A tidy number is a number whose digits are in non-decreasing order, e.g. 1234. Here is a way of finding tidy numbers written in Ruby:
def tidy_number(n)
n.to_s.chars.sort.join.to_i == n
end
p tidy_number(12345678) # true
p tidy_number(12345878) # false
I tried to write the same thing in Scala and arrived at the following:
object MyClass {
def tidy_number(n:Int) = n.toString.toList.sorted.mkString.toInt == n;
def main(args: Array[String]) {
println(tidy_number(12345678)) // true
println(tidy_number(12345878)) // false
}
}
The only way I could do it in Scala was by converting an integer to a string to a list and then sorting the list and going back again. My question: is there a better way? 'Better' in the sense there are fewer conversions. I'm mainly looking for a concise piece of Scala but I'd be grateful if someone pointed out a more concise way of doing it in Ruby as well.
You can use sorted on strings in Scala, so
def tidy_number(n: Int) = {
val s = n.toString
s == s.sorted
}
Doing it in two parts also avoids an extra toInt conversion.
I've never used ruby, but this post suggests you're doing it the best way
You can check each adjacent pair of digits to make sure that the first value is <= the second:
def tidy_number(n:Int) =
n.toString.sliding(2,1).forall(p => p(0) <= p(1))
Update following from helpful comments
As noted in the comments, this fails for single-digit numbers. Taking this and another comment together gives this:
def tidy_number(n:Int) =
(" "+n).sliding(2,1).forall(p => p(0) <= p(1))
Being even more pedantic it would be better to convert back to Int before comparing so that you don't rely on the sort order of the characters representing digits being the same as the sort order for the digits themselves.
def tidy_number(n:Int) =
(" "+n).sliding(2,1).forall(p => p(0).toInt <= p(1).toInt)
The only way I could do it in Scala was by converting an integer to a string to a list and then sorting the list and going back again. My question: is there a better way?
String conversion and sorting is not required.
def tidy_number(n :Int) :Boolean =
Iterator.iterate(n)(_/10)
.takeWhile(_>0)
.map(_%10)
.sliding(2)
.forall{case Seq(a,b) => a >= b
case _ => true} //is a single digit "tidy"?
Better? Hard to say. One minor advantage is that the math operations (/ and %) stop after the first false. So for the Int value 1234567890 there are only 3 ops (2 modulo and 1 division) before the result is returned. (Digits are compared in reverse order.)
But seeing as an Int is, at most, only 10 digits long, I'd go with Joel Berkeley's answer just for its brevity.
def tidyNum(n:Int) = {
var t =n; var b = true;
while(b && t!=0){b=t%10>=(t/10)%10;t=t/10};b
}
In Scala REPL:
scala> tidyNum(12345678)
res20: Boolean = true
scala> tidyNum(12343678)
res21: Boolean = false
scala> tidyNum(12)
res22: Boolean = true
scala> tidyNum(1)
res23: Boolean = true
scala> tidyNum(121)
res24: Boolean = false
This can work for any size with just the modification of type such as Long or BigInt in function signature like tidyNum(n:Long) or tidyNum(n:BigInt) as below:
def tidyNum(n:BigInt) = {
var t =n; var b = true;
while(b && t!=0){b=t%10>=(t/10)%10;t=t/10};b
}
scala> tidyNum(BigInt("123456789"))
res26: Boolean = true
scala> tidyNum(BigInt("1234555555555555555555555567890"))
res29: Boolean = false
scala> tidyNum(BigInt("123455555555555555555555556789"))
res30: Boolean = true
And the BigInt can be used for types Int, Long as well.
Related
I stumbled upon this challenge on stackoverflow while learning about property based testing in scala using ScalaCheck.
Find the smallest positive integer that does not occur in a given sequence
I thought of trying to write a generator driven property based test for this problem to check the validity of my program but can't seem to be able to think of a how to write a relevant test case. I understand that I could write a table driven property based testing for this use case but that limit the number of properties I could test this algo with.
import scala.annotation.tailrec
object Solution extends App {
def solution(a: Array[Int]): Int = {
val posNums = a.toSet.filter(_ > 0)
#tailrec
def checkForSmallestNum(ls: Set[Int], nextMin: Int): Int = {
if (ls.contains(nextMin)) checkForSmallestNum(ls, nextMin + 1)
else nextMin
}
checkForSmallestNum(posNums, 1)
}
}
Using Scalatest's (since you did tag scalatest) Scalacheck integration and Scalatest matchers, something like
forAll(Gen.listOf(Gen.posNum[Int]) -> "ints") { ints =>
val asSet = ints.toSet
val smallestNI = Solution.solution(ints.toArray)
asSet shouldNot contain(smallestNI)
// verify that adding non-positive ints doesn't change the result
forAll(
Gen.frequency(
1 -> Gen.const(0),
10 -> Gen.negNum[Int]
) -> "nonPos"
) { nonPos =>
// Adding a non-positive integer to the input shouldn't affect the result
Solution.solution((nonPos :: ints).toArray) shouldBe smallestNI
}
// More of a property-based approach
if (smallestNI > 1) {
forAll(Gen.oneOf(1 until smallestNI) -> "x") { x =>
asSet should contain(x)
}
} else succeed // vacuous
// Alternatively, but perhaps in a less property-based way
(1 until smallestNI).foreach { x =>
asSet should contain(x)
}
}
Note that if scalatest is set to try forAlls 100 times, the nested property check will check values 10k times. Since smallestNI will nearly always be less than the number of trials (as listOf rarely generates long lists), the exhaustive check will in practice be faster than the nested property check.
