Scala SquaredL2Updater类代码示例

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本文整理汇总了Scala中org.apache.spark.mllib.optimization.SquaredL2Updater类的典型用法代码示例。如果您正苦于以下问题：Scala SquaredL2Updater类的具体用法？Scala SquaredL2Updater怎么用？Scala SquaredL2Updater使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。

在下文中一共展示了SquaredL2Updater类的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Scala代码示例。

示例1: MllibLBFGS

//设置package包名称以及导入依赖的类
package optimizers

import breeze.linalg.{DenseVector, Vector}
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
import org.apache.spark.mllib.optimization.{L1Updater, SimpleUpdater, SquaredL2Updater, Updater}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.rdd.RDD
import utils.Functions._


class MllibLBFGS(val data: RDD[LabeledPoint],
                 loss: LossFunction,
                 regularizer: Regularizer,
                 params: LBFGSParameters
                ) extends Optimizer(loss, regularizer) {

  val opt = new LogisticRegressionWithLBFGS

  val reg: Updater = (regularizer: Regularizer) match {
    case _: L1Regularizer => new L1Updater
    case _: L2Regularizer => new SquaredL2Updater
    case _: Unregularized => new SimpleUpdater
  }

  opt.optimizer.
    setNumIterations(params.iterations).
    setConvergenceTol(params.convergenceTol).
    setNumCorrections(params.numCorrections).
    setRegParam(regularizer.lambda).
    setUpdater(reg)

  override def optimize(): Vector[Double] = {
    val model = opt.run(data)
    val w = model.weights.toArray
    return DenseVector(w)
  }
}

开发者ID:mlbench，项目名称:mlbench，代码行数:38，代码来源:MllibLBFGS.scala

示例2: SparkSGD

//设置package包名称以及导入依赖的类
package linalg.sgd
import scala.util.Random
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.optimization.GradientDescent
import org.apache.spark.mllib.optimization.SquaredL2Updater
import org.apache.spark.mllib.optimization.LogisticGradient
import org.apache.spark.SparkContext



object SparkSGD {
  def main(args: Array[String]): Unit = {
    val m = 4
    val n = 200000
    val sc = new SparkContext("local[2]", "")
    val points = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
      val random = new Random(idx)
      iter.map(i => (1.0, Vectors.dense(Array.fill(n)(random.nextDouble()))))
    }.cache()
    val (weights, loss) = GradientDescent.runMiniBatchSGD(
      points,
      new LogisticGradient,
      new SquaredL2Updater,
      0.1,
      2,
      1.0,
      1.0,
      Vectors.dense(new Array[Double](n)))
    println("w:"  + weights(0))
    println("loss:" + loss(0))
    sc.stop()

  }
}

开发者ID:PacktPublishing，项目名称:Machine-Learning-with-Spark-Second-Edition，代码行数:35，代码来源:SparkSGD.scala

示例3: MllibSGD

//设置package包名称以及导入依赖的类
package optimizers

import breeze.linalg.{DenseVector, Vector}
import org.apache.spark.mllib.classification.{LogisticRegressionWithSGD, SVMWithSGD}
import org.apache.spark.mllib.optimization.{L1Updater, SimpleUpdater, SquaredL2Updater, Updater}
import org.apache.spark.mllib.regression.{LabeledPoint, LinearRegressionWithSGD}
import org.apache.spark.rdd.RDD
import utils.Functions._

import scala.tools.cmd.gen.AnyVals.D




class MllibSGD(val data: RDD[LabeledPoint],
               loss: LossFunction,
               regularizer: Regularizer,
               params: SGDParameters,
               ctype: String
              ) extends Optimizer(loss, regularizer) {
  val opt = ctype match {
    case "SVM" => new SVMWithSGD()
    case "LR" => new LogisticRegressionWithSGD()
    case "Regression" => new LinearRegressionWithSGD()
  }

  val reg: Updater = (regularizer: Regularizer) match {
    case _: L1Regularizer => new L1Updater
    case _: L2Regularizer => new SquaredL2Updater
    case _: Unregularized => new SimpleUpdater
  }

  ctype match {
    case "SVM" => opt.asInstanceOf[SVMWithSGD].optimizer.
      setNumIterations(params.iterations).
      setMiniBatchFraction(params.miniBatchFraction).
      setStepSize(params.stepSize).
      setRegParam(regularizer.lambda).
      setUpdater(reg)
    case "LR" => opt.asInstanceOf[LogisticRegressionWithSGD].optimizer.
      setNumIterations(params.iterations).
      setMiniBatchFraction(params.miniBatchFraction).
      setStepSize(params.stepSize).
      setRegParam(regularizer.lambda).
      setUpdater(reg)
    case "Regression" => opt.asInstanceOf[LinearRegressionWithSGD].optimizer.
      setNumIterations(params.iterations).
      setMiniBatchFraction(params.miniBatchFraction).
      setStepSize(params.stepSize).
      setRegParam(regularizer.lambda).
      setUpdater(reg)
  }

  override def optimize(): Vector[Double] = {
    val model = opt.run(data)
    val w = model.weights.toArray
    DenseVector(w)
  }
}

开发者ID:mlbench，项目名称:mlbench，代码行数:60，代码来源:MllibSGD.scala

注：本文中的org.apache.spark.mllib.optimization.SquaredL2Updater类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。