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import java.util.Random
import scala.math.exp
import Vector._
import spark._
object SparkHdfsLR {
val D = 10 // Numer of dimensions
val rand = new Random(42)
case class DataPoint(x: Vector, y: Double)
def parsePoint(line: String): DataPoint = {
//val nums = line.split(' ').map(_.toDouble)
//return DataPoint(new Vector(nums.slice(1, D+1)), nums(0))
val tok = new java.util.StringTokenizer(line, " ")
var y = tok.nextToken.toDouble
var x = new Array[Double](D)
var i = 0
while (i < D) {
x(i) = tok.nextToken.toDouble; i += 1
}
return DataPoint(new Vector(x), y)
}
def main(args: Array[String]) {
if (args.length < 3) {
System.err.println("Usage: SparkHdfsLR <master> <file> <iters>")
System.exit(1)
}
val sc = new SparkContext(args(0), "SparkHdfsLR")
val lines = sc.textFile(args(1))
val points = lines.map(parsePoint _).cache()
val ITERATIONS = args(2).toInt
// Initialize w to a random value
var w = Vector(D, _ => 2 * rand.nextDouble - 1)
println("Initial w: " + w)
for (i <- 1 to ITERATIONS) {
println("On iteration " + i)
val gradient = sc.accumulator(Vector.zeros(D))
for (p <- points) {
val scale = (1 / (1 + exp(-p.y * (w dot p.x))) - 1) * p.y
gradient += scale * p.x
}
w -= gradient.value
}
println("Final w: " + w)
}
}
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