blah

src/main/scala/KMeans.scala

@@ -10,7 +10,7 @@ object KMeans {
     * Run KMeans clustering on an input RDD vector
    */
    //Create a map to store each data row with its closest cluster index as key
-   var centres : ArrayBuffer[Float] = ArrayBuffer(0.0f, 100.0f)
+   
 
   def train(dataset : DataFrame, iterations:Int) : Unit = {
      val relevantData = dataset.select("Reputation")
@@ -18,20 +18,25 @@ object KMeans {
     //val rowsAsArray = rows.map(row => List(row.getInt(0).toFloat, row.getInt(1).toFloat, row.getInt(2).toFloat) )
     val rowsAsArray = rows.map(row => row.getInt(0).toFloat )
     //rowsAsArray.foreach(println)
-    val K = 2 //number of intended clusters
+    val K = 4 //number of intended clusters
     //val n = rows.count() //number of datapoints
     val m = 1 //number of features
     //var centres = new ArrayBuffer[Row]
 
-    for (i <- 0 to iterations) {
+    var centres : ArrayBuffer[Float] = rowsAsArray.takeSample(false, K, System.nanoTime().toInt)
+
+    for (i <- 0 until iterations) {
       val clusterMap :RDD[(Int,Float)]= rowsAsArray.map(row => (assignCluster(row,centres,m,K),row))
       val newCentres = clusterMap.reduceByKey((a,b) => getAverage(a,b))
-      println("Average reputation is:")
+      
       val results = newCentres.map(x => x._2)
       val resultsOutput = results.collect()
       for (i <- 0 until K) {
         centres(i) = resultsOutput(i)
       }
+     println("Average reputation is:")
+     centres.foreach(println)
+     
     }
 
     //get random number generator r and use to select K centres randomly from dataset

src/main/scala/Main.scala

@@ -29,7 +29,7 @@ object Main {
     // get the users XML file
 
     val users = df("users")
-    val centres = KMeans.train(users, 50)
+    val centres = KMeans.train(users, 3)
     //val centresArray = centres.collect()
     //val unwrap = centresArray.map(x => x._2)
     //unwrap.foreach(println)