optimal algorithm for particular divisors - php

Not a duplicate of-
optimal algorithm for finding unique divisors
I came across this problem. I am not able to find an optimal algorithm.
The problem is :
Given a list L of natural numbers(number can be really large) and a number N, what's the optimal algorithm to determine the number of divisors of N which doesn't not divide any of the numbers present in the list L. Numbers in the list can be repetitive ie, one number can occur more than once.
Observation:
Divisors of some divisor d of N are also divisors of N.
MY approach was :
Find the divisors of N.
Sort L in reverse order(largest element being 1st element).
foreach divisor d of N, I check whether it divides any element in the list or not.(stop when you come to check for an element less than d in the list, as the list is sorted)
If d divides some number in the list L, then I don't check for any divisor of d, that is, I skip this checking.
Ultimately, left divisors which were neither divided any number in the list nor skipped are counted. This count is the final answer.
But this algorithm is not optimal for this problem.
Any ideas for a better algorithm?

What you need to look into is : co-primes (or relatively primes)
In number theory, a branch of mathematics, two integers a and b are
said to be coprime (also spelled co-prime) or relatively prime if the
only positive integer that evenly divides both of them is 1.
So to "transcode" your problem :
You basically want to find the Number of coprimes of N from the L list.
When a and b are co-primes?
If two numbers are relatively prime then their greatest common divisor (GCD)
is 1
Example code (for GCD) in PHP :
<?php
$gcd = gmp_gcd("12", "21");
echo gmp_strval($gcd) . "\n";
?>
Simply put :
$count = 0
Foreach element e in list L : calculate the GCD(e,N)
Is their GCD=1? If yes, they are coprime (so N and e have no common divisors). Count it up. $count++
And that's all there is to it.

First, factorize n and represent it in the following way: p1:k1, p2:k2,..., pm:km such that p1,p2,... are all primes and n=p1^k1 * p2^k2 ....
Now, iterate over r1, r2, r3,..., rm such that r1<=k1, r2<=k2, ..., rm<=km and check if p1^r1*p2^r2...*pm^rm divides any number in L. If not increment count by 1.
Optimization: Pick a value for r1. See if p1^r1 divides any number in L. If yes, then pick a number for r2 and so on. If p1^r1 does not divide any number in L, then increment count by (k2+1)(k3+1)..*(km+1).
Example N=72, L=[4, 5, 9, 12, 15, 20]:
Writing N as a primal product: 2:3, 3:2 (2^3*3*2 = 72).
p1=2, p2=3, k1=3, k2=2
count=0
r1=0:
r2=0:
Divides 4
r1=0:
r2=1:
Divides 9
r1=0:
r2=2:
Divides 9
r1=1:
r2=0:
Divides 4
r1=1:
r2=1:
Divides 12
r1=1:
r2=2:
L not divisible by 18. Count+=1 = 1
r1=2:
r2=0:
Divides 4
r1=2:
r2=1:
Divides 12
r1=2:
r2=2:
L not divisible by 36. Count+=1 = 2
r1=3:
r2=0:
L not divisible by 8. Count+=(k2+1) +=(2+1) = 5

<?php
class Divisors {
public $factor = array();
public function __construct($num) {
$this->num = $num;
}
// count number of divisors of a number
public function countDivisors() {
if ($this->num == 1) return 1;
$this->_primefactors();
$array_primes = array_count_values($this->factor);
$divisors = 1;
foreach($array_primes as $power) {
$divisors *= ++$power;
}
return $divisors;
}
// prime factors decomposer
private function _primefactors() {
$this->factor = array();
$run = true;
while($run && #$this->factor[0] != $this->num) {
$run = $this->_getFactors();
}
}
// get all factors of the number
private function _getFactors() {
if($this->num == 1) {
return ;
}
$root = ceil(sqrt($this->num)) + 1;
$i = 2;
while($i <= $root) {
if($this->num % $i == 0) {
$this->factor[] = $i;
$this->num = $this->num / $i;
return true;
}
$i++;
}
$this->factor[] = $this->num;
return false;
}
} // our class ends here
$example = new Divisors(4567893421);
print $example->countDivisors();
?>

