import tensorflow as tf
from ..base import TensorLike
from ..inducing_variables import InducingPoints, Multiscale, InducingPatches
from ..kernels import Kernel, SquaredExponential, Convolutional
from .dispatch import Kuf
@Kuf.register(InducingPoints, Kernel, TensorLike)
def Kuf_kernel_inducingpoints(inducing_variable: InducingPoints, kernel: Kernel, Xnew):
return kernel(inducing_variable.Z, Xnew)
@Kuf.register(Multiscale, SquaredExponential, TensorLike)
def Kuf_sqexp_multiscale(inducing_variable: Multiscale, kernel: SquaredExponential, Xnew):
Xnew, _ = kernel.slice(Xnew, None)
Zmu, Zlen = kernel.slice(inducing_variable.Z, inducing_variable.scales)
idlengthscale = kernel.lengthscale + Zlen
d = inducing_variable._cust_square_dist(Xnew, Zmu, idlengthscale)
lengthscale = tf.reduce_prod(kernel.lengthscale / idlengthscale, 1)
lengthscale = tf.reshape(lengthscale, (1, -1))
return tf.transpose(kernel.variance * tf.exp(-0.5 * d) * lengthscale)
@Kuf.register(InducingPatches, Convolutional, object)
def Kuf_conv_patch(feat, kern, Xnew):
Xp = kern.get_patches(Xnew) # N x num_patches x patch_len
bigKzx = kern.basekern.K(feat.Z, Xp) # [M, N, P] -- thanks to broadcasting of kernels
Kzx = tf.reduce_sum(bigKzx * kern.weights if hasattr(kern, 'weights') else bigKzx, [2])
return Kzx / kern.num_patches