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import model.Coord import model.Customer import model.HHCEdge import scala.collection.mutable.ArrayBuffer import util.Util import config.Conf import java.io.File import scala.io.Source object Main { def main(args: Array[String]): Unit = {
val conf = new Conf(args.toIndexedSeq)
val bufferedSource = Source.fromFile(conf.infile())
val customers = Util.getCustomers(bufferedSource) bufferedSource.close()
val hhc = new HHCSim(epsilonMax = 40, iterations = 10, WLDMax = 10) val fnl2 = hhc.go(customers) // customers.map(println(_))
// val edges3 = customers.map(Util.formAdjMatrix)
// val fnl2 = go(customers)
fnl2 match { case Left(value) => println(value) case Right(groups) => groups.foreach(c => { print(s"${c._1} -> ") c._2.foreach(e => { print(f"(${e._1}%d, ${e._2}%.2f), ") }) println }) }
// edges3 match {
// case Right(e) =>
// e.foreach { d =>
// d.foreach(g => print(f"$g%.2f, "))
// println()
// }
// case Left(e) =>
// println(s"Oops, an error occured. The error message was: $e")
// }
val coord1 = Coord(3, 4) println(s"Distance from origin = ${coord1.distance}")
val cust1 = Customer(coord1, 5) val cust2 = Customer(Coord(2, 5), 7)
println(s"Customer 1 = ${cust1}") println(s"Customer 2 = ${cust2}")
val cust3 = Customer(Coord(3, 5), 6) val cust4 = Customer(Coord(6, 8), 9) val cust5 = Customer(Coord(5, 4), 6)
val edge1 = HHCEdge(cust1, cust2) val edge2 = HHCEdge(cust3, cust4)
println(s"Edge 1 = ${edge1}") println(s"Edge 1 weight = ${edge1.weight}")
val V = Array(cust1, cust2, cust3, cust4, cust5)
val E = Array(edge1, edge2)
V.foreach(println(_)) // prints
// Customer(Coord(3, 4), 5)
// Customer(Coord(2, 5), 7)
// Customer(Coord(3, 5), 6)
// Customer(Coord(6, 8), 9)
// Customer(Coord(5, 4), 6)
E.foreach(println(_)) //prints
// HHCEdge(Customer(Coord(3, 4), 5), Customer(Coord(2, 5), 7))
// HHCEdge(Customer(Coord(3, 5), 6), Customer(Coord(6, 8), 9))
// sample adjacency
// format: off
val edgesSeq = Seq( Seq(0 , 9 , 75, 0 , 0), Seq(9 , 0 , 95, 19, 42), Seq(75, 95, 0 , 51, 66), Seq(0 , 19, 51, 0 , 31), Seq(0 , 42, 66, 31, 0) );
// adjacency list form
// 0 -> 1 -> 2
// 1 -> 0 -> 2 -> 3 -> 4
// 2 -> 0 -> 1 -> 3 -> 4
// 3 -> 1 -> 2 -> 4
// 4 -> 1 -> 2 -> 3
val edges = edgesSeq.map(e => { e.toArray }).toArray
val mst = Util.mstUsingPrims(edges)
// mst
// 0, 9 , 0 , 0 , 0
// 9, 0 , 0 , 19, 0
// 0, 0 , 0 , 51, 0
// 0, 19, 51, 0 , 31
// 0, 0 , 0 , 31, 0
// format: on
// Prim's algorithm
// Prim's algorithm result
// Edge selected 0 - 1: 9
// Edge selected 1 - 3: 19
// Edge selected 3 - 4: 31
// Edge selected 3 - 2: 51
// Verify the result with the one at https://www.programiz.com/dsa/prim-algorithm
val edges2: Array[Array[Double]] = Array.ofDim(5, 5)
// create adjacency matrix from given customers
for (i <- 0 to 4) { for (j <- 0 to 4) { edges2(i)(j) = Util.getHaversineDistance(V(i).location, V(j).location) } }
edges2.foreach { e => e.foreach { d => print(f"$d%.2f, ") } println() }
// prints
// 0.00 , 159.64, 112.79, 563.55, 225.88
// 159.64, 0.00 , 112.94, 564.16, 357.08
// 112.79, 112.94, 0.00 , 478.40, 252.42
// 563.55, 564.16, 478.40, 0.00 , 463.64
// 225.88, 357.08, 252.42, 463.64, 0.00
println() println("Initial graph:") edgesSeq.foreach { e => e.foreach { d => print(s"$d, ") } println() } println("MST: ") mst.foreach { e => e.foreach { d => print(s"$d, ") } println() }
// 0, 9, 0 , 0 , 0
// 0, 0, 0 , 19, 0
// 0, 0, 0 , 0 , 0
// 0, 0, 51, 0 , 31
// 0, 0, 0 , 0 , 0
val (mst2, centr, removed) = Util.findCentroids(mst) // val (centr2, eds2) = Util.findCentroids(edges2)
println() println(s"Centroids: \n$centr") println(s"Removed: \n$removed")
val (_, clust, _) = Util.findClusters(mst, centr, removed) val adjList = Util.makeAdjacencyList(edges, centr)
println(s"Clusters:") clust.foreach(c => { val (e, d) = c print(s"$e: ") d.foreach(f => { print(s"-> $f ") }) println() }) println()
val fnl = Util.groupClusters(centr, clust, removed)
println(s"Final cluster groups: \n$fnl")
// Output
//
// Initial graph:
// 0, 9, 75, 0, 0,
// 9, 0, 95, 19, 42,
// 75, 95, 0, 51, 66,
// 0, 19, 51, 0, 31,
// 0, 42, 66, 31, 0,
// MST:
// 0, 9, 0, 0, 0,
// 9, 0, 0, 19, 0,
// 0, 0, 0, 51, 0,
// 0, 19, 51, 0, 31,
// 0, 0, 0, 31, 0,
// Centroids:
// Vector(2, 3, 4)
// Removed:
// Vector((2,3,51), (3,2,51), (3,4,31), (4,3,31))
// Clusters:
// 2: -> 2
// 3: -> 3 -> 1 -> 0
// 4: -> 4
// Final cluster groups:
// Map(2 -> Vector((3,51)), 3 -> Vector((4,31), (2,51)), 4 -> Vector((3,31)))
} }
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