Trigger regeneration of docs

docs/src/anatomy_of_an_implementation.md

@@ -1,5 +1,8 @@
 # Anatomy of an Implementation
 
+This section is a tutorial for developers wanting to implement the LearnAPI.jl interface
+for their machine learning / statistical algorithms.
+
 LearnAPI.jl supports three core patterns. The default pattern, known as the
 [`LearnAPI.Descriminative`](@ref) pattern, looks like this:
 

docs/src/list_of_public_names.md

@@ -55,4 +55,6 @@ See [here](@ref proxy_types).
 
 - [`@trait`](@ref): for simultaneously declaring multiple traits
 
-- [`@functions`](@ref): for listing functions available for use with a learner 
+- [`@functions`](@ref): for listing functions available for use with a learner
+
+- [`LearnAPI.default_verbosity`](@ref): setter/getter for the default verbosity level 

docs/src/predict_transform.md

@@ -23,7 +23,7 @@ The above signatures apply in the case that [`LearnAPI.kind_of(learner)`](@ref)
 returns  [`LearnAPI.Generative()`](@ref), as in [density
 estimation](@ref density_estimation).
 
-## [Typical worklows](@id predict_workflow)
+## [Typical workflows](@id predict_workflow)
 
 Train some supervised `learner`:
 

docs/src/reference.md

@@ -104,16 +104,18 @@ generally requires overloading `Base.==` for the struct.
 !!! important
 
 	No LearnAPI.jl method is permitted to mutate a learner. In particular, one should make
-	deep copies of RNG hyperparameters before using them in an implementation of
-	[`fit`](@ref).
+	copies of RNG hyperparameters before using them in an implementation of
+	[`fit`](@ref). (Do not make *deep* copies as this leads to wrong behavior in the case
+	of `Random.default_rng()`, which not an actual RNG but only a pointer to the task-local
+	default RNG.)
 
 
 #### Kinds of learner
 
 As previewed in [Anatomy of an Implementation](@ref), different
 `fit`/`predict`/`transform` patterns lead to a division of learners into three distinct
-kinds, [`LearnAPI.Descriminative()`](@ref), [`LearnAPI.Generative`](@ref), and
-[`LearnAPI.Static`](@ref), which is detailed [here](@ref kinds_of_learner). See also
+kinds, [`LearnAPI.Descriminative()`](@ref), [`LearnAPI.Generative()`](@ref), and
+[`LearnAPI.Static()`](@ref), which is detailed [here](@ref kinds_of_learner). See also
 [these workflows](@ref fit_workflows) for concrete examples.
 
 
@@ -190,7 +192,7 @@ Most learners will also implement [`predict`](@ref) and/or [`transform`](@ref).
   proxies](@ref proxy) (such as probability density functions)
 
 - [`transform`](@ref operations): similar to `predict`, but for arbitrary kinds of output,
-  and which can be paired with an `inverse_transform` method
+  and which can be optionally paired with an `inverse_transform` method
 
 - [`inverse_transform`](@ref operations): for inverting the output of
   `transform` ("inverting" broadly understood)

src/fit_update.jl

@@ -141,8 +141,8 @@ model = fit(learner, data)
 model = update_observations(model, new_data, epochs => 12, learning_rate => 0.1)
 ```
 
-When following the call `fit(learner, data)`, the `update` call is semantically
-equivalent to retraining ab initio using a concatenation of `data` and `new_data`,
+When following the call `fit(learner, data)`, the `update` call is semantically equivalent
+to retraining ab initio using an observation-wise concatenation of `data` and `new_data`,
 *provided there are no hyperparameter replacements* (which rules out the example
 above). Behaviour is otherwise learner-specific.
 
@@ -178,9 +178,9 @@ Return an updated version of the `model` object returned by a previous [`fit`](@
 `update` call given the new features encapsulated in `new_data`. One may additionally
 specify hyperparameter replacements in the form `:p1 => value1, :p2 => value2, ...`.
 
-When following the call `fit(learner, data)`, the `update` call is semantically
-equivalent to retraining ab initio using a concatenation of `data` and `new_data`,
-*provided there are no hyperparameter replacements.* Behaviour is otherwise
+When following the call `fit(learner, data)`, the `update_features` call is semantically
+equivalent to retraining ab initio using a feature-wise concatenation of `data` and
+`new_data`, *provided there are no hyperparameter replacements.* Behaviour is otherwise
 learner-specific.
 
 See also [`fit`](@ref), [`update`](@ref), [`update_features`](@ref).

src/traits.jl

@@ -160,7 +160,7 @@ Return the `fit`/`predict`/`transform` signature pattern used by `learner`. See
 
 # New implementations
 
-The fallback value is [`LearnAPI.Descriminative()`].
+The fallback value is [`LearnAPI.Descriminative()`](@ref).
 
 """
 kind_of(learner) = Descriminative()