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fixed a mistake in workloadbalance

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method used immutable instance of clusters array
This commit is contained in:
Rohan Sircar 2020-04-29 16:16:49 +05:30
parent 365c028fbc
commit f34d5dce80
3 changed files with 119 additions and 416 deletions
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4
src/main/resources/logback.xml
View File

@ -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>

261
src/main/scala/sim/HHCSim2.scala
View File

@ -15,10 +15,10 @@ import cats.implicits
import scala.util.chaining._
class HHCSim2(
private val epsilonMax: Int = 0,
private val iterations: Int = 0,
private val WLDMax: Float = 0,
private val customers: String Either ArraySeq[Customer]
epsilonMax: Int = 0,
iterations: Int = 0,
WLDMax: Float = 0,
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 => {
// val (nds, rms) = assignEdges(List(i), removedEdges2)
// egdeMappings(i) = nds
// removedEdges2 = rms
}
case true =>
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)
// })
// }

270
test-output2.txt
View File

@ -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
Chosen cluster = 3
Nearest neighbour = 9
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, 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)
Current order [2, 1, 7, 10, 8, 4, 6, 9, 0, 5, 3]
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=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: 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, 42.29, 89.44, 112.740005, 55.61)
Current order [2, 1, 7, 10, 4, 6, 8, 9, 0, 5, 3]
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=6
Chosen cluster = 1
Nearest neighbour = 4
4 9 -
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-9: 3 14
Cluster-8: 12
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)
Current order [2, 1, 10, 4, 6, 8, 9, 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]
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, 9, 10, 8, 4, 1, 6, 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-2: 2
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: 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)
Current order [2, 1, 10, 4, 6, 8, 9, 0, 7, 5, 3]
maxWL = 338.0, minWL = 2.66
WLD = 335.34
Rand Num=10
Chosen cluster = 3
Nearest neighbour = 9
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
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, 70.24, 44.95, 338.0, 66.130005, 136.32, 79.13, 125.17, 55.81, 112.740005, 55.61)
Current order [2, 10, 8, 4, 1, 6, 9, 0, 7, 5, 3]
maxWL = 338.0, minWL = 44.95
WLD = 293.05
Rand Num=8
Chosen cluster = 7
Nearest neighbour = 2
7 2 -
weight = 218.49036137013215
Pairwise average = 218.49036137013215