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I have a set of items. I need to randomly pick one. The problem is that they each have a weight of 1-10. A weight of 2 means that the item is twice as likely to be picked than a weight of 1. A weight of 3 is three times as likely.
I currently fill an array with each item. If the weight is 3, I put three copies of the item in the array. Then, I pick a random item.
My method is fast, but uses a lot of memory. I am trying to think of a faster method, but nothing comes to mind. Anyone have a trick for this problem?
EDIT: My Code...
Apparently, I wasn't clear. I do not want to use (or improve) my code. This is what I did.
//Given an array $a where $a[0] is an item name and $a[1] is the weight from 1 to 100.
$b = array();
foreach($a as $t)
$b = array_merge($b, array_fill(0,$t[1],$t));
$item = $b[array_rand($b)];
This required me to check every item in $a and uses max_weight/2*size of $a memory for the array. I wanted a COMPLETELY DIFFERENT algorithm.
Further, I asked this question in the middle of the night using a phone. Typing code on a phone is nearly impossible because those silly virtual keyboards simply suck. It auto-corrects everything, ruining any code I type.
An yet further, I woke up this morning with an entirely new algorithm that uses virtual no extra memory at all and does not require checking every item in the array. I posted it as an answer below.
This ones your huckleberry.
$arr = array(
array("val" => "one", "weight" => 1),
array("val" => "two", "weight" => 2),
array("val" => "three", "weight" => 3),
array("val" => "four", "weight" => 4)
);
$weight_sum = 0;
foreach($arr as $val)
{
$weight_sum += $val['weight'];
}
$r = rand(1, $weight_sum);
print "random value is $r\n";
for($i = 0; $i < count($arr); $i++)
{
if($r <= $arr[$i]['weight'])
{
print "$r <= {$arr[$i]['weight']}, this is our match\n";
print $arr[$i]['val'] . "\n";
break;
}
else
{
print "$r > {$arr[$i]['weight']}, subtracting weight\n";
$r -= $arr[$i]['weight'];
print "new \$r is $r\n";
}
}
No need to generate arrays containing an item for every weight, no need to fill an array with n elements for a weight of n. Just generate a random number between 1 and total weight, then loop through the array until you find a weight less than your random number. If it isn't less than the number, subtract that weight from the random and continue.
Sample output:
# php wr.php
random value is 8
8 > 1, subtracting weight
new $r is 7
7 > 2, subtracting weight
new $r is 5
5 > 3, subtracting weight
new $r is 2
2 <= 4, this is our match
four
This should also support fractional weights.
modified version to use array keyed by weight, rather than by item
$arr2 = array(
);
for($i = 0; $i <= 500000; $i++)
{
$weight = rand(1, 10);
$num = rand(1, 1000);
$arr2[$weight][] = $num;
}
$start = microtime(true);
$weight_sum = 0;
foreach($arr2 as $weight => $vals) {
$weight_sum += $weight * count($vals);
}
print "weighted sum is $weight_sum\n";
$r = rand(1, $weight_sum);
print "random value is $r\n";
$found = false;
$elem = null;
foreach($arr2 as $weight => $vals)
{
if($found) break;
for($j = 0; $j < count($vals); $j ++)
{
if($r < $weight)
{
$elem = $vals[$j];
$found = true;
break;
}
else
{
$r -= $weight;
}
}
}
$end = microtime(true);
print "random element is: $elem\n";
print "total time is " . ($end - $start) . "\n";
With sample output:
# php wr2.php
weighted sum is 2751550
random value is 345713
random element is: 681
total time is 0.017189025878906
measurement is hardly scientific - and fluctuates depending on where in the array the element falls (obviously) but it seems fast enough for huge datasets.
