Here is the link to the question. Essentially, it asks to find the kth number having digit sum as 10. I have tried multiple solutions and also looked upon solutions online. Specifically this one (also shared below). The one with constant time talks about outliers in Arithmetic Progression and uses it to find the nth number having sum as 10. Obviously, the code is incorrect as it fails for test cases when k=1000 etc.
#include <bits/stdc++.h>
using namespace std;
int findNth(int n)
{
int nthElement = 19 + (n - 1) * 9;
int outliersCount = (int)log10(nthElement) - 1;
// find the nth perfect number
nthElement += 9 * outliersCount;
return nthElement;
}
int main()
{
cout << findNth(5) << endl;
return 0;
}
Eventually, I ended up writing combination of Arithmetic Progression + brute force as below
#include <bits/stdc++.h>
using namespace std;
#define ll unsigned long long
int main() {
int n;
cin >> n;
int count = 0;
ll i = 19;
for (; ; i += 9) {
int curr = i;
int localSum = 0;
while (curr) {
localSum += curr%10;
curr /= 10;
}
if (localSum == 10) {
count += 1;
}
if (count == n) {
break;
}
}
cout << i << endl;
return 0;
}
I am wondering, if there is no constant time or better algorithm that does not require me to calculate the sum, but my algorithm always hops in a way that I have number whose digit sum is 10?
Here is a Python solution that you can translate into C++.
cached_count_ds_l = {}
def count_digit_sum_length (s, l):
k = (s, l)
if k not in cached_count_ds_l:
if l < 2:
if s == 0:
return 1
elif l == 1 and s < 10:
return 1
else:
return 0
else:
ans = 0
for i in range(min(10, s+1)):
ans += count_digit_sum_length(s-i, l-1)
cached_count_ds_l[k] = ans
return cached_count_ds_l[k]
def nth_of_sum (s, n):
l = 0
while count_digit_sum_length(s, l) < n:
l += 1
digits = []
while 0 < l:
for i in range(10):
if count_digit_sum_length(s-i, l-1) < n:
n -= count_digit_sum_length(s-i, l-1)
else:
digits.append(str(i))
s -= i
l -= 1
break
return int("".join(digits))
print(nth_of_sum(10, 1000))
The idea is to use dynamic programming to find how many numbers there are of a given maximum length with a given digit sum. And then to use that to cross off whole blocks of numbers on the way to finding the right one.
The main logic goes like this:
0 numbers of length 0 sum to 10
- need longer
0 numbers of length 1 sum to 10
- need longer
9 numbers of length 2 sum to 10
- need longer
63 numbers of length 3 sum to 10
- need longer
282 numbers of length 4 sum to 10
- need longer
996 numbers of length 5 sum to 10
- need longer
2997 numbers of length 6 sum to 10
- answer has length 6
Looking for 1000th number of length 6 that sums to 10
- 996 with a leading 0 sum to 10
- Need the 4th past 99999
- 715 with a leading 1 sum to 10
- Have a leading 1
Looking for 4th number of length 5 that sums to 9
- 495 with a leading 0 sum to 9
- Have a leading 10
Looking for 4th number of length 4 that sums to 9
- 220 with a leading 0 sum to 9
- Have a leading 100
Looking for 4th number of length 3 that sums to 9
- 55 with a leading 0 sum to 9
- Have a leading 1000
Looking for 4th number of length 2 that sums to 9
- 1 with a leading 0 sum to 9
- Need the 3rd past 9
- 1 with a leading 1 sum to 9
- Need the 2nd past 19
- 1 with a leading 2 sum to 9
- Need the 1st past 29
- 1 with a leading 3 sum to 9
- Have a leading 10003
Looking for 1st number of length 1 that sums to 6
- 0 with a leading 0 sum to 6
- Need the 1st past 0
- 0 with a leading 1 sum to 6
- Need the 1st past 1
- 0 with a leading 2 sum to 6
- Need the 1st past 2
- 0 with a leading 3 sum to 6
- Need the 1st past 3
- 0 with a leading 4 sum to 6
- Need the 1st past 4
- 0 with a leading 5 sum to 6
- Need the 1st past 5
- 1 with a leading 6 sum to 6
- Have a leading 100036
And it finishes in a fraction of a second.
