in reply to Re: Average Price Algorithm
in thread Average Price Algorithm
Limbic-Region suggested I spend time comparing that algorithm with his (above) and after consideration I think there is a much better algorithm than the one I proposed above.
Limbic-Region's algorithm I'm nearly sure is provably optimal, [update: provided it is adjusted to take into account relative factorization - see below]. It insures that the largest deviations always end up in the largest baskets where they are least likely to have a significant impact. Also his algorithm uses the values with the smallest deviation from the mean first. This insures that a bucket whose size prevents an above-mean deviation from being matched with a below-mean deviation will always have the smallest possible unmatched deviation, and hence the bucket average closest to the sample mean.
On the other hand the algorithm proposed by Limbic-Region does not take full advantage of the histographic information so needs to do a binary search for each smallest deviation item, not to mention a lot of testing for best fit. This, I think can be avoided by ranking values by their distance from the mean before we begin filling the buckets.
The code for a more histographic aware version of Limbic-Region's algorithm is posted below (without correction for relative factorization). The code has been tested on Debian-Etch/Perl 5.8. For demo purposes it prints out the results of allocating 6000 items into 3 buckets for three distributions: (a) a spike with all values are at the mean (b) a symmetrical distribution, i.e. no skew and (c) a seriously skewed distribution. I've also included a demo run for the original poster's distribution.
BTW, the algorithm below is 0(N) where N=number of items to allocate to buckets. If I'm right about Limbic-Region's strategy being provably optimal, then this problem is very far from NP complete.
use strict; use warnings; sub demoAllocation($$$); demoAllocation ("Distribution: all at mean" , {a=>1000,b=>2000,c=>3000} , { '6.0' => 6000 } ); demoAllocation ("Distribution: unskewed" , {a=>1000,b=>2000,c=>3000} , { '3.0' => 300, '4.0' => 600, '5.0' => 700, '5.5' => 900 , '6.0' => 1000 , '6.5' => 900, '7.0' => 700, '8.0' => 600, '9.0' => 300 } ); demoAllocation ("Distribution: skewed" , {a=>1000,b=>2000,c=>3000} , { '3.0' => 4000, '12.0' => 2000 } ); demoAllocation ("Distribution: Original poster" , {A=>65, B=>12, C=>24, D=>19, E=>30} , {'93.8' => 5, '93.81' => 20, '93.82' => 10 , '93.83' => 15, '93.84' => 25, '93.85' => 5 , '93.87'=>20, '94.0' => 5, '94.1' => 35 , '94.2'=> 10 } ); #------------------------------------------------------------ sub demoAllocation($$$) { my ($sDescription, $hBuckets, $hFrequency) = @_; print "$sDescription\n"; my ($dAvg, $hAllocation) = allocate($hBuckets, $hFrequency); foreach my $sId (sort keys %$hAllocation) { my $hItems = $hAllocation->{$sId}; my $dSum = 0; my $iCount = 0; my ($dBucketAvg, $dDeviation); print "$sId:"; foreach my $dValue (sort keys %$hItems) { my $iFreq = $hItems->{$dValue}; printf "\t%s \@ \$%.2f\n", $iFreq, $dValue; $dSum += $dValue*$iFreq; $iCount += $iFreq; } $dBucketAvg = $dSum/$iCount; $dDeviation = $dBucketAvg - $dAvg; printf "\tbucket avg: \$%.2f, deviation: \$%.3f\n\n" , $dBucketAvg, $dDeviation; } print "\n"; } #------------------------------------------------------------ sub allocate($$) { my ($hBuckets, $hFrequency) = @_; #calculate deviations from