add PIV method

CHANGELOG.md

@@ -1,7 +1,7 @@
 ### 0.5.3 (Upcoming release)
 - Add tests for GreyNumber type
 - methods now accept Matrix (in addition to DataFrame)
-
+- PIV (Proximity Indexed Value) method implemented
 
 ### 0.5.2 
 - Implement MEREC

src/JMcDM.jl

@@ -102,6 +102,7 @@ include("mcdm.jl")
 include("copeland.jl")
 include("sd.jl")
 include("rov.jl")
+include("piv.jl")
 
 
 
@@ -136,6 +137,7 @@ import .DEMATEL: dematel, DematelResult
 import .Entropy: entropy, EntropyResult
 import .AHP: ahp, ahp_consistency, ahp_RI, AHPResult, AHPConsistencyResult
 import .MEREC: merec, MERECResult, MERECMethod
+import .PIV: piv, PIVResult, PIVMethod
 
 import .SCDM: LaplaceResult, MaximinResult, MaximaxResult, MinimaxResult, MiniminResult
 import .SCDM: SavageResult, HurwiczResult, MLEResult, ExpectedRegretResult
@@ -174,6 +176,7 @@ export PSIMethod
 export MoosraMethod
 export ROVMethod
 export MERECMethod
+export PIVMethod
 
 export MCDMSetting
 
@@ -206,6 +209,7 @@ export ROVResult
 export PSIResult
 export MoosraResult
 export MERECResult
+export PIVResult
 
 # export game type
 export GameResult
@@ -264,6 +268,7 @@ export rov
 export psi
 export moosra
 export merec
+export piv
 
 #  export SCDM tools
 export laplace

src/codas.jl

@@ -30,6 +30,8 @@ function Base.show(io::IO, result::CODASResult)
     println(io, "Best indices:")
     println(io, result.bestIndex)
 end
+
+
 """
     codas(decisionMat, weights, fs)
 Apply CODAS (COmbinative Distance-based ASsessment) method for a given matrix, weights and, type of criteria.

src/piv.jl

@@ -0,0 +1,91 @@
+module PIV
+
+export piv, PIVResult, PIVMethod
+
+import ..MCDMMethod, ..MCDMResult, ..MCDMSetting
+using ..Utilities 
+
+using DataFrames 
+
+struct PIVResult <: MCDMResult
+    decisionMatrix::DataFrame
+    normalizedMatrix::DataFrame
+    weightedNormalizedMatrix::DataFrame
+    w::Array{Float64,1}
+    scores::Vector
+    ranking::Array{Int64,1}
+    bestIndex::Int64
+end
+
+struct PIVMethod <: MCDMMethod
+end
+
+function Base.show(io::IO, result::PIVResult)
+    println(io, "Scores:")
+    println(io, result.scores)
+    println(io, "Ordering: ")
+    println(io, result.ranking)
+    println(io, "Best index:")
+    println(io, result.bestIndex)
+end
+
+"""
+    piv(decisionMat, weights, fs)
+Apply PIV (Proximity Indexed Value) method for a given matrix, weights and, type of criteria.
+
+# Arguments:
+ - `decisionMat::DataFrame`: n × m matrix of objective values for n alternatives and m criteria 
+ - `weights::Array{Float64, 1}`: m-vector of weights that sum up to 1.0. If the sum of weights is not 1.0, it is automatically normalized.
+ - `fs::Array{Function,1}`: m-vector of type of criteria. The benefit criteria shown with "maximum", and the cost criteria shown with "minimum".
+
+ # Description 
+piv() applies the PIV method to rank n alternatives subject to m criteria and criteria type vector. Alternatives 
+with lesser scores values (u_i values in the original article) are better as they represent the deviation 
+from the ideal values.
+
+
+# Output 
+- `::PIVResult`: PIVResult object that holds multiple outputs including scores, rankings, and best index.
+
+# References
+Sameera Mufazzal, S.M. Muzakkir, A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals,
+Computers & Industrial Engineering, Volume 119, 2018, Pages 427-438, ISSN 0360-8352,
+https://doi.org/10.1016/j.cie.2018.03.045.
+"""
+function piv(decisionMat::DataFrame, weights::Array{Float64,1}, fns::Array{Function,1})::PIVResult
+
+    normalized_dec_mat = Utilities.normalize(decisionMat)
+    weighted_norm_mat = weights * normalized_dec_mat
+
+    nrow, ncol = size(weighted_norm_mat)
+
+    desiredvalues = Utilities.apply_columns(fns, weighted_norm_mat)
+
+    finalmat = similar(weighted_norm_mat)
+
+    @inbounds for i in 1:nrow
+        for j in 1:ncol
+            if fns[j] == maximum
+                finalmat[i,j] = desiredvalues[j] - weighted_norm_mat[i,j]
+            elseif fns[j] == minimum 
+                finalmat[i,j] = weighted_norm_mat[i,j] - desiredvalues[j]
+            end
+        end
+    end
+
+    di = Utilities.apply_rows(sum, finalmat)
+
+    ranks = di |> sortperm 
+    bestIndex = ranks |> first 
+
+    return PIVResult(
+        decisionMat, 
+        normalized_dec_mat, 
+        weighted_norm_mat, 
+        weights, 
+        di, 
+        ranks,
+        bestIndex)
+end 
+
+end # End of Module PIV 

test.jl

@@ -1,18 +1,3 @@
 using JMcDM
 
-df = DataFrame(
-            :K1 => [105000.0, 120000, 150000, 115000, 135000],
-            :K2 => [105.0, 110, 120, 105, 115],
-            :K3 => [10.0, 15, 12, 20, 15],
-            :K4 => [4.0, 4, 3, 4, 5],
-            :K5 => [300.0, 500, 550, 600, 400],
-            :K6 => [10.0, 8, 12, 9, 9],
-        )
-        functionlist = [minimum, maximum, minimum, maximum, maximum, minimum]
 
-        w = [0.05, 0.20, 0.10, 0.15, 0.10, 0.40]
-
-        gdf = makegrey(Matrix(df))
-        result = aras(makeDecisionMatrix(gdf), w, functionlist)
-
-        @show result 

test/testmcdm.jl

@@ -1343,5 +1343,33 @@
         @test result2 isa MERECResult
         @test result2.w == result.w
     end
+
+    @testset "PIV" begin
+        tol = 0.0001
+
+        decmat = [
+         60 2.5 2540 500 990
+         6.35 6.667 1016 3000 1041
+         6.8 10 1727 1500 1676
+         10 5 1000 2000 965
+         2.5 9.8 560 500 915
+         4.5 12.5 1016 350 508
+         3 10 1778 1000 920]
+
+        w = [0.1761,	0.2042,	0.2668,	0.1243,	0.2286]
+
+        fns = [maximum, maximum, maximum, maximum, maximum]
+
+        result = piv(makeDecisionMatrix(decmat), w, makeminmax(fns))
+
+        @test result isa PIVResult
+        @test isapprox(
+            result.scores,
+            [0.22086675609968962, 0.35854940101222144, 0.2734184099704686, 0.4005382183676046, 0.4581157878699193, 0.43595371718873477, 0.3580651803072459],
+            atol = tol,
+        )
+        @test result.bestIndex == 1
+        @test result.ranking == [1, 3, 7, 2, 4, 6, 5]
+    end
 end