The overall trick, is that if something is the least positive integer for which some predicate applies, that's the same as saying that for all positive integers less than that something the predicate does not apply.
I've implemented a basic mutable tree in Scala and I want to traverse it in a functional way in order to search for an element, but I don't know how to implement it. I want also the algorithm to be tail recursive if possible.
The tree is a structure with a value and a list of leafs that are also trees.
Any help would be appreciated.
This is the code that I have (focus on getOpt method):
package main
import scala.collection.mutable.ListBuffer
sealed trait Tree[Node] {
val node: Node
val parent: Option[Tree[Node]]
val children: ListBuffer[Tree[Node]]
def add(n: Node): Tree[Node]
def size: Int
def getOpt(p: (Node) => Boolean): Option[Tree[Node]]
override def toString = {
s"""[$node${if (children.isEmpty) "" else s", Children: $children"}]"""
}
}
case class ConcreteTree(override val node: Int) extends Tree[Int] {
override val children = ListBuffer[Tree[Int]]()
override val parent: Option[Tree[Int]] = None
override def add(n: Int): ConcreteTree = {
val newNode = new ConcreteTree(n) {override val parent: Option[Tree[Int]] = Some(this)}
children += newNode
newNode
}
override def size: Int = {
def _size(t: Tree[Int]): Int = {
1 + t.children.foldLeft(0)((sum, tree) => sum + _size(tree))
}
_size(this)
}
// This method is not correct
override def getOpt(p: (Int) => Boolean): Option[Tree[Int]] = {
def _getOpt(tree: Tree[Int], p: (Int) => Boolean): Option[Tree[Int]] = {
tree.children.map {
t =>
if(p(t.node)) Some(t) else t.children.map(_getOpt(_, p))
}
}
}
}
object Main {
def main(args: Array[String]) {
val tree1 = ConcreteTree(1)
val tree2 = tree1.add(2)
val tree3 = tree1.add(3)
println(tree1.getOpt(_ == 2))
}
}
#doertsch answer is the best approach that I have at the moment.
I would actually go for something more flexible and implement a generic function to produce a lazy stream of your flattened tree, then a lot of your later work will become much easier. Something like this:
def traverse[Node](tree: Tree[Node]): Stream[Tree[Node]] =
tree #:: (tree.children map traverse).fold(Stream.Empty)(_ ++ _)
Then your getOpt reduces to this:
override def getOpt(p: (Int) => Boolean): Option[Tree[Int]] =
traverse(tree) find {x => p(x.node)}
Simplifying even further, if you're just interested in the data without the Tree structure, you can just get a stream of nodes, giving you:
def nodes[Node](tree: Tree[Node]): Stream[Node] = traverse(tree) map (_.node)
def getNode(p: (Int) => Boolean): Option[Int] = nodes(tree) find p
This opens other possibilities for very concise and readable code like nodes(tree) filter (_ > 3), nodes(tree).sum, nodes(tree) contains 12, and similar expressions.
Using the methods exists and find (which every List provides), you can achieve the "finish when a result is found" behaviour. (Although it might be argued that internally, these are not implemented in a totally functional way: https://github.com/scala/scala/blob/5adc400f5ece336f3f5ff19691204975d41e652e/src/library/scala/collection/LinearSeqOptimized.scala#L88)
Your code might look as follows:
case class Tree(nodeValue: Long, children: List[Tree]) {
def containsValue(search: Long): Boolean =
search == nodeValue || children.exists(_.containsValue(search))
def findSubTreeWithNodeValue(search: Long): Option[Tree] =
if (search == nodeValue)
Some(this)
else
children.find(_.containsValue(search)).
flatMap(_.findSubTreeWithNodeValue(search))
}
On the last two lines, the find application will return the correct subtree of the current node, in case one exists, and the flatMap part will extract the correct subtree from it recursively, or leave the None result unchanged if the value was not found.
This one, however, has the unlovely characteristic of doing parts of the traversal twice, once for determining whether a result exists, and once for extracting it from the tree that contains it. There might be a way to fix this in a more efficient way, but I can't figure it out at the moment ...
