use spark run KMeans cluster , program block? - scala

Question

when I use apache spark scala API run the KMeans cluster. my program as follow:
object KMeans {
def closestPoint(p: Vector, centers: Array[Vector]) = {
var index = 0
var bestIndex = 0
var closest = Double.PositiveInfinity
for(i <- 0 until centers.length) {
var tempDist = p.squaredDist(centers(i))
if(tempDist < closest) {
closest = tempDist
bestIndex = i
}
}
bestIndex
}
def parseVector(line: String): Vector = {
new Vector(line.split("\\s+").map(s => s.toDouble))
}
def main(args: Array[String]): Unit = {}
System.setProperty("hadoop.home.dir", "F:/OpenSoft/hadoop-2.2.0")
val sc = new SparkContext("local", "kmeans cluster",
"G:/spark-0.9.0-incubating-bin-hadoop2",
SparkContext.jarOfClass(this.getClass()))
val lines = sc.textFile("G:/testData/synthetic_control.data.txt") // RDD[String]
val count = lines.count
val data = lines.map(parseVector _) // RDD[Vector]
data.foreach(println)
val K = 6
val convergeDist = 0.1
val kPoint = data.takeSample(withReplacement = false, K, 42) // Array[Vector]
kPoint.foreach(println)
var tempDist = 1.0
while(tempDist > convergeDist) {
val closest = data.map(p => (closestPoint(p, kPoint), (p, 1)))
val pointStat = closest.reduceByKey{case ((x1, y1), (x2, y2)) =>
(x1+x2, y1+y2)}
val newKPoint = pointStat.map{pair => (
pair._1,pair._2._1/pair._2._2)}.collectAsMap()
tempDist = 0.0
for(i <- 0 until K) {
tempDist += kPoint(i).squaredDist(newKPoint(i))
}
for(newP <- newKPoint) {
kPoint(newP._1) = newP._2
}
println("Finish iteration (delta=" + tempDist + ")")
}
println("Finish centers: ")
kPoint.foreach(println)
System.exit(0)
}
when I apply run as local mode , the log info as follow:
..................
14/03/31 11:29:15 INFO HadoopRDD: Input split: hdfs://hadoop-01:9000/data/synthetic_control.data:0+288374
program begin block , no running continue........
Can anyone can help me???