Completed given algorithm
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src/main/resources/logback.xml
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13
src/main/resources/logback.xml
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@ -0,0 +1,13 @@
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<configuration>
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<appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
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<!-- encoders are assigned the type
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ch.qos.logback.classic.encoder.PatternLayoutEncoder by default -->
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<encoder>
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<pattern>%msg%n</pattern>
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</encoder>
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</appender>
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<root level="trace">
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<appender-ref ref="STDOUT" />
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</root>
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</configuration>
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@ -76,59 +76,71 @@ object Main {
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customers = customers
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customers = customers
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)
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)
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val x = hhc.go()
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// val x = hhc.go()
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x match {
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val x2 = hhc.go()
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case Left(value) => println(value)
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case Right(value) =>
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}
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x.foreach(d => {
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// x match {
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val (a, b, c, g) = d
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// case Left(value) => println(value)
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println(c)
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// case Right(value) =>
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// }
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println
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// x.foreach(d => {
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// val (a, b, c, g, groups) = d
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// // println(c)
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// // println
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// // println(b)
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// // for (i <- 0 until b.length) {
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// // if (!b(i).isEmpty) {
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// // print(s"$i ")
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// // print(b(i))
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// // // edgeMappings
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// // // b(i).foreach(e => {
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// // // print(f"(${e.toNode}%d, ${e.weight}%.2f), ")
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// // // })
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// // println
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// // }
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// // }
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// // println
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// // for (i <- 0 until g.length) {
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// // if (!g(i)._2.isEmpty) {
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// // print(s"$i ")
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// // print(g(i))
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// // // edgeMappings
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// // // b(i).foreach(e => {
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// // // print(f"(${e.toNode}%d, ${e.weight}%.2f), ")
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// // // })
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// // println
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// // }
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// // }
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// println
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// // var i = 0
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// // b.foreach(e => {
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// // // if (!e.isEmpty) {
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// // print(s"Cluster-$i: ")
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// // i += 1
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// // e.foreach(f => { print(s"$f ") })
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// // println
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// // // }
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// // })
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// // i = 0
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// // groups.foreach(e => {
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// // // if (!e.isEmpty) {
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// // print(s"Cluster-$i: ")