This way requires two random calculations but they should be faster and require about 1/4 of the memory but with some reduced accuracy if weights have disproportionate counts. (See Update for increased accuracy at the cost of some memory and processing)
Store a multidimensional array where each item is stored in the an array based on its weight:
$array[$weight][] = $item;
// example: Item with a weight of 5 would be $array[5][] = 'Item'
Generate a new array with the weights (1-10) appearing n times for n weight:
foreach($array as $n=>$null) {
for ($i=1;$i<=$n;$i++) {
$weights[] = $n;
}
}
The above array would be something like: [ 1, 2, 2, 3, 3, 3, 4, 4, 4, 4 ... ]
First calculation: Get a random weight from the weighted array we just created
$weight = $weights[mt_rand(0, count($weights)-1)];
Second calculation: Get a random key from that weight array
$value = $array[$weight][mt_rand(0, count($array[$weight])-1)];
Why this works: You solve the weighted issue by using the weighted array of integers we created. Then you select randomly from that weighted group.
Update: Because of the possibility of disproportionate counts of items per weight, you could add another loop and array for the counts to increase accuracy.
foreach($array as $n=>$null) {
$counts[$n] = count($array[$n]);
}
foreach($array as $n=>$null) {
// Calculate proportionate weight (number of items in this weight opposed to minimum counted weight)
$proportion = $n * ($counts[$n] / min($counts));
for ($i=1; $i<=$proportion; $i++) {
$weights[] = $n;
}
}
What this does is if you have 2000 10's and 100 1's, it'll add 200 10's (20 * 10, 20 because it has 20x the count, and 10 because it is weighted 10) instead of 10 10's to make it proportionate to how many are in there opposed the minimum weight count. So to be accurate, instead of adding one for EVERY possible key, you are just being proportionate based on the MINIMUM count of weights.
I greatly appreciate the answers above. Please consider this answer, which does not require checking every item in the original array.
// Given $a as an array of items
// where $a[0] is the item name and $a[1] is the item weight.
// It is known that weights are integers from 1 to 100.
for($i=0; $i<sizeof($a); $i++) // Safeguard described below
{
$item = $a[array_rand($a)];
if(rand(1,100)<=$item[1]) break;
}
This algorithm only requires storage for two variables ($i and $item) as $a was already created before the algorithm kicked in. It does not require a massive array of duplicate items or an array of intervals.
In a best-case scenario, this algorithm will touch one item in the original array and be done. In a worst-case scenario, it will touch n items in an array of n items (not necessarily every item in the array as some may be touched more than once).
If there was no safeguard, this could run forever. The safeguard is there to stop the algorithm if it simply never picks an item. When the safeguard is triggered, the last item touched is the one selected. However, in millions of tests using random data sets of 100,000 items with random weights of 1 to 10 (changing rand(1,100) to rand(1,10) in my code), the safeguard was never hit.
I made histograms comparing the frequency of items selected among my original algorithm, the ones from answers above, and the one in this answer. The differences in frequencies are trivial - easy to attribute to variances in the random numbers.
EDIT... It is apparent to me that my algorithm may be combined with the algorithm pala_ posted, removing the need for a safeguard.
In pala_'s algorithm, a list is required, which I call an interval list. To simplify, you begin with a random_weight that is rather high. You step down the list of items and subtract the weight of each one until your random_weight falls to zero (or less). Then, the item you ended on is your item to return. There are variations on this interval algorithm that I've tested and pala_'s is a very good one. But, I wanted to avoid making a list. I wanted to use only the given weighted list and never touch all the items. The following algorithm merges my use of random jumping with pala_'s interval list. Instead of a list, I randomly jump around the list. I am guaranteed to get to zero eventually, so no safeguard is needed.
// Given $a as the weighted array (described above)
$weight = rand(1,100); // The bigger this is, the slower the algorithm runs.
while($weight>0)
{
$item = $a[array_rand($a)];
$weight-= $item[1];
}
// $item is the random item you want.
I wish I could select both pala_ and this answer as the correct answers.
I'm not sure if this is "faster", but I think it may be more "balance"d between memory usage and speed.
