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These sigs are for the arities below only.

map f: (a->b)->(x->b->x)->(x->a->x)

filter pred: (a->bool)->(x->a->x)->(x->a->x)

flatmap f: (a->[b])->(x->b->x)->(x->a->x)

etc.




OK, maybe we're getting somewhere. Let me try to write this `map`, just to see. I'm using Scala, so I can put some types, and have the compiler yell at me if I'm doing something overtly wrong (I need all the help I can get!).

For clarity, let's define a type alias for reducers:

    type Reducer[X, A] = (X, A) ⇒ X
Let's define `map` to match the type definition you provided. And with that type definition, I only see one way in which the function can be implemented. So it must be:

    def map[X, A, B](f: A ⇒ B): (Reducer[X, B] ⇒ Reducer[X, A]) =
        (redB: Reducer[X, B]) ⇒ (x: X, a: A) ⇒ redB(x, f(a))
How can I use this? Let's try the following:

    def addup(zero: Int, a: List[Int]) = a.foldLeft(zero)(_ + _)
    def parseList(a: List[String]) = a.map(_.toInt)

    map(parseList)(addup)(1, List("7", "8"))
This returns 16. OK, parsing the list, and adding up starting from 1. But it doesn't look to me like `map` implements anything like the usual semantic of map. It just converts the data structure, and applies the reducer. What am I missing here?


So, a transduceMap implementation would be this I guess?

  transduceMap :: (b -> a) -> (acc -> a -> acc) -> (acc -> b -> acc)
  transduceMap f = \reduceFn -> \acc el -> reduceFn acc (f el)
lambda added for clarity (no pun intended), however types are easier to match when using this syntax:

  transduceMap :: (b -> a) -> (acc -> a -> acc) -> acc -> b -> acc
  transduceMap f reduceFn acc el = reduceFn acc (f el)




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