Merge pull request #21 from nickrobinson251/npr/use-chainrules-docs

Share docs with ChainRules

Author: nickrobinson251

.travis.yml

@@ -15,16 +15,7 @@ notifications:
 # uncomment the following lines to override the default test script
 #script:
 #  - if [[ -a .git/shallow ]]; then git fetch --unshallow; fi
-#  - julia -e 'Pkg.clone(pwd()); Pkg.build("AbstractChainRules"); Pkg.test("AbstractChainRules"; coverage=true)'
+#  - julia -e 'Pkg.clone(pwd()); Pkg.build("ChainRulesCore"); Pkg.test("ChainRulesCore"; coverage=true)'
 after_success:
   # push coverage results to Coveralls
   - julia -e 'using Pkg; Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'
-jobs:
-  include:
-    - state: "Documentation"
-      julia: 1.0
-      os: linux
-      script:
-        - julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
-        - julia --project=docs/ docs/make.jl
-      after_success: skip

README.md

@@ -2,20 +2,12 @@
 
 [![Travis](https://travis-ci.org/JuliaDiff/ChainRulesCore.jl.svg?branch=master)](https://travis-ci.org/JuliaDiff/ChainRulesCore.jl)
 [![Coveralls](https://coveralls.io/repos/github/JuliaDiff/ChainRulesCore.jl/badge.svg?branch=master)](https://coveralls.io/github/JuliaDiff/ChainRulesCore.jl?branch=master)
-[![](https://img.shields.io/badge/docs-latest-blue.svg)](https://JuliaDiff.github.io/ChainRulesCore.jl/latest)
+[![](https://img.shields.io/badge/docs-latest-blue.svg)](https://JuliaDiff.github.io/ChainRules.jl/latest)
 
-The ChainRulesCore package provides a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives.
+The ChainRulesCore package provides a light-weight dependency for defining sensitivities for functions in your packages, without you needing to depend on ChainRules itself.
 
-This package is a WIP; the framework is essentially there, but there are a bunch of TODOs, virtually no tests, etc. PRs welcome! Documentation is incoming, which should help if you'd like to contribute.
+This will allow your package to be used with [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl), which aims to provide a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives.
 
-Here are some of the basic goals for the package:
-
-- First-class support for complex differentiation via Wirtinger derivatives.
-
-- Mixed-mode composability without being coupled to a specific AD implementation.
-
-- Propagation semantics built-in, with default implementations that allow rule authors to easily opt-in to common optimizations (fusion, increment elision, memoization, etc.).
-
-- Control-inverted design: rule authors can fully specify derivatives in a concise manner while naturally allowing the caller to compute only what they need.
+This package is a work in progress; the framework is essentially there, but there are a bunch of TODOs, virtually no tests, etc. PRs welcome! The API is mostly documented, which should help if you'd like to contribute.
 
 The ChainRulesCore source code follows the [YASGuide](https://github.com/jrevels/YASGuide).

docs/Project.toml

@@ -11,7 +11,7 @@ return that calculated differential value.
 
 For example:
 
-```julia-repl
+```jldoctest
 julia> using ChainRulesCore: frule, rrule, AbstractRule
 
 julia> x, y = rand(2);
@@ -26,7 +26,7 @@ true
 
 julia> Δx, Δy = rand(2);
 
-julia> dh(Δx, Δy) == ((y / h) * Δx + (x / h) * Δy)
+julia> dh(Δx, Δy) == ((x / h) * Δx + (y / h) * Δy)
 true
 
 julia> h, (dx, dy) = rrule(hypot, x, y);
@@ -39,10 +39,10 @@ true
 
 julia> Δh = rand();
 
-julia> dx(Δh) == (y / h) * Δh
+julia> dx(Δh) == (x / h) * Δh
 true
 
-julia> dy(Δh) == (x / h) * Δh
+julia> dy(Δh) == (y / h) * Δh
 true
 ```
 
@@ -262,7 +262,7 @@ Examples:
 
 unary input, unary output scalar function:
 
-```julia-repl
+```jldoctest
 julia> x = rand();
 
 julia> sinx, dsin = frule(sin, x);
@@ -276,7 +276,7 @@ true
 
 unary input, binary output scalar function:
 
-```julia-repl
+```jldoctest
 julia> x = rand();
 
 julia> sincosx, (dsin, dcos) = frule(sincos, x);
@@ -318,7 +318,7 @@ Examples:
 
 unary input, unary output scalar function:
 
-```julia-repl
+```jldoctest
 julia> x = rand();
 
 julia> sinx, dx = rrule(sin, x);
@@ -332,18 +332,18 @@ true
 
 binary input, unary output scalar function:
 
-```julia-repl
+```jldoctest
 julia> x, y = rand(2);
 
 julia> hypotxy, (dx, dy) = rrule(hypot, x, y);
 
 julia> hypotxy == hypot(x, y)
 true
 
-julia> dx(1) == (y / hypot(x, y))
+julia> dx(1) == (x / hypot(x, y))
 true
 
-julia> dy(1) == (x / hypot(x, y))
+julia> dy(1) == (y / hypot(x, y))
 true
 ```