The goal is to code this sum into a recursive function.
Sum
I have tried so far to code it like this.
def under(u: Int): Int = {
var i1 = u/2
var i = i1+1
if ( u/2 == 1 ) then u + 1 - 2 * 1
else (u + 1 - 2 * i) + under(u-1)
}
It seems like i am running into an issue with the recursive part but i am not able to figure out what goes wrong.
In theory, under(5) should produce 10.
Your logic is wrong. It should iterate (whether through loop, recursion or collection is irrelevant) from i=1 to i=n/2. But using n and current i as they are.
(1 to (n/2)).map(i => n + 1 - 2 * i).sum
You are (more or less) running computations from i=1 to i=n (or rather n down to 1) but instead of n you use i/2 and instead of i you use i/2+1. (sum from i=1 to i=n of (n/2 + 1 - 2 * i)).
// actually what you do is more like (1 to n).toList.reverse
// rather than (1 to n)
(1 to n).map(i => i/2 + 1 - 2 * (i/2 + 1)).sum
It's a different formula. It has twice the elements to sum, and a part of each of them is changing instead of being constant while another part has a wrong value.
To implement the same logic with recursion you would have to do something like:
// as one function with default args
// tail recursive version
def under(n: Int, i: Int = 1, sum: Int = 0): Int =
if (i > n/2) sum
else under(n, i+1, sum + (n + 2 - 2 * i))
// not tail recursive
def under(n: Int, i: Int = 1): Int =
if (i > n/2) 0
else (n + 2 - 2 * i) + under(n, i + 1)
// with nested functions without default args
def under(n: Int): Int = {
// tail recursive
def helper(i: Int, sum: Int): Int =
if (i > n/2) sum
else helper(i + 1, sum + (n + 2 - 2 * i))
helper(1, 0)
}
def under(n: Int): Int = {
// not tail recursive
def helper(i: Int): Int =
if (i > n/2) 0
else (n + 2 - 2 * i) + helper(i + 1)
helper(1)
}
As a side note: there is no need to use any iteration / recursion at all. Here is an explicit formula:
def g(n: Int) = n / 2 * (n - n / 2)
that gives the same results as
def h(n: Int) = (1 to n / 2).map(i => n + 1 - 2 * i).sum
Both assume that you want floored n / 2 in the case that n is odd, i.e. both of the functions above behave the same as
def j(n: Int) = (math.ceil(n / 2.0) * math.floor(n / 2.0)).toInt
(at least until rounding errors kick in).
Related
I'm trying to write a code in Scala to calculate the sum of elements from x to y using a while loop.
I initialize x and y to for instance :
val x = 1
val y = 10
then I write a while loop to increment x :
while (x<y) x = x + 1
But println(x) gives the result 10 so I'm assuming the code basically does 1 + 1 + ... + 1 10 times, but that's not what I want.
One option would be to find the sum via a range, converted to a list:
val x = 1
val y = 10
val sum = (x to y).toList.sum
println("sum = " + sum)
Output:
sum = 55
Demo here:
Rextester
Here's how you would do it using a (yak!) while loop with vars:
var x = 1 // Note that is a "var" not a "val"
val y = 10
var sum = 0 // Must be a "var"
while(x <= y) { // Note less than or equal to
sum += x
x += 1
}
println(s"Sum is $sum") // Sum = 55 (= 1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9 + 10)
Here's another, more functional, approach using a recursive function. Note the complete lack of var types.
val y = 10
#scala.annotation.tailrec // Signifies add must be tail recursive
def add(x: Int, sum: Int): Int = {
// If x exceeds y, then return the current sum value.
if(x > y) sum
// Otherwise, perform another iteration adding 1 to x and x to sum.
else add(x + 1, sum + x)
}
// Start things off and get the result (55).
val result = add(1, 0)
println(s"Sum is $result") // Sum is 55
Here's a common functional approach that can be used with collections. Firstly, (x to y) becomes a Range of values between 1 and 10 inclusive. We then use the foldLeft higher-order function to sum the members:
val x = 1
val y = 10
val result = (x to y).foldLeft(0)(_ + _)
println(s"Sum is $result") // Sum is 55
The (0) is the initial sum value, and the (_ + _) adds the current sum to the current value. (This is Scala shorthand for ((sum: Int, i: Int) => sum + i)).
Finally, here's a simplified version of the elegant functional version that #TimBiegeleisen posted above. However, since a Range already implements a .sum member, there is no need to convert to a List first:
val x = 1
val y = 10
val result = (x to y).sum
println(s"Sum is $result") // Sum is 55
(sum can be thought of as being equivalent to the foldLeft example above, and is typically implemented in similar fashion.)
