diff options
Diffstat (limited to 'examples')
3 files changed, 133 insertions, 1 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala index e5a09ecec0..d3babc3ed1 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala @@ -18,8 +18,8 @@ package org.apache.spark.examples import scala.math.random + import org.apache.spark._ -import SparkContext._ /** Computes an approximation to pi */ object SparkPi { diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala new file mode 100644 index 0000000000..53b303d658 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala @@ -0,0 +1,80 @@ +/* + * 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 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.OFF_HEAP) + 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) + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala new file mode 100644 index 0000000000..ce78f0876e --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala @@ -0,0 +1,52 @@ +/* + * 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 scala.math.random + +import org.apache.spark._ +import org.apache.spark.storage.StorageLevel + +/** + * 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) { + System.err.println("Usage: SparkTachyonPi <master> [<slices>]") + System.exit(1) + } + val spark = new SparkContext(args(0), "SparkTachyonPi", + System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass)) + + val slices = if (args.length > 1) args(1).toInt else 2 + val n = 100000 * slices + + val rdd = spark.parallelize(1 to n, slices) + rdd.persist(StorageLevel.OFF_HEAP) + val count = rdd.map { i => + val x = random * 2 - 1 + val y = random * 2 - 1 + if (x * x + y * y < 1) 1 else 0 + }.reduce(_ + _) + println("Pi is roughly " + 4.0 * count / n) + + spark.stop() + } +} |