Related

Find all combinations of x numbers where they sum to Y

I'm trying to write this solution in PHP.
Inputs:
number of numbers = x
smallest number = 1
sum of numbers = y
I'm not dealing with very large numbers, largest x is approximatly 50, largest y is approximatly 80.
rules: Within each set of numbers, the number proceeding the previous must be equal to or greater.
For example
x = 3
min = 1
y = 6
solution:
(1,1,4),(1,2,3)
note that (3,2,1) isn't a solution as they are in descending order.
This is easily solved via recursion. The time complexity though will be high. For a better (but slightly more complex solution) use dynamic programming.
Here's the idea:
If the size of the set is 1 then the only possible solution is the desired sum.
If the set is larger than one then you can merge a number X between the minimum and the desired sum with a set of numbers which add up to the desired sum minus X.
function tuplesThatSumUpTo($desiredSum, $minimumNumber, $setSize) {
$tuples = [];
if ($setSize <= 1) {
return [ [ $desiredSum ] ]; //A set of sets of size 1 e.g. a set of the desired sum
}
for ($i = $minimumNumber;$i < $desiredSum;$i++) {
$partial = tuplesThatSumUpTo($desiredSum-$i, $minimumNumber,$setSize-1);
$tuples = array_merge($tuples, array_map(function ($tuple) use ($i) {
$res = array_merge([$i], $tuple);
sort($res);
return $res;
},$partial));
}
return array_unique($tuples,SORT_REGULAR);
}
See it run:
http://sandbox.onlinephpfunctions.com/code/1b0e507f8c2fcf06f4598005bf87ee98ad2505b3
The dynamic programming approach would have you instead hold an array of sets with partial sums and refer back to it to fill in what you need later on.

Arrangement : how many unique possibilities for x elements among n

I'm not that good at math, so I'm stuck here.
I need to get the total number of possible arrangement (I think, or permutations maybe?) of X elements amongst N.
I want to pick X distinct elements amongst N (N>=X)
order DOES matter
each element can not come more than once in a combination
=> For exemple, given $N = count(1,2,3,4,5,6,7,8,9), a valid combination of $X=6 elements could be :
- 1,4,5,3,2,8
- 4,2,1,9,7,3
What formula do I need to use in PHP to get the total number of possibilities?
There are N choices for the first element, N-1 for the second (as you have already chosen 1) then N-2 choices for the third and so on. You can express using factorials this a N! / (N-X-1)!. See https://en.wikipedia.org/wiki/Permutations
Ok, I think I got it.
$set = array(A,B,C,D,E,F,G);
$n = count($set);
$k = 6;
if($n>0)
{
if($k < $n)
{
$outcomes = gmp_fact($n) / gmp_fact($n-$k);
}
else
{
$outcomes = gmp_fact($n);
}
} else { $outcomes = 0; }
where gmp_fact($n) is the php function for $n! (n factorial), which means N x (N-1) x ... x 1

