Clustering completed

src/main/scala/Main.scala

@@ -2,6 +2,7 @@ import model.Coord
 import model.Customer
 import model.HHCEdge
 import scala.collection.mutable._
+import util.Util
 object Main {
   def main(args: Array[String]): Unit = {
 
@@ -42,45 +43,38 @@ object Main {
 
     // sample adjacency
     // format: off
-    val edges = Array(
-        Array(0 , 9 , 75, 0 , 0),
-        Array(9 , 0 , 95, 19, 42),
-        Array(75, 95, 0 , 51, 66),
-        Array(0 , 19, 51, 0 , 31),
-        Array(0 , 42, 66, 31, 0)
+    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
 
-    val selected: ArrayBuffer[Boolean] =
-      ArrayBuffer.fill(5)(false)
-
-    selected(0) = true
-
-
-    for (_ <- 0 until 4) {
-      var min = 999999
-      var x = 0
-      var y = 0
-      for (i <- 0 until 5) {
-        if (selected(i) == true) {
-          for (j <- 0 until 5) {
-            if (selected(j) == false && edges(i)(j) != 0) {
-              if (min > edges(i)(j)) {
-                min = edges(i)(j)
-                x = i
-                y = j
-              }
-            }
-          }
-        }
-      }
-      println(s"Edge selected $x - $y : ${edges(x)(y)}")
-      selected(y) = true
-    }
-
     // Prim's algorithm result
     // Edge selected 0 - 1: 9
     // Edge selected 1 - 3: 19
@@ -89,13 +83,12 @@ object Main {
 
     // Verify the result with the one at https://www.programiz.com/dsa/prim-algorithm
 
-    val edges2: ArrayBuffer[ArrayBuffer[Double]] = ArrayBuffer.empty
+    val edges2: Array[Array[Double]] = Array.ofDim(5, 5)
 
     // create adjacency matrix from given customers
     for (i <- 0 to 4) {
-      edges2.append(ArrayBuffer.empty)
       for (j <- 0 to 4) {
-        edges2(i).append(HHCEdge(V(i), V(j)).weight)
+        edges2(i)(j) = Util.getHaversineDistance(V(i).location, V(j).location)
       }
     }
 
@@ -113,5 +106,78 @@ object Main {
     // 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 (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)
+    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)))
   }
 }

src/main/scala/util/Util.scala

@@ -2,6 +2,8 @@ package util
 
 import model.Coord
 import scala.math._
+import scala.collection.mutable.ArrayBuffer
+import scala.collection.mutable
 