Incidentally the million'th is 20111220000010, the billionth is 10111000000002000000010000002100, and the trillionth is 10000000100000100000100000000000001000000000000100000000010110001000.
Related
I have a word corpus of say 3000 words such as [hello, who, this ..].
I want to find the nth 3 word combination from this corpus.I am fine with any order as long as the algorithm gives consistent output.
What would be the time complexity of the algorithm.
I have seen this answer but was looking for something simple.
(Note that I will be using 1-based indexes and ranks throughout this answer.)
To generate all combinations of 3 elements from a list of n elements, we'd take all elements from 1 to n-2 as the first element, then for each of these we'd take all elements after the first element up to n-1 as the second element, then for each of these we'd take all elements after the second element up to n as the third element. This gives us a fixed order, and a direct relation between the rank and a specific combination.
If we take element i as the first element, there are (n-i choose 2) possibilities for the second and third element, and thus (n-i choose 2) combinations with i as the first element. If we then take element j as the second element, there are (n-j choose 1) = n-j possibilities for the third element, and thus n-j combinations with i and j as the first two elements.
Linear search in tables of binomial coefficients
With tables of these binomial coefficients, we can quickly find a specific combination, given its rank. Let's look at a simplified example with a list of 10 elements; these are the number of combinations with element i as the first element:
i
1 C(9,2) = 36
2 C(8,2) = 28
3 C(7,2) = 21
4 C(6,2) = 15
5 C(5,2) = 10
6 C(4,2) = 6
7 C(3,2) = 3
8 C(2,2) = 1
---
120 = C(10,3)
And these are the number of combinations with element j as the second element:
j
2 C(8,1) = 8
3 C(7,1) = 7
4 C(6,1) = 6
5 C(5,1) = 5
6 C(4,1) = 4
7 C(3,1) = 3
8 C(2,1) = 2
9 C(1,1) = 1
So if we're looking for the combination with e.g. rank 96, we look at the number of combinations for each choice of first element i, until we find which group of combinations the combination ranked 96 is in:
i
1 36 96 > 36 96 - 36 = 60
2 28 60 > 28 60 - 28 = 32
3 21 32 > 21 32 - 21 = 11
4 15 11 <= 15
So we know that the first element i is 4, and that within the 15 combinations with i=4, we're looking for the eleventh combination. Now we look at the number of combinations for each choice of second element j, starting after 4:
j
5 5 11 > 5 11 - 5 = 6
6 4 6 > 4 6 - 4 = 2
7 3 2 <= 3
So we know that the second element j is 7, and that the third element is the second combination with j=7, which is k=9. So the combination with rank 96 contains the elements 4, 7 and 9.
Binary search in tables of running total of binomial coefficients
Instead of creating a table of the binomial coefficients and then performing a linear search, it is of course more efficient to create a table of the running total of the binomial coefficient, and then perform a binary search on it. This will improve the time complexity from O(N) to O(logN); in the case of N=3000, the two look-ups can be done in log2(3000) = 12 steps.
So we'd store:
i
1 36
2 64
3 85
4 100
5 110
6 116
7 119
8 120
and:
j
2 8
3 15
4 21
5 26
6 30
7 33
8 35
9 36
Note that when finding j in the second table, you have to subtract the sum corresponding with i from the sums. Let's walk through the example of rank 96 and combination [4,7,9] again; we find the first value that is greater than or equal to the rank:
3 85 96 > 85
4 100 96 <= 100
So we know that i=4; we then subtract the previous sum next to i-1, to get:
96 - 85 = 11
Now we look at the table for j, but we start after j=4, and subtract the sum corresponding to 4, which is 21, from the sums. then again, we find the first value that is greater than or equal to the rank we're looking for (which is now 11):
6 30 - 21 = 9 11 > 9
7 33 - 21 = 12 11 <= 12
So we know that j=7; we subtract the previous sum corresponding to j-1, to get:
11 - 9 = 2
So we know that the second element j is 7, and that the third element is the second combination with j=7, which is k=9. So the combination with rank 96 contains the elements 4, 7 and 9.
