scala减少,reduceLeft reduceRight,折叠,foldLeft foldRight

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1. 归约函数

1.1 reduce

   聚合： N -> 1,如 sum函数统计List中所有元素的和; 在scalaThe bottom layer uses pairwise aggregation,The type of the aggregated result is ANDreduce方法的返回值类型相同

object T27 {
    
  def main(args: Array[String]): Unit = {
    
    val dataList = List(1, 2, 3, 4,5)
    val i = dataList.reduce(_ - _)
    println(i)
  }
}

1.2 reduceLeft

  从左往右,Performs an operator on the elements in the collection

object T28 {
    
  def main(args: Array[String]): Unit = {
    
    val dataList = List(1, 2, 3, 4,5)
    val i = dataList.reduceLeft(_ - _)
    println(i)
  }
}

注：The operation logic is( ( ( (1 - 2) - 3) - 4) - 5),即 op( op( ... op(x_1, x_2) ..., x_{n-1}), x_n)

1.3 reduceRight

  从右往左,Performs an operator on the elements in the collection

object T29 {
    
  def main(args: Array[String]): Unit = {
    
    val dataList = List(1, 2, 3, 4, 5)
    val i = dataList.reduceRight(_ - _)
    println(i)
  }
}

注：The operation logic is(1 - (2 - (3 - (4 - 5) ) ) ),即 op(x_1, op(x_2, ..., op(x_{n-1}, x_n)...))

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2. fold、foldLeft 、foldRight

  fold函数的本质是Aggregate with the initial value and each piece of data in the collection;fold方法实际是函数柯里化,有2个参数,第一个参数表示初始值,The second parameter represents the calculation rule.foldThere are two variants of the method：foldLeft()和foldRight()：foldLeft(),第一个参数为累计值,The direction in which the collection is traversed is 从左到右;foldRight(),第二个参数为累计值,The direction in which the collection is traversed is 从右到左

如：合并2个map并keyThe same summation

object T30 {
    
  def main(args: Array[String]): Unit = {
    
    val map1 = mutable.Map("a" -> 1, "b" -> 1, "c" -> 3)
    val map2 = mutable.Map("a" -> 1, "d" -> 1, "c" -> 3)
    map1.foldLeft(map2)((map, element) => {
    
      val k = element._1
      val v = element._2
      val newV = map.getOrElse(k, 0) + v
      map.update(k, newV)
      map
    })
    println(map2)
  }
}