 object Util {
   private val r = 6471.00 // km
@@ -44,4 +46,223 @@ object Util {
     val d = r * c
     d
   }
+
+  def primTraverse(
+      arr1: Array[Array[Int]],
+      comp: Int,
+      cond: (Int, Int) => Boolean,
+      cb: (Int, Int, Array[Array[Int]], Array[Array[Int]]) => Array[Array[Int]]
+  ): Unit = {
+    val n = arr1.length
+    val selected = Array.ofDim[Boolean](n)
+
+    val arr2: Array[Array[Int]] = Array.ofDim(n, n)
+
+    selected(0) = true
+
+    // val mst: Array[Array[Int]] = Array.ofDim(5, 5)
+
+    for (_ <- 0 until n - 1) {
+      // var min = 999999
+      var x = 0
+      var y = 0
+      for (i <- 0 until n) {
+        if (selected(i) == true) {
+          for (j <- 0 until n) {
+            if (selected(j) == false && arr1(i)(j) != 0) {
+              if (cond(comp, arr1(i)(j))) {
+                x = i
+                y = j
+                cb(x, y, arr1, arr2)
+              }
+            }
+          }
+
+        }
+
+      }
+      // mst(x)(y) = mst(x)(y)
+      selected(y) = true
+    }
+  }
+  def mstUsingPrims(
+      edges: Array[Array[Int]]
+  ): Array[Array[Int]] = {
+    val n = edges.length
+    val selected: ArrayBuffer[Boolean] = ArrayBuffer.fill(n)(false)
+
+    selected(0) = true
+
+    val mst: Array[Array[Int]] = Array.ofDim(n, n)
+
+
+    for (_ <- 0 until n - 1) {
+      var min = 999999
+      var x = 0
+      var y = 0
+      for (i <- 0 until n) {
+        if (selected(i) == true) {
+          for (j <- 0 until n) {
+            if (selected(j) == false && edges(i)(j) != 0) {
+              if (min > edges(i)(j)) {
+                min = edges(i)(j)
+                x = i
+                y = j
+              }
+            }
+          }
+        }
+      }
+      // println(s"Edge selected $x - $y : ${edges(x)(y)}")
+      mst(x)(y) = edges(x)(y)
+      mst(y)(x) = edges(x)(y)
+
+      selected(y) = true
+    }
+    mst
+  }
+
+  def findClusters(
+      mst: Array[Array[Int]],
+      centroids: IndexedSeq[Int]
+  ): Map[Int, ArrayBuffer[Int]] = {
+    val n = mst.length
+    val x: Map[Int, ArrayBuffer[Int]] = centroids
+      .map(d => {
+        val y = DFS(d, mst)
+        y(0) -> y
+      })
+      .toMap
+    x
+  }
+
+  def makeAdjacencyList(
+      mst: Array[Array[Int]],
+      centroids: IndexedSeq[Int]
+  ): ArrayBuffer[ArrayBuffer[Int]] = {
+    val n = mst.length
+    // val selected: ArrayBuffer[Boolean] = ArrayBuffer.fill(n)(false)
+    val buf: ArrayBuffer[ArrayBuffer[Int]] =
+      ArrayBuffer.fill(n)(ArrayBuffer.empty)
+    // for (_ <- 0 until n -1) {
+
+    // }
+    for (i <- 0 until n) {
+      for (j <- 0 until n) {
+        if (mst(i)(j) != 0) {
+          // println(s" $i $j = ${mst(i)(j)}")
+          buf(i) += j
+        }
+      }
+    }
+    buf
+  }
+
+  def findCentroids[T](
+      mst: Array[Array[T]]
+  )(implicit ev: Numeric[T]): (IndexedSeq[Int], IndexedSeq[(Int, Int, T)]) = {
+    val n = mst.length
+    val centroids: mutable.Set[Int] = mutable.Set.empty
+    val removed: ArrayBuffer[(Int, Int, T)] = ArrayBuffer.empty
+    for (i <- 0 until n) {
+      for (j <- 0 until n) {
+        if (ev.gt(mst(i)(j), ev.fromInt(20)) && mst(i)(j) != 0) {
+          // println(s" $i $j = ${mst(i)(j)}")
+          centroids += i
+          centroids += j
+          removed.append((i, j, mst(i)(j)))
+          mst(i)(j) = ev.zero
+        }
+      }
+    }
+    (centroids.toIndexedSeq, removed.toIndexedSeq)
+  }
+
+  // def DFS(start: Int, graph: Array[Array[Int]], visited: Array[Boolean]): Unit = {
+  //   // if(start == 0) {
+  //   //   visited = Array.fill(graph.size)(false)
+  //   // }
+  //   visited(start) = true
+
+  //   println(s"$start ")
+
+  //   for(i <- 0 until graph.size) {
+  //     if (graph(start)(i) > 0 && graph(start)(i) < 20 && (!visited(i))) {
+  //           DFS(i, graph, visited);
+  //       }
+  //   }
+  // }
+  // val visited = Array.fill(mst.size)(false)
+  def DFS(
+      start: Int,
+      graph: Array[Array[Int]]
+  ): ArrayBuffer[Int] = {
+    val visited = Array.fill(graph.size)(false)
+    val buf = ArrayBuffer[Int]()
+    def loop(start: Int, graph: Array[Array[Int]], visited: Array[Boolean]) {
+      visited(start) = true
+      buf += start
+      // print(s"$start ")
+
+      for (i <- 0 until graph.size) {
+        if (graph(start)(i) > 0 && (!visited(i))) {
+          loop(i, graph, visited);
+        }
+      }
+    }
+
+    loop(start, graph, visited)
+    // println()
+    buf
+  }
+
+  def DFS(
+      start: Int,
+      graph: Array[Array[Int]],
+      num: Int,
+      cond: (Int, Int) => Boolean
+  ): ArrayBuffer[Int] = {
+    val visited = Array.fill(graph.size)(false)
+    val buf = ArrayBuffer[Int]()
+    def loop(start: Int, graph: Array[Array[Int]], visited: Array[Boolean]) {
+      visited(start) = true
+      buf += start
+      // print(s"$start ")
+
+      for (i <- 0 until graph.size) {
+        if (graph(start)(i) > 0 && cond(graph(start)(i), num) && (!visited(i))) {
+          loop(i, graph, visited);
+        }
+      }
+    }
+
+    loop(start, graph, visited)
+    // println()
+    buf
+  }
+
+  def groupClusters(
+      centroids: IndexedSeq[Int],
+      clusters: Map[Int, ArrayBuffer[Int]],
+      removed: IndexedSeq[(Int, Int, Int)]
+  ) = {
+    val groups = centroids
+      .map(c => {
+        val cluster = clusters(c)
+        val lst = removed
+          .filter(r => {
+            c == r._1
+          })
+          .sortWith((x, y) => {
+            x._3 < y._3
+          })
+          .map(l => {
+            (l._2,l._3)
+          })
+        c -> lst
+      })
+      .toMap
+    groups
+  }
+
 }