I wrote this code to find the prime numbers less than the given number i in scala.
def findPrime(i : Int) : List[Int] = i match {
case 2 => List(2)
case _ => {
val primeList = findPrime(i-1)
if(isPrime(i, primeList)) i :: primeList else primeList
}
}
def isPrime(num : Int, prePrimes : List[Int]) : Boolean = prePrimes.forall(num % _ != 0)
But, I got a feeling the findPrime function, especially this part:
case _ => {
val primeList = findPrime(i-1)
if(isPrime(i, primeList)) i :: primeList else primeList
}
is not quite in the functional style.
I am still learning functional programming. Can anyone please help me improve this code to make it more functional.
Many thanks.
Here's a functional implementation of the Sieve of Eratosthenes, as presented in Odersky's "Functional Programming Principles in Scala" Coursera course :
// Sieving integral numbers
def sieve(s: Stream[Int]): Stream[Int] = {
s.head #:: sieve(s.tail.filter(_ % s.head != 0))
}
// All primes as a lazy sequence
val primes = sieve(Stream.from(2))
// Dumping the first five primes
print(primes.take(5).toList) // List(2, 3, 5, 7, 11)
The style looks fine to me. Although the Sieve of Eratosthenes is a very efficient way to find prime numbers, your approach works well too, since you are only testing for division against known primes. You need to watch out however--your recursive function is not tail recursive. A tail recursive function does not modify the result of the recursive call--in your example you prepend to the result of the recursive call. This means that you will have a long call stack and so findPrime will not work for large i. Here is a tail-recursive solution.
def primesUnder(n: Int): List[Int] = {
require(n >= 2)
def rec(i: Int, primes: List[Int]): List[Int] = {
if (i >= n) primes
else if (prime(i, primes)) rec(i + 1, i :: primes)
else rec(i + 1, primes)
}
rec(2, List()).reverse
}
def prime(num: Int, factors: List[Int]): Boolean = factors.forall(num % _ != 0)
This solution isn't prettier--it's more of a detail to get your solution to work for large arguments. Since the list is built up backwards to take advantage of fast prepends, the list needs to be reversed. As an alternative, you could use an Array, Vector or a ListBuffer to append the results. With the Array, however, you would need to estimate how much memory to allocate for it. Fortunately we know that pi(n) is about equal to n / ln(n) so you can choose a reasonable size. Array and ListBuffer are also a mutable data types, which goes again your desire for functional style.
Update: to get good performance out of the Sieve of Eratosthenes I think you'll need to store data in a native array, which also goes against your desire for style in functional programming. There might be a creative functional implementation though!
Update: oops! Missed it! This approach works well too if you only divide by primes less than the square root of the number you are testing! I missed this, and unfortunately it's not easy to adjust my solution to do this because I'm storing the primes backwards.
Update: here's a very non-functional solution that at least only checks up to the square root.
rnative, you could use an Array, Vector or a ListBuffer to append the results. With the Array, however, you would need to estimate how much memory to allocate for it. Fortunately we know that pi(n) is about equal to n / ln(n) so you can choose a reasonable size. Array and ListBuffer are also a mutable data types, which goes again your desire for functional style.
Update: to get good performance out of the Sieve of Eratosthenes I think you'll need to store data in a native array, which also goes against your desire for style in functional programming. There might be a creative functional implementation though!
Update: oops! Missed it! This approach works well too if you only divide by primes less than the square root of the number you are testing! I missed this, and unfortunately it's not easy to adjust my solution to do this because I'm storing the primes backwards.
Update: here's a very non-functional solution that at least only checks up to the square root.
import scala.collection.mutable.ListBuffer
def primesUnder(n: Int): List[Int] = {
require(n >= 2)
val primes = ListBuffer(2)
for (i <- 3 to n) {
if (prime(i, primes.iterator)) {
primes += i
}
}
primes.toList
}
// factors must be in sorted order
def prime(num: Int, factors: Iterator[Int]): Boolean =
factors.takeWhile(_ <= math.sqrt(num).toInt) forall(num % _ != 0)
Or I could use Vectors with my original approach. Vectors are probably not the best solution because they don't have the fasted O(1) even though it's amortized O(1).
As schmmd mentions, you want it to be tail recursive, and you also want it to be lazy. Fortunately there is a perfect data-structure for this: Stream.
