Using fp-ts. I have an option of an array
const arrayofKeys: Option<Array<K>>,
and an option of a record
const record: Option<Record<K,V>>
I want to pick the Vs of the Record where Ks intersect with the Array and stick the result in an Option.
In ramda: R.pick(arrayOfKeys, record)
How do i solve this with fp-ts or other packages within the fp-ts ecosystem?
I'd personally avoid Ramda et al as in my experience they're not very well typed. Here's a pure fp-ts approach (Str.fromNumber is from fp-ts-std, trivially replaced):
declare const arrayOfKeyNums: Option<Array<number>>
const arrayOfKeys = pipe(arrayOfKeyNums, O.map(A.map(Str.fromNumber)))
declare const record: Option<Record<string, number>>
const keyIntersectedVals: O.Option<Array<number>> = pipe(
sequenceT(O.Apply)(arrayOfKeys, record),
O.map(([ks, rec]) =>
pipe(
rec,
R.foldMapWithIndex(Str.Ord)(A.getMonoid<number>())((k, v) =>
A.elem(Str.Eq)(k)(ks) ? [v] : [],
),
),
),
)
It's a bit verbose owing to the need to pass typeclass instances around. On the plus side, the use of typeclass instances means that this can be trivially updated to support any value type, including non-primitive types with any given Eq.
Here's what the body might instead look like in Haskell for comparison, where typeclass instances don't need to be passed around:
keyIntersectedVals :: Maybe [Int]
keyIntersectedVals = uncurry (M.foldMapWithKey . intersectedToList) <$> sequenceT (mkeys, mmap)
where intersectedToList ks k v
| k `elem` ks = [v]
| otherwise = []
For example, given keys O.some(["a", "c"]) and a record O.some({ a: 123, b: 456, c: 789 }), we get O.some([123, 789]).
Ramda's lift lifts a function on some values to work on a container of those values. So lift (pick) will likely do what you want, so long as fp-ts's Option supports the FantasyLand Apply specification.
const {of} = folktale.maybe
const {lift, pick} = R
const keys = of (['k', 'e', 'y', 's']) // Maybe (['k', 'e', 'y', 's'])
const record = of ({s: 1, k: 2, y: 3, b: 4, l: 5, u: 6, e: 7}) // Maybe ({s: 1, k: 2, ...})
console .log (lift (pick) (keys, record) .toString())
<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.28.0/ramda.min.js"></script>
<script src="//cdnjs.cloudflare.com/ajax/libs/folktale/2.0.0/folktale.min.js"></script>
This is a great use case for traverseArray, an optimized version of traverse. You can also use "Do notation" and apS to get a really clean, monadic pipeline. If any of these operations return a None, the entire flow will terminate early (this is a good!).
Also, lookup is a very handy function similar to get from Ramda/Lodash, but it returns an Option. Both the Record and Array modules export a version of this function.
declare const arrayofKeys: O.Option<Array<string>>
declare const record: O.Option<Record<string, number>>
export const result: O.Option<ReadonlyArray<number>> = pipe(
O.Do,
O.apS('keys', arrayofKeys),
O.apS('rec', record),
O.chain(({ keys, rec }) =>
pipe(
keys,
O.traverseArray(key => pipe(rec, R.lookup(key)))
)
)
)
Functions used:
https://gcanti.github.io/fp-ts/modules/Option.ts.html#do
https://gcanti.github.io/fp-ts/modules/Option.ts.html#aps
https://gcanti.github.io/fp-ts/modules/Option.ts.html#chain
https://gcanti.github.io/fp-ts/modules/Option.ts.html#traversearray
https://gcanti.github.io/fp-ts/modules/Record.ts.html#lookup
There is a given function, that is fixed and must not be changed:
const validate = v => v === "fred" ? "Y" : undefined
Now, because I would like to be functional and would like to avoid null-checks I've decided to use Maybe (ramda-fantasy) for validation function:
const vv = val => Maybe(val).map(v=> validate(v)).getOrElse("N")
vv should return Y if it's called with "fred" otherwise N.
vv(null) returns N -> OK
vv("fred") returns Y -> OK
vv("ding") returns undefined -> wrong, expected N
The problem is, that Maybe.map always returns Just, that I do not understand (because I'm just learning it). For me I would be beneficial if this function would behave in similar way to Maybe(val) that returns None or Just.
I have two question:
Why Maybe.map does not handle null/undefined?
How to rewrite vv that it would return expected values in all three cases?
