spark RandomRDDGeneration 源码
spark RandomRDDGeneration 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/mllib/RandomRDDGeneration.scala
/*
* 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.
*/
// scalastyle:off println
package org.apache.spark.examples.mllib
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.random.RandomRDDs
import org.apache.spark.rdd.RDD
/**
* An example app for randomly generated RDDs. Run with
* {{{
* bin/run-example org.apache.spark.examples.mllib.RandomRDDGeneration
* }}}
* If you use it as a template to create your own app, please use `spark-submit` to submit your app.
*/
object RandomRDDGeneration {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName(s"RandomRDDGeneration")
val sc = new SparkContext(conf)
val numExamples = 10000 // number of examples to generate
val fraction = 0.1 // fraction of data to sample
// Example: RandomRDDs.normalRDD
val normalRDD: RDD[Double] = RandomRDDs.normalRDD(sc, numExamples)
println(s"Generated RDD of ${normalRDD.count()}" +
" examples sampled from the standard normal distribution")
println(" First 5 samples:")
normalRDD.take(5).foreach( x => println(s" $x") )
// Example: RandomRDDs.normalVectorRDD
val normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows = numExamples, numCols = 2)
println(s"Generated RDD of ${normalVectorRDD.count()} examples of length-2 vectors.")
println(" First 5 samples:")
normalVectorRDD.take(5).foreach( x => println(s" $x") )
println()
sc.stop()
}
}
// scalastyle:on println
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