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// // i += 1
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// // e.foreach(f => { print(f"(${f._1}%d, ${f._2}%.2f), ") })
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// // println
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// // // }
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// // })
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for (i <- 0 until b.length) {
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if (!b(i).isEmpty) {
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print(s"$i ")
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print(b(i))
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// edgeMappings
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// b(i).foreach(e => {
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// print(f"(${e.toNode}%d, ${e.weight}%.2f), ")
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// })
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// })
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println
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}
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}
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println
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for (i <- 0 until g.length) {
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// x2.map(_.map(println))
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if (!g(i)._2.isEmpty) {
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print(s"$i ")
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print(g(i))
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// edgeMappings
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// b(i).foreach(e => {
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// print(f"(${e.toNode}%d, ${e.weight}%.2f), ")
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// })
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println
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}
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}
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println
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var i = 0
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a.foreach(e => {
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if (!e.isEmpty) {
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print(s"Cluster-$i: ")
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i += 1
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e.foreach(f => { print(s"$f ") })
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println
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}
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})
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})
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val x2 = hhc.go2()
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x2.map(_.map(println))
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// x.map(e => {
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// x.map(e => {
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// val (mst, rm) = e
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// val (mst, rm) = e
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@ -10,72 +10,234 @@ import model.Customer
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import scala.io.BufferedSource
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import scala.io.BufferedSource
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import model.Coord
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import model.Coord
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import scala.util.Random
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import scala.util.Random
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import com.typesafe.scalalogging.LazyLogging
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import cats.implicits
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import scala.util.chaining._
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class HHCSim2(
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class HHCSim2(
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private val epsilonMax: Int = 0,
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private val epsilonMax: Int = 0,
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private val iterations: Int = 0,
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private val iterations: Int = 0,
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private val WLDMax: Float = 0,
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private val WLDMax: Float = 0,
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private val customers: String Either ArraySeq[Customer]
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private val customers: String Either ArraySeq[Customer]
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) {
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) extends LazyLogging {
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import HHCSim2._
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import HHCSim2._
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import Ordering.Double.IeeeOrdering
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import Ordering.Double.IeeeOrdering
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private var adjMatrix: Option[GraphMatrix[Double]] = None
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def go() = {
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def go() = {
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customers
