fixed a mistake in workloadbalance

method used immutable instance of clusters array

src/main/resources/logback.xml

@@ -3,11 +3,11 @@
         <!-- encoders are assigned the type
          ch.qos.logback.classic.encoder.PatternLayoutEncoder by default -->
         <encoder>
-            <pattern>%msg%n</pattern>
+            <pattern>%d{HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n</pattern>
         </encoder>
     </appender>
 
-    <root level="trace">
+    <root level="debug">
         <appender-ref ref="STDOUT" />
     </root>
 </configuration>

src/main/scala/sim/HHCSim2.scala

@@ -15,10 +15,10 @@ import cats.implicits
 import scala.util.chaining._
 
 class HHCSim2(
-    private val epsilonMax: Int = 0,
+    epsilonMax: Int = 0,
-    private val iterations: Int = 0,
+    iterations: Int = 0,
-    private val WLDMax: Float = 0,
+    WLDMax: Float = 0,
-    private val customers: String Either ArraySeq[Customer]
+    customers: String Either ArraySeq[Customer]
 ) extends LazyLogging {
   import HHCSim2._
   import Ordering.Double.IeeeOrdering
@@ -39,7 +39,7 @@ class HHCSim2(
         case (mstUpdated, clusters, edgeMappings, removedEdges) =>
           printClusters(clusters)
           val e =
-            groupClusters2(mstUpdated, clusters, edgeMappings, removedEdges)
+            groupClusters(mstUpdated, clusters, edgeMappings, removedEdges)
           printGroups(e._5)
           e
       }
@@ -184,7 +184,7 @@ class HHCSim2(
           logger.debug(s"Nearest neighbour = $s")
           // val avg = averagePairwiseDistance(mst(s))
           val avg = adjMatrix
-            .map(m => averagePairwiseDistance(m)(clusters(s)))
+            .map(m => averagePairwiseDistance(m)(mutClusters(s)))
             .getOrElse(9999d)
           //  if (avg < epsilonMax) true else false
 
@@ -204,28 +204,9 @@ class HHCSim2(
     })
   }
 
-  // def averagePairwiseDistance[T](
-  //     lst: List[HHCEdge2[T]]
-  // )(implicit num: Numeric[T]) = {
-  //   val it = lst.combinations(2)
-  //   val sum = lst.foldLeft(0)((x, y) => x + num.toInt(y.weight))
-  //   val n = if (lst.length == 0) 1 else lst.length
-  //   val avg = sum / n
-
-  //   // it.map()
-  //   avg
-  // }
-
   def averagePairwiseDistance(graph: GraphMatrix[Double])(
       cluster: List[Int]
   ) = {
-    // val iter = cluster.combinations(2)
-    // var sum = 0d
-    // for (pair <- iter) {
-    //   val (i, j) = pair(0) -> pair(1)
-    //   sum += graph(i)(j)
-    // }
-    // val n = if (iter.length == 0) 1 else iter.length
     val (sum, size) = cluster
       .combinations(2)
       .foldLeft((0d, 0))((acc, pair) => {
@@ -266,10 +247,6 @@ object HHCSim2 extends HHCTypes {
         arr match {
           case Array(latitude, longitude, workLoad) => {
             val cust =
-              // Customer(
-              //   Coord(latitude.toDouble, longitude.toDouble),
-              //   workLoad.toFloat
-              // )
               for {
                 lat <- latitude.toDoubleOption
                 lon <- longitude.toDoubleOption
@@ -280,7 +257,6 @@ object HHCSim2 extends HHCTypes {
               case Some(c) => loop(lst += c, iter)
               case None    => Left(s"Error reading customers at line - $line")
             }
-            // loop(lst += cust, iter)
           }
           case _ => {
             if (line.equals(" ") || line.contains("\n"))
@@ -439,21 +415,8 @@ object HHCSim2 extends HHCTypes {
     val result = ArraySeq.tabulate(mstUpdated.length) { i =>
       {
         val buf = ListBuffer[Int]()
-        // mstUpdated(i).isEmpty match {
-        //   case true => { lst += i }
-        //   case false => {
-        //     if (!visited(i)) {
-        //       lst ++= DFS(i)(mstUpdated)
-        //       for (j <- lst) visited(j) = true
-        //     }
-        //   }
-        // }
         visited(i) match {
-          case true => {
+          case true =>
-            // val (nds, rms) = assignEdges(List(i), removedEdges2)
-            // egdeMappings(i) = nds
-            // removedEdges2 = rms
-          }
           case false if (!mstUpdated(i).isEmpty) => {
             val nodes = DFS(i)(mstUpdated)
             buf ++= nodes
@@ -475,8 +438,6 @@ object HHCSim2 extends HHCTypes {
 