The thought is to transform your current implementation (500000 items array) into an equal-length array (100000 items), with the lowest "origin" position as key, and origin index as value:
<?php
$set=[["a",3],["b",5]];
$current_implementation=["a","a","a","b","b","b","b","b"];
// 0=>0 means the lowest "position" 0
// points to 0 in the set;
// 3=>1 means the lowest "position" 3
// points to 1 in the set;
$my_implementation=[0=>0,3=>1];
And then randomly picks a number between 0 and highest "origin" position:
// 3 is the lowest position of the last element ("b")
// and 5 the weight of that last element
$my_implemention_pick=mt_rand(0,3+5-1);
Full code:
<?php
function randomPickByWeight(array $set)
{
$low=0;
$high=0;
$candidates=[];
foreach($set as $key=>$item)
{
$candidates[$high]=$key;
$high+=$item["weight"];
}
$pick=mt_rand($low,$high-1);
while(!array_key_exists($pick,$candidates))
{
$pick--;
}
return $set[$candidates[$pick]];
}
$cache=[];
for($i=0;$i<100000;$i++)
{
$cache[]=["item"=>"item {$i}","weight"=>mt_rand(1,10)];
}
$time=time();
for($i=0;$i<100;$i++)
{
print_r(randomPickByWeight($cache));
}
$time=time()-$time;
var_dump($time);
3v4l.org demo
3v4l.org have some time limitation on codes, so the demo didn't finished. On my laptop the above demo finished in 10 seconds (i7-4700 HQ)
ere is my offer in case I've understand you right. I offer you take a look and if there are some question I'll explain.
Some words in advance:
My sample is with only 3 stages of weight - to be clear
- With outer while I'm simulating your main loop - I count only to 100.
- The array must to be init with one set of initial numbers as shown in my sample.
- In every pass of main loop I get only one random value and I'm keeping the weight at all.
<?php
$array=array(
0=>array('item' => 'A', 'weight' => 1),
1=>array('item' => 'B', 'weight' => 2),
2=>array('item' => 'C', 'weight' => 3),
);
$etalon_weights=array(1,2,3);
$current_weights=array(0,0,0);
$ii=0;
while($ii<100){ // Simulates your main loop
// Randomisation cycle
if($current_weights==$etalon_weights){
$current_weights=array(0,0,0);
}
$ft=true;
while($ft){
$curindex=rand(0,(count($array)-1));
$cur=$array[$curindex];
if($current_weights[$cur['weight']-1]<$etalon_weights[$cur['weight']-1]){
echo $cur['item'];
$array[]=$cur;
$current_weights[$cur['weight']-1]++;
$ft=false;
}
}
$ii++;
}
?>
I'll use this input array for my explanation:
$values_and_weights=array(
"one"=>1,
"two"=>8,
"three"=>10,
"four"=>4,
"five"=>3,
"six"=>10
);
The simple version isn't going to work for you because your array is so large. It requires no array modification but may need to iterate the entire array, and that's a deal breaker.
/*$pick=mt_rand(1,array_sum($values_and_weights));
$x=0;
foreach($values_and_weights as $val=>$wgt){
if(($x+=$wgt)>=$pick){
echo "$val";
break;
}
}*/
For your case, re-structuring the array will offer great benefits.
The cost in memory for generating a new array will be increasingly justified as:
array size increases and
number of selections increases.
The new array requires the replacement of "weight" with a "limit" for each value by adding the previous element's weight to the current element's weight.
Then flip the array so that the limits are the array keys and the values are the array values.
The selection logic is: the selected value will have the lowest limit that is >= $pick.
// Declare new array using array_walk one-liner:
array_walk($values_and_weights,function($v,$k)use(&$limits_and_values,&$x){$limits_and_values[$x+=$v]=$k;});
//Alternative declaration method - 4-liner, foreach() loop:
/*$x=0;
foreach($values_and_weights as $val=>$wgt){
$limits_and_values[$x+=$wgt]=$val;
}*/
var_export($limits_and_values);
$limits_and_values looks like this:
array (
1 => 'one',
9 => 'two',
19 => 'three',
23 => 'four',
26 => 'five',
36 => 'six',
)
Now to generate the random $pick and select the value:
// $x (from walk/loop) is the same as writing: end($limits_and_values); $x=key($limits_and_values);
$pick=mt_rand(1,$x); // pull random integer between 1 and highest limit/key
while(!isset($limits_and_values[$pick])){++$pick;} // smallest possible loop to find key
echo $limits_and_values[$pick]; // this is your random (weighted) value
This approach is brilliant because isset() is very fast and the maximum number of isset() calls in the while loop can only be as many as the largest weight (not to be confused with limit) in the array.