EDIT: I would like to explain why validate should not be changed: it's just simple example of function coming from external library. I wanted to see how easy/hard is to integrate such libraries into functional programming. So is not about string operations, just about streaming values when at some point it evaluates to null.
EDIT2:
This solves my problem:
Either.ofNullable = Either.prototype.ofNullable = function (value) {
return value == null ? Either.Left("is null") : Either.Right(value);
};
EDIT3:
I've implemented my own Either with missing functionality: https://github.com/maciejmiklas/functional-ts/blob/main/src/either.ts
Note: Ramda Fantasy is no longer maintained. The team recommends that you use other implementations of these concepts.
But we can still answer this question, as it's likely to be true of any reasonable Maybe implementation
Basically, that's not how Maybe is designed to work. The idea is that you can have a Just holding absolutely any value. That includes the values null and undefined.
Ramda added a convenience constructor, Maybe (val), which turns into Just (val) if val is not a nil value, and into Nothing () if it is. But that doesn't mean that you cannot create a Just (null). The main construction technique is to use the static Maybe .of. And you can note that
Maybe (null) //=> Nothing ()
Maybe.of (null) //=> Just (null)
So we're probably not going to make that technique work. We couldn't just map such an existing validate over our Maybe and expect it to work. We could, however, work with a version like this:
const validate = v => v === "fred" ? Just ("Y") : Nothing ()
Here, we still have one problem. Maybe ('fred') .map (validate) yields Just (Just ('Y')). We have extra nesting. But this is exactly what chain is for. It removes such an additional level, so that Maybe ('fred') .chain (validate) yields Just ('Y'). We can then use it like this:
const validate = v => v === "fred" ? Just ("Y") : Nothing ()
const vv = val => Maybe (val) .chain (validate) .getOrElse ('N')
console .log ([
vv (null), // 'N'
vv ('fred'), // 'Y'
vv ('ding') // 'N'
])
There is an idiomatic approach to converting a Pair into a List:
Pair(a, b).toList()
No I am searching for the opposite process. My best approach looks like this:
Pair(list[0], list[1])
My problem with this is that I need to make a List value in code first for this to work. I would love something like this:
listOf(a, b).toPair()
For a more general solution you could use the extension function zipWithNext* which
Returns a list of pairs of each two adjacent elements in this collection.
The example in the documentation explains it better:
val letters = ('a'..'f').toList()
val pairs = letters.zipWithNext()
println(letters) // [a, b, c, d, e, f]
println(pairs) // [(a, b), (b, c), (c, d), (d, e), (e, f)]
*note that this function is available since v1.2 of Kotlin.
You can make this extension for yourself:
fun <T> List<T>.toPair(): Pair<T, T> {
if (this.size != 2) {
throw IllegalArgumentException("List is not of length 2!")
}
return Pair(this[0], this[1])
}
The error handling is up to you, you could also return a Pair<T, T>? with null being returned in the case where the List is not the correct length. You could also only check that it has at least 2 elements, and not require it to have exactly 2.
Usage is as you've described:
listOf(a, b).toPair()
Here's a variation on #zsmb13's solution that avoids creating the exception explicitly and dereferencing the list by index:
fun <T> List<T>.toPair(): Pair<T, T> {
require (this.size == 2) { "List is not of length 2!" }
val (a, b) = this
return Pair(a, b)
}
If you're looking for an option that is chainable (for example I came across this when mapping a list of strings that I turned into a pair by splitting), I find this pattern useful:
yourList.let{ it[0] to it[1] } // this assumes yourList always has at least 2 values
Here it is in context:
val pairs: List<Pair<String,String>> = lines.map{ it.split(",").let{ it[0] to it[1] } }
I understand that abstraction is about taking something more concrete and making it more abstract. That something may be either a data structure or a procedure. For example:
Data abstraction: A rectangle is an abstraction of a square. It concentrates on the fact a square has two pairs of opposite sides and it ignores the fact that adjacent sides of a square are equal.
Procedural abstraction: The higher order function map is an abstraction of a procedure which performs some set of operations on a list of values to produce an entirely new list of values. It concentrates on the fact that the procedure loops through every item of the list in order to produce a new list and ignores the actual operations performed on every item of the list.
So my question is this: how is abstraction any different from generalization? I'm looking for answers primarily related to functional programming. However if there are parallels in object-oriented programming then I would like to learn about those as well.
