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I am looking to calculate the scalar product of two lists. Let's say we have two Lists, l1 = List(1,2,3) and l2 = List(4,5,6), the result should be List(4,10,18)
The code below works:
def scalarProduct(l1 : List[Int], l2 : List[Int]):List[Int] = {
val l3 = l1 zip(l2); l3 map(xy => xy._1*xy._2)
}
However, the following fails to compile, and says Cannot resolve reference map with such signature :
def scalarProduct(l1 : List[Int], l2 : List[Int]):List[Int] = {
val l3 = l1 zip(l2); l3 map((x:Int,y:Int) => x*y)
}
This zip() would return a list of Int pairs, and the above map is also taking a function which takes an Int pair.
Could someone point out why does the second variant fail in this case?
Your second example fails because you provide a function with 2 parameters to the map, while map takes a function with 1 parameter.
Have a look, here's a (simplified) signature of the map function:
def map[B, That](f: A => B): That
The function f is the one that you have to pass to do the conversion. As you can see, it has type A => B, i.e. accept a single parameter.
Now take a look at the (simplified) zip function signature:
def zip [B](that : List[B]) : List[(A, B)]
It actually produces a list whose members are tuples. Tuple of 2 elements looks like this: (A, B). When you call map on the list of tuples, you have to provide the function f that takes a tuple of 2 elements as a parameter, exactly like you do in your first example.
Since it's inconvenient to work with tuples directly, you could extract values of tuple's members to a separate variables using pattern matching.
Here's an REPL session to illustrate this.
scala> List(1, 2, 3)
res0: List[Int] = List(1, 2, 3)
scala> List(2, 3, 4)
res1: List[Int] = List(2, 3, 4)
scala> res0 zip res1
res2: List[(Int, Int)] = List((1,2), (2,3), (3,4))
Here's how you do a standard tuple values extraction with pattern matching:
scala> res2.map(t => t match {
| case (x, y) => x * y
| })
res3: List[Int] = List(2, 6, 12)
It's important to note here that pattern matching expects a partial function as an argument. I.e. the following expression is actually a partial function:
{
case (x, y) => x * y
}
The partial function has its own type in Scala: trait PartialFunction[-A, +B] extends (A) => B, and you could read more about it, for example, here.
Partial function is a normal function, since it extends (A) => B, and that's why you can pass a partial function to the map call:
scala> res2.map { case (x, y) => x * y }
res4: List[Int] = List(2, 6, 12)
You actually use special Scala syntax here, that allows for functions invocations (map in our case) without parentheses around its parameters. You can alternatively write this with parentheses as follows:
scala> res2.map ({ case (x, y) => x * y })
res5: List[Int] = List(2, 6, 12)
There's no difference between the 2 last calls at all.
The fact that you don't have to declare a parameter of anonymous function you pass to the map before you do pattern matching on it, is actually Scala's syntactic sugar. When you call res2.map { case (x, y) => x * y }, what's really going on is pattern matching with partial function.
Hope this helps.
you need something like:
def scalarProduct(l1 : List[Int], l2 : List[Int]):List[Int] = {
val l3 = l1 zip(l2); l3 map{ case (x:Int,y:Int) => x*y}
}
You can have a look at this link to help you with this type of problems.
I want to extract range of elements from a list, meeting the following requirements:
First element of range has to be an element previous to element matching specific condition
Last element of range has to be an element next to element matching specific condition
Example: For list (1,1,1,10,2,10,1,1,1) and condition x >= 10 I want to get (1,10,2,10,1)
This is very simple to program imperatively, but I am just wondering if there is some smart Scala-functional way to achieve it. Is it?
Keeping it in the scala standard lib, I would solve this using recursion:
def f(_xs: List[Int])(cond: Int => Boolean): List[Int] = {
def inner(xs: List[Int], res: List[Int]): List[Int] = xs match {
case Nil => Nil
case x :: y :: tail if cond(y) && res.isEmpty => inner(tail, res ++ (x :: y :: Nil))
case x :: y :: tail if cond(x) && res.nonEmpty => res ++ (x :: y :: Nil)
case x :: tail if res.nonEmpty => inner(tail, res :+ x)
case x :: tail => inner(tail, res)
}
inner(_xs, Nil)
}
scala> f(List(1,1,1,10,2,10,1,1,1))(_ >= 10)
res3: List[Int] = List(1, 10, 2, 10, 1)
scala> f(List(2,10,2,10))(_ >= 10)
res4: List[Int] = List()
scala> f(List(2,10,2,10,1))(_ >= 10)
res5: List[Int] = List(2, 10, 2, 10, 1)
Maybe there is something I did not think of in this solution, or I missunderstood something, but I think you will get the basic idea.