FOR YOUR CASE, THIS APPROACH WILL FIND THE VALUE IN 10 ITERATIONS OR LESS!
Here is my Demo that will accept a weighted array (like $values_and_weights), then in just four lines:
Restructure the array,
Generate a random number,
Find the correct value, and
Display it.
If I wanted a random number between one and three I could do $n = mt_rand(1,3).
There is a 33% chance that $n = 1, a 33% chance it's 2, and a 33% chance that it's 3.
What if I want to make it more difficult to get a 3 than a 1?
Say I want a 50% chance that a 1 is drawn, a 30% chance that a 2 is drawn and a 20% chance that a 3 is drawn?
I need a scalable solution as the possible range will vary between 1-3 and 1-100, but in general I'd like the lower numbers to be drawn more often than the higher ones.
How can I accomplish this?
There is a simple explanation of how you can use standard uniform random variable to produce random variable with a distribution similar to the one you want:
https://math.stackexchange.com/a/241543
This is maths.
In your example the just chose a random number between 0 and 99.
Values returned between 0 to 49 - call it 1
Values returned between 50 - 69 - Call it 2
Values returned between 70 - 99 - Call it 3
Simple if statement will do this or populate an array for the distribution required
Assuming a 1 - 10 scale, you can use a simple if statement and have the numbers represent percentages. And just have each if statement set $n to a specific. Only downfall, it isn't universal.
$dummy = mt_rand(1,10);
// represents 50%
if ($dummy <= 5) {
$n = 1;
}
// represents 40%
if ($dummy >= 6 && $dummy <= 9) {
$n = 2;
} else {
// represents 10%
$n = 3;
}
I want a hint on a efficient algorithm of solving the following:
(Okay, The question was not that, I just made this for simplicity).
Example: Out of N Advert boards there should be M advert of a company in K consecutive boards. and the Adverts will be used such a way that only MINIMUM number of Adverts will be used
if N=3,K=2,M=1. The answer will be one 010;
if N=6, K=3, M=2 the answer will be 6.
011011,
011101,
011110,
101101,
101110,
110110.
I took the approach of Creating all the combinations(with binary approach) with iterative and recursive method.After that I filtered it. It is working well except if N is big it will crash.(as expected). So Is there any efficient method of solving these?
I just took another approach but it is not going to well .. :(
Second approach
if($n%$k == 0){
$perm = $m*($n/$k);
}else{
$perm = $m*ceil($n/$k);
}
Then I will do the nCr... and now I'm lost
First Approach
<?php
$n = $input1;$k = $input2;$l=$input3; $total = 0;
$flag = false;$arrays = array();$z=0;$min=$n;$x=0;
$total_permutations = pow(2,$n);
for($i=0;$i<$total_permutations;$i++){
$binary = base_convert($i, 10, 2);
$binary = str_pad($binary, $n,"0", STR_PAD_LEFT);
for($j=0;$j<=($n-$k);$j++){
$flag = false;$x=0;
for($m=0;$m<$k;$m++){
$x += intval($binary{$j+$m});
}
if($x<$l){
$flag = true;
break;
}
}
if(!$flag){
$arrays[$z]=str_split($binary);
$arrays[$z] = array_map('intval', $arrays[$z]);
$z++;
echo $binary."<br />";
}
unset($binary);
}
$min = min(array_map('array_sum',$arrays));
echo "------------<br />";
foreach($arrays as $val){
$sum = array_sum($val);
if($sum == $min){
echo implode("",$val);echo "<br>";
$total++;
}
}
return $total;
}
?>
To elaborate on my comment, here is one potential solution (Though, I would be very disappointed if there wasn't a better one)
1) First, calculate the minimum number of 1's needed in a valid solution. (I believe this is simply max(M, floor(n/k)*m + max(0, n%k-(k-m))). The formula is derived from building a string one letter at a time, placing 0's whenever possible)
2) Now, assume N > K (otherwise the answer is trivial).
