| Conditions | 9 |
| Paths | 128 |
| Total Lines | 88 |
| Code Lines | 41 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 0 | ||
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
| 1 | <?php |
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| 20 | protected function fillPartitions(Partitions $partitions, Partition $dataset, int $chunks): void |
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| 21 | { |
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| 22 | $dataset = $dataset->getArrayCopy(); |
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| 23 | |||
| 24 | // See https://github.com/technically-php/linear-partitioning for |
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| 25 | // original version of this algorithm. |
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| 26 | |||
| 27 | // An array S of non-negative numbers {s1, ... ,sn} |
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| 28 | $s = \array_merge([null], $dataset); // adapt indices here: [0..n-1] => [1..n] |
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| 29 | |||
| 30 | // Integer K - number of ranges to split items into |
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| 31 | $k = $chunks; |
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| 32 | $n = \count($dataset); |
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| 33 | |||
| 34 | // Let D[n,k] be the position of K-th divider |
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| 35 | // which produces the minimum possible cost partitioning of N elements to K ranges |
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| 36 | $d = []; |
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| 37 | |||
| 38 | // Let p be the sum of first i elements (cost calculation optimization) |
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| 39 | $p = []; |
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| 40 | |||
| 41 | // 1) Init prefix sums array |
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| 42 | // pi = sum of {s1, ..., si} |
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| 43 | $p[0] = $this->getPartitionItemFactory()::create(0); |
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| 44 | for ($i = 1; $i <= $n; ++$i) { |
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| 45 | $p[$i] = $this->getPartitionItemFactory()::create($p[$i - 1]->getWeight() + $s[$i]->getWeight()); |
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| 46 | } |
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| 47 | |||
| 48 | // Let M[n,k] be the minimum possible cost over all partitionings of N elements to K ranges |
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| 49 | $m = []; |
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| 50 | |||
| 51 | // 2) Init boundaries |
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| 52 | for ($i = 1; $i <= $n; ++$i) { |
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| 53 | // The only possible partitioning of i elements to 1 range is a single all-elements range |
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| 54 | // The cost of that partitioning is the sum of those i elements |
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| 55 | $m[$i][1] = $p[$i]; // sum of {s1, ..., si} -- optimized using pi |
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| 56 | } |
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| 57 | |||
| 58 | for ($j = 1; $j <= $k; ++$j) { |
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| 59 | // The only possible partitioning of 1 element into j ranges is a single one-element range |
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| 60 | // The cost of that partitioning is the value of first element |
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| 61 | $m[1][$j] = $s[1]; |
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| 62 | } |
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| 63 | // 3) Main recurrence (fill the rest of values in table M) |
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| 64 | for ($i = 2; $i <= $n; ++$i) { |
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| 65 | for ($j = 2; $j <= $k; ++$j) { |
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| 66 | $solutions = []; |
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| 67 | for ($x = 1; ($i - 1) >= $x; ++$x) { |
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| 68 | $solutions[] = [ |
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| 69 | 0 => $this->getPartitionItemFactory()::create( |
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| 70 | \max( |
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| 71 | $m[$x][$j - 1]->getWeight(), |
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| 72 | $p[$i]->getWeight() - $p[$x]->getWeight() |
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| 73 | ) |
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| 74 | ), |
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| 75 | 1 => $x, |
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| 76 | ]; |
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| 77 | } |
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| 78 | |||
| 79 | \usort( |
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| 80 | $solutions, |
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| 81 | static function (array $x, array $y) { |
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| 82 | return $x[0] <=> $y[0]; |
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| 83 | } |
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| 84 | ); |
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| 85 | |||
| 86 | $best_solution = $solutions[0]; |
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| 87 | $m[$i][$j] = $best_solution[0]; |
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| 88 | $d[$i][$j] = $best_solution[1]; |
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| 89 | } |
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| 90 | } |
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| 91 | |||
| 92 | // 4) Reconstruct partitioning |
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| 93 | $i = $n; |
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| 94 | $j = $k; |
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| 95 | $partition = []; |
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| 96 | while (0 < $j) { |
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| 97 | // delimiter position |
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| 98 | $dp = $d[$i][$j] ?? 0; |
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| 99 | // Add elements after delimiter {sdp, ..., si} to resulting $partition. |
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| 100 | $partition[] = \array_slice($s, $dp + 1, $i - $dp); |
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| 101 | // Step forward: look for delimiter position for partitioning M[$dp, $j-1] |
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| 102 | $i = $dp; |
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| 103 | --$j; |
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| 104 | } |
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| 105 | |||
| 106 | foreach ($partition as $i => $p) { |
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| 107 | $partitions->partition($i)->exchangeArray($p); |
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| 108 | } |
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| 111 |