# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
from pyro.distributions.transforms.discrete_cosine import DiscreteCosineTransform
from .unit_jacobian import UnitJacobianReparam
[docs]class DiscreteCosineReparam(UnitJacobianReparam):
"""
Discrete Cosine reparameterizer, using a
:class:`~pyro.distributions.transforms.DiscreteCosineTransform` .
This is useful for sequential models where coupling along a time-like axis
(e.g. a banded precision matrix) introduces long-range correlation. This
reparameterizes to a frequency-domain representation where posterior
covariance should be closer to diagonal, thereby improving the accuracy of
diagonal guides in SVI and improving the effectiveness of a diagonal mass
matrix in HMC.
When reparameterizing variables that are approximately continuous along the
time dimension, set ``smooth=1``. For variables that are approximately
continuously differentiable along the time axis, set ``smooth=2``.
This reparameterization works only for latent variables, not likelihoods.
:param int dim: Dimension along which to transform. Must be negative.
This is an absolute dim counting from the right.
:param float smooth: Smoothing parameter. When 0, this transforms white
noise to white noise; when 1 this transforms Brownian noise to to white
noise; when -1 this transforms violet noise to white noise; etc. Any
real number is allowed. https://en.wikipedia.org/wiki/Colors_of_noise.
:param bool experimental_allow_batch: EXPERIMENTAL allow coupling across a
batch dimension. The targeted batch dimension and all batch dimensions
to the right will be converted to event dimensions. Defaults to False.
"""
def __init__(self, dim=-1, smooth=0.0, *, experimental_allow_batch=False):
transform = DiscreteCosineTransform(dim=dim, smooth=smooth, cache_size=1)
super().__init__(
transform, suffix="dct", experimental_allow_batch=experimental_allow_batch
)