We define the sub-problem to be: "Given a prefix P of length K, and a budget B of 1's to place, how many ways are there to fill out N characters whilst still enforcing the M rule?"
For example, consider that our string is "101XXXXXX", with K = 3, M = 2, and B = 4. Here, N = 6, P = "101". We solve this sub-problem like so:
a) Check if the budget is big enough in O(N) time. Return 0 if it isn't
If N=0, return 1 trivially
b) Set total possible solutions to 0
c) Set the first unknown character to 1. Compute the new prefix P'
(by stripping off the first character, and adding a "1" to the end),
the new B' = B-1, the new N' = N-1, and solve the new sub-problem:
Add its result to our total
d) Set the unknown character to 0 instead: But only if the new prefix P'
(strip, add "0") has at least M 1's. If so, set B' = B-1, N' = N-1,
and solve the new sub-problem. Add its result to our total
e) Return the total
To solve the original problem, simply consider all possible prefixes P (all nCr(K,M) of them), and sum up the solutions to the derived sub-problems. By caching the results to sub-problems based on unique inputs P, N, and B, we reduce the amount of duplicate work drastically. Amortized analysis should show that the full solution runs in O(N*nCr(K,M)) time
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 $num falls.
I am trying to create a little php script that can make my life a bit easier.
Basically, I am going to have 21 text fields on a page where I am going to input 20 different numbers. In the last field I will enter a number let's call it the TOTAL AMOUNT. All I want the script to do is to point out which numbers from the 20 fields added up will come up to TOTAL AMOUNT.
Example:
field1 = 25.23
field2 = 34.45
field3 = 56.67
field4 = 63.54
field5 = 87.54
....
field20 = 4.2
Total Amount = 81.90
Output: field1 + fields3 = 81.90
Some of the fields might have 0 as value because sometimes I only need to enter 5-15 fields and the maximum will be 20.
If someone can help me out with the php code for this, will be greatly appreciated.
If you look at oezis algorithm one drawback is immediately clear: It spends very much time summing up numbers which are already known not to work. (For example if 1 + 2 is already too big, it doesn't make any sense to try 1 + 2 + 3, 1 + 2 + 3 + 4, 1 + 2 + 3 + 4 + 5, ..., too.)
Thus I have written an improved version. It does not use bit magic, it makes everything manual. A drawback is, that it requires the input values to be sorted (use rsort). But that shouldn't be a big problem ;)
function array_sum_parts($vals, $sum){
$solutions = array();
$pos = array(0 => count($vals) - 1);
$lastPosIndex = 0;
$currentPos = $pos[0];
$currentSum = 0;
while (true) {
$currentSum += $vals[$currentPos];
if ($currentSum < $sum && $currentPos != 0) {
$pos[++$lastPosIndex] = --$currentPos;
} else {
if ($currentSum == $sum) {
$solutions[] = array_slice($pos, 0, $lastPosIndex + 1);
}
if ($lastPosIndex == 0) {
break;
}
$currentSum -= $vals[$currentPos] + $vals[1 + $currentPos = --$pos[--$lastPosIndex]];
}
}
return $solutions;
}
A modified version of oezis testing program (see end) outputs:
possibilities: 540
took: 3.0897309780121
So it took only 3.1 seconds to execute, whereas oezis code executed 65 seconds on my machine (yes, my machine is very slow). That's more than 20 times faster!