Hard-coding the look-up tables
It is of course unnecessary to generate these look-up tables again every time we want to perform a look-up. We only need to generate them once, and then hard-code them into the rank-to-combination algorithm; this should take only 2998 * 64-bit + 2998 * 32-bit = 35kB of space, and make the algorithm incredibly fast.
Inverse algorithm
The inverse algorithm, to find the rank given a combination of elements [i,j,k] then means:
Finding the index of the elements in the list; if the list is sorted (e.g. words sorted alphabetically) this can be done with a binary search in O(logN).
Find the sum in the table for i that corresponds with i-1.
Add to that the sum in the table for j that corresponds with j-1, minus the sum that corresponds with i.
Add to that k-j.
Let's look again at the same example with the combination of elements [4,7,9]:
i=4 -> table_i[3] = 85
j=7 -> table_j[6] - table_j[4] = 30 - 21 = 9
k=9 -> k-j = 2
rank = 85 + 9 + 2 = 96
Look-up tables for N=3000
This snippet generates the look-up table with the running total of the binomial coefficients for i = 1 to 2998:
function C(n, k) { // binomial coefficient (Pascal's triangle)
if (k < 0 || k > n) return 0;
if (k > n - k) k = n - k;
if (! C.t) C.t = [[1]];
while (C.t.length <= n) {
C.t.push([1]);
var l = C.t.length - 1;
for (var i = 1; i < l / 2; i++)
C.t[l].push(C.t[l - 1][i - 1] + C.t[l - 1][i]);
if (l % 2 == 0)
C.t[l].push(2 * C.t[l - 1][(l - 2) / 2]);
}
return C.t[n][k];
}
for (var total = 0, x = 2999; x > 1; x--) {
total += C(x, 2);
document.write(total + ", ");
}
This snippet generates the look-up table with the running total of the binomial coefficients for j = 2 to 2999:
for (var total = 0, x = 2998; x > 0; x--) {
total += x;
document.write(total + ", ");
}
Code example
Here's a quick code example, unfortunately without the full hardcoded look-up tables, because of the size restriction on answers on SO. Run the snippets above and paste the results into the arrays iTable and jTable (after the leading zeros) to get the faster version with hard-coded look-up tables.
function combinationToRank(i, j, k) {
return iTable[i - 1] + jTable[j - 1] - jTable[i] + k - j;
}
function rankToCombination(rank) {
var i = binarySearch(iTable, rank, 1);
rank -= iTable[i - 1];
rank += jTable[i];
var j = binarySearch(jTable, rank, i + 1);
rank -= jTable[j - 1];
var k = j + rank;
return [i, j, k];
function binarySearch(array, value, first) {
var last = array.length - 1;
while (first < last - 1) {
var middle = Math.floor((last + first) / 2);
if (value > array[middle]) first = middle;
else last = middle;
}
return (value <= array[first]) ? first : last;
}
}
var iTable = [0]; // append look-up table values here
var jTable = [0, 0]; // and here
// remove this part when using hard-coded look-up tables
function C(n,k){if(k<0||k>n)return 0;if(k>n-k)k=n-k;if(!C.t)C.t=[[1]];while(C.t.length<=n){C.t.push([1]);var l=C.t.length-1;for(var i=1;i<l/2;i++)C.t[l].push(C.t[l-1][i-1]+C.t[l-1][i]);if(l%2==0)C.t[l].push(2*C.t[l-1][(l-2)/2])}return C.t[n][k]}
for (var iTotal = 0, jTotal = 0, x = 2999; x > 1; x--) {
iTable.push(iTotal += C(x, 2));
jTable.push(jTotal += x - 1);
}
document.write(combinationToRank(500, 1500, 2500) + "<br>");
document.write(rankToCombination(1893333750) + "<br>");
Let's say I have 15 elements. I want to group them such a way that:
group1 = 1 - 5
group2 = 6 - 9
group3 = 10 - 12
group4 = 13 - 14
group5 = 15
This way I'll get elements in each group as below:
group1 = 5
group2 = 4
group3 = 3
group4 = 2
group5 = 1
As you can see loop interval is decreasing.
