Deleting extra LogisticRegressionGenerator and RidgeRegressionGenerator

mllib/src/main/scala/spark/mllib/classification/LogisticRegressionGenerator.scala

-package spark.mllib.classification
-
-import scala.util.Random
-
-import org.jblas.DoubleMatrix
-
-import spark.{RDD, SparkContext}
-import spark.mllib.util.MLUtils
-
-object LogisticRegressionGenerator {
-
-  def main(args: Array[String]) {
-    if (args.length != 5) {
-      println("Usage: LogisticRegressionGenerator " +
-        "<master> <output_dir> <num_examples> <num_features> <num_partitions>")
-      System.exit(1)
-    }
-
-    val sparkMaster: String = args(0)
-    val outputPath: String = args(1)
-    val nexamples: Int = if (args.length > 2) args(2).toInt else 1000
-    val nfeatures: Int = if (args.length > 3) args(3).toInt else 2
-    val parts: Int = if (args.length > 4) args(4).toInt else 2
-    val eps = 3
-
-    val sc = new SparkContext(sparkMaster, "LogisticRegressionGenerator")
-
-    val data: RDD[(Double, Array[Double])] = sc.parallelize(0 until nexamples, parts).map { idx =>
-      val rnd = new Random(42 + idx)
-
-      val y = if (idx % 2 == 0) 0 else 1
-      val x = Array.fill[Double](nfeatures) {
-        rnd.nextGaussian() + (y * eps)
-      }
-      (y, x)
-    }
-
-    MLUtils.saveLabeledData(data, outputPath)
-    sc.stop()
-  }
-}

mllib/src/main/scala/spark/mllib/regression/RidgeRegressionGenerator.scala

-package spark.mllib.regression
-
-import scala.util.Random
-
-import org.jblas.DoubleMatrix
-
-import spark.{RDD, SparkContext}
-import spark.mllib.util.MLUtils
-
-
-object RidgeRegressionGenerator {
-
-  def main(args: Array[String]) {
-    if (args.length != 5) {
-      println("Usage: RidgeRegressionGenerator " +
-        "<master> <output_dir> <num_examples> <num_features> <num_partitions>")
-      System.exit(1)
-    }
-
-    val sparkMaster: String = args(0)
-    val outputPath: String = args(1)
-    val nexamples: Int = if (args.length > 2) args(2).toInt else 1000
-    val nfeatures: Int = if (args.length > 3) args(3).toInt else 100
-    val parts: Int = if (args.length > 4) args(4).toInt else 2
-    val eps = 10
-
-    org.jblas.util.Random.seed(42)
-    val sc = new SparkContext(sparkMaster, "RidgeRegressionGenerator")
-
-    // Random values distributed uniformly in [-0.5, 0.5]
-    val w = DoubleMatrix.rand(nfeatures, 1).subi(0.5)
-    w.put(0, 0, 10)
-    w.put(1, 0, 10)
-
-    val data: RDD[(Double, Array[Double])] = sc.parallelize(0 until parts, parts).flatMap { p =>
-      org.jblas.util.Random.seed(42 + p)
-      val examplesInPartition = nexamples / parts
-
-      val X = DoubleMatrix.rand(examplesInPartition, nfeatures)
-      val y = X.mmul(w)
-
-      val rnd = new Random(42 + p)
-
-      val normalValues = Array.fill[Double](examplesInPartition)(rnd.nextGaussian() * eps)
-      val yObs = new DoubleMatrix(normalValues).addi(y)
-
-      Iterator.tabulate(examplesInPartition) { i =>
-        (yObs.get(i, 0), X.getRow(i).toArray)
-      }
-    }
-
-    MLUtils.saveLabeledData(data, outputPath)
-    sc.stop()
-  }
-}