the mean my $dAvg=calcWeightedAvg($hFrequency); my ($iFreqAvg, $aAbove, $aBelow) = calcDeviations($hFrequency, $dAvg); #sort buckets by size: smallest first my @aBuckets = sort { $hBuckets->{$a} <=> $hBuckets->{$b} } keys %$hBuckets; #allocate items to buckets, smallest first my %hAllocations; my $iFirstAbove = 0; my $iFirstBelow = 0; foreach my $sId (@aBuckets) { my $iSize = $hBuckets->{$sId}; $hAllocations{$sId} = fillBucket($iSize, $dAvg, \$iFreqAvg , $aAbove, \$iFirstAbove , $aBelow, \$iFirstBelow); } return ($dAvg, \%hAllocations); } #------------------------------------------------------------ # SUPPORTING FUNCTIONS - alphabetical order #------------------------------------------------------------ sub calcDeviations($$) { my ($hFrequency, $dAvg) = @_; my @aAbove; my @aBelow; my $iFreqAvg = 0; #calculate deviations from mean while (my ($dValue,$iFreq) = each(%$hFrequency)) { if ($dValue == $dAvg) { $iFreqAvg+=$iFreq; next; } my $dDeviation = $dValue - $dAvg; if (0 < $dDeviation) { push @aAbove, [ $dDeviation, $dValue, $iFreq ]; } else { push @aBelow, [ -$dDeviation, $dValue, $iFreq ]; } } #sort with smallest deviations first return ( $iFreqAvg , [ sort { compareDeviations($a,$b) } @aAbove ] , [ sort { compareDeviations($a,$b) } @aBelow ] ); } #------------------------------------------------------------ sub compareDeviations($$) { my ($x, $y) = @_; return $x->[0] <=> $y->[0]; } #------------------------------------------------------------ sub calcWeightedAvg($) { my $hFrequency = shift @_; my $dSum=0; my $iCount=0; while (my ($dValue,$iFreq) = each(%$hFrequency)) { $dSum+=$dValue*$iFreq; $iCount+=$iFreq; } return $dSum/$iCount; } #------------------------------------------------------------ sub fillBucket($$$$$$$) { my ($iNeeded, $dAvg, $rFreqAvg , $aAbove, $rFirstAbove , $aBelow, $rFirstBelow) = @_; #take items that are at the mean, if we can if ($iNeeded <= $$rFreqAvg) { $$rFreqAvg-=$iNeeded; return { $dAvg => $iNeeded }; } my $hItems = {}; my $aUp = $aAbove->[$$rFirstAbove]; my $aDown = $aBelow->[$$rFirstBelow]; my $dNetDeviation = 0; if (0 < $$rFreqAvg) { $iNeeded -= $$rFreqAvg; $hItems->{$dAvg} = $$rFreqAvg; $$rFreqAvg = 0; } #take whatever creates the smallest net deviation # [0] deviation # [1] value # [2] frequency while ($iNeeded > 0) { my $bUseUp = 0; if ($aUp) { if ($aDown) { my $dNetUp = $dNetDeviation + $aUp->[0]; my $dNetDown = $dNetDeviation - $aDown->[0]; if (abs($dNetUp) < abs($dNetDown)) { $bUseUp = 1; $dNetDeviation = $dNetUp; } else { $bUseUp = 0; $dNetDeviation = $dNetDown; } } else { $bUseUp = 1; } } else { $bUseUp = 0; } if ($bUseUp) { $hItems->{$aUp->[1]} ++; $aUp->[2]--; $$rFirstAbove++ unless $aUp->[2]; $aUp = $aAbove->[$$rFirstAbove]; } else { $hItems->{$aDown->[1]} ++; $aDown->[2]--; $$rFirstBelow++ unless $aDown->[2]; $aDown = $aBelow->[$$rFirstBelow]; } $iNeeded--; } return $hItems; }
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Re^3: Average Price Algorithm
by GrandFather (Saint) on Feb 01, 2009 at 01:21 UTC | |
by ELISHEVA (Prior) on Feb 01, 2009 at 07:45 UTC | |
Re^3: Average Price Algorithm
by Limbic~Region (Chancellor) on Feb 02, 2009 at 19:11 UTC | |
by ELISHEVA (Prior) on Feb 02, 2009 at 20:05 UTC | |
by Limbic~Region (Chancellor) on Feb 02, 2009 at 20:38 UTC | |
by ELISHEVA (Prior) on Feb 02, 2009 at 21:31 UTC | |
by Limbic~Region (Chancellor) on Feb 03, 2009 at 13:32 UTC | |
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