Generate a random number from a given set of numbers and chances

I have a list of numbers like
$list = array(1,5,19,23,59,51,24)
in actual code this is generated from database, so this array will hold up to 500 numbers that are different from each other.
each of these numbers in the database has a probability of occurring recorded. So i have a data from previous executions to generate random numbers from 1 to 500 and recorded the probabilities of each number generated for like 1000 times.
Now having list of numbers and probabilities for each number i want to write a function that will generate a random number from these 500 numbers based on their probabilities.
For example:
number 1 has a chance of: 0.00123 //0.123%
number 6 has a chance of: 0.0421 //4.21%
number 11 has a chance of: 0.0133 //1.33%
so variable $finallist will look something like this:
$finallist[1] = 0.00123;
$finallist[6] = 0.0421;
$finallist[11] = 0.0133;
Now if i run my function and pass in $finallist as a parameter i want to retrieve a random number between 1 and 6 but number 6 will have higher possibility of coming out than 1 and 11 will have higher possibility to come out than 1.
I have some functions written that deal with returning the random number based on its chance but it only takes 1 value as a parameter.
private function randomWithProbability($chance, $num, $range = false)
{
/* first generate a number 0 and 1 and see if that number is in the range of chance */
$rand = $this->getRandomFloatValue(0, 1);
if ($rand <= $chance)
{
/* the number should be returned */
return $num;
}
else
{
/* otherwise return a random number */
if ($range !== false)
{
/* make sure that this number is not same as the number for which we specified the chance */
$rand = mt_rand(1, $range);
while ($rand == $num)
{
$rand = mt_rand(1, $range);
}
return $rand;
}
}
}
if anyone knows a solution/algorithm to do this or if there is anything built in to PHP would be a big help. Thank you so much.
The basic algorithm you're looking for:
add all the probabilities together and determine the maximum
pick a random number between 0 and 1 and multiply it by the max
find the entry that corresponds with that value
Example code:
<?php
// create some weighted sample data (id => weight)
$samples = array(
'a' => 0.001,
'b' => 0.004,
'c' => 0.006,
'd' => 0.05,
'e' => 0.01,
'f' => 0.015,
'g' => 0.1
);
class Accumulator {
function __construct($samples) {
// accumulate all samples into a cumulative amount (a running total)
$this->acc = array();
$this->ids = array();
$this->max = 0;
foreach($samples as $k=>$v) {
$this->max += $v;
array_push($this->acc, $this->max);
array_push($this->ids, $k);
}
}
function pick() {
// selects a random number between 0 and 1, increasing the multiple here increases the granularity
// and randomness; it should probably at least match the precision of the sample data (in this case 3 decimal digits)
$random = mt_rand(0,1000)/1000 * $this->max;
for($i=0; $i < count($this->acc); $i++) {
// looks through the values until we find our random number, this is our seletion
if( $this->acc[$i] >= $random ) {
return $this->ids[$i];
}
}
throw new Exception('this is mathematically impossible?');
}
private $max; // the highest accumulated number
private $acc; // the accumulated totals for random selection
private $ids; // a list of the associated ids
}
$acc = new Accumulator($samples);
// create a results object to test our random generator
$results = array_fill_keys(array_keys($samples), 0);
// now select some data and test the results
print "picking 10000 random numbers...\n";
for($i=0; $i < 10000; $i++) {
$results[ $acc->pick() ]++;
}
// now show what we found out
foreach($results as $k=>$v) {
print "$k picked $v times\n";
}
The results:
> php.exe rand.php
picking 10000 random numbers...
a picked 52 times
b picked 198 times
c picked 378 times
d picked 2655 times
e picked 543 times
f picked 761 times
g picked 5413 times
Running the same code with this sample:
// samples with even weight
$samples = array(
'a' => 0.1,
'b' => 0.1,
'c' => 0.1,
'd' => 0.1
);
Produces these results:
> php.exe rand.php
picking 10000 random numbers...
a picked 2520 times
b picked 2585 times
c picked 2511 times
d picked 2384 times

Get result based on probability distribution

In a browser game we have items that occur based on their probabilities.
P(i1) = 0.8
P(i2) = 0.45
P(i3) = 0.33
P(i4) = 0.01
How do we implement a function in php that returns a random item based on its probability chance?
edit
The items have a property called rarity which varies from 1 to 100 and represents the probability to occcur. The item that occurs is chosen from a set of all items of a certain type. (e.x the given example above represents all artifacts tier 1)
I don't know if its the best solution but when I had to solve this a while back this is what I found:
Function taken from this blog post:
// Given an array of values, and weights for those values (any positive int)
// it will select a value randomly as often as the given weight allows.
// for example:
// values(A, B, C, D)
// weights(30, 50, 100, 25)
// Given these values C should come out twice as often as B, and 4 times as often as D.
function weighted_random($values, $weights){
$count = count($values);
$i = 0;
$n = 0;
$num = mt_rand(0, array_sum($weights));
while($i < $count){
$n += $weights[$i];
if($n >= $num){
break;
}
$i++;
}
return $values[$i];
}
Example call:
$values = array('A','B','C');
$weights = array(1,50,100);
$weighted_value = weighted_random($values, $weights);
It's somewhat unwieldy as obviously the values and weights need to be supplied separately but this could probably be refactored to suit your needs.
Tried to understand how Bulk's function works, and here is how I understand based on Benjamin Kloster answer:
https://softwareengineering.stackexchange.com/questions/150616/return-random-list-item-by-its-weight
Generate a random number n in the range of 0 to sum(weights), in this case $num so lets say from this: weights(30, 50, 100, 25).
Sum is 205.
Now $num has to be 0-30 to get A,
30-80 to get B
80-180 to get C
and 180-205 to get D
While loop finds in which interval the $num falls.

