updating docs

Author: zuhengxu

docs/make.jl

@@ -1,15 +1,18 @@
 using NormalizingFlows
 using Documenter
 
+using Random
+using Distributions
+
 DocMeta.setdocmeta!(
     NormalizingFlows, :DocTestSetup, :(using NormalizingFlows); recursive=true
 )
 
 makedocs(;
     modules=[NormalizingFlows],
-    repo="https://github.com/TuringLang/NormalizingFlows.jl/blob/{commit}{path}#{line}",
     sitename="NormalizingFlows.jl",
-    format=Documenter.HTML(),
+    repo="https://github.com/TuringLang/NormalizingFlows.jl/blob/{commit}{path}#{line}",
+    format=Documenter.HTML(; prettyurls=get(ENV, "CI", nothing) == "true"),
     pages=[
         "Home" => "index.md",
         "API" => "api.md",

docs/src/index.md

@@ -4,7 +4,7 @@ CurrentModule = NormalizingFlows
 
 # NormalizingFlows.jl
 
-Documentation for [NormalizingFlows](https://github.com/TuringLang/NormalizingFlows.jl).
+Documentation for [NormalizingFlows.jl](https://github.com/TuringLang/NormalizingFlows.jl).
 
 
 The purpose of this package is to provide a simple and flexible interface for 
@@ -15,8 +15,6 @@ construct (e.g., define customized flow layers) and combine various components
 for variational approximation of general target distributions, 
 *without being tied to specific probabilistic programming frameworks or applications*. 
 
-See the [documentation](https://turinglang.org/NormalizingFlows.jl/dev/) for more.  
-
 ## Installation
 To install the package, run the following command in the Julia REPL:
 ```
@@ -67,7 +65,7 @@ optimization problems:
 \text{Reverse KL:}\quad
 &\argmin _{\theta} \mathbb{E}_{q_{\theta}}\left[\log q_{\theta}(Z)-\log p(Z)\right] \\
 &= \argmin _{\theta} \mathbb{E}_{q_0}\left[\log \frac{q_\theta(T_N\circ \cdots \circ T_1(Z_0))}{p(T_N\circ \cdots \circ T_1(Z_0))}\right] \\
-&= \argmax _{\theta} \mathbb{E}_{q_0}\left[ \log p\left(T_N \circ \cdots \circ T_1(Z_0)\right)-\log q_0(X)+\sum_{n=1}^N \log J_n\left(F_n \circ \cdots \circ F_1(X)\right)\right]
+&= \argmax _{\theta} \mathbb{E}_{q_0}\left[ \log p\left(T_N \circ \cdots \circ T_1(Z_0)\right)-\log q_0(X)+\sum_{n=1}^N \log J_n\left(T_n \circ \cdots \circ T_1(X)\right)\right]
 \end{aligned}
 ```
 and 
@@ -82,4 +80,4 @@ Both problems can be solved via standard stochastic optimization algorithms,
 such as stochastic gradient descent (SGD) and its variants. 
 
 
-
+For a detailed introduction of normalizing flows, please refer to

src/flows/realnvp.jl

@@ -186,12 +186,9 @@ end
 
 Default constructor of RealNVP with 10 layers, 
 each coupling function has 2 hidden layers with 32 units. 
-Following the general architecture as in [^ASD2020] (see Apdx. E).
+Following the general architecture as in the Apdx. E of [^ASD2020].
 
-
-[^ASD2020]: Agrawal, A., & Sheldon, D., & Domke, J. (2020). 
-Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. 
-In *NeurIPS*.
+[^ASD2020]: Agrawal, A., & Sheldon, D., & Domke, J. (2020). Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. In *NeurIPS*.
 """
 realnvp(q0; paramtype::Type{T} = Float64) where {T<:AbstractFloat} = realnvp(
     q0, [32, 32], 10; paramtype=paramtype