Good functional algorithm design practice is all about breaking complex problems into simpler ones.
The principle is called Divide and Conquer.
It's easy to extract two simpler subproblems from the subject problem:
Get a list of all elements after the matching one, preceded with this matching element,
preceded with an element before it.
Get a list of all elements up to the latest matching one, followed by the matching element and
the element after it.
The named problems are simple enough for the appropriate functions to be implemented, so no subdivision is required.
Here's the implementation of the first function:
def afterWithPredecessor
[ A ]
( elements : List[ A ] )
( test : A => Boolean )
: List[ A ]
= elements match {
case Nil => Nil
case a :: tail if test( a ) => Nil // since there is no predecessor
case a :: b :: tail if test( b ) => a :: b :: tail
case a :: tail => afterWithPredecessor( tail )( test )
}
Since the second problem can be seen as a direct inverse of the first one, it can be easily implemented by reversing the input and output:
def beforeWithSuccessor
[ A ]
( elements : List[ A ] )
( test : A => Boolean )
: List[ A ]
= afterWithPredecessor( elements.reverse )( test ).reverse
But here's an optimized version of this:
def beforeWithSuccessor
[ A ]
( elements : List[ A ] )
( test : A => Boolean )
: List[ A ]
= elements match {
case Nil => Nil
case a :: b :: tail if test( a ) =>
a :: b :: beforeWithSuccessor( tail )( test )
case a :: tail =>
beforeWithSuccessor( tail )( test ) match {
case Nil => Nil
case r => a :: r
}
}
Finally, composing the above functions together to produce the function solving your problem becomes quite trivial:
def range[ A ]( elements : List[ A ] )( test : A => Boolean ) : List[ A ]
= beforeWithSuccessor( afterWithPredecessor( elements )( test ) )( test )
Tests:
scala> range( List(1,1,1,10,2,10,1,1,1) )( _ >= 10 )
res0: List[Int] = List(1, 10, 2, 10, 1)
scala> range( List(1,1,1,10,2,10,1,1,1) )( _ >= 1 )
res1: List[Int] = List()
scala> range( List(1,1,1,10,2,10,1,1,1) )( _ == 2 )
res2: List[Int] = List(10, 2, 10)
The second test returns an empty list since the outermost elements satisfying the predicate have no predecessors (or successors).
def range[T](elements: List[T], condition: T => Boolean): List[T] = {
val first = elements.indexWhere(condition)
val last = elements.lastIndexWhere(condition)
elements.slice(first - 1, last + 2)
}
scala> range[Int](List(1,1,1,10,2,10,1,1,1), _ >= 10)
res0: List[Int] = List(1, 10, 2, 10, 1)
scala> range[Int](List(2,10,2,10), _ >= 10)
res1: List[Int] = List(2, 10, 2, 10)
scala> range[Int](List(), _ >= 10)
res2: List[Int] = List()
Zip and map to the rescue
val l = List(1, 1, 1, 10, 2, 1, 1, 1)
def test (i: Int) = i >= 10
((l.head :: l) zip (l.tail :+ l.last)) zip l filter {
case ((a, b), c) => (test (a) || test (b) || test (c) )
} map { case ((a, b), c ) => c }
That should work. I only have my smartphone and am miles from anywhere I could test this, so apologise for any typos or minor syntax errors
Edit: works now. I hope it's obvious that my solution shuffles the list to the right and to the left to create two new lists. When these are zipped together and zipped again with the original list, the result is a list of tuples, each containing the original element and a tuple of its neighbours. This is then trivial to filter and map back to a simple list.
Making this into a more general function (and using collect rather than filter -> map)...
def filterWithNeighbours[E](l: List[E])(p: E => Boolean) = l match {
case Nil => Nil
case li if li.size < 3 => if (l exists p) l else Nil
case _ => ((l.head :: l) zip (l.tail :+ l.last)) zip l collect {
case ((a, b), c) if (p (a) || p (b) || p (c) ) => c
}
}
This is less efficient than the recursive solution but makes the test much simpler and more clear. It can be difficult to match the right sequence of patterns in a recursive solution, as the patterns often express the shape of the chosen implementation rather than the original data. With the simple functional solution, each element is clearly and simply being compared to its neighbours.