Finding n-th permutation without computing others

Given an array of N elements representing the permutation atoms, is there an algorithm like that:
function getNthPermutation( $atoms, $permutation_index, $size )
where $atoms is the array of elements, $permutation_index is the index of the permutation and $size is the size of the permutation.
For instance:
$atoms = array( 'A', 'B', 'C' );
// getting third permutation of 2 elements
$perm = getNthPermutation( $atoms, 3, 2 );
echo implode( ', ', $perm )."\n";
Would print:
B, A
Without computing every permutation until $permutation_index ?
I heard something about factoradic permutations, but every implementation i've found gives as result a permutation with the same size of V, which is not my case.
Thanks.
As stated by RickyBobby, when considering the lexicographical order of permutations, you should use the factorial decomposition at your advantage.
From a practical point of view, this is how I see it:
Perform a sort of Euclidian division, except you do it with factorial numbers, starting with (n-1)!, (n-2)!, and so on.
Keep the quotients in an array. The i-th quotient should be a number between 0 and n-i-1 inclusive, where i goes from 0 to n-1.
This array is your permutation. The problem is that each quotient does not care for previous values, so you need to adjust them. More explicitly, you need to increment every value as many times as there are previous values that are lower or equal.
The following C code should give you an idea of how this works (n is the number of entries, and i is the index of the permutation):
/**
* #param n The number of entries
* #param i The index of the permutation
*/
void ithPermutation(const int n, int i)
{
int j, k = 0;
int *fact = (int *)calloc(n, sizeof(int));
int *perm = (int *)calloc(n, sizeof(int));
// compute factorial numbers
fact[k] = 1;
while (++k < n)
fact[k] = fact[k - 1] * k;
// compute factorial code
for (k = 0; k < n; ++k)
{
perm[k] = i / fact[n - 1 - k];
i = i % fact[n - 1 - k];
}
// readjust values to obtain the permutation
// start from the end and check if preceding values are lower
for (k = n - 1; k > 0; --k)
for (j = k - 1; j >= 0; --j)
if (perm[j] <= perm[k])
perm[k]++;
// print permutation
for (k = 0; k < n; ++k)
printf("%d ", perm[k]);
printf("\n");
free(fact);
free(perm);
}
For example, ithPermutation(10, 3628799) prints, as expected, the last permutation of ten elements:
9 8 7 6 5 4 3 2 1 0
Here's a solution that allows to select the size of the permutation. For example, apart from being able to generate all permutations of 10 elements, it can generate permutations of pairs among 10 elements. Also it permutes lists of arbitrary objects, not just integers.
function nth_permutation($atoms, $index, $size) {
for ($i = 0; $i < $size; $i++) {
$item = $index % count($atoms);
$index = floor($index / count($atoms));
$result[] = $atoms[$item];
array_splice($atoms, $item, 1);
}
return $result;
}
Usage example:
for ($i = 0; $i < 6; $i++) {
print_r(nth_permutation(['A', 'B', 'C'], $i, 2));
}
// => AB, BA, CA, AC, BC, CB
How does it work?
There's a very interesting idea behind it. Let's take the list A, B, C, D. We can construct a permutation by drawing elements from it like from a deck of cards. Initially we can draw one of the four elements. Then one of the three remaining elements, and so on, until finally we have nothing left.
Here is one possible sequence of choices. Starting from the top we're taking the third path, then the first, the the second, and finally the first. And that's our permutation #13.
Think about how, given this sequence of choices, you would get to the number thirteen algorithmically. Then reverse your algorithm, and that's how you can reconstruct the sequence from an integer.
Let's try to find a general scheme for packing a sequence of choices into an integer without redundancy, and unpacking it back.
One interesting scheme is called decimal number system. "27" can be thought of as choosing path #2 out of 10, and then choosing path #7 out of 10.