     // println(s"Removed edges size: ${removedEdges2.size}")
 
-    // result
-    // (result, ArraySeq.unsafeWrapArray(egdeMappings), removedEdges)
     (
       mstUpdated,
       result.filterNot(_.isEmpty),
@@ -503,31 +464,11 @@ object HHCSim2 extends HHCTypes {
         val node = iter.next
         val (filt, rm) =
           removedEdges.partition(e => e.fromNode == node)
-        // removedEdges.partition(e => e.toNode == node || e.fromNode == node)
         loop(nodes, rm, iter, filt)
       }
     }
-    // val filtered = ListBuffer.empty[HHCEdge2[T]]
-    // val updated = ListBuffer.empty[HHCEdge2[T]]
-    // for (node <- nodes) {
-    //   val (filt, rm) =
-    //     removedEdges.partition(e => e.toNode == node || e.fromNode == node)
-    //   if (!filtered.isEmpty) {
-    //     // return (filt.toList, rm.toList)
-    //   } else {
-    //     filtered ++= filt
-    //     updated ++= rm
-    //   }
-    // }
-
-    // print(filtered)
-    // (filtered.toList, updated.toList)
     val (edges, removedEdgesUpdated) =
       loop(nodes, removedEdges, it, List.empty[HHCEdge2[T]])
-    // val edges2 = edges.sortWith {
-    //   case (HHCEdge2(_, _, weight1), HHCEdge2(_, _, weight2)) =>
-    //     weight1 < weight2
-    // }
     (edges, removedEdgesUpdated)
   }
 