This is a very efficient prime calculator implemented as a Stream, with a few optimisations:
object Prime {
def is(i: Long): Boolean =
if (i == 2) true
else if ((i & 1) == 0) false // efficient div by 2
else prime(i)
def primes: Stream[Long] = 2 #:: prime3
private val prime3: Stream[Long] = {
#annotation.tailrec
def nextPrime(i: Long): Long =
if (prime(i)) i else nextPrime(i + 2) // tail
def next(i: Long): Stream[Long] =
i #:: next(nextPrime(i + 2))
3 #:: next(5)
}
// assumes not even, check evenness before calling - perf note: must pass partially applied >= method
def prime(i: Long): Boolean =
prime3 takeWhile (math.sqrt(i).>= _) forall { i % _ != 0 }
}
Prime.is is the prime check predicate, and Prime.primes returns a Stream of all prime numbers. prime3 is where the Stream is computed, using the prime predicate to check for all prime divisors less than the square root of i.
/**
* #return Bitset p such that p(x) is true iff x is prime
*/
def sieveOfEratosthenes(n: Int) = {
val isPrime = mutable.BitSet(2 to n: _*)
for (p <- 2 to Math.sqrt(n) if isPrime(p)) {
isPrime --= p*p to n by p
}
isPrime.toImmutable
}
A sieve method is your best bet for small lists of numbers (up to 10-100 million or so).
see: Sieve of Eratosthenes
Even if you want to find much larger numbers, you can use the list you generate with this method as divisors for testing numbers up to n^2, where n is the limit of your list.
#mfa has mentioned using a Sieve of Eratosthenes - SoE and #Luigi Plinge has mentioned that this should be done using functional code, so #netzwerg has posted a non-SoE version; here, I post a "almost" functional version of the SoE using completely immutable state except for the contents of a mutable BitSet (mutable rather than immutable for performance) that I posted as an answer to another question:
object SoE {
def makeSoE_Primes(top: Int): Iterator[Int] = {
val topndx = (top - 3) / 2
val nonprms = new scala.collection.mutable.BitSet(topndx + 1)
def cullp(i: Int) = {
import scala.annotation.tailrec; val p = i + i + 3
#tailrec def cull(c: Int): Unit = if (c <= topndx) { nonprms += c; cull(c + p) }
cull((p * p - 3) >>> 1)
}
(0 to (Math.sqrt(top).toInt - 3) >>> 1).filterNot { nonprms }.foreach { cullp }
Iterator.single(2) ++ (0 to topndx).filterNot { nonprms }.map { i: Int => i + i + 3 }
}
}
How about this.
def getPrimeUnder(n: Int) = {
require(n >= 2)
val ol = 3 to n by 2 toList // oddList
def pn(ol: List[Int], pl: List[Int]): List[Int] = ol match {
case Nil => pl
case _ if pl.exists(ol.head % _ == 0) => pn(ol.tail, pl)
case _ => pn(ol.tail, ol.head :: pl)
}
pn(ol, List(2)).reverse
}
It's pretty fast for me, in my mac, to get all prime under 100k, its take around 2.5 sec.
A scalar fp approach
// returns the list of primes below `number`
def primes(number: Int): List[Int] = {
number match {
case a
if (a <= 3) => (1 to a).toList
case x => (1 to x - 1).filter(b => isPrime(b)).toList
}
}
// checks if a number is prime
def isPrime(number: Int): Boolean = {
number match {
case 1 => true
case x => Nil == {
2 to math.sqrt(number).toInt filter(y => x % y == 0)
}
}
}
def primeNumber(range: Int): Unit ={
val primeNumbers: immutable.IndexedSeq[AnyVal] =
for (number :Int <- 2 to range) yield {
val isPrime = !Range(2, Math.sqrt(number).toInt).exists(x => number % x == 0)
if(isPrime) number
}
for(prime <- primeNumbers) println(prime)
}
object Primes {
private lazy val notDivisibleBy2: Stream[Long] = 3L #:: notDivisibleBy2.map(_ + 2)
private lazy val notDivisibleBy2Or3: Stream[Long] = notDivisibleBy2
.grouped(3)
.map(_.slice(1, 3))
.flatten
.toStream
private lazy val notDivisibleBy2Or3Or5: Stream[Long] = notDivisibleBy2Or3
.grouped(10)
.map { g =>
g.slice(1, 7) ++ g.slice(8, 10)
}
.flatten
.toStream
lazy val primes: Stream[Long] = 2L #::
notDivisibleBy2.head #::
notDivisibleBy2Or3.head #::
notDivisibleBy2Or3Or5.filter { i =>
i < 49 || primes.takeWhile(_ <= Math.sqrt(i).toLong).forall(i % _ != 0)
}
def apply(n: Long): Stream[Long] = primes.takeWhile(_ <= n)
def getPrimeUnder(n: Long): Long = Primes(n).last
}
Related
I am trying to learn Scala and functional programming ideology by rewriting basic exercises. Currently I have trouble with naive approach for generating primes "trial division".