But each digit can only encode choices from 10 alternatives. Other systems that have a fixed radix, like binary and hexadecimal, also can only encode sequences of choices from a fixed number of alternatives. We want a system with a variable radix, kind of like time units, "14:05:29" is hour 14 from 24, minute 5 from 60, second 29 from 60.
What if we take generic number-to-string and string-to-number functions, and fool them into using mixed radixes? Instead of taking a single radix, like parseInt('beef', 16) and (48879).toString(16), they will take one radix per each digit.
function pack(digits, radixes) {
var n = 0;
for (var i = 0; i < digits.length; i++) {
n = n * radixes[i] + digits[i];
}
return n;
}
function unpack(n, radixes) {
var digits = [];
for (var i = radixes.length - 1; i >= 0; i--) {
digits.unshift(n % radixes[i]);
n = Math.floor(n / radixes[i]);
}
return digits;
}
Does that even work?
// Decimal system
pack([4, 2], [10, 10]); // => 42
// Binary system
pack([1, 0, 1, 0, 1, 0], [2, 2, 2, 2, 2, 2]); // => 42
// Factorial system
pack([1, 3, 0, 0, 0], [5, 4, 3, 2, 1]); // => 42
And now backwards:
unpack(42, [10, 10]); // => [4, 2]
unpack(42, [5, 4, 3, 2, 1]); // => [1, 3, 0, 0, 0]
This is so beautiful. Now let's apply this parametric number system to the problem of permutations. We'll consider length 2 permutations of A, B, C, D. What's the total number of them? Let's see: first we draw one of the 4 items, then one of the remaining 3, that's 4 * 3 = 12 ways to draw 2 items. These 12 ways can be packed into integers [0..11]. So, let's pretend we've packed them already, and try unpacking:
for (var i = 0; i < 12; i++) {
console.log(unpack(i, [4, 3]));
}
// [0, 0], [0, 1], [0, 2],
// [1, 0], [1, 1], [1, 2],
// [2, 0], [2, 1], [2, 2],
// [3, 0], [3, 1], [3, 2]
These numbers represent choices, not indexes in the original array. [0, 0] doesn't mean taking A, A, it means taking item #0 from A, B, C, D (that's A) and then item #0 from the remaining list B, C, D (that's B). And the resulting permutation is A, B.
Another example: [3, 2] means taking item #3 from A, B, C, D (that's D) and then item #2 from the remaining list A, B, C (that's C). And the resulting permutation is D, C.
This mapping is called Lehmer code. Let's map all these Lehmer codes to permutations:
AB, AC, AD, BA, BC, BD, CA, CB, CD, DA, DB, DC
That's exactly what we need. But if you look at the unpack function you'll notice that it produces digits from right to left (to reverse the actions of pack). The choice from 3 gets unpacked before the choice from 4. That's unfortunate, because we want to choose from 4 elements before choosing from 3. Without being able to do so we have to compute the Lehmer code first, accumulate it into a temporary array, and then apply it to the array of items to compute the actual permutation.
But if we don't care about the lexicographic order, we can pretend that we want to choose from 3 elements before choosing from 4. Then the choice from 4 will come out from unpack first. In other words, we'll use unpack(n, [3, 4]) instead of unpack(n, [4, 3]). This trick allows to compute the next digit of Lehmer code and immediately apply it to the list. And that's exactly how nth_permutation() works.
One last thing I want to mention is that unpack(i, [4, 3]) is closely related to the factorial number system. Look at that first tree again, if we want permutations of length 2 without duplicates, we can just skip every second permutation index. That'll give us 12 permutations of length 4, which can be trimmed to length 2.
for (var i = 0; i < 12; i++) {
var lehmer = unpack(i * 2, [4, 3, 2, 1]); // Factorial number system
console.log(lehmer.slice(0, 2));
}
It depends on the way you "sort" your permutations (lexicographic order for example).
One way to do it is the factorial number system, it gives you a bijection between [0 , n!] and all the permutations.
Then for any number i in [0,n!] you can compute the ith permutation without computing the others.
This factorial writing is based on the fact that any number between [ 0 and n!] can be written as :