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customers
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.flatMap(checkEmpty)
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.flatMap(checkEmpty)
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.map(formAdjMatrix)
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.map(formAdjMatrix)
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.tap(logGraph)
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.tap(_.foreach(e => adjMatrix = Some(e)))
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.map(e => mstUsingPrims(e))
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.map(e => mstUsingPrims(e))
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.tap(logMST)
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.map { case (mst, epsilon) => removeEdges(mst)(epsilon) }
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.map { case (mst, epsilon) => removeEdges(mst)(epsilon) }
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.tap(logMST2)
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.map { case (mstUpdated, removed) => findClusters(mstUpdated)(removed) }
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.map { case (mstUpdated, removed) => findClusters(mstUpdated)(removed) }
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.map {
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.map {
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case (clusters, edgeMappings, removedEdges) =>
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case (mstUpdated, clusters, edgeMappings, removedEdges) =>
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groupClusters(clusters, edgeMappings, removedEdges)
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printClusters(clusters)
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val e =
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groupClusters2(mstUpdated, clusters, edgeMappings, removedEdges)
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printGroups(e._5)
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e
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}
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}
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}
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.flatMap(value => {
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def go2() = {
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val (mst, clusters, edgeMappings, removed, clusterGroups) = value
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val res = go().flatMap(value => {
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balanceWorkload(clusters, clusterGroups)
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val (clusters, edgeMappings, removed, clusterGroups) = value
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workloadBalance(clusters, edgeMappings, removed, clusterGroups)
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})
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})
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res
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}
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// def go2() = {
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// val res = go().flatMap(value => {
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// val (mst, clusters, edgeMappings, removed, clusterGroups) = value
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// balanceWorkload(mst, clusters, clusterGroups)
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// })
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// res
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// }
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def printGraph(graph: GraphMatrix[Double]) = graph.foreach { e =>
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e.foreach { d =>
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print(f"$d%.2f, ")
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}
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println
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}
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}
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def workloadBalance[N: Numeric](
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def printGraph(graph: Graph[Double]) = {
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for (i <- 0 until graph.size) {
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print(s"$i: ")
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for (edge <- graph(i)) {
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print(f"(${edge.toNode}, ${edge.weight}%.2f), ")
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}
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println
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}
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}
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def logGraph(maybeGraph: Either[String, GraphMatrix[Double]]): Unit =
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maybeGraph.foreach(
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graph => {
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logger.whenDebugEnabled {
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logger.debug("Graph: ")
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printGraph(graph)
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}
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}
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)
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def logMST(maybeMST: Either[String, (Graph[Double], Double)]): Unit =