A very interesting question indeed. I found this article on the topic, which concisely states that:
While abstraction reduces complexity by hiding irrelevant detail, generalization reduces complexity by replacing multiple entities which perform similar functions with a single construct.
Lets take the old example of a system that manages books for a library. A book has tons of properties (number of pages, weight, font size(s), cover,...) but for the purpose of our library we may only need
Book(title, ISBN, borrowed)
We just abstracted from the real books in our library, and only took the properties that interested us in the context of our application.
Generalization on the other hand does not try to remove detail but to make functionality applicable to a wider (more generic) range of items. Generic containers are a very good example for that mindset: You wouldn't want to write an implementation of StringList, IntList, and so on, which is why you'd rather write a generic List which applies to all types (like List[T] in Scala). Note that you haven't abstracted the list, because you didn't remove any details or operations, you just made them generically applicable to all your types.
Round 2
#dtldarek's answer is really a very good illustration! Based on it, here's some code that might provide further clarification.
Remeber the Book I mentioned? Of course there are other things in a library that one can borrow (I'll call the set of all those objects Borrowable even though that probably isn't even a word :D):
All of these items will have an abstract representation in our database and business logic, probably similar to that of our Book. Additionally, we might define a trait that is common to all Borrowables:
trait Borrowable {
def itemId:Long
}
We could then write generalized logic that applies to all Borrowables (at that point we don't care if its a book or a magazine):
object Library {
def lend(b:Borrowable, c:Customer):Receipt = ...
[...]
}
To summarize: We stored an abstract representation of all the books, magazines and DVDs in our database, because an exact representation is neither feasible nor necessary. We then went ahead and said
It doesn't matter whether a book, a magazine or a DVD is borrowed by a customer. It's always the same process.
Thus we generalized the operation of borrowing an item, by defining all things that one can borrow as Borrowables.
Object:
Abstraction:
Generalization:
Example in Haskell:
The implementation of the selection sort by using priority queue with three different interfaces:
an open interface with the queue being implemented as a sorted list,
an abstracted interface (so the details are hidden behind the layer of abstraction),
a generalized interface (the details are still visible, but the implementation is more flexible).
{-# LANGUAGE RankNTypes #-}
module Main where
import qualified Data.List as List
import qualified Data.Set as Set
{- TYPES: -}
-- PQ new push pop
-- by intention there is no build-in way to tell if the queue is empty
data PriorityQueue q t = PQ (q t) (t -> q t -> q t) (q t -> (t, q t))
-- there is a concrete way for a particular queue, e.g. List.null
type ListPriorityQueue t = PriorityQueue [] t
-- but there is no method in the abstract setting
newtype AbstractPriorityQueue q = APQ (forall t. Ord t => PriorityQueue q t)
{- SOLUTIONS: -}
-- the basic version
list_selection_sort :: ListPriorityQueue t -> [t] -> [t]
list_selection_sort (PQ new push pop) list = List.unfoldr mypop (List.foldr push new list)
where
mypop [] = Nothing -- this is possible because we know that the queue is represented by a list
mypop ls = Just (pop ls)
-- here we abstract the queue, so we need to keep the queue size ourselves
abstract_selection_sort :: Ord t => AbstractPriorityQueue q -> [t] -> [t]
abstract_selection_sort (APQ (PQ new push pop)) list = List.unfoldr mypop (List.foldr mypush (0,new) list)
where
mypush t (n, q) = (n+1, push t q)
mypop (0, q) = Nothing
mypop (n, q) = let (t, q') = pop q in Just (t, (n-1, q'))
-- here we generalize the first solution to all the queues that allow checking if the queue is empty
class EmptyCheckable q where
is_empty :: q -> Bool
generalized_selection_sort :: EmptyCheckable (q t) => PriorityQueue q t -> [t] -> [t]
generalized_selection_sort (PQ new push pop) list = List.unfoldr mypop (List.foldr push new list)
where
mypop q | is_empty q = Nothing
mypop q | otherwise = Just (pop q)
{- EXAMPLES: -}
-- priority queue based on lists
priority_queue_1 :: Ord t => ListPriorityQueue t
priority_queue_1 = PQ [] List.insert (\ls -> (head ls, tail ls))
instance EmptyCheckable [t] where