I think, you are looking for something like this:
#tailrec
def findInTree[T](value: T, stack: List[Node[T]]): Option[Node[T]] = stack match {
case Nil => None
case Node(value) :: _ => stack.headOption
case head :: tail => findInTree(value, head.children ++ tail)
}
This does not traverse parts of the tree twice, and is also tail-recursive.
I changed your class definitions a little bit for clarity, but it's the same idea.
You call it something like this: findInTree(valueToFind, List(root))
When turning a collection into a view, all transformers like map are implemented lazily. This means elements are only processed as required. This should solve your problem:
override def getOpt(p: (Int) => Boolean): Option[Tree[Int]] = {
if (p(node)) Some(this)
else children.view.map(_.getOpt(p)).find(_.isDefined).getOrElse(None)
}
So we are mapping (lazily) over the children, turning them into Options of the searched node. Subsequently we find the first such Option being not None. The final getOrElse(None) is required to "flatten" the nested Options since find returns an Option itself.
I didn't actually run the code, so please excuse minor mistakes. However, the general approach should have become clear.
In my imperative-style Scala code, I have an algorithm:
def myProcessor(val items: List) {
var numProcessed = 0
while(numProcessed < items.size) {
val processedSoFar = items.size - numProcessed
numProcessed += processNextBlockOfItems(items, processedSoFar)
}
}
I would like to keep the "block processing" functionality, and not just do a "takeWhile" on the items list. How can I rewrite this in functional style?
You need to change it to a recursive style wherein you "pass" in the "state" of each loop
#tailrec
def myProcessor(items: List[A], count: Int = 0): Int = items match{
case Nil => count
case x :: xs =>
processNextBlockOfItems(items, count)
myProcessor(xs, count + 1)
}
assuming that "processedSoFar" is not an index. If you can work with the current "head" of the list:
#tailrec
def myProcessor(items: List[A], count: Int = 0): Int = items match{
case Nil => count
case x :: xs =>
process(x)
myProcessor(xs, count + 1)
}
where process would only process the current "head" of the List.
So, this depends on what you consider to be more functional, but here's a version without the 'var'
def myProcessorFunctional(items: List[Int]) {
def myProcessorHelper(items: List[Int], numProcessed: Int) {
if (numProcessed < items.size) {
val processedSoFar = items.size - numProcessed
myProcessorHelper(items,
numProcessed + processNextBlockOfItems(items, processedSoFar))
}
}
myProcessorHelper(items, 0)
}
(making it a list of Ints just for simplicity, it would be easy to make it work with a generic List)
I have to say it's one of those cases where I don't mind the mutable variable - it's clear, no reference to it escapes the method.
But as I said in a comment above, processNextBlockOfItems is inherently non-functional anyway, since it's called for its side effects. A more functional way would be for it to return the state of its processing so far, and this state would be updated (and returned) on a subsequent call. Right now, if you in the middle of processing two different items lists, you'd have the issue of maintaining two different partially-processed states within processNextBlockOfItems...
Later:
Still ignoring the state issue, one convenient change would be if processNextBlockOfItems always processed the first block of the items list passed to it, returned the remaining items it had not processed (this is convenient and efficient if using List, so much so I'm wondering why you're using indicies).
This would yield something like:
def myProcessorMoreFunctional(items: List[Int]) {
if (!items.isEmpty) {
myProcessorMoreFunctional(processNextBlockOfItems(items))
}
}
I have a Validation object
val v = Validation[String, Option[Int]]
I need to make a second validation, to check if actual Integer value is equals to 100 for example. If I do
val vv = v.map(_.map(intValue => if (intValue == 100)
intValue.success[String]
else
"Bad value found".fail[Integer]))
I get:
Validation[String, Option[Validation[String, Int]]]
How is it possible to get vv also as Validation[String, Option[Int]] in most concise way
=========
Found possible solution from my own:
val validation: Validation[String, Option[Int]] = Some(100).success[String]
val validatedTwice: Validation[String, Option[Int]] = validation.fold(
_ => validation, // if Failure then return it
_.map(validateValue _) getOrElse validation // validate Successful result
)
def validateValue(value: Int): Validation[String, Option[Int]] = {
if (value == 100)
Some(value).success[String]
else
"Bad value".fail[Option[Int]]
}
Looks not concise and elegant although it works
==============
Second solution from my own, but also looks over-compicated:
val validatedTwice2: Validation[String, Option[Int]] = validation.flatMap(
_.map(validateValue _).map(_.map(Some(_))) getOrElse validation)
def validateValue(value: Int): Validation[String, Int] = {
if (value == 100)
value.success[String]
else
"Bad value".fail[Int]
}
Your solution is over-complicated. The following will suffice!
