Updated docs about it!

Author: theogf

docs/src/api.md

@@ -71,6 +71,13 @@ getvariance
 getlengthscales
 ```
 
+## Prior Means
+
+```@docs
+ZeroMean
+ConstantMean
+EmpiricalMean
+```
 
 ## Index
 

docs/src/comparison.md

@@ -43,11 +43,13 @@ AGP.jl stands for AugmentedGaussianProcesses.jl and GP.jl for GaussianProcesses.
 | Sum of kernel | ✖ | ✖ | ✖ |
 | Product of kernels | ✖ | ✖ | ✖ |
 
+Note that the kernels will be defered to `MLKernels.jl` in the future.
+
 # Other features
 
 | Feature | AGP.jl | Stheno.jl | GP.jl |
 | --- | :-: | :-: | :-: |
 | Sparse GP | ✓ | ✖ | ✖ |
-| Custom prior Mean | ✖ | ✓ | ✓ |
+| Custom prior Mean | ✓ | ✓ | ✓ |
 | Hyperparam. Opt. | ✓ | ? | ? |
 | MultiOutput | ✖ | ✓ | ✖ |

src/prior/constantmean.jl

@@ -4,9 +4,13 @@ mutable struct ConstantMean{T<:Real} <: MeanPrior{T}
 end
 
 """
-ConstantMean(c)
-Construct a constant mean with constant `c`
-Optionally give an optimizer `opt` (`Adam(α=0.01)` by default)
+**ConstantMean**
+```julia
+ConstantMean(c::T=1.0;opt::Optimizer=Adam(α=0.01))
+```
+
+Construct a prior mean with constant `c`
+Optionally set an optimizer `opt` (`Adam(α=0.01)` by default)
 """
 function ConstantMean(c::T=1.0;opt::Optimizer=Adam(α=0.01)) where {T<:Real}
     ConstantMean{T}(c,opt)

src/prior/empiricalmean.jl

@@ -4,7 +4,10 @@ mutable struct EmpiricalMean{T<:Real,V<:AbstractVector{<:Real}} <: MeanPrior{T}
 end
 
 """
-EmpiricalMean(c)
+**EmpiricalMean**
+```julia`
+function EmpiricalMean(c::V=1.0;opt::Optimizer=Adam(α=0.01)) where {V<:AbstractVector{<:Real}}
+```
 Construct a constant mean with values `c`
 Optionally give an optimizer `opt` (`Adam(α=0.01)` by default)
 """

src/prior/zeromean.jl

@@ -2,8 +2,11 @@ mutable struct ZeroMean{T<:Real} <: MeanPrior{T}
 end
 
 """
-ZeroMean(c)
-Construct a constant mean with constant 0
+ZeroMean
+```julia
+ZeroMean()
+```
+Construct a mean prior set to 0 and cannot be changed.
 """
 function ZeroMean()
     ZeroMean{Float64}()