BTW, if you just want to sum values from 1 to 10, the following code does this very succinctly:
(1 to 10).sum
Although you can use Scala to write imperative code (which uses vars, while loops, etc. and which inherently leads to shared mutable state), I strongly recommend that you consider functional alternatives. Functional programming avoids the side-effects and complexities of shared mutable state and often results in simpler, more elegant code. Note that all but the first examples are all functional.
var x = 1
var y = 10
var temp = 0
while (x < y) {
temp = temp+x
x = x + 1
}
println(temp)
This gives required result
I am trying to convert this recursive function into a tail recursive function
def sumOfFractions(n: Int): Double = {
require(n > 0, "Parameter n has to be greater than 0");
if (n==1)
1.0
else
1.0 / n + sumOfFractions(n - 1)
}
I thought that this solution would work but when it runs it just returns 1.0
def sumOfFractions(n:Int):Double = {
def inner(acc:Int, n:Int): Double={
if(n <= 1)1.0
else
{
inner(acc+(1/n),n-1)
}
}
inner(0,n)
}
I think this is because the accumulator is not being updated correctly but I don't understand why. The code is in Scala but an example in any language would be helpful.
You need the base case (n <= 1) to return the accumulator, not 1.0. You'll also run into problems because the accumulator is an Int instead of a Double, which means that + (1 / n) is just adding 0 (the result of dividing 1: Int by any n: Int greater than one).
You can fix this by changing acc's type and making the numerator of the reciprocal a literal double:
def sumOfFractions(n: Int):Double = {
def inner(acc: Double, n: Int): Double =
if (n <= 1) acc else inner(acc + (1.0 / n), n - 1)
inner(0, n)
}
This should work.
Correct your code
1) Return acc (accumulator) when n <= 1
2) Your acc should be Double type
3) Division should be floating point division
def sumOfFractions(n: Int): Double = {
def inner(acc: Double, n:Int): Double = if(n <= 1) acc
else inner(acc + (1.0 / n), n - 1)
inner(0,n)
}
Using foldLeft
def sumOfFractions(n: Int): Double =
(1 to n).foldLeft(0.0)((r, c) => r + (1.0 / c))
I would like to know, if there is a Scala built-in method to get the length of the decimal representation of an integer ?
Example: 45 has length 2; 10321 has length 5.
One could get the length with 10321.toString.length, but this smells a bit because of the overhead when creating a String object. Is there a nicer way or a built-in method ?
UPDATE:
With 'nicer' I mean a faster solution
I am only interested in positive integers
This is definitely personal preference, but I think the logarithm method looks nicer without a branch. For positive values only, the abs can be omitted of course.
def digits(x: Int) = {
import math._
ceil(log(abs(x)+1)/log(10)).toInt
}
toString then get length of int will not work for negative integers. This code will work not only for positive numbers but also negatives.
def digits(n:Int) = if (n==0) 1 else math.log10(math.abs(n)).toInt + 1;
If you want speed then something like the following is pretty good, assuming random distribution:
def lengthBase10(x: Int) =
if (x >= 1000000000) 10
else if (x >= 100000000) 9
else if (x >= 10000000) 8
else if (x >= 1000000) 7
else if (x >= 100000) 6
else if (x >= 10000) 5
else if (x >= 1000) 4
else if (x >= 100) 3
else if (x >= 10) 2
else 1
Calculating logarithms to double precision isn't efficient if all you want is the floor.
The conventional recursive way would be:
def len(x: Int, i: Int = 1): Int =
if (x < 10) i
else len(x / 10, i + 1)
which is faster than taking logs for integers in the range 0 to 10e8.
lengthBase10 above is about 4x faster than everything else though.
Something like this should do the job:
def numericLength(n: Int): Int = BigDecimal(n).precision
Take log to the base 10, take the floor and add 1.
The easiest way is:
def numberLength(i : Int): Int = i.toString.length
You might add a guarding-condition because negative Int will have the length of their abs + 1.
Another possibility can be:
private lazy val lengthList = (1 until 19).map(i => i -> math.pow(10, i).toLong)
def numberSize(x: Long): Int =
if (x >= 0) positiveNumberSize(x)
else positiveNumberSize(-x) + 1
private def positiveNumberSize(x: Long): Int =
lengthList
.collectFirst {
case (l, p) if x < p => l
}
.getOrElse(19)
Most people gave the most efficient answer of (int) log(number)+1
But I want to get a bit deeper into understanding why this works.
Let N be a 3 digit number. This means N can be any number between 100 and 1000, or :
100 < N < 1000
=> 10^2 < N < 10^3
The Logarithmic function is continuous , therefore :
log(10^2) < log(N) < log(10^3)
=> 2 < log(N) < 3
We can conclude that N's logarithm is a number between 2 and 3 , or in other words , any 3 digit numbers' logarithm is between 2 and 3.
