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Diffstat (limited to 'examples/src/main/scala/SparkLR.scala')
-rw-r--r-- | examples/src/main/scala/SparkLR.scala | 49 |
1 files changed, 49 insertions, 0 deletions
diff --git a/examples/src/main/scala/SparkLR.scala b/examples/src/main/scala/SparkLR.scala new file mode 100644 index 0000000000..71f9aea624 --- /dev/null +++ b/examples/src/main/scala/SparkLR.scala @@ -0,0 +1,49 @@ +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) + } +} |