What is occurring on this line, x is being concatenated to xs1 but x and xs1 are not defined anywhere?
case (x :: xs1, y :: ys1) =>
Also here, what value do have x and y below? Is merge being recursively called as part of the case class?
if( x < y) x :: merge(xs1 , ys)
Here is the complete Scala code :
object mergesort {
def msort(xs: List[Int]): List[Int] = {
val n = xs.length / 2
if(n == 0) xs
else {
def merge(xs: List[Int], ys: List[Int]): List[Int] = (xs , ys) match {
case (Nil, ys) => ys
case (xs, Nil) => xs
case (x :: xs1, y :: ys1) =>
if( x < y) x :: merge(xs1 , ys)
else y :: merge(xs, ys1)
}
val (fst, snd) = xs splitAt n
merge(msort(fst), msort(snd))
}
} //> msort: (xs: List[Int])List[Int]
val nums = List(2, -4, 5, 7, 1) //> nums : List[Int] = List(2, -4, 5, 7, 1)
msort(nums) //> res0: List[Int] = List(-4, 1, 2, 5, 7)
}
In
case (x :: xs1, y :: ys1) =>
:: is a syntactic sugar in pattern matching to de-construct a list in to head and tail
the list xs is de-constructed in to head x and tail xs.
In pattern matching :: de-constructs' a list, exact reverse of what it actually does in normal, construct a list.
Read De-Constructing objects in The Point of Pattern Matching in Scala
This
(xs , ys) match {
...
case (x :: xs1, y :: ys1)
is a pattern match that declares the variables x, xs1 etc. in the same statement as asserting a sequence match.
The code above is checking that xs can be decomposed into a sequence with head x and tail xs1, and if so, making the head/tail available to the successive code block in those two variables.
To answer your second question (since nobody else has!), yes, the merge function (declared within the outer function) is being called recursively.
Here's an example of how scala allows you to do pattern matching on a List:
scala> List(1,2,3)
res0: List[Int] = List(1, 2, 3)
scala> res0 match {
| case h :: t => "more than two elements, " + h + " is the first"
| case _ => "less than two elements"
| }
res1: java.lang.String = more than two elements, 1 is the first
Note that :: on the left side of the case decomposes the list in its head ( 1 ) and its tail (the rest of the list 2, 3) and binds the values to h and t, that are created and scoped only inside the first case.
Here's how you decompose a tuple:
scala> val tp = ("a", 1)
tp: (java.lang.String, Int) = (a,1)
scala> tp match {
| case (a, b) => a + " is a string, " + b + " is a number"
| case _ => "something missing"
| }
res2: java.lang.String = a is a string, 1 is a number
In the code in your question you're mixing both things and pattern matching on a tuple of Lists (xs , ys).
case (x :: xs1, y :: ys1) is both decomposing the tuple in its two lists and decomposing its two lists in their respective heads and tails.
The match-case keywords are used in scala to perform pattern matching, which is a way to match/decompose objects using several mechanisms like case classes and extractors. Google for scala pattern matching and you'll find the answers you need.
I'm trying to combine two Option[Iterable[_]] into a new Option[Iterable[_]]. I would like to return a Some if one (or both) of the elements is a Some and a None otherwise. It seems like there should be an idiomatic way of doing this, but I can't seem to find one. The following seems to do what I want, but isn't quite the slick solution I was hoping for.
def merge(
i1: Option[Iterable[_]], i2: Option[Iterable[_]]
): Option[Iterable[_]] = (i1, i2) match {
case (Some(as), Some(bs)) => Some(as ++ bs)
case (a # Some(as), None) => a
case (None, b # Some(bs)) => b
case _ => None
}
Any tips are appreciated. Thanks!
If you're willing to put up with a bit of abstract algebra, there's a nice generalization here: Iterable[_] is a monoid under concatenation, where a monoid's just a set of things (iterable collections, in this case) and an addition-like operation (concatenation) with some simple properties and an identity element (the empty collection).