The trouble described below is that I could not rewrite well-known algorithm in functional style preserving efficiency, because I have no suitable immutable data structure, like a List but with fast operations not only on head, but also on the very end.
I started with writing java code which for every odd number tests its divisibility by already found primes (limited by square root of value being tested) - and adds it to the end of the list if no divisor was found.
http://ideone.com/QE8U0I
List<Integer> primes = new ArrayList<>();
primes.add(2);
int cur = 3;
while (primes.size() < 100000) {
for (Integer x : primes) {
if (x * x > cur) {
primes.add(cur);
break;
}
if (cur % x == 0) {
break;
}
}
cur += 2;
}
Now I tried to rewrite it in "functional way" - there was no problem with using recursion instead of loops, but I stuck with immutable collections. Core idea is as following:
http://ideone.com/4DQ6mi
def primes(n: Int) = {
#tailrec
def divisibleByAny(x: Int, list: List[Int]): Boolean = {
if (list.isEmpty) false else {
val h = list.head
h * h <= x && (x % h == 0 || divisibleByAny(x, list.tail))
}
}
#tailrec
def morePrimes(from: Int, prev: List[Int]): List[Int] = {
if (prev.size == n) prev else
morePrimes(from + 2, if (divisibleByAny(from, prev)) prev else prev :+ from)
}
morePrimes(3, List(2))
}
But it is slow - if I understand correctly because operation of adding to the end of immutable list requires creation of new copy of the whole stuff.
I searched over documentation to find more suitable data structure and tried to substitute list with immutable Queue, for it is said:
Adding items to the queue always has cost O(1) ... Removing an item is on average O(1).
But it is still even slower:
http://ideone.com/v8BsuQ
def primes(n: Int) = {
#tailrec
def divisibleByAny(x: Int, list: Queue[Int]): Boolean = {
if (list.isEmpty) false else {
val (h, t) = list.dequeue
h * h <= x && (x % h == 0 || divisibleByAny(x, t))
}
}
#tailrec
def morePrimes(from: Int, prev: Queue[Int]): Queue[Int] = {
if (prev.size == n) prev else
morePrimes(from + 2, if (divisibleByAny(from, prev)) prev else prev.enqueue(from))
}
morePrimes(3, Queue(2))
}
What is going wrong or am I missing something?
P.S. I believe there are other algorithms for generating primes which are more suitable for functional style. I think I've seen some paper. But now I'm interested in this one, or more precisely in existence of suitable data structure.
According to http://docs.scala-lang.org/overviews/collections/performance-characteristics.html Vectors have an amortised constant cost for appending, prepending and seeking. Indeed, using vectors instead of lists in your solution is much faster
def primes(n: Int) = {
#tailrec
def divisibleByAny(x: Int, list: Vector[Int]): Boolean = {
if (list.isEmpty) false else {
val (h +: t) = list
h * h <= x && (x % h == 0 || divisibleByAny(x, t))
}
}
#tailrec
def morePrimes(from: Int, prev: Vector[Int]): Vector[Int] = {
if (prev.length == n) prev else
morePrimes(from + 2, if (divisibleByAny(from, prev)) prev else prev :+ from)
}
morePrimes(3, Vector(2))
}
http://ideone.com/x3k4A3
I think you have 2 main options
Use a Vector - which is better than a list for appending. It is a Bitmapped Trie data structure (http://en.wikipedia.org/wiki/Trie). It’s “effectively” O(1) for appending to (i.e. O(1) on average)
Or...possibly the answer you're not looking for
Use a mutable data structure like ListBuffer - immutability it great to try achieve, and should be your go to collections - but sometimes for efficiency reasons, you may use mutable structures . What is key it to make sure it does not “leak out” of your classes. If you look at the List.scala implementation, you’ll see ListBuffer used a lot internally. However, its coverted back to a List just before it leaves the class. If its good enough for the core Scala libraries, its probably ok for you to use under exceptional cases that warrant it.