is_empty = List.null
-- priority queue based on sets
priority_queue_2 :: Ord t => PriorityQueue Set.Set t
priority_queue_2 = PQ Set.empty Set.insert Set.deleteFindMin
instance EmptyCheckable (Set.Set t) where
is_empty = Set.null
-- an arbitrary type and a queue specially designed for it
data ABC = A | B | C deriving (Eq, Ord, Show)
-- priority queue based on counting
data PQ3 t = PQ3 Integer Integer Integer
priority_queue_3 :: PriorityQueue PQ3 ABC
priority_queue_3 = PQ new push pop
where
new = (PQ3 0 0 0)
push A (PQ3 a b c) = (PQ3 (a+1) b c)
push B (PQ3 a b c) = (PQ3 a (b+1) c)
push C (PQ3 a b c) = (PQ3 a b (c+1))
pop (PQ3 0 0 0) = undefined
pop (PQ3 0 0 c) = (C, (PQ3 0 0 (c-1)))
pop (PQ3 0 b c) = (B, (PQ3 0 (b-1) c))
pop (PQ3 a b c) = (A, (PQ3 (a-1) b c))
instance EmptyCheckable (PQ3 t) where
is_empty (PQ3 0 0 0) = True
is_empty _ = False
{- MAIN: -}
main :: IO ()
main = do
print $ list_selection_sort priority_queue_1 [2, 3, 1]
-- print $ list_selection_sort priority_queue_2 [2, 3, 1] -- fail
-- print $ list_selection_sort priority_queue_3 [B, C, A] -- fail
print $ abstract_selection_sort (APQ priority_queue_1) [B, C, A] -- APQ hides the queue
print $ abstract_selection_sort (APQ priority_queue_2) [B, C, A] -- behind the layer of abstraction
-- print $ abstract_selection_sort (APQ priority_queue_3) [B, C, A] -- fail
print $ generalized_selection_sort priority_queue_1 [2, 3, 1]
print $ generalized_selection_sort priority_queue_2 [B, C, A]
print $ generalized_selection_sort priority_queue_3 [B, C, A]-- power of generalization
-- fail
-- print $ let f q = (list_selection_sort q [2,3,1], list_selection_sort q [B,C,A])
-- in f priority_queue_1
-- power of abstraction (rank-n-types actually, but never mind)
print $ let f q = (abstract_selection_sort q [2,3,1], abstract_selection_sort q [B,C,A])
in f (APQ priority_queue_1)
-- fail
-- print $ let f q = (generalized_selection_sort q [2,3,1], generalized_selection_sort q [B,C,A])
-- in f priority_queue_1
The code is also available via pastebin.
Worth noticing are the existential types. As #lukstafi already pointed out, abstraction is similar to existential quantifier and generalization is similar to universal quantifier.
Observe that there is a non-trivial connection between the fact that ∀x.P(x) implies ∃x.P(x) (in a non-empty universe), and that there rarely is a generalization without abstraction (even c++-like overloaded functions form a kind of abstraction in some sense).
Credits:
Portal cake by Solo.
Dessert table by djttwo.
The symbol is the cake icon from material.io.
I'm going to use some examples to describe generalisation and abstraction, and I'm going to refer to this article.
To my knowledge, there is no official source for the definition of abstraction and generalisation in the programming domain (Wikipedia is probably the closest you'll get to an official definition in my opinion), so I've instead used an article which I deem credible.
Generalization
The article states that:
"The concept of generalization in OOP means that an object encapsulates
common state and behavior for a category of objects."
So for example, if you apply generalisation to shapes, then the common properties for all types of shape are area and perimeter.
Hence a generalised shape (e.g. Shape) and specialisations of it (e.g. a Circle), can be represented in classes as follows (note that this image has been taken from the aforementioned article)
Similarly, if you were working in the domain of jet aircraft, you could have a Jet as a generalisation, which would have a wingspan property. A specialisation of a Jet could be a FighterJet, which would inherit the wingspan property and would have its own property unique to fighter jets e.g. NumberOfMissiles.
Abstraction
The article defines abstraction as:
"the process of identifying common patterns that have systematic
variations; an abstraction represents the common pattern and provides
a means for specifying which variation to use" (Richard Gabriel)"
In the domain of programming:
An abstract class is a parent class that allows inheritance but can
never be instantiated.
Hence in the example given in the Generalization section above, a Shape is abstract as:
In the real world, you never calculate the area or perimeter of a
generic shape, you must know what kind of geometric shape you have
because each shape (eg. square, circle, rectangle, etc.) has its own
area and perimeter formulas.
However, as well as being abstract a shape is also a generalisation (because it "encapsulates common state and behavior for a category of objects" where in this case the objects are shapes).
