如何参数化类和实现方法取决于Scala中的类型 [英] How to parametrize class and implement method depends on type in Scala

问题描述

这是我尝试过的.取决于用户在我要向新的Chunk中添加String或Double的函数中输入了什么.

This is what I tried. Depends on what does user put into the function I want to add String or Double to new Chunk.

package org.apache.spark.h2o.utils

import water.fvec.{NewChunk, Frame, Chunk}
import water._
import water.parser.ValueString

class ReplaceNa[T >: Any](a: T) extends MRTask{
  override def map(c: Chunk, nc: NewChunk): Unit = {
    for (row <- 0 until c.len()) {

        a match{
             case s: ValueString if(c.isNA(row)) => nc.addStr(s)           
             case d: Double      if(c.isNA(row)) => nc.addNum(d)

      }
    }
  }
}

但是我遇到了错误

 error: value outputFrame is not a member of Nothing
          pred.add(new ReplaceNa(3).doAll(1, pred.vec(4)).outputFrame(Array("s"), null))

感谢您的帮助！

推荐答案

我有几点评论:

• 检查switch分支之外的NA
• 您缺少非NA情况，因此生成的向量比输入向量短(我希望您希望生成相同长度的向量)

关于泛型，您需要提供类型专门化.例如，类似于以下代码段:

Regarding generics, you need to provide type specialization. For example, something like the following snippet:

class ReplaceNA[T](val value: T)(implicit add: TAdd[T]) extends MRTask[ReplaceNA[T]] {
  override def map(c: Chunk, nc: NewChunk): Unit = {
    for (row <- 0 until c.len()) {
      // Replace NAs by given value
      if (c.isNA(row)) {
        add.addValue(nc, value)
      } else {
        // Do something with default value
        nc.addNA()
      }
    }
  }

}

trait TAdd[T] extends Serializable {
  def addValue(nc: NewChunk, value: T)
}

object TAdd extends Serializable {
  implicit val addDouble = new TAdd[Double] { def addValue(nc: NewChunk, value: Double) = nc.addNum(value) }
  implicit val addFloat = new TAdd[Float] { def addValue(nc: NewChunk, value: Float) = nc.addNum(value) }
  implicit val addValueString = new TAdd[ValueString] { def addValue(nc: NewChunk, value: ValueString) = nc.addStr(value) }
}

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