Similarly, if A is a monoid, then Option[A] is also a monoid under a slightly more general version of your merge:
Some(xs) + Some(ys) == Some(xs + ys)
Some(xs) + None == Some(xs)
None + Some(ys) == Some(ys)
None + None == None
(Note that we need the fact that A is a monoid to know what to do in the first line.)
The Scalaz library captures all these generalizations in its Monoid type class, which lets you write your merge like this:
import scalaz._, Scalaz._
def merge(i1: Option[Iterable[_]], i2: Option[Iterable[_]]) = i1 |+| i2
Which works as expected:
scala> merge(Some(1 to 5), None)
res0: Option[Iterable[_]] = Some(Range(1, 2, 3, 4, 5))
scala> merge(Some(1 to 5), Some(4 :: 3 :: 2 :: 1 :: Nil))
res1: Option[Iterable[_]] = Some(Vector(1, 2, 3, 4, 5, 4, 3, 2, 1))
scala> merge(None, None)
res2: Option[Iterable[_]] = None
(Note that there are other operations that would give valid Monoid instances for Iterable and Option, but yours are the most commonly used, and the ones that Scalaz provides by default.)
This works:
def merge(i1: Option[Iterable[_]], i2: Option[Iterable[_]]): Option[Iterable[_]] =
(for (a <- i1; b <- i2) yield a ++ b).orElse(i1).orElse(i2)
The for/yield portion will add the contents of the options if and only if both are Some.
You can also drop some of the dots and parentheses if you want:
(for (a <- i1; b <- i2) yield a ++ b) orElse i1 orElse i2
You could use this for arbitrary arity:
def merge(xs: Option[Iterable[_]]*) =
if (xs.forall(_.isEmpty)) None else Some(xs.flatten.flatten)
I am doing a pattern matching on a list. Is there anyway I can access the first and last element of the list to compare?
I want to do something like..
case List(x, _*, y) if(x == y) => true
or
case x :: _* :: y =>
or something similar...
where x and y are first and last elements of the list..
How can I do that.. any Ideas?
Use the standard :+ and +: extractors from the scala.collection package
ORIGINAL ANSWER
Define a custom extractor object.
object :+ {
def unapply[A](l: List[A]): Option[(List[A], A)] = {
if(l.isEmpty)
None
else
Some(l.init, l.last)
}
}
Can be used as:
val first :: (l :+ last) = List(3, 89, 11, 29, 90)
println(first + " " + l + " " + last) // prints 3 List(89, 11, 29) 90
(For your case: case x :: (_ :+ y) if(x == y) => true)
In case you missed the obvious:
case list # (head :: tail) if head == list.last => true
The head::tail part is there so you don’t match on the empty list.
simply:
case head +: _ :+ last =>
for example:
scala> val items = Seq("ham", "spam", "eggs")
items: Seq[String] = List(ham, spam, eggs)
scala> items match {
| case head +: _ :+ last => Some((head, last))
| case List(head) => Some((head, head))
| case _ => None
| }
res0: Option[(String, String)] = Some((ham,eggs))
Lets understand the concept related to this question, there is a difference between '::', '+:' and ':+':
1st Operator:
'::' - It is right associative operator which works specially for lists
scala> val a :: b :: c = List(1,2,3,4)
a: Int = 1
b: Int = 2
c: List[Int] = List(3, 4)
2nd Operator:
'+:' - It is also right associative operator but it works on seq which is more general than just list.
scala> val a +: b +: c = List(1,2,3,4)
a: Int = 1
b: Int = 2
c: List[Int] = List(3, 4)
3rd Operator:
':+' - It is also left associative operator but it works on seq which is more general than just list
scala> val a :+ b :+ c = List(1,2,3,4)
a: List[Int] = List(1, 2)
b: Int = 3
c: Int = 4
The associativity of an operator is determined by the operator’s last character. Operators ending in a colon ‘:’ are right-associative. All other operators are left-associative.
A left-associative binary operation e1;op;e2 is interpreted as e1.op(e2)
If op is right-associative, the same operation is interpreted as { val x=e1; e2.op(x) }, where x is a fresh name.
Now comes answer for your question:
So now if you need to get first and last element from the list, please use following code
scala> val firstElement +: b :+ lastElement = List(1,2,3,4)
firstElement: Int = 1
b: List[Int] = List(2, 3)
lastElement: Int = 4