Merge pull request #8 from JuliaTrustworthyAI/fix-docstrings

updated project
JuliaTrustworthyAI · Nov 13, 2024 · 063f74e · 063f74e · pat-alt · Nov 13, 2024
2 parents 926f37c + 202ee77
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -4,6 +4,12 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## Version [1.0.2] - 2024-11-13
+
+### Added
+
+- Improved some docstrings.
+
 ## Version [1.0.1] - 2024-10-26
 
 ### Added

diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "EnergySamplers"
 uuid = "f446124b-5d5e-4171-a6dd-a1d99768d3ce"
 authors = ["Patrick Altmeyer and contributors"]
-version = "1.0.1"
+version = "1.0.2"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"

diff --git a/src/utils.jl b/src/utils.jl
@@ -31,7 +31,7 @@ end
 @doc raw"""
     _energy(f, x, y::Int; agg=mean)
 
-Computes the energy for conditional samples $x \sim p_{\theta}(x|y)$: $E(x)=- f_{\theta}(x)[y]$. $DOC_Grathwohl
+Computes the energy for conditional samples $x \sim p_{\theta}(x|y)$: $E(x)=- f_{\theta}(x)[y]$. Here `f` is the model, `x` is the input and `y` is the **index** of the target label. $DOC_Grathwohl 
 """
 function _energy(f, x, y::Int; agg=mean)
     if f isa Flux.Chain
@@ -52,7 +52,7 @@ end
 @doc raw"""
     energy_differential(f, xgen, xsampled, y::Int; agg=mean)
 
-Computes the energy differential between a conditional sample ``x_{\text{gen}} \sim p_{\theta}(x|y)`` and an observed sample ``x_{\text{sample}} \sim p(x|y)`` as ``E(x_{\text{sample}}|y) - E(x_{\text{gen}}|y)`` with ``E(x|y) = -f_{\theta}(x)[y]``. $DOC_Grathwohl
+Computes the energy differential between a conditional sample ``x_{\text{gen}} \sim p_{\theta}(x|y)`` and an observed sample ``x_{\text{sample}} \sim p(x|y)`` as ``E(x_{\text{sample}}|y) - E(x_{\text{gen}}|y)`` with ``E(x|y) = -f_{\theta}(x)[y]``. Here `f` is the model, `xgen` are the generated samples, `xsampled` are the observed training samples and `y` is the **index** of the target label. $DOC_Grathwohl
 """
 function energy_differential(f, xgen, xsampled, y::Int; agg=mean)
     neg_loss = _energy(f, xgen, y; agg=agg)         # negative loss associated with generated samples
@@ -64,7 +64,7 @@ end
 @doc raw"""
     energy_penalty(f, xgen, xsampled, y::Int; agg=mean)
 
-Computes the a Ridge penalty for the overall energies of the conditional samples ``x_{\text{gen}} \sim p_{\theta}(x|y)`` and an observed sample ``x_{\text{sample}} \sim p(x|y)``. $DOC_Grathwohl
+Computes the a Ridge penalty for the overall energies of the conditional samples ``x_{\text{gen}} \sim p_{\theta}(x|y)`` and an observed sample ``x_{\text{sample}} \sim p(x|y)``. Here `f` is the model, `xgen` are the generated samples, `xsampled` are the observed training samples and `y` is the **index** of the target label. $DOC_Grathwohl
 """
 function energy_penalty(f, xgen, xsampled, y::Int; agg=mean, p=1)
     neg_loss = _energy(f, xgen, y; agg=agg)         # negative loss associated with generated samples