add Null and Divide all by norm Normalizations

Author: jbytecode

src/main/scala/normalization.scala

@@ -4,6 +4,8 @@ import org.expr.mcdm.Mat
 import org.expr.mcdm.Vec
 import org.expr.mcdm.Direction
 
+import scala.math
+
 object Normalization:
 
   def VectorNormNormalization(
@@ -52,3 +54,31 @@ object Normalization:
       then decmat(i)(j) / columnmaxs(j)
       else columnmins(j) / decmat(i)(j)
     )
+
+  def NullNormalization(
+      decmat: Mat,
+      weights: Vec,
+      directions: Array[Direction]
+  ): Mat =
+    decmat
+  
+  def DivideByAllNormNormalization(
+      decmat: Mat,
+      weights: Vec,
+      directions: Array[Direction]
+  ): Mat =
+
+    // return mat ./ sqrt(sum(mat .* mat))
+    val (n, m) = Matrix.size(decmat)
+
+    val matmat = Array.tabulate(n, m)((i, j) => decmat(i)(j) * decmat(i)(j))
+
+    val sumofall = matmat.flatten.sum
+
+    val sqrtsum = math.sqrt(sumofall)
+
+    Array.tabulate(n, m)((i, j) => decmat(i)(j) / sqrtsum)
+
+
+    
+

src/main/scala/saw.scala

@@ -1,6 +1,7 @@
 package org.expr.mcdm
 
-import org.expr.mcdm.Direction
+import org.expr.mcdm._
+
 
 case class SawResult(
     normalizedDecisionMat: Mat,

src/test/scala/testnormalization.scala

@@ -80,5 +80,38 @@ class TestNormalizations extends munit.FunSuite {
 
     A.assert(Matrix.elementwise_equal(result, expected, 1e-5))
   }
+
+  test("Divide By All Norm Normalization"){
+      val mat = Array(
+       Array(0.0264859,  0.0565293,  0.960557,  0.765195,  0.135447),
+       Array(0.31746  ,  0.631628 ,  0.142819,  0.94584 ,  0.553917),
+       Array(0.166361 ,  0.197306 ,  0.725104,  0.334121,  0.202895))
+
+      val directions = Array(Maximize, Minimize, Maximize, Minimize, Maximize)
+
+      val result = Normalization.DivideByAllNormNormalization(mat, Array.emptyDoubleArray, directions)
+
+      val expected = Array(
+         Array(0.0132476,  0.0282746,  0.480447 ,  0.382732,  0.0677471),
+         Array(0.158786 ,  0.315925 ,  0.0714348,  0.473086,  0.277056),
+         Array(0.0832097,  0.0986878,  0.36268  ,  0.167119,  0.101483))
+
+      A.assert(Matrix.elementwise_equal(result, expected, 1e-5))
+  }
+
+  test("Null Normalization"){
+      val mat = Array(
+       Array(0.0264859,  0.0565293,  0.960557,  0.765195,  0.135447),
+       Array(0.31746  ,  0.631628 ,  0.142819,  0.94584 ,  0.553917),
+       Array(0.166361 ,  0.197306 ,  0.725104,  0.334121,  0.202895))
+
+      val directions = Array(Maximize, Minimize, Maximize, Minimize, Maximize)
+
+      val result = Normalization.NullNormalization(mat, Array.emptyDoubleArray, directions)
+
+      val expected = mat
+
+      A.assert(Matrix.elementwise_equal(result, expected, 1e-5))
+  }
 
 }