Project.toml

@@ -4,13 +4,10 @@ authors = ["Dimitri Lozeve <dimitri@lozeve.com>"]
 version = "0.1.0"
 
 [deps]
-CPLEX = "a076750e-1247-5638-91d2-ce28b192dca0"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
-JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"

src/GardenOptim.jl

@@ -17,7 +17,6 @@ export update!, fillgardenrandomly!, gardenevolution!, outputgarden
 include("classification.jl")
 include("loaddata.jl")
 include("mcmc.jl")
-include("optim.jl")
 
 "Save the garden to a CSV file."
 function outputgarden(garden::Matrix{Int}, plants::Vector{Symbol})

src/optim.jl

@@ -1,67 +0,0 @@
-using JuMP
-using CPLEX
-
-function neighbourindices(mask::Matrix, idx::Int)::Vector{Int}
-    if mask[idx] == 0
-        return []
-    end
-
-    m, n = size(mask)
-    i, j = Tuple(CartesianIndices(mask)[idx])
-    indices = [(i, j-1), (i, j+1), (i-1, j), (i+1, j)]
-    # cells filled with 0 are not part of the garden
-    cartesianindices = [
-        CartesianIndex(k, l) for (k, l) in indices
-        if 0 < k <= m && 0 < l <= n && mask[k, l] != 0
-    ]
-    # convert to linear indices
-    LinearIndices(mask)[cartesianindices]
-end
-
-function neighbourmatrix(mask::Matrix)::Matrix{Bool}
-    N = length(mask)
-    d = zeros(N, N)
-    for i in 1:N
-        for j in neighbourindices(mask, i)
-            d[i, j] = 1
-        end
-    end
-    d
-end
-
-function definemodel(plantcounts::Vector, garden::Matrix, mask::Matrix, costs::Matrix)
-    N = length(mask)
-    Q = size(costs, 1)
-
-    optimizer = optimizer_with_attributes(
-        CPLEX.Optimizer, "CPX_PARAM_OPTIMALITYTARGET" => 2, "CPX_PARAM_EPINT" => 1e-8)
-    model = Model(optimizer)
-
-    @variable(model, x[1:N, 1:Q], Bin)
-
-    @objective(
-        model,
-        Min,
-        sum(
-            costs[q, r] * x[i, q] * x[j, r]
-            for i = 1:N, q = 1:Q, r = 1:Q for j = neighbourindices(mask, i)
-        )
-    )
-
-    for i = 1:N
-        @constraint(model, sum(x[i, q] for q = 1:Q) <= 1)
-        if mask[i] == 0
-            for q = 1:Q
-                @constraint(model, x[i, q] == 0)
-            end
-        end
-        if garden[i] != 0
-            @constraint(model, x[i, garden[i]] == 1)
-        end
-    end
-
-    for q = 1:Q
-        @constraint(model, sum(x[i, q] for i = 1:N) >= plantcounts[q])
-    end
-    model
-end

test/optim.jl

@@ -1,15 +0,0 @@
-using GardenOptim
-using LinearAlgebra
-using Test
-
-@testset "optim" begin
-    mask = zeros(Bool, 5, 7)
-    mask[[1, 2, 6, 7]] .= 1
-    @test GardenOptim.neighbourindices(mask, 1) == [6, 2]
-    @test GardenOptim.neighbourindices(mask, 2) == [7, 1]
-    @test GardenOptim.neighbourindices(mask, 3) == []
-    neighbourcount = sum([length(GardenOptim.neighbourindices(mask, i)) for i = 1:length(mask)])
-    @test sum(GardenOptim.neighbourmatrix(mask)) == neighbourcount
-    @test issymmetric(GardenOptim.neighbourmatrix(mask))
-end
-

test/runtests.jl

@@ -3,5 +3,4 @@ using Test
 @testset "GardenOptim.jl" begin
     include("classification.jl")
     include("mcmc.jl")
-    include("optim.jl")
 end