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maybeMST.foreach(
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t => {
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logger.whenDebugEnabled {
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logger.debug(s"Epsilon = ${t._2}")
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logger.debug("MST: ")
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printGraph(graph = t._1)
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}
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}
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)
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def logMST2(it: Either[String, (Graph[Double], RemovedEdges[Double])]): Unit =
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it.foreach(
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t => {
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logger.whenDebugEnabled {
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logger.debug(s"Removed Edges = ${t._2}")
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logger.debug("MST: ")
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printGraph(graph = t._1)
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}
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}
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)
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def printClusters(clusters: Clusters) = {
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logger.whenDebugEnabled {
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logger.debug("Clusters:")
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var i = 0
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clusters.foreach(e => {
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print(s"Cluster-$i: ")
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i += 1
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e.foreach(f => print(s"$f "))
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println
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})
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}
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}
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def printClusters(clusters: Array[List[Int]]) = {
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logger.whenDebugEnabled {
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logger.debug("Clusters:")
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var i = 0
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clusters.foreach(e => {
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print(s"Cluster-$i: ")
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i += 1
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e.foreach(f => print(s"$f "))
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println
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})
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}
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}
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def printGroups(groups: ArraySeq[List[(Int, Double)]]) = {
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logger.whenDebugEnabled {
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logger.debug("Neighbours: ")
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var i = 0
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groups.foreach(e => {
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print(s"Cluster-$i: ")
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i += 1
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e.foreach(f => { print(f"(${f._1}%d, ${f._2}%.2f), ") })
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println
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})
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}
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}
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def balanceWorkload[N: Numeric](
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clusters: Clusters,
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clusters: Clusters,
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edgeMappings: ArraySeq[List[HHCEdge2[N]]],
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groups: ArraySeq[List[(Int, N)]]
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removedEdges: RemovedEdges[N],
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groups: ArraySeq[(Int, List[(Int, N)])]
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) = {
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) = {
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customers.map { c =>
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customers.map(c => {
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val k = clusters.size
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val mutClusters =
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val mutClusters = clusters.toArray
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clusters.toArray
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val worlkLoads =
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val clusterOrder = (0 until clusters.length).toArray
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mutClusters.map(_.map(j => c(j)).foldLeft(0f)((x, y) => x + y.workload))
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logger.debug(s"Initial order: $clusterOrder")
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val sortedClusters =
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val k = mutClusters.size