@@ -543,50 +484,6 @@ object HHCSim2 extends HHCTypes {
   }
 
   def groupClusters[N: Ordering](
-      clusters: Clusters,
-      edgeMappings: ArraySeq[List[HHCEdge2[N]]],
-      removed: RemovedEdges[N]
-  ) = {
-    var k = -1
-    val clustMap = collection.mutable.Map.empty[Int, Int]
-    val groups = ArraySeq.tabulate(edgeMappings.size)(i => {
-      val buf = ListBuffer.empty[(Int, N)]
-      val lst = edgeMappings(i)
-      val y = lst.foreach(e => {
-        if (edgeMappings(e.toNode).isEmpty) {
-          // var buf = ListBuffer.empty[(Int, N)]
-          for (j <- 0 until clusters.size) {
-            for (edge <- clusters(j)) {
-              if (edge == e.toNode)
-                buf += (j -> e.weight)
-            }
-          }
-          // Left(buf.toList)
-        } else {
-          buf += ((e.toNode, e.weight))
-        }
-        // Right(lst.map {
-        //   case HHCEdge2(fromNode, toNode, weight) => (toNode, weight)
-        // })
-      })
-      if (!clusters(i).isEmpty) {
-        k += 1
-        clustMap += k -> i
-      }
-      // val lst2 = lst.map {
-      //   case HHCEdge2(fromNode, toNode, weight) => (toNode, weight)
-      // }
-      // if (buf.isEmpty) lst2 else buf.toList ::: lst2
-      // if (lt.isEmpty) rt else lt
-      (k, buf.sortWith {
-        case ((_, weight1), (_, weight2)) =>
-          weight1 < weight2
-      }.toList)
-    })
-    (clusters, clustMap.toMap, removed, groups)
-  }
-
-  def groupClusters2[N: Ordering](
       mst: Graph[N],
       clusters: Clusters,
       edgeMappings: ArraySeq[List[HHCEdge2[N]]],
@@ -607,147 +504,3 @@ object HHCSim2 extends HHCTypes {
     (mst, clusters, edgeMappings, removed, groups)
   }
 }
-// def fun2[N: Numeric](
-//       clusters: Clusters,
-//       clustMap: Map[Int, Int],
-//       removedEdges: RemovedEdges[N],
-//       groups: ArraySeq[(Int, List[(Int, N)])]
-//   ) = {
-//     customers.map(c => {
-//       val mutClusters =
-//         clusters.view.filterNot(_.isEmpty).zipWithIndex.toArray
-//       val k = mutClusters.size
-//       // val k = clustMap.size
-//       for (_ <- 0 until iterations) {
-//         val WL = mutClusters.view
-//         // .filter(!_._1.isEmpty)
-//           .map(e => {
-//             val (clust, _) = e
-//             val customers = clust.map(j => c(j))
-//             val wl = customers.foldLeft(0f)(_ + _.workload)
-//             wl
-//           })
-//           .to(ArraySeq)
-//         // logger.debug(s"$mutClusters")
-//         logger.debug(s"$WL")
-//         var counter = 0
-//         mutClusters.sortInPlaceWith((x, y) => {
-//           val l = WL(counter)
-//           if (counter < mutClusters.length - 1) counter += 1
-//           println(s"$counter")
-//           val r = WL(counter)
-//           l < r
-//           // WL(x._2) < WL(y._2)
-//         })
-//         val (minWL, maxWL) = WL.foldLeft((99999f, 0f))((x, y) => {
-//           val (currMin, currMax) = x
-//           if (y < currMin) (y, currMax)
-//           else if (y > currMax) (currMin, y)
-//           else x
-//         })
-//         // val mutSortedClust = mutClusters.sort
-//         val WLD = maxWL - minWL
-//         // logger.debug(s"Iterations = $iterations")
-
-//         logger.debug(s"maxWL = $maxWL, minWL = $minWL")
-//         logger.debug(s"WLD = $WLD")
-//         if (WLD > WLDMax) {
-//           val clustNum = Random.between(((k / 2) + 1), k)
-//           // while (clusters(groupNum).isEmpty) {
-//           //   print(s"num = $groupNum")
-//           //   groupNum = Random.between(((k / 2) + 1), k)