Except using Vector, also consider using higher-order functions instead of recursion. That's also a completely valid functional style. On my machine the following implementation of divisibleByAny is about 8x faster, than #Pyetras tailrec implementation when running primes(1000000):
def divisibleByAny(x: Int, list: Vector[Int]): Boolean =
list.view.takeWhile(el => el * el <= x).exists(x % _ == 0)
I came across another codechef problem which I am attempting to solve in Scala. The problem statement is as follows:
Stepford Street was a dead end street. The houses on Stepford Street
were bought by wealthy millionaires. They had them extensively altered
so that as one progressed along the street, the height of the
buildings increased rapidly. However, not all millionaires were
created equal. Some refused to follow this trend and kept their houses
at their original heights. The resulting progression of heights was
thus disturbed. A contest to locate the most ordered street was
announced by the Beverly Hills Municipal Corporation. The criteria for
the most ordered street was set as follows: If there exists a house
with a lower height later in the street than the house under
consideration, then the pair (current house, later house) counts as 1
point towards the disorderliness index of the street. It is not
necessary that the later house be adjacent to the current house. Note:
No two houses on a street will be of the same height For example, for
the input: 1 2 4 5 3 6 The pairs (4,3), (5,3) form disordered pairs.
Thus the disorderliness index of this array is 2. As the criteria for
determining the disorderliness is complex, the BHMC has requested your
help to automate the process. You need to write an efficient program
that calculates the disorderliness index of a street.
A sample input output provided is as follows:
Input: 1 2 4 5 3 6
Output: 2
The output is 2 because of two pairs (4,3) and (5,3)
To solve this problem I thought I should use a variant of MergeSort,incrementing by 1 when the left element is greater than the right element.
My scala code is as follows:
def dysfunctionCalc(input:List[Int]):Int = {
val leftHalf = input.size/2
println("HalfSize:"+leftHalf)
val isOdd = input.size%2
println("Is odd:"+isOdd)
val leftList = input.take(leftHalf+isOdd)
println("LeftList:"+leftList)
val rightList = input.drop(leftHalf+isOdd)
println("RightList:"+rightList)
if ((leftList.size <= 1) && (rightList.size <= 1)){
println("Entering input where both lists are <= 1")
if(leftList.size == 0 || rightList.size == 0){
println("One of the lists is less than 0")
0
}
else if(leftList.head > rightList.head)1 else 0
}
else{
println("Both lists are greater than 1")
dysfunctionCalc(leftList) + dysfunctionCalc(rightList)
}
}
First off, my logic is wrong,it doesn't have a merge stage and I am not sure what would be the best way to percolate the result of the base-case up the stack and compare it with the other values. Also, using recursion to solve this problem may not be the most optimal way to go since for large lists, I maybe blowing up the stack. Also, there might be stylistic issues with my code as well.
I would be great if somebody could point out other flaws and the right way to solve this problem.
Thanks
Suppose you split your list into three pieces: the item you are considering, those on the left, and those on the right. Suppose further that those on the left are in a sorted set. Now you just need to walk through the list, moving items from "right" to "considered" and from "considered" to "left"; at each point, you look at the size of the subset of the sorted set that is greater than your item. In general, the size lookup can be done in O(log(N)) as can the add-element (with a Red-Black or AVL tree, for instance). So you have O(N log N) performance.
Now the question is how to implement this in Scala efficiently. It turns out that Scala has a Red-Black tree used for its TreeSet sorted set, and the implementation is actually quite simple (here in tail-recursive form):
import collection.immutable.TreeSet
final def calcDisorder(xs: List[Int], left: TreeSet[Int] = TreeSet.empty, n: Int = 0): Int = xs match {
case Nil => n
case x :: rest => calcDisorder(rest, left + x, n + left.from(x).size)
}
Unfortunately, left.from(x).size takes O(N) time (I believe), which yields a quadratic execution time. That's no good--what you need is an IndexedTreeSet which can do indexOf(x) in O(log(n)) (and then iterate with n + left.size - left.indexOf(x) - 1). You can build your own implementation or find one on the web. For instance, I found one here (API here) for Java that does exactly the right thing.
Incidentally, the problem with doing a mergesort is that you cannot easily work cumulatively. With merging a pair, you can keep track of how out-of-order it is. But when you merge in a third list, you must see how out of order it is with respect to both other lists, which spoils your divide-and-conquer strategy. (I am not sure whether there is some invariant one could find that would allow you to calculate directly if you kept track of it.)