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mutClusters.map(_.sortWith((x, y) => c(x).workload < c(y).workload))
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val minWL =
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c.foldLeft(c(0).workload)(_ min _.workload)
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val maxWL =
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c.foldLeft(0f)(_ max _.workload)
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val WLD = maxWL - minWL
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if (WLD > WLDMax) {
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var t = Random.between(((k / 2) + 1), k)
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while (!mutClusters(t).isEmpty) t = Random.between(((k / 2) + 1), k)
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val Ds = groups(t)._2(0)
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mutClusters.updated(Ds._1, t)
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}
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for (_ <- 0 until iterations) {
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for (_ <- 0 until iterations) {
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// val workloads = clusters.flatMap(_.map(j => c(j)))
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val WL = mutClusters.view
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.map(nodes => {
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val customers = nodes.map(node => c(node))
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val wl = customers.foldLeft(0f)(_ + _.workload)
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wl
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})
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.to(ArraySeq)
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logger.debug(s"$WL")
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clusterOrder.sortInPlaceWith((x, y) => WL(x) < WL(y))
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logger.debug(s"Current order $clusterOrder")
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val (minWL, maxWL) = WL.foldLeft((99999f, 0f))((acc, wl) => {
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val (currMin, currMax) = acc
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if (wl < currMin) (wl, currMax)
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else if (wl > currMax) (currMin, wl)
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else acc
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})
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val WLD = maxWL - minWL
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logger.debug(s"maxWL = $maxWL, minWL = $minWL")
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logger.debug(s"WLD = $WLD")
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if (WLD > WLDMax) {
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val randNum = Random.between(((k / 2) + 1), k)
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logger.debug(s"Rand Num=$randNum")
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val effectiveNum = clusterOrder(randNum)
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logger.debug(s"Chosen cluster = $effectiveNum")
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val (s, _) = groups(effectiveNum)(0)
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logger.debug(s"Nearest neighbour = $s")
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// val avg = averagePairwiseDistance(mst(s))
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val avg = adjMatrix
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.map(m => averagePairwiseDistance(m)(clusters(s)))
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.getOrElse(9999d)
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// if (avg < epsilonMax) true else false
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logger.debug(s"Pairwise average = $avg")
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if (avg < epsilonMax) {
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logger.debug("Average less than epsilonMax. Updating Clusters.")
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if (!mutClusters(s).contains(effectiveNum)) {
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mutClusters(s) = effectiveNum :: mutClusters(s)
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}
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// mutClusters.foreach(println)
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printClusters(mutClusters)
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}
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}
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}
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}
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}
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}
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ArraySeq.unsafeWrapArray(mutClusters)
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})
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}
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def checkAveragePairwiseDistance[T](
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// def averagePairwiseDistance[T](
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lst: List[(Int, T)]
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// lst: List[HHCEdge2[T]]
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)(implicit num: Numeric[T]) = {
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// )(implicit num: Numeric[T]) = {
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val sum = lst.foldLeft(0)((x, y) => x + num.toInt(y._2))