-//           // }
-//           // val clustT = mutClusters(groupNum)
-//           logger.debug(s"Chosen Num=$clustNum")
-//           // val t = groups(groupNum)._1
-//           val p = mutClusters(clustNum)._2
-//           logger.debug(s"Clust num =$p")
-//           val t = clustMap(p)
-//           // val t = mutClusters(clustNum)._2
-//           logger.debug(s"T=$t")
-//           val s = groups(t)._2(0)
-//           logger.debug(s"Nearest neighbour = ${s._1}")
-//         }
-//       }
-//       ArraySeq.unsafeWrapArray(mutClusters)
-//     })
-//   }
-//  def workloadBalance[N: Numeric](
-//       clusters: Clusters,
-//       edgeMappings: ArraySeq[List[HHCEdge2[N]]],
-//       removedEdges: RemovedEdges[N],
-//       groups: ArraySeq[(Int, List[(Int, N)])]
-//   ) =
-//     customers.map { c =>
-//       val k = clusters.size
-//       val mutClusters = clusters.toArray
-//       for (_ <- 0 until iterations) {
-//         val worlkLoads =
-//           mutClusters.map(
-//             _.map(j => c(j)).foldLeft(0f)((x, y) => x + y.workload)
-//           )
-//         val sortedClusters =
-//           mutClusters.map(_.sortWith((x, y) => c(x).workload < c(y).workload))
-//         val minWL =
-//           c.foldLeft(c(0).workload)(_ min _.workload)
-//         val maxWL =
-//           c.foldLeft(0f)(_ max _.workload)
-//         val WLD = maxWL - minWL
-//         if (WLD > WLDMax) {
-//           var t = Random.between(((k / 2) + 1), k)
-//           while (!mutClusters(t).isEmpty) t = Random.between(((k / 2) + 1), k)
-//           val Ds = groups(t)._2(0)
-//           mutClusters.updated(Ds._1, t)
-//         }
-//       }
-//     }
-
-//   def fun[N: Numeric](
-//       clusters: Clusters,
-//       clustMap: Map[Int, Int],
-//       removedEdges: RemovedEdges[N],
-//       groups: ArraySeq[(Int, List[(Int, N)])]
-//   ) = {
-//     customers.map(c => {
-//       val mutClusters = clusters.toArray
-//       // val k = mutClusters.size
-//       val k = clustMap.size
-//       for (_ <- 0 until iterations) {
-//         val WL = mutClusters.view
-//           .filter(!_.isEmpty)
-//           .map(clust => {
-//             val customers = clust.map(j => c(j))
-//             val wl = customers.foldLeft(0f)(_ + _.workload)
-//             wl
-//           })
-//           .to(ArraySeq)
-//         val (minWL, maxWL) = WL.foldLeft((99999f, 0f))((x, y) => {
-//           val (currMin, currMax) = x
-//           if (y < currMin) (y, currMax)
-//           else if (y > currMax) (currMin, y)
-//           else x
-//         })
-//         // val mutSortedClust = mutClusters.sort
-//         val WLD = maxWL - minWL
-//         // logger.debug(s"Iterations = $iterations")
-//         logger.debug(s"$WL")
-//         logger.debug(s"maxWL = $maxWL, minWL = $minWL")
-//         logger.debug(s"WLD = $WLD")
-//         if (WLD > WLDMax) {
-//           var clustNum = Random.between(((k / 2) + 1), k)
-//           // while (clusters(groupNum).isEmpty) {
-//           //   print(s"num = $groupNum")
-//           //   groupNum = Random.between(((k / 2) + 1), k)
-//           // }
-//           // val clustT = mutClusters(groupNum)
-//           logger.debug(s"Clust Num=$clustNum")
-//           // val t = groups(groupNum)._1
-//           val t = clustMap(clustNum)
-//           logger.debug(s"T=$t")
-//           val s = groups(t)._2(0)
-//           logger.debug(s"Nearest neighbour = ${s._1}")
-//         }
-//       }
-//       ArraySeq.unsafeWrapArray(mutClusters)
-//     })
-//   }

