TL;DR

On with TASK #2 from The Weekly Challenge #237. Enjoy!

# The challenge

You are given an array of integers.

Write a script to permute the give array such that you get the maximum possible greatness.

To determine greatness, nums[i] < perm[i] where 0 <= i < nums.length

Example 1

Input: @nums = (1, 3, 5, 2, 1, 3, 1)
Output: 4

One possible permutation: (2, 5, 1, 3, 3, 1, 1) which returns 4 greatness as below:
nums < perm
nums < perm
nums < perm
nums < perm


Example 2

Input: @ints = (1, 2, 3, 4)
Output: 3

One possible permutation: (2, 3, 4, 1) which returns 3 greatness as below:
nums < perm
nums < perm
nums < perm


# The questions

My first question is… what is the question? I mean, not my question, but the challenge’s question. It seems we’re asked for a permutation, but examples seem to indicate that we need to calculate the greatness.

# The solution

A permutation will generally have “great” positions (i.e. positions that contribute to greatness) and other “not-great” positions. In each great position, the corresponding number from the original input is strictly lower.

If we rearrange all such correspondent pairs in the respective permutations, nothing changes regarding the greatness, so it’s an invariant with respect to pre-arranging the input permutation in some way. For this reason, it will be useful to think the initial input permutation as sorted in descending order, e.g. the first example would become:

5 3 3 2 1 1 1


We are also optimistic folks, so we start big and assume that every position can be a great one!

greatness = 7
5 3 3 2 1 1 1


At this point we can observe that the first position can never be great: it holds the maximum value, so by definition there’s nothing inside the permutation that can be greater than it. One point lost to greatness. Anyway, we put this value in a pool of values that we can later use to fill in great positions.

greatness = 6
5 3 3 2 1 1 1
-
P


Where should we put this value, though? It can actually fill any position past its own, because it’s the maximum and there’s only one of it. We can observe, though, that it makes sense to allocate it to the value immediately below it, because if we e.g. “waste” it on a 2 or a 1 we might miss an opportunity. As a sub-example, consider all different values, like:

4 3 2 1


It only makes sense to allocate the 4 to a 3, because otherwise we will just “lose” the 3 and get a greatness score of 2 instead of 3.

Back to the original example and algorithm explanation, then, we use the pool as soon as it makes sense, i.e. the first 3 that we find. We fit the 5 in second position and remove it from the pool, because we can (and must) use it exactly once:

greatness = 6
5 3 3 2 1 1 1
- 5
x


As we pass, we collect this first 3 in the pool for possible great positions further down the road:

greatness = 6
5 3 3 2 1 1 1
- 5
x P


Now we move on to the following 3, and we find that there’s nothing in the pool to make this a great position. Hence we keep our pool as it is and declare this a miss, lowering the greatness value by 1 again. We also collect this 3 in the pool, by the way.

greatness = 5
5 3 3 2 1 1 1
- 5 -
x P P


Moving on, we find the 2, which can be made a great position thanks to the 3 that we have in the pool. Taking also into account that we collect the 2 in the pool, the step becomes:

greatness = 5
5 3 3 2 1 1 1
- 5 - 3
x x P P


We can move on with the same algorithm:

greatness = 5
5 3 3 2 1 1 1
- 5 - 3 3
x x x P P

greatness = 5
5 3 3 2 1 1 1
- 5 - 3 3 2
x x x x P P


The last position cannot be great, because we only have 1 in the pool, so we end up with:

greatness = 4
5 3 3 2 1 1 1
- 5 - 3 3 2 -
x x x x P P P


At this point we have found our greatness value. To find one permutation, we can just use what’s left in the pool inside the positions that we skipped so far:

greatness = 4
5 3 3 2 1 1 1
1 5 1 3 3 2 1


This approach guarantees us that we will always find both the maximum greatness and a corresponding permutation that has that value of greatness.

We can observe that we can establish the greatness at the end of the first sweep, by subtracting the size of the pool from the size of the input data. This is what happens in this Perl implementation:

#!/usr/bin/env perl
use v5.24;
use warnings;
use experimental 'signatures';
use JSON::PP;

my ($greatness,$permutation) = great_permutation(@ARGV);
say $greatness, ' -> ', JSON::PP->new->encode($permutation);

sub great_permutation (@inputs) {
my @sorted_indexes = sort { $inputs[$b] <=> $inputs[$a] } 0 .. $#inputs; my @permutation = (undef) x @inputs; # first pass - set greatness! my @pool; my @not_great; for my$index (@sorted_indexes) {
my $value =$inputs[$index]; if (@pool &&$pool > $value) {$permutation[$index] = shift @pool; } else { push @not_great,$index;
}
push @pool, $value; } my$greatness = scalar(@inputs) - scalar(@pool);

# second pass - fill the rest
@permutation[@not_great] = @pool;

return ($greatness, \@permutation); }  The code above contains some initial index-calculation to make it possible to find a permutation for the actual input, not the one sorted descendingly that we used in our algorithm. Basically, we operate on the sorted one, but keep the original indexes so that our allocations end up in the right place. If we just need the value of greatness (as it seems from the output of the examples), this can be further squeezed by working on whole blocks of same-valued positions. We still keep the pool, but it’s just a count at this point. In our initial example, we first turn the input: 1 3 5 2 1 3 1  into an array of pairs of counts, sorted in descending order for the value (not the count): (5 1) (3 2) (2 1) (1 3)  At this point, we can observe that we just need these values to be ordered by the original value, but we don’t need the value any more actually because we only care about “greater-than” relations, not actual values: 1 2 1 3  This means that we have 1 top value, then 2 (same) values, then 1 value, then 3 (same) values, all values being sorted descendingly. The pool starts from 0; at each count, in order, what’s in the pool goes to “cover” that count as much as possible, with two possibilies: • the pool value is more than, or equal to, the count. This means that the greatness is not impacted, because all corresponding slots can be made great; in this case we “consume” the exact amount of count from the pool, while at the same time also gaining all of them for the next slot, so actually nothing changes! • the pool value is less than the count. This means that we will lose the difference in greatness, exhausting the current pool and resetting it with the count of the current slot (for the following slots). All of this… in Perl: sub greatness (@inputs) { my %count_for;$count_for{$_}++ for @inputs; my @counts = @count_for{sort {$a <=> $b } keys %count_for}; my$greatness = @inputs;
my $pool = 0; for my$count (@counts) {
next if $count <=$pool; # win & accumulate the same quantity
$greatness -=$count - $pool; # not enough in pool, lose some$pool = $count; # restart pool from this slot } return$greatness;
}


… and, of course, in Raku:

#!/usr/bin/env raku
use v6;
sub MAIN (*@args) { put greatness(@args) }

sub greatness (@inputs) {
my %count-for;
%count-for{$_}++ for @inputs; my @counts = %count-for{ %count-for.keys.sort({$^a.Int <=> $^b.Int}) }; my$greatness = @inputs.elems;
my $pool = 0; for @counts ->$count {
next if $count <=$pool; # win and accumulate the same quantity
$greatness -=$count - $pool; # not enough in pool, lose some$pool = $count; # restart pool from this slot } return$greatness;
}


Well, I guess I bored every single one of you future me:s at this point, to the rest please stay safe and have fun!

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