implement Mabac

Author: jbytecode

src/main/scala/mabac.scala

@@ -0,0 +1,38 @@
+package org.expr.mcdm
+
+case class MabacResult(
+    scores: Vec
+) extends MCDMResult
+
+def mabac(
+    decmat: Mat,
+    weights: Vec,
+    directions: Array[Direction],
+    normalization: NormalizationFunction =
+      Normalization.MaxMinRangeNormalization,
+    options: Map[String, Any] = Map.empty 
+): MabacResult =
+
+    val (row, col) = Matrix.size(decmat)
+
+    val colMax = Matrix.colmaxs(decmat)
+
+    val colMin = Matrix.colmins(decmat)
+
+    val A = normalization(decmat, weights, directions)
+
+    // wA = Utilities.weightise((A .+ one(zerotype)), w)
+    val wA = Matrix.weightizeColumns(A.map(row => row.map(_ + 1.0)), weights)
+
+    // g[i] = geomean(wA[:, i])
+    val g = Matrix.applyFunctionToColumns(wA, Statistics.geomean)
+
+    // Q = wA .- g'
+    val Q = wA.map(row => row.zip(g).map { case (x, y) => x - y })
+
+
+    // scores[i] = sum(Q[i, :])
+    val scores = Q.map(row => row.sum)
+
+    MabacResult(scores)
+

src/main/scala/statistics.scala

@@ -1,5 +1,7 @@
 package org.expr.mcdm
 
+import scala.math.{log, exp}
+
 object Statistics:
 
   def mean(a: Vec): Double = a.sum / a.length
@@ -48,3 +50,4 @@ object Statistics:
       case n if n % 2 == 0 => (sorted(n / 2 - 1) + sorted(n / 2)) / 2.0
       case _ => sorted(L / 2)
 
+

src/test/scala/testmabac.scala

@@ -0,0 +1,38 @@
+import munit.Assertions as A
+
+import org.expr.mcdm.mabac
+import org.expr.mcdm.Direction.{Maximize, Minimize}
+import org.expr.mcdm.Matrix
+
+class TestMabac extends munit.FunSuite {
+  test("Mabac Example - 1") {
+
+    val decmat = Array(
+        Array(2.0, 1, 4, 7, 6, 6, 7, 3000),
+        Array(4.0, 1, 5, 6, 7, 7, 6, 3500),
+        Array(3.0, 2, 6, 6, 5, 6, 8, 4000),
+        Array(5.0, 1, 5, 7, 6, 7, 7, 3000),
+        Array(4.0, 2, 5, 6, 7, 7, 6, 3000),
+        Array(3.0, 2, 6, 6, 6, 6, 6, 3500))
+
+    val weights = Array(0.293, 0.427, 0.067, 0.027, 0.053, 0.027, 0.053, 0.053)
+
+    val fns = Array(
+        Maximize,
+        Maximize,
+        Maximize,
+        Maximize,
+        Maximize,
+        Maximize,
+        Maximize,
+        Minimize
+    )
+
+    val result = mabac(decmat, weights, fns)
+
+    val expected_scores = Array(-0.31132, -0.10898, 0.20035, 0.04218, 0.34452, 0.20035)
+
+    A.assert(Matrix.elementwise_equal(result.scores, expected_scores, 0.0001))
+  }
+
+}