I took 15 just for an example. In actual programme it's user driven parameter which can be anything (hopefully few thousand).
Now what I'm looking for is:
Whatever is in group1 should have variable "loop" value 0, group2 should have 1, group3 should have 2 and so on... "loop" is an int variable which is being used to calculate some other stuff.
Let's put in other words too
I have an int variable called "loop". I want to assign value to it such a way that:
First n frames loop value 0 next (n -1) frames loop value 1 then next (n - 2) frames loop value 2 all the way to loop value (n - 1)
Let's say I have 15 frames on my timeline.
So n will be 5 ====>>>>> (5 + 4 + 3 + 2 + 1 = 15; as interval is decreasing by 1)
then
first 5 frames(1 - 5) loop is 0 then next 4 frames(6 - 9) loop is 1 then next 3 frames(10 - 12) loop is 2 then next 2 frames(13 - 14) loop is 3 and for last frame(15) loop is 4.
frames "loop" value
1 - 5 => 0
6 - 9 => 1
10 - 12 => 2
13 - 14 => 3
15 => 4
I've tried with modulo(%). But the issue is on frame 12 loop is 2 so (12 % (5 - 2)) remainder is 0 so it increments loop value.
The following lines are sample code which is running inside a solver. #loop is by default 0 and #Frame is current processing frame number.
int loopint = 5 - #loop;
if (#Frame % loopint == 0)
#loop += 1;
If I understand this correctly, then
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
int main(int argc, char *argv[]) {
int n = atoi(argv[1]);
for(int i = 1; i <= n; ++i) {
printf("%d: %f\n", i, ceil((sqrt(8 * (n - i + 1) + 1) - 1) / 2));
}
}
is an implementation in C.
The math behind this is as follows: The 1 + 2 + 3 + 4 + 5 you have there is a Gauß sum, which has a closed form S = n * (n + 1) / 2 for n terms. Solving this for n, we get
n = (sqrt(8 * S + 1) - 1) / 2
Rounding this upward would give us the solution if you wanted the short stretches at the beginning, that is to say 1, 2, 2, 3, 3, 3, ...
Since you want the stretches to become progressively shorter, we have to invert the order, so S becomes (n - S + 1). Therefore the formula up there.
EDIT: Note that unless the number of elements in your data set fits the n * (n+1) / 2 pattern precisely, you will have shorter stretches either at the beginning or in the end. This implementation places the irregular stretch at the beginning. If you want them at the end,
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
int main(int argc, char *argv[]) {
int n = atoi(argv[1]);
int n2 = (int) ceil((sqrt(8 * n + 1) - 1) / 2);
int upper = n2 * (n2 + 1) / 2;
for(int i = 1; i <= n; ++i) {
printf("%d: %f\n", i, n2 - ceil((sqrt(8 * (upper - i + 1) + 1) - 1) / 2));
}
}
does it. This calculates the next such number beyond your element count, then calculates the numbers you would have if you had that many elements.
My challenge is to output the total number of five-digit numbers that have a digit 5, but no digits 8. My only two answers so far have been 0, or 90000. Can anyone help me?