Here is my try, I don't use MergeSort but it seems to solve the problem:
def calcDisorderness(myList:List[Int]):Int = myList match{
case Nil => 0
case t::q => q.count(_<t) + calcDisorderness(q)
}
scala> val input = List(1,2,4,5,3,6)
input: List[Int] = List(1, 2, 4, 5, 3, 6)
scala> calcDisorderness(input)
res1: Int = 2
The question is, is there a way to have a lower complexity?
Edit: tail recursive version of the same function and cool usage of default values in function arguments.
def calcDisorderness(myList:List[Int], disorder:Int=0):Int = myList match{
case Nil => disorder
case t::q => calcDisorderness(q, disorder + q.count(_<t))
}
A solution based on Merge Sort. Not super fast, potential slowdown could be in "xs.length".
def countSwaps(a: Array[Int]): Long = {
var disorder: Long = 0
def msort(xs: List[Int]): List[Int] = {
import Stream._
def merge(left: List[Int], right: List[Int], inc: Int): Stream[Int] = {
(left, right) match {
case (x :: xs, y :: ys) if x > y =>
cons(y, merge(left, ys, inc + 1))
case (x :: xs, _) => {
disorder += inc
cons(x, merge(xs, right, inc))
}
case _ => right.toStream
}
}
val n = xs.length / 2
if (n == 0)
xs
else {
val (ys, zs) = xs splitAt n
merge(msort(ys), msort(zs), 0).toList
}
}
msort(a.toList)
disorder
}
Another solution based on Merge Sort. Very fast: no FP or for-loop.
def countSwaps(a: Array[Int]): Count = {
var swaps: Count = 0
def mergeRun(begin: Int, run_len: Int, src: Array[Int], dst: Array[Int]) = {
var li = begin
val lend = math.min(begin + run_len, src.length)
var ri = begin + run_len
val rend = math.min(begin + run_len * 2, src.length)
var ti = begin
while (ti < rend) {
if (ri >= rend) {
dst(ti) = src(li); li += 1
swaps += ri - begin - run_len
} else if (li >= lend) {
dst(ti) = src(ri); ri += 1
} else if (a(li) <= a(ri)) {
dst(ti) = src(li); li += 1
swaps += ri - begin - run_len
} else {
dst(ti) = src(ri); ri += 1
}
ti += 1
}
}
val b = new Array[Int](a.length)
var run = 0
var run_len = 1
while (run_len < a.length) {
var begin = 0
while (begin < a.length) {
val (src, dst) = if (run % 2 == 0) (a, b) else (b, a)
mergeRun(begin, run_len, src, dst)
begin += run_len * 2
}
run += 1
run_len *= 2
}
swaps
}
Convert the above code to Functional style: no mutable variable, no loop.
All recursions are tail calls, thus the performance is good.
def countSwaps(a: Array[Int]): Count = {
def mergeRun(li: Int, lend: Int, rb: Int, ri: Int, rend: Int, di: Int, src: Array[Int], dst: Array[Int], swaps: Count): Count = {
if (ri >= rend && li >= lend) {
swaps
} else if (ri >= rend) {
dst(di) = src(li)
mergeRun(li + 1, lend, rb, ri, rend, di + 1, src, dst, ri - rb + swaps)
} else if (li >= lend) {
dst(di) = src(ri)
mergeRun(li, lend, rb, ri + 1, rend, di + 1, src, dst, swaps)
} else if (src(li) <= src(ri)) {
dst(di) = src(li)
mergeRun(li + 1, lend, rb, ri, rend, di + 1, src, dst, ri - rb + swaps)
} else {
dst(di) = src(ri)
mergeRun(li, lend, rb, ri + 1, rend, di + 1, src, dst, swaps)
}
}
val b = new Array[Int](a.length)
def merge(run: Int, run_len: Int, lb: Int, swaps: Count): Count = {
if (run_len >= a.length) {
swaps
} else if (lb >= a.length) {
merge(run + 1, run_len * 2, 0, swaps)
} else {
val lend = math.min(lb + run_len, a.length)
val rb = lb + run_len
val rend = math.min(rb + run_len, a.length)
val (src, dst) = if (run % 2 == 0) (a, b) else (b, a)
val inc_swaps = mergeRun(lb, lend, rb, rb, rend, lb, src, dst, 0)
merge(run, run_len, lb + run_len * 2, inc_swaps + swaps)
}
}
merge(0, 1, 0, 0)
}
It seems to me that the key is to break the list into a series of ascending sequences. For example, your example would be broken into (1 2 4 5)(3 6). None of the items in the first list can end a pair. Now you do a kind of merge of these two lists, working backwards:
6 > 5, so 6 can't be in any pairs
3 < 5, so its a pair
3 < 4, so its a pair
3 > 2, so we're done
I'm not clear from the definition on how to handle more than 2 such sequences.