Furthermore you may notice, that my code found 540 instead of 338 possibilities. This is because I adjusted the testing program to use integers instead of floats. Direct floating point comparison is rarely the right thing to do, this is a great example why: You sometimes get 59.959999999999 instead of 59.96 and thus the match will not be counted. So, if I run oezis code with integers it finds 540 possibilities, too ;)
Testing program:
// Inputs
$n = array();
$n[0] = 6.56;
$n[1] = 8.99;
$n[2] = 1.45;
$n[3] = 4.83;
$n[4] = 8.16;
$n[5] = 2.53;
$n[6] = 0.28;
$n[7] = 9.37;
$n[8] = 0.34;
$n[9] = 5.82;
$n[10] = 8.24;
$n[11] = 4.35;
$n[12] = 9.67;
$n[13] = 1.69;
$n[14] = 5.64;
$n[15] = 0.27;
$n[16] = 2.73;
$n[17] = 1.63;
$n[18] = 4.07;
$n[19] = 9.04;
$n[20] = 6.32;
// Convert to Integers
foreach ($n as &$num) {
$num *= 100;
}
$sum = 57.96 * 100;
// Sort from High to Low
rsort($n);
// Measure time
$start = microtime(true);
echo 'possibilities: ', count($result = array_sum_parts($n, $sum)), '<br />';
echo 'took: ', microtime(true) - $start;
// Check that the result is correct
foreach ($result as $element) {
$s = 0;
foreach ($element as $i) {
$s += $n[$i];
}
if ($s != $sum) echo '<br />FAIL!';
}
var_dump($result);
sorry for adding a new answer, but this is a complete new solution to solve all problems of life, universe and everything...:
function array_sum_parts($n,$t,$all=false){
$count_n = count($n); // how much fields are in that array?
$count = pow(2,$count_n); // we need to do 2^fields calculations to test all possibilities
# now i want to look at every number from 1 to $count, where the number is representing
# the array and add up all array-elements which are at positions where my actual number
# has a 1-bit
# EXAMPLE:
# $i = 1 in binary mode 1 = 01 i'll use ony the first array-element
# $i = 10 in binary mode 10 = 1010 ill use the secont and the fourth array-element
# and so on... the number of 1-bits is the amount of numbers used in that try
for($i=1;$i<=$count;$i++){ // start calculating all possibilities
$total=0; // sum of this try
$anzahl=0; // counter for 1-bits in this try
$k = $i; // store $i to another variable which can be changed during the loop
for($j=0;$j<$count_n;$j++){ // loop trough array-elemnts
$total+=($k%2)*$n[$j]; // add up if the corresponding bit of $i is 1
$anzahl+=($k%2); // add up the number of 1-bits
$k=$k>>1; //bit-shift to the left for looking at the next bit in the next loop
}
if($total==$t){
$loesung[$i] = $anzahl; // if sum of this try is the sum we are looking for, save this to an array (whith the number of 1-bits for sorting)
if(!$all){
break; // if we're not looking for all solutions, make a break because the first one was found
}
}
}
asort($loesung); // sort all solutions by the amount of numbers used
// formating the solutions to getting back the original array-keys (which shoud be the return-value)
foreach($loesung as $val=>$anzahl){
$bit = strrev(decbin($val));
$total=0;
$ret_this = array();
for($j=0;$j<=strlen($bit);$j++){
if($bit[$j]=='1'){
$ret_this[] = $j;
}
}
$ret[]=$ret_this;
}
return $ret;
}
// Inputs
$n[0]=6.56;
$n[1]=8.99;
$n[2]=1.45;
$n[3]=4.83;
$n[4]=8.16;
$n[5]=2.53;
$n[6]=0.28;
$n[7]=9.37;
$n[8]=0.34;
$n[9]=5.82;
$n[10]=8.24;
$n[11]=4.35;
$n[12]=9.67;
$n[13]=1.69;
$n[14]=5.64;
$n[15]=0.27;
$n[16]=2.73;
$n[17]=1.63;
$n[18]=4.07;
$n[19]=9.04;
$n[20]=6.32;
// Output
$t=57.96;
var_dump(array_sum_parts($n,$t)); //returns one possible solution (fuc*** fast)
var_dump(array_sum_parts($n,$t,true)); // returns all possible solution (relatively fast when you think of all the needet calculations)
if you don't use the third parameter, it returns the best (whith the least amount numbers used) solution as array (whith keys of the input-array) - if you set the third parameter to true, ALL solutions are returned (for testing, i used the same numbers as zaf in his post - there are 338 solutions in this case, found in ~10sec on my machine).
EDIT:
if you get all, you get the results ordered by which is "best" - whithout this, you only get the first found solution (which isn't necessarily the best).
