initialize Matrix individuals, fixes #55

src/api/utilities.jl

@@ -86,7 +86,18 @@ end
 Initialize population by replicating the `inividual` vector.
 """
 initial_population(method::M, individual::I) where {M<:AbstractOptimizer, I<:AbstractVector} =
-    return [copy(individual) for i in 1:population_size(method)]
+    [copy(individual) for i in 1:population_size(method)]
+
+"""
+    initial_population(method, individuals::AbstractVector{<:AbstractVector})
+
+Initialize population from the collection of `inividuals` vectors.
+"""
+function initial_population(method::M, individuals::AbstractVector{I}) where {M<:AbstractOptimizer, I<:AbstractVector}
+    n = population_size(method)
+    @assert length(individuals) ==  n "Size of initial population must be $n"
+    individuals
+end
 
 """
     initial_population(method, individual::Function)
@@ -97,14 +108,12 @@ initial_population(method::M, individualFunc::Function) where {M<:AbstractOptimi
     [individualFunc() for i in 1:population_size(method)]
 
 """
-    initial_population(method, individuals::AbstractMatrix)
+    initial_population(method, individual::AbstractMatrix)
 
-Initialize population from the `inividuals` matrix where each individual is a column.
+Initialize population by replicating the `inividual` matrix.
 """
-function initial_population(method::M, individuals::I) where {M<:AbstractOptimizer, I<:AbstractMatrix}
-    n = population_size(method)
-    @assert size(individuals,2) >= n "Size of initial population must be no smaller then $n"
-    return [individuals[:,i] for i in 1:n]
+function initial_population(method::M, individual::I) where {M<:AbstractOptimizer, I<:AbstractMatrix}
+    [copy(individual) for i in 1:population_size(method)]
 end
 
 """
@@ -133,5 +142,5 @@ function initial_population(method::M, bounds::ConstraintBounds) where {M<:Abstr
             indv[j,i] += l
         end
     end
-    initial_population(method, indv)
+    initial_population(method,  [collect(i) for i in  eachcol(indv)])
 end

src/cmaes.jl

@@ -113,10 +113,13 @@ function update_state!(objfun, state::CMAESState{T,TI}, population::AbstractVect
     weights = view(w, 1:μ)
     E_NormN = sqrt(N)*(1-1/(4*N)+1/(21*N*N))
 
+    parent = reshape(state.parent,N)
+    parentshape = size(state.parent)
+
     z = zeros(T, N, λ)
     # y = zeros(T, N, λ)
     ȳ = zeros(T, N)
-    offspring = Array{TI}(undef, λ)
+    offspring = Array{Vector{T}}(undef, λ)
     fitoff = fill(Inf, λ)
 
     B, D = try
@@ -141,23 +144,23 @@ function update_state!(objfun, state::CMAESState{T,TI}, population::AbstractVect
         # y[:,i] =  B * D * z[:,i]
         # y[:,i] =  SqrtC * z[:,i]
         # offspring[i] = state.parent + σ * y[:,i]
-        offspring[i] = state.parent + σ * B * D * z[:,i]
-        fitoff[i] = value(objfun, offspring[i]) # Evaluate fitness
+        offspring[i] = parent + σ * B * D * z[:,i]
+        fitoff[i] = value(objfun, reshape(offspring[i],parentshape...)) # Evaluate fitness
     end
 
     # Select new parent population
     idx = sortperm(fitoff)
     for i in 1:μ
         o = offspring[idx[i]]
-        population[i] = o
-        ȳ .+=(o.-state.parent).*(w[i]/σ)
+        population[i] = reshape(o,parentshape...)
+        ȳ .+=(o.-parent).*(w[i]/σ)
         state.fitpop[i] = fitoff[idx[i]]
     end
 
     # y_λ = view(y,:,idx)
     z_λ = view(z,:,idx)
     z̄ = vec(sum(z_λ[:,1:μ].*weights', dims=2))
-    state.parent += (cₘ*σ).*ȳ  #  forming recombinant perent for next generation
+    parent += (cₘ*σ).*ȳ  #  forming recombinant perent for next generation
     state.s_σ = (1 - c_σ)*state.s_σ + sqrt(μ_eff*c_σ*(2 - c_σ))*(B*z̄)
     # state.s_σ = (1 - c_σ)*state.s_σ + sqrt(μ_eff*c_σ*(2 - c_σ))*(SqrtC*ȳ)
     state.σ = σ * exp((c_σ/d_σ)*(norm(state.s_σ)/E_NormN - 1))
@@ -175,6 +178,7 @@ function update_state!(objfun, state::CMAESState{T,TI}, population::AbstractVect
     state.C = (1 - c_1 - c_μ*sum(w)).*state.C + rank_1 + rank_μ
 
     state.fittest = population[1]
+    state.parent = reshape(parent,parentshape...)
 
     return false
 end

test/interface.jl

@@ -111,13 +111,20 @@
     ##############
     pop = Evolutionary.initial_population(mthd, BitVector(ones(dimension)))
     @test length(pop) == population_size(mthd)
+    @test size(first(pop)) == (dimension,)
+
+    @test_throws AssertionError Evolutionary.initial_population(mthd, fill(BitVector(ones(dimension)),4))
+    pop = Evolutionary.initial_population(mthd, fill(BitVector(ones(dimension)),5))
+    @test length(pop) == population_size(mthd)
+    @test size(first(pop)) == (dimension,)
 
-    @test_throws AssertionError Evolutionary.initial_population(mthd, BitMatrix(ones(dimension,4)))
     pop = Evolutionary.initial_population(mthd, BitMatrix(ones(dimension,6)))
     @test length(pop) == population_size(mthd)
+    @test size(first(pop)) == (dimension,6)
 
     pop = Evolutionary.initial_population(mthd, (()->rand(Bool,dimension)))
     @test length(pop) == population_size(mthd)
+    @test size(first(pop)) == (dimension,)
 
     lb = [0,1,2,-1]
     ub = [0,3,2,1]

test/regression.jl

@@ -0,0 +1,21 @@
+@testset "Rastrigin" begin
+    ## data
+    m, n, n2 = 9, 6, 3
+    X, A, E = randn(m, n), randn(n, n2), randn(m, n2) * 0.1
+    Y = X * A + E
+    r = 0.1
+
+    linear(w) = sum(abs2, Y .- X*w)
+    ridge(w) = linear(w) + r*sum(abs2, w)
+    ridgevec(w) = ridge(reshape(w,n,n2))
+
+    # using vector individual
+    result = Evolutionary.optimize(ridgevec, randn(n*n2), CMAES(μ=100, c_1=0.05))
+    @test sum(abs, inv(X'X + r * I)*X'*Y .- reshape(Evolutionary.minimizer(result), n, n2)) ≈ 0.001 atol=1e-3
+
+    # using matrix individual
+    res1 = Evolutionary.optimize(linear, randn(n,n2), CMAES(μ=100, c_1=0.05))
+    res2 = Evolutionary.optimize(ridge, randn(n,n2), CMAES(μ=100, c_1=0.05))
+    @test sum(abs, inv(X'X )*X'*Y .- Evolutionary.minimizer(res1)) ≈ 0.001 atol=1e-3
+    @test sum(abs, inv(X'X + r * I)*X'*Y .- Evolutionary.minimizer(res2)) ≈ 0.001 atol=1e-3
+end

test/runtests.jl

@@ -14,7 +14,8 @@ for tests in [
     "rastrigin.jl",
     "n-queens.jl",
     "knapsack.jl",
-    "onemax.jl"
+    "onemax.jl",
+    "regression.jl"
 ]
     include(tests)
 end