So if we take only the integer part of a numbers logarithm(eg. the integer part of 2.567 is 2) and add 1 we get the digit length of the number.
Here is the solution:
number.toString.toCharArray.size
input - output
45 - 2
100 - 3
Given n ( say 3 people ) and s ( say 100$ ), we'd like to partition s among n people.
So we need all possible n-tuples that sum to s
My Scala code below:
def weights(n:Int,s:Int):List[List[Int]] = {
List.concat( (0 to s).toList.map(List.fill(n)(_)).flatten, (0 to s).toList).
combinations(n).filter(_.sum==s).map(_.permutations.toList).toList.flatten
}
println(weights(3,100))
This works for small values of n. ( n=1, 2, 3 or 4).
Beyond n=4, it takes a very long time, practically unusable.
I'm looking for ways to rework my code using lazy evaluation/ Stream.
My requirements : Must work for n upto 10.
Warning : The problem gets really big really fast. My results from Matlab -
---For s =100, n = 1 thru 5 results are ---
n=1 :1 combinations
n=2 :101 combinations
n=3 :5151 combinations
n=4 :176851 combinations
n=5: 4598126 combinations
---
You need dynamic programming, or memoization. Same concept, anyway.
Let's say you have to divide s among n. Recursively, that's defined like this:
def permutations(s: Int, n: Int): List[List[Int]] = n match {
case 0 => Nil
case 1 => List(List(s))
case _ => (0 to s).toList flatMap (x => permutations(s - x, n - 1) map (x :: _))
}
Now, this will STILL be slow as hell, but there's a catch here... you don't need to recompute permutations(s, n) for numbers you have already computed. So you can do this instead:
val memoP = collection.mutable.Map.empty[(Int, Int), List[List[Int]]]
def permutations(s: Int, n: Int): List[List[Int]] = {
def permutationsWithHead(x: Int) = permutations(s - x, n - 1) map (x :: _)
n match {
case 0 => Nil
case 1 => List(List(s))
case _ =>
memoP getOrElseUpdate ((s, n),
(0 to s).toList flatMap permutationsWithHead)
}
}
And this can be even further improved, because it will compute every permutation. You only need to compute every combination, and then permute that without recomputing.
To compute every combination, we can change the code like this:
val memoC = collection.mutable.Map.empty[(Int, Int, Int), List[List[Int]]]
def combinations(s: Int, n: Int, min: Int = 0): List[List[Int]] = {
def combinationsWithHead(x: Int) = combinations(s - x, n - 1, x) map (x :: _)
n match {
case 0 => Nil
case 1 => List(List(s))
case _ =>
memoC getOrElseUpdate ((s, n, min),
(min to s / 2).toList flatMap combinationsWithHead)
}
}
Running combinations(100, 10) is still slow, given the sheer numbers of combinations alone. The permutations for each combination can be obtained simply calling .permutation on the combination.
Here's a quick and dirty Stream solution:
def weights(n: Int, s: Int) = (1 until s).foldLeft(Stream(Nil: List[Int])) {
(a, _) => a.flatMap(c => Stream.range(0, n - c.sum + 1).map(_ :: c))
}.map(c => (n - c.sum) :: c)
It works for n = 6 in about 15 seconds on my machine:
scala> var x = 0
scala> weights(100, 6).foreach(_ => x += 1)
scala> x
res81: Int = 96560646
As a side note: by the time you get to n = 10, there are 4,263,421,511,271 of these things. That's going to take days just to stream through.
