finish re-instating docs for testing new implementations

Author: ablaom

docs/Project.toml

@@ -2,6 +2,7 @@
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
 LearnAPI = "92ad9a40-7767-427a-9ee6-6e577f1266cb"
+LearnTestAPI = "3111ed91-c4f2-40e7-bb19-7f6c618409b8"
 MLCore = "c2834f40-e789-41da-a90e-33b280584a8c"
 ScientificTypesBase = "30f210dd-8aff-4c5f-94ba-8e64358c1161"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"

docs/make.jl

@@ -1,13 +1,14 @@
 using Documenter
 using LearnAPI
+using LearnTestAPI
 using ScientificTypesBase
 using DocumenterInterLinks
 # using LearnTestAPI
 
 const  REPO = Remotes.GitHub("JuliaAI", "LearnAPI.jl")
 
 makedocs(
-    modules=[LearnAPI, ], #LearnTestAPI],
+    modules=[LearnAPI, LearnTestAPI],
     format=Documenter.HTML(
         prettyurls = true,#get(ENV, "CI", nothing) == "true",
         collapselevel = 1,

docs/src/anatomy_of_an_implementation.md

@@ -44,7 +44,7 @@ refer to the [demonstration](@ref workflow) of the implementation given later.
 	1. Implement `fit` (probably following an existing example). Read the [`fit`](@ref) document string to see what else may need to be implemented, paying particular attention to the "New implementations" section.
 	3. Rinse and repeat with each new method implemented.
 	4. Identify any additional [learner traits](@ref traits) that have appropriate overloadings; use the [`@trait`](@ref) macro to define these in one block.
-	5. Ensure your implementation includes the compulsory method [`LearnAPI.learner`](@ref) and compulsory traits [`LearnAPI.constructor`](@ref) and [`LearnAPI.functions`](@ref). Read and apply "[Testing your implementation](@ref)".
+	5. Ensure your implementation includes the compulsory method [`LearnAPI.learner`](@ref) and compulsory traits [`LearnAPI.constructor`](@ref) and [`LearnAPI.functions`](@ref). Read and apply "[Testing an Implementation](@ref)".
 
 	If you get stuck, refer back to this tutorial and the [Reference](@ref reference) sections.
 

docs/src/common_implementation_patterns.md

@@ -11,7 +11,7 @@ which introduces the main interface objects and terminology.
 Although an implementation is defined purely by the methods and traits it implements, many
 implementations fall into one (or more) of the informally understood patterns or tasks
 below. While some generally fall into one of the core `Descriminative`, `Generative` or
-`Static` patterns detailed [here](@id kinds_of_learner), there are exceptions (such as
+`Static` patterns detailed [here](@ref kinds_of_learner), there are exceptions (such as
 clustering, which has both `Descriminative` and `Static` variations).
 
 - [Regression](@ref): Supervised learners for continuous targets

docs/src/testing_an_implementation.md

@@ -30,6 +30,17 @@ LearnTestAPI.@testapi
 LearnTestAPI.jl provides some simple, tested, LearnAPI.jl implementations, which may be
 useful for testing learner wrappers and meta-algorithms.
 
+- [`LearnTestAPI.Ridge`](@ref)
+- [`LearnTestAPI.BabyRidge`](@ref)
+- [`LearnTestAPI.ConstantClassifier`](@ref)
+- [`LearnTestAPI.TruncatedSVD`](@ref)
+- [`LearnTestAPI.Selector`](@ref)
+- [`LearnTestAPI.FancySelector`](@ref)
+- [`LearnTestAPI.NormalEstimator`](@ref)
+- [`LearnTestAPI.Ensemble`](@ref)
+- [`LearnTestAPI.StumpRegressor`](@ref)
+
+
 ```@docs
 LearnTestAPI.Ridge
 LearnTestAPI.BabyRidge