Made examples and core subprojects

1 files changed, 51 insertions, 0 deletions

diff --git a/examples/src/main/scala/SparkHdfsLR.scala b/examples/src/main/scala/SparkHdfsLR.scala
new file mode 100644
index 0000000000..f14d48b17c
--- /dev/null
+++ b/examples/src/main/scala/SparkHdfsLR.scala
@@ -0,0 +1,51 @@
+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)
+  }
+}