I was creating a faster string splitter method. First, I wrote a non-tail recursive version returning List. Next, a tail recursive one using ListBuffer and then calling toList (+= and toList are O(1)). I fully expected the tail recursive version to be faster, but that is not the case.
Can anyone explain why?
Original version:
def split(s: String, c: Char, i: Int = 0): List[String] = if (i < 0) Nil else {
val p = s indexOf (c, i)
if (p < 0) s.substring(i) :: Nil else s.substring(i, p) :: split(s, c, p + 1)
}
Tail recursive one:
import scala.annotation.tailrec
import scala.collection.mutable.ListBuffer
def split(s: String, c: Char): Seq[String] = {
val buffer = ListBuffer.empty[String]
#tailrec def recurse(i: Int): Seq[String] = {
val p = s indexOf (c, i)
if (p < 0) {
buffer += s.substring(i)
buffer.toList
} else {
buffer += s.substring(i, p)
recurse(p + 1)
}
}
recurse(0)
}
This was benchmarked with code here, with results here, by #scala's jyxent.
You're simply doing more work in the second case. In the first case, you might overflow your stack, but every operation is really simple, and :: is as small of a wrapper as you can get (all you have to do is create the wrapper and point it to the head of the other list). In the second case, not only do you create an extra collection initially and have to form a closure around s and buffer for the nested method to use, but you also use the heavierweight ListBuffer which has to check for each += whether it's already been copied out to a list, and uses different code paths depending on whether it's empty or not (in order to get the O(1) append to work).
You expect the tail recursive version to be faster due to the tail call optimization and I think this is right, if you compare apples to apples:
def split3(s: String, c: Char): Seq[String] = {
#tailrec def recurse(i: Int, acc: List[String] = Nil): Seq[String] = {
val p = s indexOf (c, i)
if (p < 0) {
s.substring(i) :: acc
} else {
recurse(p + 1, s.substring(i, p) :: acc)
}
}
recurse(0) // would need to reverse
}
I timed this split3 to be faster, except of course to get the same result it would need to reverse the result.
It does seem ListBuffer introduces inefficiencies that the tail recursion optimization cannot make up for.
Edit: thinking about avoiding the reverse...
def split3(s: String, c: Char): Seq[String] = {
#tailrec def recurse(i: Int, acc: List[String] = Nil): Seq[String] = {
val p = s lastIndexOf (c, i)
if (p < 0) {
s.substring(0, i + 1) :: acc
} else {
recurse(p - 1, s.substring(p + 1, i + 1) :: acc)
}
}
recurse(s.length - 1)
}
This has the tail call optimization and avoids ListBuffer.
I wrote an answer to the first Project Euler question:
Add all the natural numbers below one thousand that are multiples of 3 or 5.
The first thing that came to me was:
(1 until 1000).filter(i => (i % 3 == 0 || i % 5 == 0)).foldLeft(0)(_ + _)
but it's slow (it takes 125 ms), so I rewrote it, simply thinking of 'another way' versus 'the faster way'
(1 until 1000).foldLeft(0){
(total, x) =>
x match {
case i if (i % 3 == 0 || i % 5 ==0) => i + total // Add
case _ => total //skip
}
}
This is much faster (only 2 ms). Why? I'm guess the second version uses only the Range generator and doesn't manifest a fully realized collection in any way, doing it all in one pass, both faster and with less memory. Am I right?
Here the code on IdeOne: http://ideone.com/GbKlP
The problem, as others have said, is that filter creates a new collection. The alternative withFilter doesn't, but that doesn't have a foldLeft. Also, using .view, .iterator or .toStream would all avoid creating the new collection in various ways, but they are all slower here than the first method you used, which I thought somewhat strange at first.
But, then... See, 1 until 1000 is a Range, whose size is actually very small, because it doesn't store each element. Also, Range's foreach is extremely optimized, and is even specialized, which is not the case of any of the other collections. Since foldLeft is implemented as a foreach, as long as you stay with a Range you get to enjoy its optimized methods.