#include <iostream>
using namespace std;
int main() {
int number;
int counter=10000;
int ncounter=0;
while (counter >= 10000 && counter <= 99999) {
int n1,n2,n3,n4,n5;
counter = counter + 1;
n1 = number%10;
number /= 10;
n2 = number%10;
number /= 10;
n3 = number%10;
number /= 10;
n4 = number%10;
number /=10;
n5 = number%10;
number /= 10;
if (n1 == 5||n2 == 5||n3 == 5||n4 == 5||n5 == 5)
if (n1!=8)
if (n2!=8)
if (n3!=8)
if(n4!=8)
if (n5!=8)
ncounter=ncounter+1;
}
cout<<ncounter<<endl;
return 0;
}
(num with 5 but not 8) = (num without 8) - (num with neither 8 nor 5) = 8*9*9*9*9 - 7*8*8*8*8= 23816
Each number is a selection of 5 digits (with repetitions).
Since you cannot select the digit 8, you have 9 possible digits, so this problem is equivalent to the same problem, base 9 (instead of base 10).
If you make 1 digit a 5, there are 4 non-5 and non-8 digits remaining. The number of these can be calculated as 8^4 (because there are 8 available digits to choose from, and you need to choose 4 of these). With a single 5, there are 5 ways to position the 5, so multiply by 5.
Similarly with 2 5's, there are 10 ways to position the 5s relative to other digits.
Therefore, we have the following table:
number of digits==5 remaining digits ways to position 5s
1 8^4 5
2 8^3 10 = 5*4/2
3 8^2 10
4 8^1 5
5 8^0 1
There are 5*8^4 + 10*8^3 + 10*8^2 + 5*8^1 + 8^0 = 26281 numbers <10^5 with a 5 but not an 8.
There are 4*8^3 + 6*8^2 + 4*8^1 + 8^0 = 2465 numbers <10^4 with a 5 but not an 8. Therefore, there are 23816 numbers satisfying your criteria.
This is actually a mathematical problem. Here we have three conditions:
First digit is not zero, as it should be a five-digit number.
No digits are 8
One or more digits are 5
There can be numbers with one to five 5s, where first digit is or is not 5 (except for 55555). That's nine cases to count.
If first digit is not 5, it has 7 options: [1234679]; if any other digit is not 5, it has 8 options: [12346790].
Here C(5) is number of combinations to place 5s, and C(o) - to place other digits.
N(5). 1st? C(5) C(o)
1 Y 1 * 8^4
1 N 4 * 7*8^3
2 Y 4 * 8^3
2 N 6 * 7*8^2
3 Y 6 * 8^2
3 N 4 * 7*8
4 Y 4 * 8
4 N 1 * 7
5 Y 1
Sum: 23816
How is this code working for multiple of 5
bool isMultipleof5(int n)
{
/* If n is a multiple of 5 then we make sure that last
digit of n is 0 */
if ( (n&1) == 1 )
n <<= 1;
float x = n;
x = ( (int)(x*0.1) )*10;
/* If last digit of n is 0 then n will be equal to (int)x */
if ( (int)x == n )
return true;
return false;
}
It first makes n divisable by 2.
Next, it checks if it is divisable by 10 by multiplying with 0.1 and again with 10. The idea that if it is divisable by 10, you will get back to the original, and only then.
So, if the modifies n is divisable by 10 - it is certainly divisable by 5 as well, and since modified n is always divisable by 2, if it is divisable by 5 it will be divisable by 10, and the algorithm works.
NOTE: This is very unsuggested and especially might break with large values due to floating point precision issues. using the % operator should be prefered: return (n % 5) == 0
This is how the code works with some examples.
if ( (n&1) == 1 ) //Checks if the number is odd
n <<= 1; //Multiplies the number by 2 if odd
x = ( (int)(x * 0.1) //Divides the number 10 then truncates any decimal places
* 10 ) //Multiplies it back by 10
if ( (int)x == n ) //If the floating point value equals the (semi) original value its divisible by 5
return true;
return false; //Other wise false
Example:
15 & 1 == 1 //15 is odd
15 <<= 1; //n is now 30
30 / 10 = 3;
3 * 10 = 30; //x is now 30
30 == 30 //15 is a multiple of 5
17 & 1 == 1 //17 is odd
17 <<= 1; //n is now 34
34 / 10 = 3.4;
((int)3.4 = 3) * 10 = 30; //x is now 30
30 != 34 //17 is not a multiple of 5.
