rename method set_user_solutions

jmejia8 · Mar 8, 2023 · dcf87a4 · dcf87a4
1 parent e657910
commit dcf87a4
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Showing 4 changed files with 17 additions and 17 deletions.
diff --git a/docs/src/api.md b/docs/src/api.md
@@ -107,7 +107,7 @@ Metaheuristics.gen_initial_state
 ```
 
 ```@docs
-set_inital_solutions!
+set_user_solutions!
 ```
 
 ## Variation 

diff --git a/src/Metaheuristics.jl b/src/Metaheuristics.jl
@@ -26,7 +26,7 @@ export GenerationalReplacement, ElitistReplacement
 export sample, LatinHypercubeSampling, Grid 
 export εDE, Restart
 export optimize!
-export set_inital_solutions!
+export set_user_solutions!
 
 include("externals.jl")
 
@@ -52,7 +52,7 @@ include("common/repair.jl")
 include("common/stop.jl")
 include("common/compare.jl")
 include("common/non-dominated-sorting.jl")
-include("common/set_inital_solutions.jl")
+include("common/set_user_solutions.jl")
 
 
 

diff --git a/src/common/set_inital_solutions.jl → src/common/set_user_solutions.jl b/src/common/set_inital_solutions.jl → src/common/set_user_solutions.jl
@@ -1,7 +1,7 @@
 include("gen_initial_state.jl")
 
 """
-    set_inital_solutions!(optimizer, x, fx)
+    set_user_solutions!(optimizer, x, fx)
 
 Provide initial solutions to the `optimizer`.
 - `x` can be a `Vector` and `fx` a function or `fx = f(x)`
@@ -18,12 +18,12 @@ julia> algo  = ECA(N = 61); # optimizer
 julia> # one solution can be provided
        x0 = [0.5, 0.5 0.5];
 
-julia> set_inital_solutions!(algo, x0, f);
+julia> set_user_solutions!(algo, x0, f);
 
 julia> # providing multiple solutions
        X0 = rand(30, 3); # 30 solutions with dim 3
 
-julia> set_inital_solutions!(algo, X0, f);
+julia> set_user_solutions!(algo, X0, f);
 
 julia> optimize(f, [0 0 0; 1 1 1.0], algo)
 +=========== RESULT ==========+
@@ -37,7 +37,7 @@ stop reason: Small difference of objective function values.
 ```
 
 """
-function set_inital_solutions!(algo::AbstractAlgorithm, solution::AbstractSolution)
+function set_user_solutions!(algo::AbstractAlgorithm, solution::AbstractSolution)
     status = algo.status
     if !isnothing(status.best_sol) && !isempty(status.population)
         push!(status.population, solution)
@@ -48,16 +48,16 @@ function set_inital_solutions!(algo::AbstractAlgorithm, solution::AbstractSoluti
 end
 
 
-function set_inital_solutions!(algo::AbstractAlgorithm, x::AbstractVector, fx)
-    set_inital_solutions!(algo, create_child(x, fx))
+function set_user_solutions!(algo::AbstractAlgorithm, x::AbstractVector, fx)
+    set_user_solutions!(algo, create_child(x, fx))
 end
 
-function set_inital_solutions!(algo::AbstractAlgorithm, x::AbstractVector, f::Function)
-    set_inital_solutions!(algo, x, f(x))
+function set_user_solutions!(algo::AbstractAlgorithm, x::AbstractVector, f::Function)
+    set_user_solutions!(algo, x, f(x))
 end
 
 
-function set_inital_solutions!(algo::AbstractAlgorithm, X::AbstractMatrix, fX::AbstractVector)
+function set_user_solutions!(algo::AbstractAlgorithm, X::AbstractMatrix, fX::AbstractVector)
     n = size(X, 1)
     m = length(fX)
 
@@ -72,15 +72,15 @@ function set_inital_solutions!(algo::AbstractAlgorithm, X::AbstractMatrix, fX::A
 
     # TODO: this part can be parallelized
     for i in 1:n
-        set_inital_solutions!(algo, X[i,:], fX[i])
+        set_user_solutions!(algo, X[i,:], fX[i])
     end
 
     # TODO check population size provided in algo.parameters.N
 
     algo
 end
 
-function set_inital_solutions!(algo::AbstractAlgorithm, X::AbstractMatrix, f::Function)
-    set_inital_solutions!(algo, X, [f(X[i,:]) for i in 1:size(X,1)])
+function set_user_solutions!(algo::AbstractAlgorithm, X::AbstractMatrix, f::Function)
+    set_user_solutions!(algo, X, [f(X[i,:]) for i in 1:size(X,1)])
 end
 
diff --git a/test/initial_solution.jl b/test/initial_solution.jl
@@ -23,7 +23,7 @@
                 # initial solutions
                 X = rand(10, size(bounds, 2))
                 X[1,:] = x_optimum #zeros(size(bounds, 2))
-                set_inital_solutions!(algo, X, f)
+                set_user_solutions!(algo, X, f)
 
                 res = optimize(f, bounds, algo)
 
@@ -59,7 +59,7 @@
                 # initial solutions
                 X = rand(10, size(bounds, 2))
                 X[1,:] = x_optimum
-                set_inital_solutions!(algo, X, f)
+                set_user_solutions!(algo, X, f)
 
                 res = optimize(f, bounds, algo)
                 @test length(res.population) == algo.parameters.N