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// val it = lst.combinations(2)
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val avg = sum / lst.length
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// val sum = lst.foldLeft(0)((x, y) => x + num.toInt(y.weight))
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if (avg < epsilonMax) true else false
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// val n = if (lst.length == 0) 1 else lst.length
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// val avg = sum / n
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// // it.map()
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// avg
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// }
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def averagePairwiseDistance(graph: GraphMatrix[Double])(
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cluster: List[Int]
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) = {
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// val iter = cluster.combinations(2)
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// var sum = 0d
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// for (pair <- iter) {
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// val (i, j) = pair(0) -> pair(1)
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// sum += graph(i)(j)
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// }
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// val n = if (iter.length == 0) 1 else iter.length
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val (sum, size) = cluster
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.combinations(2)
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.foldLeft((0d, 0))((acc, pair) => {
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val (sum, size) = acc
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||||||
|
val (i, j) = pair(0) -> pair(1)
|
||||||
|
logger.trace(s"$i $j - ")
|
||||||
|
logger.trace(s"weight = ${graph(i)(j)}")
|
||||||
|
(sum + graph(i)(j), size + 1)
|
||||||
|
})
|
||||||
|
|
||||||
|
val n = if (size == 0) 1 else size
|
||||||
|
sum / n
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -104,19 +266,29 @@ object HHCSim2 extends HHCTypes {
|
|||||||
arr match {
|
arr match {
|
||||||
case Array(latitude, longitude, workLoad) => {
|
case Array(latitude, longitude, workLoad) => {
|
||||||
val cust =
|
val cust =
|
||||||
Customer(
|
// Customer(
|
||||||
Coord(latitude.toDouble, longitude.toDouble),
|
// Coord(latitude.toDouble, longitude.toDouble),
|
||||||
workLoad.toFloat
|
// workLoad.toFloat
|
||||||
)
|
// )
|
||||||
loop(lst += cust, iter)
|
for {
|
||||||
|
lat <- latitude.toDoubleOption
|
||||||
|
lon <- longitude.toDoubleOption
|
||||||
|
coord = Coord(lat, lon)
|
||||||
|
wl <- workLoad.toFloatOption
|
||||||
|
} yield (Customer(coord, wl))
|
||||||
|
cust match {
|
||||||
|
case Some(c) => loop(lst += c, iter)
|
||||||
|
case None => Left(s"Error reading customers at line - $line")
|
||||||
|
}
|
||||||
|
// loop(lst += cust, iter)
|
||||||
}
|
}
|
||||||
case _ => {
|
case _ => {
|
||||||
if (arr.mkString.equals(" ") || arr.mkString.contains("\n"))
|
if (line.equals(" ") || line.contains("\n"))
|
||||||
Left("Error newline")
|
Left("Error newline")
|
||||||
else {
|
else {
|
||||||
Left(
|
Left(
|
||||||
"Error reading customers from" +
|
"Error reading customers from" +
|
||||||
s" file - ${arr.mkString(", ")}"
|
s" file at line - $line}"
|
||||||
)
|
)
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
@ -188,7 +360,6 @@ object HHCSim2 extends HHCTypes {
|
|||||||
min = num.toInt(edges(i)(j))
|
min = num.toInt(edges(i)(j))
|
||||||
x = i
|
x = i
|
||||||
y = j
|
y = j
|
||||||
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
@ -200,11 +371,11 @@ object HHCSim2 extends HHCTypes {
|
|||||||
selected(y) = true
|
selected(y) = true
|
||||||
}
|
}
|
||||||
val adjList2 = adjList.map(l => {
|
val adjList2 = adjList.map(l => {
|
||||||
count += 1
|
count += l.size
|
||||||
l.toList
|
l.toList
|
||||||
})
|
})
|
||||||
adjList2.foreach(println)
|
// adjList2.foreach(println)
|
||||||
(ArraySeq.unsafeWrapArray(adjList2), num.fromInt(sum / count))
|
(ArraySeq.unsafeWrapArray(adjList2), num.fromInt(sum / (count / 2)))
|
||||||
}
|
}
|
||||||
|
|
||||||
def removeEdges[N: Ordering](
|
def removeEdges[N: Ordering](
|
||||||
@ -226,8 +397,8 @@ object HHCSim2 extends HHCTypes {
|
|||||||
filtered
|
filtered
|
||||||
}
|
}
|
||||||
println
|
println
|
||||||
result.foreach(println)
|
// result.foreach(println)
|
||||||
println
|
// println
|
||||||
(result, removed.toList)
|
(result, removed.toList)
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -259,11 +430,12 @@ object HHCSim2 extends HHCTypes {
|
|||||||
|
|
||||||
def findClusters[T: Numeric](mstUpdated: Graph[T])(
|
def findClusters[T: Numeric](mstUpdated: Graph[T])(
|
||||||
removedEdges: RemovedEdges[T]
|
removedEdges: RemovedEdges[T]
|
||||||
): (Clusters, ArraySeq[List[HHCEdge2[T]]], RemovedEdges[T]) = {
|
): (Graph[T], Clusters, ArraySeq[List[HHCEdge2[T]]], RemovedEdges[T]) = {
|
||||||
val visited = Array.fill[Boolean](mstUpdated.length)(false)
|
val visited = Array.fill[Boolean](mstUpdated.length)(false)
|
||||||
var removedEdges2 = removedEdges
|
var removedEdges2 = removedEdges
|
||||||
val egdeMappings =
|
val egdeMappings =
|
||||||
Array.fill(mstUpdated.length)(List.empty[HHCEdge2[T]])
|
Array.fill(mstUpdated.length)(List.empty[HHCEdge2[T]])
|
||||||
|
val nodeToClusterMappings = Array.fill(mstUpdated.length)(0)
|
||||||
val result = ArraySeq.tabulate(mstUpdated.length) { i =>
|
val result = ArraySeq.tabulate(mstUpdated.length) { i =>
