本文整理汇总了Scala中org.apache.spark.mllib.optimization.L1Updater类的典型用法代码示例。如果您正苦于以下问题：Scala L1Updater类的具体用法？Scala L1Updater怎么用？Scala L1Updater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L1Updater类的2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)
}
} 示例2: 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)
}
}