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import java.util.Random
import scala.math.exp
import Vector._
import spark._
object SparkLR {
val N = 10000 // Number of data points
val D = 10 // Numer of dimensions
val R = 0.7 // Scaling factor
val ITERATIONS = 5
val rand = new Random(42)
case class DataPoint(x: Vector, y: Double)
def generateData = {
def generatePoint(i: Int) = {
val y = if(i % 2 == 0) -1 else 1
val x = Vector(D, _ => rand.nextGaussian + y * R)
DataPoint(x, y)
}
Array.fromFunction(generatePoint _)(N)
}
def main(args: Array[String]) {
if (args.length == 0) {
System.err.println("Usage: SparkLR <host> [<slices>]")
System.exit(1)
}
val sc = new SparkContext(args(0), "SparkLR")
val numSlices = if (args.length > 1) args(1).toInt else 2
val data = generateData
// 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 <- sc.parallelize(data, numSlices)) {
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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