As others said though just simply use the mod operator %.
This is how it works:
Double the number. Now anything ending in 5 will be divisible 10 (and also divisible by 5). n <<= 1; (the check for oddness is unnecessary (n&1) == 1)
Divide it by 10, and cast away the fractional part. (int)(x*0.1)
Multiply it by 10, so now we have the same number as in step 1 only if the number in step 1 was already divisible by 10.
The use of floating point to divide by 10 makes this algorithm dangerous and probably incorrect for large values.
Try this
bool isMultipleof5(int n)
{
return (n%5) == 0;
}
A simpler way would be
bool isMultipleof5(int n)
{
return 0 == ( n % 5 ) ;
}
#define IS_MULTIPLE_OF_5(n) (((n)%5) ? 0 : 1)
I'd agree that (n % 5) == 0 would be an ideal solution, but that wasn't really the question.
This code works because it first checks if the input is odd. If it is, it multiplies by two. Since all odd multiples of 5 end with a 5, multiplying by 2 gives a number that ends with 0.
Then it checks if the last digit is 0. This can only happen if it started as a 0 (i.e. was even, we didn't change it) or if it was odd and ended in a 5 (we multiplied by 2). So, if it ends in 0 then the input must have been divisible by 5.
I'd add that this is also an awkward way to check the value of the last digit. I'd suggest n % 10 == 0 instead, but like others mentioned... you could have just used n % 5 == 0 in the first place ;).
I'm reading about permutations and I'm interested in ranking/unranking methods.
From the abstract of a paper:
A ranking function for the permutations on n symbols assigns a unique
integer in the range [0, n! - 1] to each of the n! permutations. The corresponding
unranking function is the inverse: given an integer between 0 and n! - 1, the
value of the function is the permutation having this rank.
I made a ranking and an unranking function in C++ using next_permutation. But this isn't practical for n>8. I'm looking for a faster method and factoradics seem to be quite popular.
But I'm not sure if this also works with duplicates. So what would be a good way to rank/unrank permutations with duplicates?
I will cover one half of your question in this answer - 'unranking'. The goal is to find the lexicographically 'K'th permutation of an ordered string [abcd...] efficiently.
We need to understand Factorial Number System (factoradics) for this. A factorial number system uses factorial values instead of powers of numbers (binary system uses powers of 2, decimal uses powers of 10) to denote place-values (or base).
The place values (base) are –
5!= 120 4!= 24 3!=6 2!= 2 1!=1 0!=1 etc..
The digit in the zeroth place is always 0. The digit in the first place (with base = 1!) can be 0 or 1. The digit in the second place (with base 2!) can be 0,1 or 2 and so on. Generally speaking, the digit at nth place can take any value between 0-n.
First few numbers represented as factoradics-
0 -> 0 = 0*0!
1 -> 10 = 1*1! + 0*0!
2 -> 100 = 1*2! + 0*1! + 0*0!
3 -> 110 = 1*2! + 1*1! + 0*0!
4 -> 200 = 2*2! + 0*1! + 0*0!
5 -> 210 = 2*2! + 1*1! + 0*0!
6 -> 1000 = 1*3! + 0*2! + 0*1! + 0*0!
7 -> 1010 = 1*3! + 0*2! + 1*1! + 0*0!
8 -> 1100 = 1*3! + 1*2! + 0*1! + 0*0!
9 -> 1110
10-> 1200
There is a direct relationship between n-th lexicographical permutation of a string and its factoradic representation.
For example, here are the permutations of the string “abcd”.
0 abcd 6 bacd 12 cabd 18 dabc
1 abdc 7 badc 13 cadb 19 dacb
2 acbd 8 bcad 14 cbad 20 dbac
3 acdb 9 bcda 15 cbda 21 dbca
4 adbc 10 bdac 16 cdab 22 dcab
5 adcb 11 bdca 17 cdba 23 dcba
We can see a pattern here, if observed carefully. The first letter changes after every 6-th (3!) permutation. The second letter changes after 2(2!) permutation. The third letter changed after every (1!) permutation and the fourth letter changes after every (0!) permutation. We can use this relation to directly find the n-th permutation.