(_: Range).foreach:
#inline final override def foreach[#specialized(Unit) U](f: Int => U) {
if (length > 0) {
val last = this.last
var i = start
while (i != last) {
f(i)
i += step
}
f(i)
}
}
(_: Range).view.foreach
def foreach[U](f: A => U): Unit =
iterator.foreach(f)
(_: Range).view.iterator
override def iterator: Iterator[A] = new Elements(0, length)
protected class Elements(start: Int, end: Int) extends BufferedIterator[A] with Serializable {
private var i = start
def hasNext: Boolean = i < end
def next: A =
if (i < end) {
val x = self(i)
i += 1
x
} else Iterator.empty.next
def head =
if (i < end) self(i) else Iterator.empty.next
/** $super
* '''Note:''' `drop` is overridden to enable fast searching in the middle of indexed sequences.
*/
override def drop(n: Int): Iterator[A] =
if (n > 0) new Elements(i + n, end) else this
/** $super
* '''Note:''' `take` is overridden to be symmetric to `drop`.
*/
override def take(n: Int): Iterator[A] =
if (n <= 0) Iterator.empty.buffered
else if (i + n < end) new Elements(i, i + n)
else this
}
(_: Range).view.iterator.foreach
def foreach[U](f: A => U) { while (hasNext) f(next()) }
And that, of course, doesn't even count the filter between view and foldLeft:
override def filter(p: A => Boolean): This = newFiltered(p).asInstanceOf[This]
protected def newFiltered(p: A => Boolean): Transformed[A] = new Filtered { val pred = p }
trait Filtered extends Transformed[A] {
protected[this] val pred: A => Boolean
override def foreach[U](f: A => U) {
for (x <- self)
if (pred(x)) f(x)
}
override def stringPrefix = self.stringPrefix+"F"
}
Try making the collection lazy first, so
(1 until 1000).view.filter...
instead of
(1 until 1000).filter...
That should avoid the cost of building an intermediate collection. You might also get better performance from using sum instead of foldLeft(0)(_ + _), it's always possible that some collection type might have a more efficient way to sum numbers. If not, it's still cleaner and more declarative...
Looking through the code, it looks like filter does build a new Seq on which the foldLeft is called. The second skips that bit. It's not so much the memory, although that can't but help, but that the filtered collection is never built at all. All that work is never done.
Range uses TranversableLike.filter, which looks like this:
def filter(p: A => Boolean): Repr = {
val b = newBuilder
for (x <- this)
if (p(x)) b += x
b.result
}
I think it's the += on line 4 that's the difference. Filtering in foldLeft eliminates it.
filter creates a whole new sequence on which then foldLeft is called. Try:
(1 until 1000).view.filter(i => (i % 3 == 0 || i % 5 == 0)).reduceLeft(_+_)
This will prevent said effect, merely wrapping the original thing. Exchanging foldLeft with reduceLeft is only cosmetic (in this case).
Now the challenge is, can you think of a yet more efficient way? Not that your solution is too slow in this case, but how well does it scale? What if instead of 1000, it was 1000000000? There is a solution that could compute the latter case just as quickly as the former.
I have created solution for PE P12 in Scala but is very very slow. Can somebody can tell me why? How to optimize this? calculateDevisors() - naive approach and calculateNumberOfDivisors() - divisor function has the same speed :/
import annotation.tailrec
def isPrime(number: Int): Boolean = {
if (number < 2 || (number != 2 && number % 2 == 0) || (number != 3 && number % 3 == 0))
false
else {
val sqrtOfNumber = math.sqrt(number) toInt
#tailrec def isPrimeInternal(divisor: Int, increment: Int): Boolean = {
if (divisor > sqrtOfNumber)
true
else if (number % divisor == 0)
false
else
isPrimeInternal(divisor + increment, 6 - increment)
}
isPrimeInternal(5, 2)
}
}
def generatePrimeNumbers(count: Int): List[Int] = {
#tailrec def generatePrimeNumbersInternal(number: Int = 3, index: Int = 0,
primeNumbers: List[Int] = List(2)): List[Int] = {
if (index == count)
primeNumbers
else if (isPrime(number))
generatePrimeNumbersInternal(number + 2, index + 1, primeNumbers :+ number)
else
generatePrimeNumbersInternal(number + 2, index, primeNumbers)
}
generatePrimeNumbersInternal();
}
val primes = Stream.cons(2, Stream.from(3, 2) filter {isPrime(_)})
def calculateDivisors(number: Int) = {
for {
divisor <- 1 to number
if (number % divisor == 0)
} yield divisor
}