test-output2.txt

@@ -1,4 +1,3 @@
-Graph: 
 0.00, 1169.81, 464.59, 945.75, 1282.33, 1037.66, 640.89, 335.29, 111.67, 1142.56, 876.33, 845.31, 1211.84, 587.31, 735.31, 497.33, 994.87, 922.07, 767.10, 1206.08, 
 1169.81, 0.00, 722.99, 682.83, 191.14, 527.48, 777.24, 929.62, 1122.63, 195.17, 605.52, 512.54, 221.44, 833.01, 862.90, 703.24, 856.59, 540.04, 634.58, 278.41, 
 464.59, 722.99, 0.00, 724.38, 859.17, 599.15, 302.60, 218.49, 402.17, 732.89, 621.64, 398.11, 747.67, 294.91, 643.37, 234.41, 848.21, 485.90, 527.86, 813.31, 
@@ -93,14 +92,28 @@ ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 42.29, 55.81, 23.
 Current order [2, 1, 9, 7, 10, 8, 4, 6, 0, 5, 3]
 maxWL = 338.0, minWL = 2.66
 WLD = 335.34
-Rand Num=10
+Rand Num=7
-Chosen cluster = 3
+Chosen cluster = 6
-Nearest neighbour = 9
+Nearest neighbour = 5
+5 17 - 
+weight = 115.60102365831875
+5 11 - 
+weight = 202.1756669521256
+17 11 - 
+weight = 95.71999741615204
+Pairwise average = 137.83222934219881
+ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 42.29, 55.81, 23.83, 55.61)
+Current order [2, 1, 9, 7, 10, 8, 4, 6, 0, 5, 3]
+maxWL = 338.0, minWL = 2.66
+WLD = 335.34
+Rand Num=6
+Chosen cluster = 4
+Nearest neighbour = 1
 Pairwise average = 0.0
 Average less than epsilonMax. Updating Clusters.
 Clusters:
 Cluster-0: 0 8 
-Cluster-1: 1 
+Cluster-1: 4 1 
 Cluster-2: 2 
 Cluster-3: 3 10 18 16 
 Cluster-4: 4 9 19 
@@ -108,31 +121,52 @@ Cluster-5: 5 17 11
 Cluster-6: 6 13 
 Cluster-7: 7 
 Cluster-8: 12 
-Cluster-9: 3 14 
+Cluster-9: 14 
 Cluster-10: 15 
-ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 42.29, 55.81, 112.740005, 55.61)
+ArraySeq(115.13, 70.24, 2.66, 338.0, 66.130005, 136.32, 79.13, 42.29, 55.81, 23.83, 55.61)
-Current order [2, 1, 7, 10, 8, 4, 6, 9, 0, 5, 3]
+Current order [2, 9, 7, 10, 8, 4, 1, 6, 0, 5, 3]
 maxWL = 338.0, minWL = 2.66
 WLD = 335.34
-Rand Num=9
+Rand Num=7
-Chosen cluster = 5
+Chosen cluster = 6
-Nearest neighbour = 8
+Nearest neighbour = 5
-Pairwise average = 0.0
+5 17 - 
-Average less than epsilonMax. Updating Clusters.
+weight = 115.60102365831875
-Clusters:
+5 11 - 
-Cluster-0: 0 8 
+weight = 202.1756669521256
-Cluster-1: 1 
+17 11 - 
-Cluster-2: 2 
+weight = 95.71999741615204
-Cluster-3: 3 10 18 16 
+Pairwise average = 137.83222934219881
-Cluster-4: 4 9 19 
+ArraySeq(115.13, 70.24, 2.66, 338.0, 66.130005, 136.32, 79.13, 42.29, 55.81, 23.83, 55.61)
-Cluster-5: 5 17 11 
+Current order [2, 9, 7, 10, 8, 4, 1, 6, 0, 5, 3]
-Cluster-6: 6 13 
+maxWL = 338.0, minWL = 2.66
-Cluster-7: 7 
+WLD = 335.34
-Cluster-8: 5 12 
+Rand Num=6
-Cluster-9: 3 14 
+Chosen cluster = 1
-Cluster-10: 15 
+Nearest neighbour = 4
-ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 42.29, 89.44, 112.740005, 55.61)
+4 9 - 
-Current order [2, 1, 7, 10, 4, 6, 8, 9, 0, 5, 3]
+weight = 148.32786590021598
+4 19 - 
+weight = 152.85018337083108
+9 19 - 
+weight = 99.82544752586941
+Pairwise average = 133.66783226563882