|
||||||
{
|
{
|
||||||
val buf = ListBuffer[Int]()
|
val buf = ListBuffer[Int]()
|
||||||
@ -304,8 +476,13 @@ object HHCSim2 extends HHCTypes {
|
|||||||
// println(s"Removed edges size: ${removedEdges2.size}")
|
// println(s"Removed edges size: ${removedEdges2.size}")
|
||||||
|
|
||||||
// result
|
// result
|
||||||
(result, ArraySeq.unsafeWrapArray(egdeMappings), removedEdges)
|
// (result, ArraySeq.unsafeWrapArray(egdeMappings), removedEdges)
|
||||||
// (result.filterNot(_.isEmpty), ArraySeq.unsafeWrapArray(egdeMappings), removedEdges)
|
(
|
||||||
|
mstUpdated,
|
||||||
|
result.filterNot(_.isEmpty),
|
||||||
|
ArraySeq.unsafeWrapArray(egdeMappings.filterNot(_.isEmpty)),
|
||||||
|
removedEdges
|
||||||
|
)
|
||||||
}
|
}
|
||||||
|
|
||||||
def assignEdges[T: Ordering](
|
def assignEdges[T: Ordering](
|
||||||
@ -313,6 +490,7 @@ object HHCSim2 extends HHCTypes {
|
|||||||
removedEdges: RemovedEdges[T]
|
removedEdges: RemovedEdges[T]
|
||||||
) = {
|
) = {
|
||||||
val it = nodes.iterator
|
val it = nodes.iterator
|
||||||
|
@annotation.tailrec
|
||||||
def loop(
|
def loop(
|
||||||
nodes: List[Int],
|
nodes: List[Int],
|
||||||
removedEdges: RemovedEdges[T],
|
removedEdges: RemovedEdges[T],
|
||||||
@ -353,13 +531,25 @@ object HHCSim2 extends HHCTypes {
|
|||||||
(edges, removedEdgesUpdated)
|
(edges, removedEdgesUpdated)
|
||||||
}
|
}
|
||||||
|
|
||||||
|
def mapNodestoClusters[N: Ordering](mst: Graph[N], clusters: Clusters) = {
|
||||||
|
val arr = Array.fill(mst.size)(0)
|
||||||
|
for (i <- 0 until clusters.size) {
|
||||||
|
val nodes = clusters(i)
|
||||||
|
for (node <- nodes) {
|
||||||
|
arr(node) = i
|
||||||
|
}
|
||||||
|
}
|
||||||
|
ArraySeq.unsafeWrapArray(arr)
|
||||||
|
}
|
||||||
|
|
||||||
def groupClusters[N: Ordering](
|
def groupClusters[N: Ordering](
|
||||||
clusters: Clusters,
|
clusters: Clusters,
|
||||||
edgeMappings: ArraySeq[List[HHCEdge2[N]]],
|
edgeMappings: ArraySeq[List[HHCEdge2[N]]],
|
||||||
removed: RemovedEdges[N]
|
removed: RemovedEdges[N]
|
||||||
) = {
|
) = {
|
||||||
var k = -1
|
var k = -1
|
||||||
val x = ArraySeq.tabulate(edgeMappings.size)(i => {
|
val clustMap = collection.mutable.Map.empty[Int, Int]
|
||||||
|
val groups = ArraySeq.tabulate(edgeMappings.size)(i => {
|
||||||
val buf = ListBuffer.empty[(Int, N)]
|
val buf = ListBuffer.empty[(Int, N)]
|
||||||
val lst = edgeMappings(i)
|
val lst = edgeMappings(i)
|
||||||
val y = lst.foreach(e => {
|
val y = lst.foreach(e => {
|
||||||
@ -368,7 +558,7 @@ object HHCSim2 extends HHCTypes {
|
|||||||
for (j <- 0 until clusters.size) {
|
for (j <- 0 until clusters.size) {
|
||||||
for (edge <- clusters(j)) {
|
for (edge <- clusters(j)) {
|
||||||
if (edge == e.toNode)
|
if (edge == e.toNode)
|
||||||
buf += ((j, e.weight))
|
buf += (j -> e.weight)
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
// Left(buf.toList)
|
// Left(buf.toList)
|
||||||
@ -379,17 +569,185 @@ object HHCSim2 extends HHCTypes {
|
|||||||
// case HHCEdge2(fromNode, toNode, weight) => (toNode, weight)
|
// case HHCEdge2(fromNode, toNode, weight) => (toNode, weight)
|
||||||
// })
|
// })
|
||||||
})
|
})
|
||||||
if (!clusters(i).isEmpty) k += 1
|
if (!clusters(i).isEmpty) {
|
||||||
|
k += 1
|
||||||
|
clustMap += k -> i
|
||||||
|
}
|
||||||
// val lst2 = lst.map {
|
// val lst2 = lst.map {
|
||||||
// case HHCEdge2(fromNode, toNode, weight) => (toNode, weight)
|
// case HHCEdge2(fromNode, toNode, weight) => (toNode, weight)
|
||||||
// }
|
// }
|
||||||
// if (buf.isEmpty) lst2 else buf.toList ::: lst2
|
// if (buf.isEmpty) lst2 else buf.toList ::: lst2
|
||||||
// if (lt.isEmpty) rt else lt
|
// if (lt.isEmpty) rt else lt
|
||||||
(k, buf.toList.sortWith {
|
(k, buf.sortWith {
|
||||||
case ((_, weight1), (_, weight2)) =>
|
case ((_, weight1), (_, weight2)) =>
|
||||||
weight1 < weight2
|
weight1 < weight2
|
||||||
|
}.toList)
|
||||||
})
|
})
|
||||||
|
(clusters, clustMap.toMap, removed, groups)
|
||||||
|
}
|
||||||
|
|
||||||
|
def groupClusters2[N: Ordering](
|
||||||
|
mst: Graph[N],
|
||||||
|
clusters: Clusters,
|
||||||
|
edgeMappings: ArraySeq[List[HHCEdge2[N]]],
|
||||||
|
removed: RemovedEdges[N]
|
||||||
|
) = {
|
||||||
|
val nodeMap = mapNodestoClusters(mst, clusters)
|
||||||
|
val groups = ArraySeq.tabulate(edgeMappings.size)(i => {
|
||||||
|
val buf = ListBuffer.empty[(Int, N)]
|
||||||
|
val lst = edgeMappings(i)
|
||||||
|
lst.foreach(e => {
|
||||||
|
buf += nodeMap(e.toNode) -> e.weight
|
||||||
})
|
})
|
||||||
(clusters, edgeMappings, removed, x)
|
buf.sortWith {
|
||||||
|
case ((_, weight1), (_, weight2)) =>
|
||||||
|
weight1 < weight2
|
||||||
|
}.toList
|
||||||
|
})
|
||||||
|
(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)
|
||||||
|
// })
|
||||||
|
// }
|
||||||
|
|||||||
300
test-output2.txt
Normal file
300
test-output2.txt
Normal file
@ -0,0 +1,300 @@
|
|||||||
|
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,
|
||||||
|
945.75, 682.83, 724.38, 0.00, 637.37, 1033.19, 987.82, 912.29, 968.32, 509.20, 110.33, 883.65, 881.27, 1003.66, 270.07, 509.33, 176.62, 969.29, 197.94, 488.88,
|
||||||
|
1282.33, 191.14, 859.17, 637.37, 0.00, 718.08, 951.89, 1073.97, 1248.13, 148.33, 586.96, 697.31, 390.07, 1004.07, 862.04, 793.34, 797.85, 730.02, 646.87, 152.85,
|
||||||
|
1037.66, 527.48, 599.15, 1033.19, 718.08, 0.00, 429.37, 711.89, 949.07, 688.28, 927.00, 202.18, 362.68, 495.69, 1097.48, 749.46, 1204.22, 115.60, 891.82, 786.43,
|
||||||
|
640.89, 777.24, 302.60, 987.82, 951.89, 429.37, 0.00, 305.61, 540.80, 858.29, 879.23, 283.82, 711.61, 66.61, 940.60, 532.22, 1128.59, 321.60, 799.37, 953.47,
|
||||||
|
