implement critic method

Author: jbytecode

.gitignore

@@ -10,3 +10,5 @@ project/**
 .metals/**
 .bsp/**
 .bloop/**
+
+.scalafmt.conf

src/main/scala/critic.scala

@@ -0,0 +1,40 @@
+package org.expr.mcdm
+
+import org.expr.mcdm.Vec 
+import org.expr.mcdm.Mat
+
+case class CriticResult(
+    weights: Vec,
+    corMat: Mat,
+    scores: Vec
+) extends MCDMResult
+
+def critic(
+    mat: Mat, 
+    directions: Array[Direction], 
+    normalization: NormalizationFunction = Normalization.MaxMinRangeNormalization
+    ): CriticResult = 
+
+        val (n, m) = Matrix.size(mat)
+
+        val normalizedMat = normalization(mat, Array.emptyDoubleArray, directions)
+
+        val onesMat = Matrix.ones(m, m)
+        val correlations = Statistics.correlation(normalizedMat)
+
+        val corMat = Matrix.subtract(onesMat, correlations)
+
+        val stds = Matrix.applyFunctionToColumns(normalizedMat, Statistics.std)
+    
+        var scores = Array.fill(m)(0.0)
+        for i <- 0 until m do
+            scores(i) = Matrix.getcolat(corMat, i).sum * stds(i)
+
+        val w = scores.map(_ / scores.sum)
+
+        CriticResult(
+            w,
+            corMat,
+            scores)
+
+

src/main/scala/matrix.scala

@@ -128,3 +128,6 @@ object Matrix:
       case Direction.Maximize => Direction.Minimize
     }
 
+  def subtract(a: Mat, b: Mat): Mat =
+    a.zip(b).map((rowa, rowb) => rowa.zip(rowb).map((x, y) => x - y))
+

src/main/scala/statistics.scala

@@ -7,6 +7,8 @@ object Statistics:
     def variance(a: Vec) : Double =
       val m = mean(a)
       a.map(x => (x - m) * (x - m)).sum / (a.length - 1.0)
+
+    def std(a: Vec): Double = math.sqrt(variance(a))
 
     def correlation(a: Vec, b: Vec): Double =
       val ma = mean(a)

src/test/scala/testcritic.scala

@@ -0,0 +1,31 @@
+import munit.Assertions as A
+
+import org.expr.mcdm.Matrix
+import org.expr.mcdm.Direction
+import org.expr.mcdm.critic
+
+class TestCritic extends munit.FunSuite {
+    
+    test("Critic Example - 1"){
+
+        val decmat = Array(
+            Array(12.9918,  0.7264,  -1.1009, 1.598139592),
+            Array(4.1201 , 5.8824 , 3.4483  , 1.021563567),
+            Array(4.1039 , 0.0000 , -0.5076 , 0.984469444)
+        )
+
+        val directions = Array(
+            Direction.Maximize,
+            Direction.Maximize,
+            Direction.Minimize,
+            Direction.Maximize
+        )
+
+        val result = critic(decmat, directions)
+
+        val expectedWeights = Array(0.16883925, 0.418444976, 0.249124763, 0.163591012)
+
+        A.assert(Matrix.elementwise_equal(result.weights, expectedWeights, 1e-5))
+    }
+
+}

src/test/scala/testmatrix.scala

@@ -352,6 +352,25 @@ class TestMatrix extends munit.FunSuite {
       Direction.Maximize)
     direction.zip(expected).foreach{case (a, b) => A.assertNotEquals(a, b)}
   }
+  test("Matrix subtract"){
+    val a = Array(
+      Array(1.0, 5.0, 6.0, 10.0, 10.0),
+      Array(-1.0, 11.0, 9.0, 11.0, 11.0),
+      Array(9.0, 17.0, 12.0, 12.0, 12.0)
+    )
+    val b = Array(
+      Array(2.0, 5.0, 6.0, 10.0, 10.0),
+      Array(-1.0, 10.0, 9.0, 11.0, 11.0),
+      Array(9.0, 17.0, 12.0, 12.0, 13.0)
+    )
+    val result = Matrix.subtract(a, b)
+    val expected = Array(
+      Array(-1.0, 0.0, 0.0, 0.0, 0.0),
+      Array(0.0, 1.0, 0.0, 0.0, 0.0),
+      Array(0.0, 0.0, 0.0, 0.0, -1.0)
+    )
+    A.assert(Matrix.elementwise_equal(result, expected, 1e-5))
+  }
 
 }