v flatMap (_.filter(_ == 100).toSuccess("Bad value found"))
The toSuccess comes from OptionW and converts an Option[A] into a Validation[X, A] taking the value provided for the failure case in the event that the option is empty. The flatMap works like this:
Validation[X, A]
=> (A => Validation[X, B])
=> (via flatMap) Validation[X, B]
That is, flatMap maps and then flattens (join in scalaz-parlance):
Validation[X, A]
=> (A => Validation[X, B]]
=> (via map) Validation[X, Validation[X, B]]
=> (via join) Validation[X, B]
First, let's set up some type aliases because typing this out repeatedly will get old pretty fast. We'll tidy up your validation logic a little too while we're here.
type V[X] = Validation[String, X]
type O[X] = Option[X]
def checkInt(i: Int): V[Int] = Validation.fromEither(i != 100 either "Bad value found" or i)
val v: V[O[Int]] = _
this is where we're starting out - b1 is equivalent to your vv situation
val b1: V[O[V[Int]]] = v.map(_.map(checkInt))
so let's sequence the option to flip over the V[O[V[Int]]] into a V[V[O[Int]]]
val b2: V[V[O[Int]]] = v.map(_.map(checkInt)).map(_.sequence[V, Int])
or if you're feeling lambda-y it could have been
sequence[({type l[x] = Validation[String, x]})#l, Int]
next we flatten out that nested validation - we're going to pull in the Validation monad because we actually do want the fastfail behaviour here, although it's generally not the right thing to do.
implicit val monad = Validation.validationMonad[String]
val b3: V[O[Int]] = v.map(_.map(checkInt)).map(_.sequence[V, Int]).join
So now we've got a Validation[String, Option[Int]], so we're there, but this is still pretty messy. Lets use some equational reasoning to tidy it up
By the second functor law we know that:
X.map(_.f).map(_.g) = X.map(_.f.g) =>
val i1: V[O[Int]] = v.map(_.map(checkInt).sequence[V, Int]).join
and by the definition of a monad:
X.map(f).join = X.flatMap(f) =>
val i2: V[O[Int]] = v.flatMap(_.map(checkInt).sequence[V, Int])
and then we apply the free theorem of traversal:
(I struggled with that bloody paper so much, but it looks like some of it sunk in!):
X.map(f).sequence = X.traverse(f andThen identity) = X.traverse(f) =>
val i3: V[O[Int]] = v.flatMap(_.traverse[V, Int](checkInt))
so now we're looking at something a bit more civilised. I imagine there's some trickery to be played with the flatMap and traverse, but I've run out of inspiration.
Use flatMap, like so:
v.flatMap(_.parseInt.fail.map(_.getMessage).validation)
I am new to Scala and am trying to get a list of random double values:
The thing is, when I try to run this, it takes way too long compared to its Java counterpart. Any ideas on why this is or a suggestion on a more efficient approach?
def random: Double = java.lang.Math.random()
var f = List(0.0)
for (i <- 1 to 200000)
( f = f ::: List(random*100))
f = f.tail
You can also achieve it like this:
List.fill(200000)(math.random)
the same goes for e.g. Array ...
Array.fill(200000)(math.random)
etc ...
You could construct an infinite stream of random doubles:
def randomList(): Stream[Double] = Stream.cons(math.random, randomList)
val f = randomList().take(200000)
This will leverage lazy evaluation so you won't calculate a value until you actually need it. Even evaluating all 200,000 will be fast though. As an added bonus, f no longer needs to be a var.
Another possibility is:
val it = Iterator.continually(math.random)
it.take(200000).toList
Stream also has a continually method if you prefer.
First of all, it is not taking longer than java because there is no java counterpart. Java does not have an immutable list. If it did, performance would be about the same.
Second, its taking a lot of time because appending lists have linear performance, so the whole thing has quadratic performance.
Instead of appending, prepend, which had constant performance.
if your using mutable state anyways you should use a mutable collection like buffer which you can add too with += (which then would be the real counterpart to java code).
but why dont u use list comprehension?
val f = for (_ <- 1 to 200000) yield (math.random * 100)
by the way: var f = List(0.0) ... f = f.tail can be replaced by var f: List[Double] = Nil in your example. (no more performance but more beauty ;)
Yet more options! Tail recursion:
def randlist(n: Int, part: List[Double] = Nil): List[Double] = {
if (n<=0) part
else randlist(n-1, 100*random :: part)
}
or mapped ranges:
(1 to 200000).map(_ => 100*random).toList
Looks like you want to use Vector instead of List. List has O(1) prepend, Vector has O(1) append. Since you are appending, but using concatenation, it'll be faster to use Vector:
def random: Double = java.lang.Math.random()
var f: Vector[Double] = Vector()
for (i <- 1 to 200000)
f = f :+ (random*100)
Got it?