fix aqua tests

Author: BatyLeo

src/regularized/abstract_regularized.jl

@@ -32,12 +32,7 @@ with g and h functions of y.
 
 - `(regularized::AbstractRegularized)(θ; kwargs...)`: return `ŷ(θ)`
 - `compute_regularization(regularized, y)`: return `Ω(y)`
-
-# Available implementations
-
-- [`SoftArgmax`](@ref)
-- [`SparseArgmax`](@ref)
-- [`RegularizedFrankWolfe`](@ref)
+- `get_maximizer(regularized)`: return the associated `GeneralizedMaximizer` optimizer
 """
 abstract type AbstractRegularizedGeneralizedMaximizer <: AbstractRegularized end
 
@@ -48,16 +43,18 @@ Return the convex penalty `Ω(y)` associated with an `AbstractRegularized` layer
 """
 function compute_regularization end
 
+"""
+    get_maximizer(regularized)
+
+Return the associated optimizer.
+"""
+function get_maximizer end
+
 @required AbstractRegularized begin
     #(regularized::AbstractRegularized)(θ::AbstractArray; kwargs...)
     compute_regularization(::AbstractRegularized, ::AbstractArray)
 end
 
-"""
-TODO
-"""
-function get_maximizer end
-
 @required AbstractRegularizedGeneralizedMaximizer begin
     get_maximizer(::AbstractRegularizedGeneralizedMaximizer)
 end

src/regularized/soft_argmax.jl

@@ -7,8 +7,8 @@ Corresponds to regularized prediction on the probability simplex with entropic p
 """
 struct SoftArgmax <: AbstractRegularized end
 
-(::SoftArgmax)(z; kwargs...) = soft_argmax(z)
-compute_regularization(::SoftArgmax, y) = soft_argmax_regularization(y)
+(::SoftArgmax)(z::AbstractVector; kwargs...) = soft_argmax(z)
+compute_regularization(::SoftArgmax, y::AbstractVector) = soft_argmax_regularization(y)
 
 function soft_argmax(z::AbstractVector)
     s = exp.(z)

src/regularized/sparse_argmax.jl

@@ -7,8 +7,8 @@ Corresponds to regularized prediction on the probability simplex with square nor
 """
 struct SparseArgmax <: AbstractRegularized end
 
-(::SparseArgmax)(z; kwargs...) = sparse_argmax(z)
-compute_regularization(::SparseArgmax, y) = sparse_argmax_regularization(y)
+(::SparseArgmax)(z::AbstractVector; kwargs...) = sparse_argmax(z)
+compute_regularization(::SparseArgmax, y::AbstractVector) = sparse_argmax_regularization(y)
 
 function sparse_argmax(z::AbstractVector; kwargs...)
     p, _ = simplex_projection_and_support(z)