EDIT2:
to forfil the desire of some explanation, i commented the essential parts of the code . if anyone needs more explanation, please ask
1. Check and eliminate fields values more than 21st field
2. Check highest of the remaining, Add smallest,
3. if its greater than 21st eliminate highest (iterate this process)
4. If lower: Highest + second Lowest, if equal show result.
5. if higher go to step 7
6. if lower go to step 4
7. if its lower than add second lowest, go to step 3.
8. if its equal show result
This is efficient and will take less execution time.
Following method will give you an answer... almost all of the time. Increase the iterations variable to your taste.
<?php
// Inputs
$n[1]=8.99;
$n[2]=1.45;
$n[3]=4.83;
$n[4]=8.16;
$n[5]=2.53;
$n[6]=0.28;
$n[7]=9.37;
$n[8]=0.34;
$n[9]=5.82;
$n[10]=8.24;
$n[11]=4.35;
$n[12]=9.67;
$n[13]=1.69;
$n[14]=5.64;
$n[15]=0.27;
$n[16]=2.73;
$n[17]=1.63;
$n[18]=4.07;
$n[19]=9.04;
$n[20]=6.32;
// Output
$t=57.96;
// Let's try to do this a million times randomly
// Relax, thats less than a blink
$iterations=1000000;
while($iterations-->0){
$z=array_rand($n, mt_rand(2,20));
$total=0;
foreach($z as $x) $total+=$n[$x];
if($total==$t)break;
}
// If we did less than a million times we have an answer
if($iterations>0){
$total=0;
foreach($z as $x){
$total+=$n[$x];
print("[$x] + ". $n[$x] . " = $total<br/>");
}
}
?>
One solution:
[1] + 8.99 = 8.99
[4] + 8.16 = 17.15
[5] + 2.53 = 19.68
[6] + 0.28 = 19.96
[8] + 0.34 = 20.3
[10] + 8.24 = 28.54
[11] + 4.35 = 32.89
[13] + 1.69 = 34.58
[14] + 5.64 = 40.22
[15] + 0.27 = 40.49
[16] + 2.73 = 43.22
[17] + 1.63 = 44.85
[18] + 4.07 = 48.92
[19] + 9.04 = 57.96
A probably inefficient but simple solution with backtracking
function subset_sums($a, $val, $i = 0) {
$r = array();
while($i < count($a)) {
$v = $a[$i];
if($v == $val)
$r[] = $v;
if($v < $val)
foreach(subset_sums($a, $val - $v, $i + 1) as $s)
$r[] = "$v $s";
$i++;
}
return $r;
}
example
$ns = array(1, 2, 6, 7, 11, 5, 8, 9, 3);
print_r(subset_sums($ns, 11));
result
Array
(
[0] => 1 2 5 3
[1] => 1 2 8
[2] => 1 7 3
[3] => 2 6 3
[4] => 2 9
[5] => 6 5
[6] => 11
[7] => 8 3
)
i don't think the answer isn't as easy as nik mentioned. let's ay you have the following numbers:
1 2 3 6 8
looking for an amount of 10
niks solution would do this (if i understand it right):
1*8 = 9 = too low
adding next lowest (2) = 11 = too high
now he would delete the high number and start again taking the new highest
1*6 = 7 = too low
adding next lowest (2) = 9 = too low
adding next lowest (3) = 12 = too high
... and so on, where the perfect answer would simply
be 8+2 = 10... i think the only solution is trying every possible combination of
numbers and stop if the amaunt you are looking for is found (or realy calculate all, if there are different solutions and save which one has used least numbers).
EDIT: realy calculating all possible combiations of 21 numbers will end up in realy, realy, realy much calculations - so there must be any "intelligent" solution for adding numbers in a special order (lik that one in niks post - with some improvements, maybe that will bring us to a reliable solution)
Without knowing if this is a homework assignment or not, I can give you some pseudo code as a hint for a possible solution, note the solution is not very efficient, more of a demonstration.
Hint:
Compare each field value to all field value and at each iteration check if their sum is equal to TOTAL_AMOUNT.
Pseudo code:
for i through field 1-20
for j through field 1-20
if value of i + value of j == total_amount
return i and j
Update:
What you seem to be having is the Subset sum problem, given within the Wiki link is pseudo code for the algorithm which might help point you in the right direction.