#inline def decomposeToPrimeNumbers(number: Int) = {
val sqrtOfNumber = math.sqrt(number).toInt
#tailrec def decomposeToPrimeNumbersInternal(number: Int, primeNumberIndex: Int = 0,
factors: List[Int] = List.empty[Int]): List[Int] = {
val primeNumber = primes(primeNumberIndex)
if (primeNumberIndex > sqrtOfNumber)
factors
else if (number % primeNumber == 0)
decomposeToPrimeNumbersInternal(number / primeNumber, primeNumberIndex, factors :+ primeNumber)
else
decomposeToPrimeNumbersInternal(number, primeNumberIndex + 1, factors)
}
decomposeToPrimeNumbersInternal(number) groupBy {n => n} map {case (n: Int, l: List[Int]) => (n, l size)}
}
#inline def calculateNumberOfDivisors(number: Int) = {
decomposeToPrimeNumbers(number) map {case (primeNumber, exponent) => exponent + 1} product
}
#tailrec def calculate(number: Int = 12300): Int = {
val triangleNumber = ((number * number) + number) / 2
val startTime = System.currentTimeMillis()
val numberOfDivisors = calculateNumberOfDivisors(triangleNumber)
val elapsedTime = System.currentTimeMillis() - startTime
printf("%d: V: %d D: %d T: %dms\n", number, triangleNumber, numberOfDivisors, elapsedTime)
if (numberOfDivisors > 500)
triangleNumber
else
calculate(number + 1)
}
println(calculate())
You could first check what is slow. Your prime calculation, for instance, is very, very slow. For each number n, you try to divide n by each each number from 5 to sqrt(n), skipping multiples of 2 and 3. Not only you do not skip numbers you already know are not primes, but even if you fix this, the complexity of this algorithm is much worse than the traditional Sieve of Eratosthenes. See one Scala implementation for the Sieve here.
That is not to say that the rest of your code isn't suboptimal as well, but I'll leave that for others.
EDIT
Indeed, indexed access to Stream is terrible. Here's a rewrite that works with Stream, instead of converting everything to Array. Also, note the remark before the first if for a possible bug in your code.
#tailrec def decomposeToPrimeNumbersInternal(number: Int, primes: Stream[Int],
factors: List[Int] = List.empty[Int]): List[Int] = {
val primeNumber = primes.head
// Comparing primeNumberIndex with sqrtOfNumber didn't make any sense
if (primeNumber > sqrtOfNumber)
factors
else if (number % primeNumber == 0)
decomposeToPrimeNumbersInternal(number / primeNumber, primes, factors :+ primeNumber)
else
decomposeToPrimeNumbersInternal(number, primes.tail, factors)
}
Slow compared to....? How do you know it's an issue with Scala, and not with your algorithm?
An admittedly quick read of the code suggests you might be recalculating primes and other values over and over. isPrimeInternal jumps out as a possible case where this might be a problem.
Your code is not compilable, some parts are missing, so I'm guessing here. Some thing that frequently hurts performance is boxing/unboxing taking place in collections. Another thing that I noted is that you cunstruct your primes as a Stream - which is a good thing - but don't take advantage of this in your isPrime function, which uses a primitive 2,3-wheel (1 and 5 mod 6) instead. I might be wrong, but try to replace it by
def isPrime(number: Int): Boolean = {
val sq = math.sqrt(number + 0.5).toInt
! primes.takeWhile(_ <= sq).exists(p => number % p == 0)
}
My scala algorithm that calculates divisors of a given number. It worked fine in the solution of
Project Euler Problem 12.
def countDivisors(numberToFindDivisor: BigInt): Int = {
def countWithAcc(numberToFindDivisor: BigInt, currentCandidate: Int, currentCountOfDivisors: Int,limit: BigInt): Int = {
if (currentCandidate >= limit) currentCountOfDivisors
else {
if (numberToFindDivisor % currentCandidate == 0)
countWithAcc(numberToFindDivisor, currentCandidate + 1, currentCountOfDivisors + 2, numberToFindDivisor / currentCandidate)
else
countWithAcc(numberToFindDivisor, currentCandidate + 1, currentCountOfDivisors, limit)
}
}
countWithAcc(numberToFindDivisor, 1, 0, numberToFindDivisor + 1)
}
calculateDivisors can be greatly improved by only checking for divisors up to the square root of the number. Each time you find a divisor below the sqrt, you also find one above.
def calculateDivisors(n: Int) = {
var res = 1
val intSqrt = Math.sqrt(n).toInt
for (i <- 2 until intSqrt) {
if (n % i == 0) {
res += 2
}
}
if (n == intSqrt * intSqrt) {
res += 1
}
res
}