335.29, 929.62, 218.49, 912.29, 1073.97, 711.89, 305.61, 0.00, 237.99, 950.40, 816.13, 528.89, 927.99, 253.89, 785.04, 403.11, 1016.59, 597.07, 714.52, 1031.72,
|
||||||
|
111.67, 1122.63, 402.17, 968.32, 1248.13, 949.07, 540.80, 237.99, 0.00, 1113.40, 889.10, 762.34, 1146.42, 484.14, 780.69, 486.64, 1035.62, 833.92, 780.22, 1183.88,
|
||||||
|
1142.56, 195.17, 732.89, 509.20, 148.33, 688.28, 858.29, 950.40, 1113.40, 0.00, 447.69, 626.97, 415.82, 905.04, 719.84, 649.85, 677.65, 676.77, 500.88, 99.83,
|
||||||
|
876.33, 605.52, 621.64, 110.33, 586.96, 927.00, 879.23, 816.13, 889.10, 447.69, 0.00, 774.18, 793.21, 896.67, 276.70, 415.77, 277.38, 860.62, 109.33, 447.88,
|
||||||
|
845.31, 512.54, 398.11, 883.65, 697.31, 202.18, 283.82, 528.89, 762.34, 626.97, 774.18, 0.00, 427.82, 347.93, 916.77, 551.90, 1047.64, 95.72, 723.98, 726.38,
|
||||||
|
1211.84, 221.44, 747.67, 881.27, 390.07, 362.68, 711.61, 927.99, 1146.42, 415.82, 793.21, 427.82, 0.00, 775.20, 1030.70, 795.38, 1057.48, 415.82, 802.10, 499.04,
|
||||||
|
587.31, 833.01, 294.91, 1003.66, 1004.07, 495.69, 66.61, 253.89, 484.14, 905.04, 896.67, 347.93, 775.20, 0.00, 938.28, 529.17, 1137.55, 388.20, 811.29, 998.51,
|
||||||
|
735.31, 862.90, 643.37, 270.07, 862.04, 1097.48, 940.60, 785.04, 780.69, 719.84, 276.70, 916.77, 1030.70, 938.28, 0.00, 409.27, 260.66, 1010.99, 229.37, 724.57,
|
||||||
|
497.33, 703.24, 234.41, 509.33, 793.34, 749.46, 532.22, 403.11, 486.64, 649.85, 415.77, 551.90, 795.38, 529.17, 409.27, 0.00, 618.90, 647.39, 311.83, 709.32,
|
||||||
|
994.87, 856.59, 848.21, 176.62, 797.85, 1204.22, 1128.59, 1016.59, 1035.62, 677.65, 277.38, 1047.64, 1057.48, 1137.55, 260.66, 618.90, 0.00, 1135.78, 330.72, 646.05,
|
||||||
|
922.07, 540.04, 485.90, 969.29, 730.02, 115.60, 321.60, 597.07, 833.92, 676.77, 860.62, 95.72, 415.82, 388.20, 1010.99, 647.39, 1135.78, 0.00, 814.86, 776.56,
|
||||||
|
767.10, 634.58, 527.86, 197.94, 646.87, 891.82, 799.37, 714.52, 780.22, 500.88, 109.33, 723.98, 802.10, 811.29, 229.37, 311.83, 330.72, 814.86, 0.00, 519.68,
|
||||||
|
1206.08, 278.41, 813.31, 488.88, 152.85, 786.43, 953.47, 1031.72, 1183.88, 99.83, 447.88, 726.38, 499.04, 998.51, 724.57, 709.32, 646.05, 776.56, 519.68, 0.00,
|
||||||
|
Epsilon = 187.0
|
||||||
|
MST:
|
||||||
|
0: (8, 111.67),
|
||||||
|
1: (12, 221.44), (4, 191.14),
|
||||||
|
2: (7, 218.49), (15, 234.41),
|
||||||
|
3: (10, 110.33), (16, 176.62),
|
||||||
|
4: (1, 191.14), (9, 148.33),
|
||||||
|
5: (17, 115.60), (12, 362.68),
|
||||||
|
6: (13, 66.61), (11, 283.82),
|
||||||
|
7: (8, 237.99), (2, 218.49), (13, 253.89),
|
||||||
|
8: (0, 111.67), (7, 237.99),
|
||||||
|
9: (4, 148.33), (19, 99.83),
|
||||||
|
10: (18, 109.33), (3, 110.33),
|
||||||
|
11: (6, 283.82), (17, 95.72),
|
||||||
|
12: (5, 362.68), (1, 221.44),
|
||||||
|
13: (7, 253.89), (6, 66.61),
|
||||||
|
14: (18, 229.37),
|
||||||
|
15: (2, 234.41), (18, 311.83),
|
||||||
|
16: (3, 176.62),
|
||||||
|
17: (11, 95.72), (5, 115.60),
|
||||||
|
18: (15, 311.83), (10, 109.33), (14, 229.37),
|
||||||
|
19: (9, 99.83),
|
||||||
|
|
||||||
|
Removed Edges = List(HHCEdge2(1,12,221.437941520422), HHCEdge2(1,4,191.1371918225627), HHCEdge2(2,7,218.49036137013215), HHCEdge2(2,15,234.40677699868428), HHCEdge2(4,1,191.1371918225627), HHCEdge2(5,12,362.6802853094028), HHCEdge2(6,11,283.817433688036), HHCEdge2(7,8,237.98544482631425), HHCEdge2(7,2,218.49036137013215), HHCEdge2(7,13,253.88652915186032), HHCEdge2(8,7,237.98544482631425), HHCEdge2(11,6,283.817433688036), HHCEdge2(12,5,362.6802853094028), HHCEdge2(12,1,221.437941520422), HHCEdge2(13,7,253.88652915186032), HHCEdge2(14,18,229.36669205588458), HHCEdge2(15,2,234.40677699868428), HHCEdge2(15,18,311.8336340813276), HHCEdge2(18,15,311.8336340813276), HHCEdge2(18,14,229.36669205588458))
|
||||||
|
MST:
|
||||||
|
0: (8, 111.67),
|
||||||
|
1:
|
||||||
|
2:
|
||||||
|
3: (10, 110.33), (16, 176.62),
|
||||||
|
4: (9, 148.33),
|
||||||
|
5: (17, 115.60),
|
||||||
|
6: (13, 66.61),
|
||||||
|
7:
|
||||||
|
8: (0, 111.67),
|
||||||
|
9: (4, 148.33), (19, 99.83),
|
||||||
|
10: (18, 109.33), (3, 110.33),
|
||||||
|
11: (17, 95.72),
|
||||||
|
12:
|
||||||
|
13: (6, 66.61),
|
||||||
|
14:
|
||||||
|
15:
|
||||||
|
16: (3, 176.62),
|
||||||
|
17: (11, 95.72), (5, 115.60),
|
||||||
|
18: (10, 109.33),
|
||||||
|
19: (9, 99.83),
|
||||||
|
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: 12
|
||||||
|
Cluster-9: 14
|
||||||
|
Cluster-10: 15
|
||||||
|
Neighbours:
|
||||||
|
Cluster-0: (7, 237.99),
|
||||||
|
Cluster-1: (4, 191.14), (8, 221.44),
|
||||||
|
Cluster-2: (7, 218.49), (10, 234.41),
|
||||||
|
Cluster-3: (9, 229.37), (10, 311.83),
|
||||||
|
Cluster-4: (1, 191.14),
|
||||||
|
Cluster-5: (8, 362.68),
|
||||||
|
Cluster-6: (5, 283.82),
|
||||||
|
Cluster-7: (2, 218.49), (0, 237.99), (6, 253.89),
|
||||||
|
Cluster-8: (1, 221.44), (5, 362.68),
|
||||||
|
Cluster-9: (3, 229.37),
|
||||||
|
Cluster-10: (2, 234.41), (3, 311.83),
|
||||||
|
Initial order: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
|
||||||
|
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=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: 7
|
||||||
|
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, 42.29, 55.81, 112.740005, 55.61)
|
||||||
|
Current order [2, 1, 7, 10, 8, 4, 6, 9, 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]
|
||||||
|
maxWL = 338.0, minWL = 2.66
|
||||||
|
WLD = 335.34
|
||||||
|
Rand Num=8
|
||||||
|
Chosen cluster = 0
|
||||||
|
Nearest neighbour = 7
|
||||||
|
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=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=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=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
|
||||||
Loading…
Reference in New Issue
Block a user