add the SparkTachyonHdfsLR example and some comments

Author: RongGu

examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala

@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples
+
+import java.util.Random
+import scala.math.exp
+import org.apache.spark.util.Vector
+import org.apache.spark._
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.scheduler.InputFormatInfo
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * Logistic regression based classification. 
+ * This example uses Tachyon to persist rdds during computation.
+ */
+object SparkTachyonHdfsLR {
+  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
+    }
+    DataPoint(new Vector(x), y)
+  }
+
+  def main(args: Array[String]) {
+    if (args.length < 3) {
+      System.err.println("Usage: SparkTachyonHdfsLR <master> <file> <iters>")
+      System.exit(1)
+    }
+    val inputPath = args(1)
+    val conf = SparkHadoopUtil.get.newConfiguration()
+    val sc = new SparkContext(args(0), "SparkTachyonHdfsLR",
+      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass), Map(), 
+      InputFormatInfo.computePreferredLocations(
+        Seq(new InputFormatInfo(conf, classOf[org.apache.hadoop.mapred.TextInputFormat], inputPath))
+      ))
+    val lines = sc.textFile(inputPath)
+    val points = lines.map(parsePoint _).persist(StorageLevel.TACHYON)
+    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 = points.map { p =>
+        (1 / (1 + exp(-p.y * (w dot p.x))) - 1) * p.y * p.x
+      }.reduce(_ + _)
+      w -= gradient
+    }
+
+    println("Final w: " + w)
+    System.exit(0)
+  }
+}

examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala

@@ -22,7 +22,9 @@ import scala.math.random
 import org.apache.spark._
 import org.apache.spark.storage.StorageLevel
 
-/** Computes an approximation to pi */
+/** Computes an approximation to pi 
+ *  This example uses Tachyon to persist rdds during computation.
+ */
 object SparkTachyonPi {
   def main(args: Array[String]) {
     if (args.length == 0) {