Commit ece8b5f5 authored by Martin Reinecke's avatar Martin Reinecke

Merge branch 'fix_docs7' into 'NIFTy_7'

Fix Sphinx warnings

See merge request !572
parents f5d87cdd 49549b8e
Pipeline #85063 passed with stages
in 28 minutes and 2 seconds
......@@ -224,7 +224,7 @@ Thus, only the gradient of the KL is needed with respect to this, which can be e
We stochastically estimate the KL-divergence and gradients with a set of samples drawn from the approximate posterior distribution.
The particular structure of the covariance allows us to draw independent samples solving a certain system of equations.
This KL-divergence for MGVI is implemented in the class :class:`~minimization.metric_gaussian_kl.MetricGaussianKL` within NIFTy7.
This KL-divergence for MGVI is implemented in the class :class:`~nifty.7minimization.metric_gaussian_kl.MetricGaussianKL` within NIFTy7.
The demo `getting_started_3.py` for example not only infers a field this way, but also the power spectrum of the process that has generated the field.
......
......@@ -41,7 +41,7 @@ def NormalTransform(mean, sigma, key, N_copies=0):
N_copies : integer
If == 0, target will be a scalar field.
If >= 1, target will be an
:class:`~nifty7.unstructured_domain.UnstructuredDomain`.
:class:`~nifty7.domains.unstructured_domain.UnstructuredDomain`.
"""
if N_copies == 0:
domain = DomainTuple.scalar_domain()
......@@ -71,7 +71,7 @@ def LognormalTransform(mean, sigma, key, N_copies):
N_copies : integer
If == 0, target will be a scalar field.
If >= 1, target will be an
:class:`~nifty7.unstructured_domain.UnstructuredDomain`.
:class:`~nifty7.domains.unstructured_domain.UnstructuredDomain`.
"""
logmean, logsigma = lognormal_moments(mean, sigma, N_copies)
return NormalTransform(logmean, logsigma, key, N_copies).ptw("exp")
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