SUM( ai.(i!) for i in range [0,n-1]) where ai <i
(it's pretty similar to base decomposition)
for more information on this decomposition, have a look at this thread : https://math.stackexchange.com/questions/53262/factorial-decomposition-of-integers
hope it helps
As stated on this wikipedia article this approach is equivalent to computing the lehmer code :
An obvious way to generate permutations of n is to generate values for
the Lehmer code (possibly using the factorial number system
representation of integers up to n!), and convert those into the
corresponding permutations. However the latter step, while
straightforward, is hard to implement efficiently, because it requires
n operations each of selection from a sequence and deletion from it,
at an arbitrary position; of the obvious representations of the
sequence as an array or a linked list, both require (for different
reasons) about n2/4 operations to perform the conversion. With n
likely to be rather small (especially if generation of all
permutations is needed) that is not too much of a problem, but it
turns out that both for random and for systematic generation there are
simple alternatives that do considerably better. For this reason it
does not seem useful, although certainly possible, to employ a special
data structure that would allow performing the conversion from Lehmer
code to permutation in O(n log n) time.
So the best you can do for a set of n element is O(n ln(n)) with an adapted data structure.
Here's an algorithm to convert between permutations and ranks in linear time. However, the ranking it uses is not lexicographic. It's weird, but consistent. I'm going to give two functions, one that converts from a rank to a permutation, and one that does the inverse.
First, to unrank (go from rank to permutation)
Initialize:
n = length(permutation)
r = desired rank
p = identity permutation of n elements [0, 1, ..., n]
unrank(n, r, p)
if n > 0 then
swap(p[n-1], p[r mod n])
unrank(n-1, floor(r/n), p)
fi
end
Next, to rank:
Initialize:
p = input permutation
q = inverse input permutation (in linear time, q[p[i]] = i for 0 <= i < n)
n = length(p)
rank(n, p, q)
if n=1 then return 0 fi
s = p[n-1]
swap(p[n-1], p[q[n-1]])
swap(q[s], q[n-1])
return s + n * rank(n-1, p, q)
end
The running time of both of these is O(n).
There's a nice, readable paper explaining why this works: Ranking & Unranking Permutations in Linear Time, by Myrvold & Ruskey, Information Processing Letters Volume 79, Issue 6, 30 September 2001, Pages 281–284.
http://webhome.cs.uvic.ca/~ruskey/Publications/RankPerm/MyrvoldRuskey.pdf
Here is a short and very fast (linear in the number of elements) solution in python, working for any list of elements (the 13 first letters in the example below) :
from math import factorial
def nthPerm(n,elems):#with n from 0
if(len(elems) == 1):
return elems[0]
sizeGroup = factorial(len(elems)-1)
q,r = divmod(n,sizeGroup)
v = elems[q]
elems.remove(v)
return v + ", " + ithPerm(r,elems)
Examples :
letters = ['a','b','c','d','e','f','g','h','i','j','k','l','m']
ithPerm(0,letters[:]) #--> a, b, c, d, e, f, g, h, i, j, k, l, m
ithPerm(4,letters[:]) #--> a, b, c, d, e, f, g, h, i, j, m, k, l
ithPerm(3587542868,letters[:]) #--> h, f, l, i, c, k, a, e, g, m, d, b, j
Note: I give letters[:] (a copy of letters) and not letters because the function modifies its parameter elems (removes chosen element)
The following code computes the kth permutation for given n.
i.e n=3.
The various permutations are
123
132
213
231
312
321
If k=5, return 312.
In other words, it gives the kth lexicographical permutation.
public static String getPermutation(int n, int k) {
char temp[] = IntStream.range(1, n + 1).mapToObj(i -> "" + i).collect(Collectors.joining()).toCharArray();
return getPermutationUTIL(temp, k, 0);
}
private static String getPermutationUTIL(char temp[], int k, int start) {
if (k == 1)
return new String(temp);
int p = factorial(temp.length - start - 1);
int q = (int) Math.floor(k / p);
if (k % p == 0)
q = q - 1;
if (p <= k) {
char a = temp[start + q];