Once we represent n in factoradic representation, we consider each digit in it and add a character from the given string to the output. If we need to find the 14-th permutation of ‘abcd’. 14 in factoradics -> 2100.
Start with the first digit ->2, String is ‘abcd’. Assuming the index starts at 0, take the element at position 2, from the string and add it to the Output.
Output String
c abd
2 012
The next digit -> 1.String is now ‘abd’. Again, pluck the character at position 1 and add it to the Output.
Output String
cb ad
21 01
Next digit -> 0. String is ‘ad’. Add the character at position 1 to the Output.
Output String
cba d
210 0
Next digit -> 0. String is ‘d’. Add the character at position 0 to the Output.
Output String
cbad ''
2100
To convert a given number to Factorial Number System,successively divide the number by 1,2,3,4,5 and so on until the quotient becomes zero. The reminders at each step forms the factoradic representation.
For eg, to convert 349 to factoradic,
Quotient Reminder Factorial Representation
349/1 349 0 0
349/2 174 1 10
174/3 58 0 010
58/4 14 2 2010
14/5 2 4 42010
2/6 0 2 242010
Factoradic representation of 349 is 242010.
One way is to rank and unrank the choice of indices by a particular group of equal numbers, e.g.,
def choose(n, k):
c = 1
for f in xrange(1, k + 1):
c = (c * (n - f + 1)) // f
return c
def rank_choice(S):
k = len(S)
r = 0
j = k - 1
for n in S:
for i in xrange(j, n):
r += choose(i, j)
j -= 1
return r
def unrank_choice(k, r):
S = []
for j in xrange(k - 1, -1, -1):
n = j
while r >= choose(n, j):
r -= choose(n, j)
n += 1
S.append(n)
return S
def rank_perm(P):
P = list(P)
r = 0
for n in xrange(max(P), -1, -1):
S = []
for i, p in enumerate(P):
if p == n:
S.append(i)
S.reverse()
for i in S:
del P[i]
r *= choose(len(P) + len(S), len(S))
r += rank_choice(S)
return r
def unrank_perm(M, r):
P = []
for n, m in enumerate(M):
S = unrank_choice(m, r % choose(len(P) + m, m))
r //= choose(len(P) + m, m)
S.reverse()
for i in S:
P.insert(i, n)
return tuple(P)
if __name__ == '__main__':
for i in xrange(60):
print rank_perm(unrank_perm([2, 3, 1], i))
For large n-s you need arbitrary precision library like GMP.
this is my previous post for an unranking function written in python, I think it's readable, almost like a pseudocode, there is also some explanation in the comments: Given a list of elements in lexicographical order (i.e. ['a', 'b', 'c', 'd']), find the nth permutation - Average time to solve?
based on this you should be able to figure out the ranking function, it's basically the same logic ;)
Java, from https://github.com/timtiemens/permute/blob/master/src/main/java/permute/PermuteUtil.java (my public domain code, minus the error checking):
public class PermuteUtil {
public <T> List<T> nthPermutation(List<T> original, final BigInteger permutationNumber) {
final int size = original.size();
// the return list:
List<T> ret = new ArrayList<>();
// local mutable copy of the original list:
List<T> numbers = new ArrayList<>(original);
// Our input permutationNumber is [1,N!], but array indexes are [0,N!-1], so subtract one:
BigInteger permNum = permutationNumber.subtract(BigInteger.ONE);
for (int i = 1; i <= size; i++) {
BigInteger factorialNminusI = factorial(size - i);
// casting to integer is ok here, because even though permNum _could_ be big,
// the factorialNminusI is _always_ big
int j = permNum.divide(factorialNminusI).intValue();
permNum = permNum.mod(factorialNminusI);
// remove item at index j, and put it in the return list at the end
T item = numbers.remove(j);
ret.add(item);
}
return ret;
}
}