My solution of this problem, it can computer n till 6:
object Partition {
implicit def i2p(n: Int): Partition = new Partition(n)
def main(args : Array[String]) : Unit = {
for(n <- 1 to 6) println(100.partitions(n).size)
}
}
class Partition(n: Int){
def partitions(m: Int):Iterator[List[Int]] = new Iterator[List[Int]] {
val nums = Array.ofDim[Int](m)
nums(0) = n
var hasNext = m > 0 && n > 0
override def next: List[Int] = {
if(hasNext){
val result = nums.toList
var idx = 0
while(idx < m-1 && nums(idx) == 0) idx = idx + 1
if(idx == m-1) hasNext = false
else {
nums(idx+1) = nums(idx+1) + 1
nums(0) = nums(idx) - 1
if(idx != 0) nums(idx) = 0
}
result
}
else Iterator.empty.next
}
}
}
1
101
5151
176851
4598126
96560646
However , we can just show the number of the possible n-tuples:
val pt: (Int,Int) => BigInt = {
val buf = collection.mutable.Map[(Int,Int),BigInt]()
(s,n) => buf.getOrElseUpdate((s,n),
if(n == 0 && s > 0) BigInt(0)
else if(s == 0) BigInt(1)
else (0 to s).map{k => pt(s-k,n-1)}.sum
)
}
for(n <- 1 to 20) printf("%2d :%s%n",n,pt(100,n).toString)
1 :1
2 :101
3 :5151
4 :176851
5 :4598126
6 :96560646
7 :1705904746
8 :26075972546
9 :352025629371
10 :4263421511271
11 :46897636623981
12 :473239787751081
13 :4416904685676756
14 :38393094575497956
15 :312629484400483356
16 :2396826047070372396
17 :17376988841260199871
18 :119594570260437846171
19 :784008849485092547121
20 :4910371215196105953021
Here is some imperative code:
var sum = 0
val spacing = 6
var x = spacing
for(i <- 1 to 10) {
sum += x * x
x += spacing
}
Here are two of my attempts to "functionalize" the above code:
// Attempt 1
(1 to 10).foldLeft((0, 6)) {
case((sum, x), _) => (sum + x * x, x + spacing)
}
// Attempt 2
Stream.iterate ((0, 6)) { case (sum, x) => (sum + x * x, x + spacing) }.take(11).last
I think there might be a cleaner and better functional way to do this. What would be that?
PS: Please note that the above is just an example code intended to illustrate the problem; it is not from the real application code.
Replacing 10 by N, you have spacing * spacing * N * (N + 1) * (2 * N + 1) / 6
This is by noting that you're summing (spacing * i)^2 for the range 1..N. This sum factorizes as spacing^2 * (1^2 + 2^2 + ... + N^2), and the latter sum is well-known to be N * (N + 1) * (2 * N + 1) / 6 (see Square Pyramidal Number)
I actually like idea of lazy sequences in this case. You can split your algorithm in 2 logical steps.
At first you want to work on all natural numbers (ok.. not all, but up to max int), so you define them like this:
val naturals = 0 to Int.MaxValue
Then you need to define knowledge about how numbers, that you want to sum, can be calculated:
val myDoubles = (naturals by 6 tail).view map (x => x * x)
And putting this all together:
val naturals = 0 to Int.MaxValue
val myDoubles = (naturals by 6 tail).view map (x => x * x)
val mySum = myDoubles take 10 sum
I think it's the way mathematician will approach this problem. And because all collections are lazily evaluated - you will not get out of memory.
Edit
If you want to develop idea of mathematical notation further, you can actually define this implicit conversion:
implicit def math[T, R](f: T => R) = new {
def ∀(range: Traversable[T]) = range.view map f
}
and then define myDoubles like this:
val myDoubles = ((x: Int) => x * x) ∀ (naturals by 6 tail)
My personal favourite would have to be:
val x = (6 to 60 by 6) map {x => x*x} sum
Or given spacing as an input variable:
val x = (spacing to 10*spacing by spacing) map {x => x*x} sum
or
val x = (1 to 10) map (spacing*) map {x => x*x} sum
There are two different directions to go. If you want to express yourself, assuming that you can't use the built-in range function (because you actually want something more complicated):
Iterator.iterate(spacing)(x => x+spacing).take(10).map(x => x*x).foldLeft(0)(_ + _)
This is a very general pattern: specify what you start with and how to get the next given the previous; then take the number of items you need; then transform them somehow; then combine them into a single answer. There are shortcuts for almost all of these in simple cases (e.g. the last fold is sum) but this is a way to do it generally.
But I also wonder--what is wrong with the mutable imperative approach for maximal speed? It's really quite clear, and Scala lets you mix the two styles on purpose:
var x = spacing
val last = spacing*10
val sum = 0
while (x <= last) {
sum += x*x
x += spacing
}
(Note that the for is slower than while since the Scala compiler transforms for loops to a construct of maximum generality, not maximum speed.)
Here's a straightforward translation of the loop you wrote to a tail-recursive function, in an SML-like syntax.
val spacing = 6
fun loop (sum: int, x: int, i: int): int =
if i > 0 then loop (sum+x*x, x+spacing, i-1)
else sum
val sum = loop (0, spacing, 10)
Is this what you were looking for? (What do you mean by a "cleaner" and "better" way?)
What about this?
def toSquare(i: Int) = i * i
val spacing = 6
val spaceMultiples = (1 to 10) map (spacing *)
val squares = spaceMultiples map toSquare
println(squares.sum)
You have to split your code in small parts. This can improve readability a lot.
Here is a one-liner:
(0 to 10).reduceLeft((u,v)=>u + spacing*spacing*v*v)
Note that you need to start with 0 in order to get the correct result (else the first value 6 would be added only, but not squared).
Another option is to generate the squares first:
(1 to 2*10 by 2).scanLeft(0)(_+_).sum*spacing*spacing