for (int j = start + q; j > start; j--)
temp[j] = temp[j - 1];
temp[start] = a;
}
return k - p >= 0 ? getPermutationUTIL(temp, k - (q * p), start + 1) : getPermutationUTIL(temp, k, start + 1);
}
private static void swap(char[] arr, int j, int i) {
char temp = arr[i];
arr[i] = arr[j];
arr[j] = temp;
}
private static int factorial(int n) {
return n == 0 ? 1 : (n * factorial(n - 1));
}
It is calculable. This is a C# code that does it for you.
using System;
using System.Collections.Generic;
namespace WpfPermutations
{
public class PermutationOuelletLexico3<T>
{
// ************************************************************************
private T[] _sortedValues;
private bool[] _valueUsed;
public readonly long MaxIndex; // long to support 20! or less
// ************************************************************************
public PermutationOuelletLexico3(T[] sortedValues)
{
if (sortedValues.Length <= 0)
{
throw new ArgumentException("sortedValues.Lenght should be greater than 0");
}
_sortedValues = sortedValues;
Result = new T[_sortedValues.Length];
_valueUsed = new bool[_sortedValues.Length];
MaxIndex = Factorial.GetFactorial(_sortedValues.Length);
}
// ************************************************************************
public T[] Result { get; private set; }
// ************************************************************************
/// <summary>
/// Return the permutation relative to the index received, according to
/// _sortedValues.
/// Sort Index is 0 based and should be less than MaxIndex. Otherwise you get an exception.
/// </summary>
/// <param name="sortIndex"></param>
/// <param name="result">Value is not used as inpu, only as output. Re-use buffer in order to save memory</param>
/// <returns></returns>
public void GetValuesForIndex(long sortIndex)
{
int size = _sortedValues.Length;
if (sortIndex < 0)
{
throw new ArgumentException("sortIndex should be greater or equal to 0.");
}
if (sortIndex >= MaxIndex)
{
throw new ArgumentException("sortIndex should be less than factorial(the lenght of items)");
}
for (int n = 0; n < _valueUsed.Length; n++)
{
_valueUsed[n] = false;
}
long factorielLower = MaxIndex;
for (int index = 0; index < size; index++)
{
long factorielBigger = factorielLower;
factorielLower = Factorial.GetFactorial(size - index - 1); // factorielBigger / inverseIndex;
int resultItemIndex = (int)(sortIndex % factorielBigger / factorielLower);
int correctedResultItemIndex = 0;
for(;;)
{
if (! _valueUsed[correctedResultItemIndex])
{
resultItemIndex--;
if (resultItemIndex < 0)
{
break;
}
}
correctedResultItemIndex++;
}
Result[index] = _sortedValues[correctedResultItemIndex];
_valueUsed[correctedResultItemIndex] = true;
}
}
// ************************************************************************
/// <summary>
/// Calc the index, relative to _sortedValues, of the permutation received
/// as argument. Returned index is 0 based.
/// </summary>
/// <param name="values"></param>
/// <returns></returns>
public long GetIndexOfValues(T[] values)
{
int size = _sortedValues.Length;
long valuesIndex = 0;
List<T> valuesLeft = new List<T>(_sortedValues);
for (int index = 0; index < size; index++)
{
long indexFactorial = Factorial.GetFactorial(size - 1 - index);
T value = values[index];
int indexCorrected = valuesLeft.IndexOf(value);
valuesIndex = valuesIndex + (indexCorrected * indexFactorial);
valuesLeft.Remove(value);
}
return valuesIndex;
}
// ************************************************************************
}
}
If you store all the permutations in memory, for example in an array, you should be able to bring them back out one at a time in O(1) time.
This does mean you have to store all the permutations, so if computing all permutations takes a prohibitively long time, or storing them takes a prohibitively large space then this may not be a solution.
My suggestion would be to try it anyway, and come back if it is too big/slow - there's no point looking for a "clever" solution if a naive one will do the job.

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