Use proper ordered lists in docstrings

Lists were already formatted as bullet lists with
extraneous numbering or as manual plaintext lists.

Author: abhro

src/hyperparam/one_dimensional_range_methods.jl

@@ -66,31 +66,31 @@ In the first case iteration is over all `values` stored in the range
 iteration is over approximately `n` ordered values, generated as
 follows:
 
-(i) First, exactly `n` values are generated between `U` and `L`, with a
-spacing determined by `r.scale` (uniform if `scale=:linear`) where `U`
-and `L` are given by the following table:
-
-| `r.lower`   | `r.upper`  | `L`                 | `U`                 |
-|-------------|------------|---------------------|---------------------|
-| finite      | finite     | `r.lower`           | `r.upper`           |
-| `-Inf`      | finite     | `r.upper - 2r.unit` | `r.upper`           |
-| finite      | `Inf`      | `r.lower`           | `r.lower + 2r.unit` |
-| `-Inf`      | `Inf`      | `r.origin - r.unit` | `r.origin + r.unit` |
-
-(ii) If a callable `f` is provided as `scale`, then a uniform spacing
-is always applied in (i) but `f` is broadcast over the results. (Unlike
-ordinary scales, this alters the effective range of values generated,
-instead of just altering the spacing.)
-
-(iii) If `r` is a discrete numeric range (`r isa NumericRange{<:Integer}`)
-then the values are additionally rounded, with any duplicate values
-removed. Otherwise all the values are used (and there are exacltly `n`
-of them).
-
-(iv) Finally, if a random number generator `rng` is specified, then the values are
-returned in random order (sampling without replacement), and otherwise
-they are returned in numeric order, or in the order provided to the
-range constructor, in the case of a `NominalRange`.
+1. First, exactly `n` values are generated between `U` and `L`, with a
+   spacing determined by `r.scale` (uniform if `scale=:linear`) where `U`
+   and `L` are given by the following table:
+
+   | `r.lower`   | `r.upper`  | `L`                 | `U`                 |
+   |-------------|------------|---------------------|---------------------|
+   | finite      | finite     | `r.lower`           | `r.upper`           |
+   | `-Inf`      | finite     | `r.upper - 2r.unit` | `r.upper`           |
+   | finite      | `Inf`      | `r.lower`           | `r.lower + 2r.unit` |
+   | `-Inf`      | `Inf`      | `r.origin - r.unit` | `r.origin + r.unit` |
+
+2. If a callable `f` is provided as `scale`, then a uniform spacing
+   is always applied in (1) but `f` is broadcast over the results. (Unlike
+   ordinary scales, this alters the effective range of values generated,
+   instead of just altering the spacing.)
+
+3. If `r` is a discrete numeric range (`r isa NumericRange{<:Integer}`)
+   then the values are additionally rounded, with any duplicate values
+   removed. Otherwise all the values are used (and there are exacltly `n`
+   of them).
+
+4. Finally, if a random number generator `rng` is specified, then the values are
+   returned in random order (sampling without replacement), and otherwise
+   they are returned in numeric order, or in the order provided to the
+   range constructor, in the case of a `NominalRange`.
 
 """
 iterator(rng::AbstractRNG, r::ParamRange, args...) =

src/machines.jl

@@ -572,31 +572,31 @@ the true model given by `getproperty(composite, model)`. See also [`machine`](@r
 For the action to be a no-operation, either `mach.frozen == true` or
 or none of the following apply:
 
-- (i) `mach` has never been trained (`mach.state == 0`).
+1. `mach` has never been trained (`mach.state == 0`).
 
-- (ii) `force == true`.
+2. `force == true`.
 
-- (iii) The `state` of some other machine on which `mach` depends has
-  changed since the last time `mach` was trained (ie, the last time
-  `mach.state` was last incremented).
+3. The `state` of some other machine on which `mach` depends has
+   changed since the last time `mach` was trained (ie, the last time
+   `mach.state` was last incremented).
 
-- (iv) The specified `rows` have changed since the last retraining and
-  `mach.model` does not have `Static` type.
+4.  The specified `rows` have changed since the last retraining and
+   `mach.model` does not have `Static` type.
 
-- (v) `mach.model` is a model and different from the last model used for training, but has
-  the same type.
+5.  `mach.model` is a model and different from the last model used for training, but has
+   the same type.
 
-- (vi) `mach.model` is a model but has a type different from the last model used for
-  training.
+6. `mach.model` is a model but has a type different from the last model used for
+   training.
 
-- (vii) `mach.model` is a symbol and `(composite, mach.model)` is different from the last
-  model used for training, but has the same type.
+7. `mach.model` is a symbol and `(composite, mach.model)` is different from the last
+   model used for training, but has the same type.
 
-- (viii) `mach.model` is a symbol and `(composite, mach.model)` has a different type from
-  the last model used for training.
+8. `mach.model` is a symbol and `(composite, mach.model)` has a different type from
+   the last model used for training.
 
-In any of the cases (i) - (iv), (vi), or (viii), `mach` is trained ab initio. If (v) or
-(vii) is true, then a training update is applied.
+In any of the cases (1) - (4), (6), or (8), `mach` is trained ab initio.
+If (5) or (7) is true, then a training update is applied.
 
 To freeze or unfreeze `mach`, use `freeze!(mach)` or `thaw!(mach)`.