+ArraySeq(115.13, 70.24, 2.66, 338.0, 66.130005, 136.32, 79.13, 42.29, 55.81, 23.83, 55.61)
+Current order [2, 9, 7, 10, 8, 4, 1, 6, 0, 5, 3]
+maxWL = 338.0, minWL = 2.66
+WLD = 335.34
+Rand Num=7
+Chosen cluster = 6
+Nearest neighbour = 5
+5 17 - 
+weight = 115.60102365831875
+5 11 - 
+weight = 202.1756669521256
+17 11 - 
+weight = 95.71999741615204
+Pairwise average = 137.83222934219881
+ArraySeq(115.13, 70.24, 2.66, 338.0, 66.130005, 136.32, 79.13, 42.29, 55.81, 23.83, 55.61)
+Current order [2, 9, 7, 10, 8, 4, 1, 6, 0, 5, 3]
 maxWL = 338.0, minWL = 2.66
 WLD = 335.34
 Rand Num=8
@@ -142,40 +176,51 @@ Pairwise average = 0.0
 Average less than epsilonMax. Updating Clusters.
 Clusters:
 Cluster-0: 0 8 
-Cluster-1: 1 
+Cluster-1: 4 1 
 Cluster-2: 2 
 Cluster-3: 3 10 18 16 
 Cluster-4: 4 9 19 
 Cluster-5: 5 17 11 
 Cluster-6: 6 13 
 Cluster-7: 0 7 
-Cluster-8: 5 12 
+Cluster-8: 12 
-Cluster-9: 3 14 
+Cluster-9: 14 
 Cluster-10: 15 
-ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 125.17, 89.44, 112.740005, 55.61)
+ArraySeq(115.13, 70.24, 2.66, 338.0, 66.130005, 136.32, 79.13, 125.17, 55.81, 23.83, 55.61)
-Current order [2, 1, 10, 4, 6, 8, 9, 0, 7, 5, 3]
+Current order [2, 9, 10, 8, 4, 1, 6, 0, 7, 5, 3]
-maxWL = 338.0, minWL = 2.66
-WLD = 335.34
-Rand Num=6
-Chosen cluster = 9
-Nearest neighbour = 3
-3 10 - 
-weight = 110.32563662919385
-3 18 - 
-weight = 197.93532036986213
-3 16 - 
-weight = 176.6244692117106
-10 18 - 
-weight = 109.32984942622367
-10 16 - 
-weight = 277.38062276155625
-18 16 - 
-weight = 330.7167343396919
-Pairwise average = 200.3854387897064
-ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 125.17, 89.44, 112.740005, 55.61)
-Current order [2, 1, 10, 4, 6, 8, 9, 0, 7, 5, 3]
 maxWL = 338.0, minWL = 2.66
 WLD = 335.34
+Rand Num=8
+Chosen cluster = 7
+Nearest neighbour = 2
+Pairwise average = 0.0
+Average less than epsilonMax. Updating Clusters.
+Clusters:
+Cluster-0: 0 8 
+Cluster-1: 4 1 
+Cluster-2: 7 2 
+Cluster-3: 3 10 18 16 
+Cluster-4: 4 9 19 
+Cluster-5: 5 17 11 
+Cluster-6: 6 13 
+Cluster-7: 0 7 
+Cluster-8: 12 
+Cluster-9: 14 
+Cluster-10: 15 
+ArraySeq(115.13, 70.24, 44.95, 338.0, 66.130005, 136.32, 79.13, 125.17, 55.81, 23.83, 55.61)
+Current order [9, 2, 10, 8, 4, 1, 6, 0, 7, 5, 3]
+maxWL = 338.0, minWL = 23.83
+WLD = 314.17
+Rand Num=7
+Chosen cluster = 0
+Nearest neighbour = 7
+0 7 - 
+weight = 335.29122627935715
+Pairwise average = 335.29122627935715
+ArraySeq(115.13, 70.24, 44.95, 338.0, 66.130005, 136.32, 79.13, 125.17, 55.81, 23.83, 55.61)
+Current order [9, 2, 10, 8, 4, 1, 6, 0, 7, 5, 3]
+maxWL = 338.0, minWL = 23.83
+WLD = 314.17
 Rand Num=10
 Chosen cluster = 3
 Nearest neighbour = 9
@@ -183,118 +228,23 @@ Pairwise average = 0.0
 Average less than epsilonMax. Updating Clusters.
 Clusters:
 Cluster-0: 0 8 
-Cluster-1: 1 
+Cluster-1: 4 1 
-Cluster-2: 2 
+Cluster-2: 7 2 
 Cluster-3: 3 10 18 16 
 Cluster-4: 4 9 19 
 Cluster-5: 5 17 11 
 Cluster-6: 6 13 
 Cluster-7: 0 7 
-Cluster-8: 5 12 
+Cluster-8: 12 
