1 files changed, 0 insertions, 46 deletions
diff --git a/src/main/scala/com/nsrddyn/FPU/CholeskyDecomposition.scala b/src/main/scala/com/nsrddyn/FPU/CholeskyDecomposition.scala
deleted file mode 100644
index 895473a..0000000
--- a/src/main/scala/com/nsrddyn/FPU/CholeskyDecomposition.scala
+++ /dev/null
@@ -1,46 +0,0 @@
-package com.nsrddyn.fpu
-
-import scala.math._
-import scala.collection.immutable.ListSet
-import scala.collection.mutable.ArrayBuffer
-
-class CholeskyDecomposition {
-
-  /*
-   *  Floating point operation to stress the cpu
-   *  Calculate the number of KFLOPS / FLOPS
-   *  implementation of the Cholesky decomposition
-   *  More information on the Cholesky decomposition at: 
-   *  https://en.wikipedia.org/wiki/Cholesky_decomposition
-   *  
-   *  Linpack uses the cholesky decomposition
-   *  https://www.netlib.org/linpack/
-   *
-   *  https://www.geeksforgeeks.org/dsa/cholesky-decomposition-matrix-decomposition/
-   *
-   *  The Cholesky decomposition maps matrix A into the product of A = L · LH where L is the lower triangular matrix and LH is the transposed,
-   *  complex conjugate or Hermitian, and therefore of upper triangular form (Fig. 13.6).
-   *  This is true because of the special case of A being a square, conjugate symmetric matrix. 
-   */ 
-
-  def run(matrix: Vector[Vector[Int]]): Unit = {
-
-    val size: Int = matrix.size
-    val lower: ArrayBuffer[ArrayBuffer[Int]] = ArrayBuffer[ArrayBuffer[Int]]()
-
-    for 
-      i <- 0 to size 
-      j <- 0 until i 
-    do 
-      if i == j then lower(i)(j) = getSquaredSummation(lower, i, j, matrix) else lower(j)(j) = getReversedSummation(lower, i, j, matrix)
-
-  }
-
-  private def getReversedSummation(lower: ArrayBuffer[ArrayBuffer[Int]], i: Int, j: Int, matrix: Vector[Vector[Int]]) = {
-    math.sqrt(matrix(j)(j) - (0 until j).map { k => lower(i)(k) * lower(j)(k) }.sum).toInt
-  }
-  private def getSquaredSummation(lower: ArrayBuffer[ArrayBuffer[Int]], i: Int, j: Int, matrix:  Vector[Vector[Int]]) = {
-    ((matrix(i)(j) - (0 until j).map { k => math.pow(lower(j)(k), 2)}.sum) / lower(j)(j)).toInt
-  }
-}
-