Going back to the example I gave about Jets and FighterJets, a Jet is not abstract as a concrete instance of a Jet is feasible, as one can exist in the real world, unlike a shape i.e. in the real world you cant hold a shape you hold an instance of a shape e.g. a cube. So in the aircraft example, a Jet is not abstract, it is a generalisation as it is possible to have a "concrete" instance of a jet.
Not addressing credible / official source: an example in Scala
Having "Abstraction"
trait AbstractContainer[E] { val value: E }
object StringContainer extends AbstractContainer[String] {
val value: String = "Unflexible"
}
class IntContainer(val value: Int = 6) extends AbstractContainer[Int]
val stringContainer = new AbstractContainer[String] {
val value = "Any string"
}
and "Generalization"
def specialized(c: StringContainer.type) =
println("It's a StringContainer: " + c.value)
def slightlyGeneralized(s: AbstractContainer[String]) =
println("It's a String container: " + s.value)
import scala.reflect.{ classTag, ClassTag }
def generalized[E: ClassTag](a: AbstractContainer[E]) =
println(s"It's a ${classTag[E].toString()} container: ${a.value}")
import scala.language.reflectiveCalls
def evenMoreGeneral(d: { def detail: Any }) =
println("It's something detailed: " + d.detail)
executing
specialized(StringContainer)
slightlyGeneralized(stringContainer)
generalized(new IntContainer(12))
evenMoreGeneral(new { val detail = 3.141 })
leads to
It's a StringContainer: Unflexible
It's a String container: Any string
It's a Int container: 12
It's something detailed: 3.141
Abstraction
Abstraction is specifying the framework and hiding the implementation level information. Concreteness will be built on top of the abstraction. It gives you a blueprint to follow to while implementing the details. Abstraction reduces the complexity by hiding low level details.
Example: A wire frame model of a car.
Generalization
Generalization uses a “is-a” relationship from a specialization to the generalization class. Common structure and behaviour are used from the specializtion to the generalized class. At a very broader level you can understand this as inheritance. Why I take the term inheritance is, you can relate this term very well. Generalization is also called a “Is-a” relationship.
Example: Consider there exists a class named Person. A student is a person. A faculty is a person. Therefore here the relationship between student and person, similarly faculty and person is generalization.
I'd like to offer an answer for the greatest possible audience, hence I use the Lingua Franca of the web, Javascript.
Let's start with an ordinary piece of imperative code:
// some data
const xs = [1,2,3];
// ugly global state
const acc = [];
// apply the algorithm to the data
for (let i = 0; i < xs.length; i++) {
acc[i] = xs[i] * xs[i];
}
console.log(acc); // yields [1, 4, 9]
In the next step I introduce the most important abstraction in programming - functions. Functions abstract over expressions:
// API
const foldr = f => acc => xs => xs.reduceRight((acc, x) => f(x) (acc), acc);
const concat = xs => ys => xs.concat(ys);
const sqr_ = x => [x * x]; // weird square function to keep the example simple
// some data
const xs = [1,2,3];
// applying
console.log(
foldr(x => acc => concat(sqr_(x)) (acc)) ([]) (xs) // [1, 4, 9]
)
As you can see a lot of implementation details are abstracted away. Abstraction means the suppression of details.
Another abstraction step...
// API
const comp = (f, g) => x => f(g(x));
const foldr = f => acc => xs => xs.reduceRight((acc, x) => f(x) (acc), acc);
const concat = xs => ys => xs.concat(ys);
const sqr_ = x => [x * x];
// some data
const xs = [1,2,3];
// applying
console.log(
foldr(comp(concat, sqr_)) ([]) (xs) // [1, 4, 9]
);
And another one:
// API
const concatMap = f => foldr(comp(concat, f)) ([]);
const comp = (f, g) => x => f(g(x));
const foldr = f => acc => xs => xs.reduceRight((acc, x) => f(x) (acc), acc);
const concat = xs => ys => xs.concat(ys);
const sqr_ = x => [x * x];
// some data
const xs = [1,2,3];
// applying
console.log(
concatMap(sqr_) (xs) // [1, 4, 9]
);
The underlying principle should now be clear. I'm still dissatisfied with concatMap though, because it only works with Arrays. I want it to work with every data type that is foldable:
// API
const concatMap = foldr => f => foldr(comp(concat, f)) ([]);
const concat = xs => ys => xs.concat(ys);
const sqr_ = x => [x * x];
const comp = (f, g) => x => f(g(x));
// Array
const xs = [1, 2, 3];
const foldr = f => acc => xs => xs.reduceRight((acc, x) => f(x) (acc), acc);
// Option (another foldable data type)
const None = r => f => r;
const Some = x => r => f => f(x);
const foldOption = f => acc => tx => tx(acc) (x => f(x) (acc));
// applying
console.log(
concatMap(foldr) (sqr_) (xs), // [1, 4, 9]
concatMap(foldOption) (sqr_) (Some(3)), // [9]
concatMap(foldOption) (sqr_) (None) // []
);
I broadened the application of concatMap to encompass a larger domain of data types, nameley all foldable datatypes. Generalization emphasizes the commonalities between different types, (or rather objects, entities).
