square(exponent_mahalanobis),
1) # NxM
exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis) # NxM
sqrt_det_L = tf.reduce_prod(sqexp_kern_lengthscale)
sqrt_det_L_plus_Xcov = tf.exp(
tf.reduce_sum(tf.math.log(tf.linalg.diag_part(chol_L_plus_Xcov)),
axis=1))
determinants = sqrt_det_L / sqrt_det_L_plus_Xcov # N
eKxz_sqexp = sqexp_kern.variance * (
determinants[:, None] * exponent_mahalanobis
) ## NxM <- End RBF eKxz code
tiled_Z = tf.tile(tf.expand_dims(Z_transpose, 0), (N, 1, 1)) # NxDxM
z_L_inv_Xcov = tf.linalg.matmul(tiled_Z,
Xcov / sqexp_kern_lengthscale[:, None]**2.,
transpose_a=True) # NxMxD
cross_eKzxKxz = tf.linalg.cholesky_solve(
chol_L_plus_Xcov,
(lin_kern_variances * sqexp_kern_lengthscale**2.)[..., None] *
tiled_Z) # NxDxM
cross_eKzxKxz = tf.linalg.matmul(
(z_L_inv_Xcov + Xmu[:, None, :]) * eKxz_sqexp[..., None],
cross_eKzxKxz) # NxMxM
return cross_eKzxKxz
@dispatch.expectation.register((Gaussian, DiagonalGaussian), kernels.Linear,
InducingPoints, kernels.SquaredExponential, InducingPoints)
def _E(p, lin_kern, feat1, sqexp_kern, feat2, nghp=None):
"""
Compute the expectation:
expectation[n] = <Ka_{Z1, x_n} Kb_{x_n, Z2}>_p(x_n)
- K_lin_{.,.} :: Linear kernel
- K_sqexp_{.,.} :: sqexp kernel
Different Z1 and Z2 are handled if p is diagonal and K_lin and K_sqexp have disjoint
active_dims, in which case the joint expectations simplify into a product of expectations
:return: NxM1xM2
"""
return tf.linalg.adjoint(
expectation(p, (sqexp_kern, feat2), (lin_kern, feat1)))
```

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