 Cluster-9: 3 14 
 Cluster-10: 15 
-ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 125.17, 89.44, 112.740005, 55.61)
+ArraySeq(115.13, 70.24, 44.95, 338.0, 66.130005, 136.32, 79.13, 125.17, 55.81, 112.740005, 55.61)
-Current order [2, 1, 10, 4, 6, 8, 9, 0, 7, 5, 3]
+Current order [2, 10, 8, 4, 1, 6, 9, 0, 7, 5, 3]
-maxWL = 338.0, minWL = 2.66
+maxWL = 338.0, minWL = 44.95
-WLD = 335.34
+WLD = 293.05
-Rand Num=10
+Rand Num=8
-Chosen cluster = 3
+Chosen cluster = 7
-Nearest neighbour = 9
+Nearest neighbour = 2
-Pairwise average = 0.0
+7 2 - 
-Average less than epsilonMax. Updating Clusters.
+weight = 218.49036137013215
-Clusters:
+Pairwise average = 218.49036137013215
-Cluster-0: 0 8 
-Cluster-1: 1 
-Cluster-2: 2 
-Cluster-3: 3 10 18 16 
-Cluster-4: 4 9 19 
-Cluster-5: 5 17 11 
-Cluster-6: 6 13 
-Cluster-7: 0 7 
-Cluster-8: 5 12 
-Cluster-9: 3 14 
-Cluster-10: 15 
-ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 125.17, 89.44, 112.740005, 55.61)
-Current order [2, 1, 10, 4, 6, 8, 9, 0, 7, 5, 3]
-maxWL = 338.0, minWL = 2.66
-WLD = 335.34
-Rand Num=9
-Chosen cluster = 5
-Nearest neighbour = 8
-Pairwise average = 0.0
-Average less than epsilonMax. Updating Clusters.
-Clusters:
-Cluster-0: 0 8 
-Cluster-1: 1 
-Cluster-2: 2 
-Cluster-3: 3 10 18 16 
-Cluster-4: 4 9 19 
-Cluster-5: 5 17 11 
-Cluster-6: 6 13 
-Cluster-7: 0 7 
-Cluster-8: 5 12 
-Cluster-9: 3 14 
-Cluster-10: 15 
-ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 125.17, 89.44, 112.740005, 55.61)
-Current order [2, 1, 10, 4, 6, 8, 9, 0, 7, 5, 3]
-maxWL = 338.0, minWL = 2.66
-WLD = 335.34
-Rand Num=9
-Chosen cluster = 5
-Nearest neighbour = 8
-Pairwise average = 0.0
-Average less than epsilonMax. Updating Clusters.
-Clusters:
-Cluster-0: 0 8 
-Cluster-1: 1 
-Cluster-2: 2 
-Cluster-3: 3 10 18 16 
-Cluster-4: 4 9 19 
-Cluster-5: 5 17 11 
-Cluster-6: 6 13 
-Cluster-7: 0 7 
-Cluster-8: 5 12 
-Cluster-9: 3 14 
-Cluster-10: 15 
-ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 125.17, 89.44, 112.740005, 55.61)
-Current order [2, 1, 10, 4, 6, 8, 9, 0, 7, 5, 3]
-maxWL = 338.0, minWL = 2.66
-WLD = 335.34
-Rand Num=9
-Chosen cluster = 5
-Nearest neighbour = 8
-Pairwise average = 0.0
-Average less than epsilonMax. Updating Clusters.
-Clusters:
-Cluster-0: 0 8 
-Cluster-1: 1 
-Cluster-2: 2 
-Cluster-3: 3 10 18 16 
-Cluster-4: 4 9 19 
-Cluster-5: 5 17 11 
-Cluster-6: 6 13 
-Cluster-7: 0 7 
-Cluster-8: 5 12 
-Cluster-9: 3 14 
-Cluster-10: 15 
-ArraySeq(115.13, 21.65, 2.66, 338.0, 66.130005, 136.32, 79.13, 125.17, 89.44, 112.740005, 55.61)
-Current order [2, 1, 10, 4, 6, 8, 9, 0, 7, 5, 3]
-maxWL = 338.0, minWL = 2.66
-WLD = 335.34
-Rand Num=9
-Chosen cluster = 5
-Nearest neighbour = 8
-Pairwise average = 0.0
-Average less than epsilonMax. Updating Clusters.
-Clusters:
-Cluster-0: 0 8 
-Cluster-1: 1 
-Cluster-2: 2 
-Cluster-3: 3 10 18 16 
-Cluster-4: 4 9 19 
-Cluster-5: 5 17 11 
-Cluster-6: 6 13 
-Cluster-7: 0 7 
-Cluster-8: 5 12 
-Cluster-9: 3 14 
-Cluster-10: 15