Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4495#discussion_r24392633
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala
---
@@ -0,0 +1,176 @@
+/*
+ * 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.mllib
+
+import org.apache.log4j.{Level, Logger}
+import org.apache.spark.mllib.clustering.PowerIterationClustering
+import org.apache.spark.rdd.RDD
+import org.apache.spark.{SparkConf, SparkContext}
+import scopt.OptionParser
+
+/**
+ * An example Power Iteration Clustering app. Takes an input of K
concentric circles
+ * with a total of "n" sampled points (total here means "across ALL of the
circles").
+ * The output should be K clusters - each cluster containing precisely the
points associated
+ * with each of the input circles.
+ *
+ * Run with
+ * {{{
+ * ./bin/run-example
org.apache.spark.examples.mllib.PowerIterationClusteringExample [options]
+ *
+ * Where options include:
+ * k: Number of circles/ clusters
+ * n: Total number of sampled points. There are proportionally more
points within the
+ * outer/larger circles
+ * numIterations: Number of Power Iterations
+ * outerRadius: radius of the outermost of the concentric circles
+ * }}}
+ *
+ * Here is a sample run and output:
+ *
+ * ./bin/run-example
org.apache.spark.examples.mllib.PowerIterationClusteringExample -k 3 --n 30
--numIterations 15
+ *
+ * Cluster assignments: 1 -> [0,1,2,3,4],2 -> [5,6,7,8,9,10,11,12,13,14],
+ * 0 -> [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]
+ *
+ *
+ * If you use it as a template to create your own app, please use
`spark-submit` to submit your app.
+ */
+object PowerIterationClusteringExample {
+
+ case class Params(
+ input: String = null,
+ k: Int = 3,
+ numPoints: Int = 30,
+ numIterations: Int = 10,
+ outerRadius: Double = 3.0
+ ) extends AbstractParams[Params]
+
+ def main(args: Array[String]) {
+ val defaultParams = Params()
+
+ val parser = new OptionParser[Params]("PIC Circles") {
+ head("PowerIterationClusteringExample: an example PIC app using
concentric circles.")
+ opt[Int]('k', "k")
+ .text(s"number of circles (/clusters), default:
${defaultParams.k}")
+ .action((x, c) => c.copy(k = x))
+ opt[Int]('n', "n")
+ .text(s"number of points, default: ${defaultParams.numPoints}")
+ .action((x, c) => c.copy(numPoints = x))
+ opt[Int]("numIterations")
+ .text(s"number of iterations, default:
${defaultParams.numIterations}")
+ .action((x, c) => c.copy(numIterations = x))
+ }
+
+ parser.parse(args, defaultParams).map { params =>
+ run(params)
+ }.getOrElse {
+ sys.exit(1)
+ }
+ }
+
+ def generateCircle(n: Int, r: Double): Array[(Double, Double)] = {
+ val pi2 = 2 * math.Pi
+ (0.0 until pi2 by pi2 / n).map { x =>
+ (r * math.cos(x), r * math.sin(x))
+ }.toArray
+ }
+
+ def generateCirclesRdd(sc: SparkContext, nCircles: Int = 3,
nTotalPoints: Int = 30,
--- End diff --
chop down the arguments and use 4-space indentation
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