I achieved this by means of dictionary passing (concatMap's additional argument in my example). Now it is somewhat annoying to pass these type dicts around throughout your code. Hence the Haskell folks introduced type classes to, ...um, abstract over type dicts:
concatMap :: Foldable t => (a -> [b]) -> t a -> [b]
concatMap (\x -> [x * x]) ([1,2,3]) -- yields [1, 4, 9]
concatMap (\x -> [x * x]) (Just 3) -- yields [9]
concatMap (\x -> [x * x]) (Nothing) -- yields []
So Haskell's generic concatMap benefits from both, abstraction and generalization.
Let me explain in the simplest manner possible.
"All pretty girls are female." is an abstraction.
"All pretty girls put on make-up." is a generalization.
Abstraction is usually about reducing complexity by eliminating unnecessary details. For example, an abstract class in OOP is a parent class that contains common features of its children but does not specify the exact functionality.
Generalization does not necessarily require to avoid details but rather to have some mechanism to allow for applying the same function to different argument. For instance, polymorphic types in functional programming languages allow you not to bother about the arguments, rather focus on the operation of the function. Similarly, in java you can have generic type which is an "umbrella" to all types while the function is the same.
Here is a more general (pun intended) description.
abstraction operation
changes the representation of an entity by hiding/reducing its properties that are not necessary for the desired conceptualization
has an inherent information loss, which makes it less flexible but more conclusive
generalization operation
doesn't change the representation of an entity, but defines similarities between entities of different kind
applies knowledge previously acquired to unseen circumstances or extends that knowledge beyond the scope of the original problem (knowledge transfer)
can be seen as a hypothesis that a set of entities of different kind have similar properties and will behave consistently when applied in a certain way
has no inherent information loss, which makes it more flexible but less conclusive (more error-prone)
Both operations reduce complexity either by hiding details or by reducing entities that perform similar functions to a single construct.
Scala's List classes have indexWhere methods, which return a single index for a List element which matches the supplied predicate (or -1 if none exists).
I recently found myself wanting to gather all indices in a List which matched a given predicate, and found myself writing an expression like:
list.zipWithIndex.filter({case (elem, _) => p(elem)}).map({case (_, index) => index})
where p here is some predicate function for selecting matching elements. This seems a bit of an unwieldy expression for such a simple requirement (but I may be missing a trick or two).
I was half expecting to find an indicesWhere function on List which would allow me to write instead:
list.indicesWhere(p)
Should something like this be part of the Scala's List API, or is there a much simpler expression than what I've shown above for doing the same thing?
Well, here's a shorter expression that removes some of the syntactic noise you have in yours (modified to use Travis's suggestion):
list.zipWithIndex.collect { case (x, i) if p(x) => i }
Or alternatively:
for ((x,i) <- list.zipWithIndex if p(x)) yield i
But if you use this frequently, you should just add it as an implicit method:
class EnrichedWithIndicesWhere[T, CC[X] <: Seq[X]](xs: CC[T]) {
def indicesWhere(p: T => Boolean)(implicit bf: CanBuildFrom[CC[T], Int, CC[Int]]): CC[Int] = {
val b = bf()
for ((x, i) <- xs.zipWithIndex if p(x)) b += i
b.result
}
}
implicit def enrichWithIndicesWhere[T, CC[X] <: Seq[X]](xs: CC[T]) = new EnrichedWithIndicesWhere(xs)
val list = List(1, 2, 3, 4, 5)
def p(i: Int) = i % 2 == 1
list.indicesWhere(p) // List(0, 2, 4)
You could use unzip to replace the map:
list.zipWithIndex.filter({case (elem, _) => p(elem)}).unzip._2