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 |
||
20 | protected function fillPartitions(Partitions $partitions, Partition $dataset, int $chunks): void |
||
21 | { |
||
22 | $dataset = $dataset->getArrayCopy(); |
||
23 | |||
24 | // See https://github.com/technically-php/linear-partitioning for |
||
25 | // original version of this algorithm. |
||
26 | |||
27 | // An array S of non-negative numbers {s1, ... ,sn} |
||
28 | $s = \array_merge([null], $dataset); // adapt indices here: [0..n-1] => [1..n] |
||
29 | |||
30 | // Integer K - number of ranges to split items into |
||
31 | $k = $chunks; |
||
32 | $n = \count($dataset); |
||
33 | |||
34 | // Let D[n,k] be the position of K-th divider |
||
35 | // which produces the minimum possible cost partitioning of N elements to K ranges |
||
36 | $d = []; |
||
37 | |||
38 | // Let p be the sum of first i elements (cost calculation optimization) |
||
39 | $p = []; |
||
40 | |||
41 | // 1) Init prefix sums array |
||
42 | // pi = sum of {s1, ..., si} |
||
43 | $p[0] = $this->getPartitionItemFactory()::create(0); |
||
44 | for ($i = 1; $i <= $n; ++$i) { |
||
45 | $p[$i] = $this->getPartitionItemFactory()::create($p[$i - 1]->getWeight() + $s[$i]->getWeight()); |
||
46 | } |
||
47 | |||
48 | // Let M[n,k] be the minimum possible cost over all partitionings of N elements to K ranges |
||
49 | $m = []; |
||
50 | |||
51 | // 2) Init boundaries |
||
52 | for ($i = 1; $i <= $n; ++$i) { |
||
53 | // The only possible partitioning of i elements to 1 range is a single all-elements range |
||
54 | // The cost of that partitioning is the sum of those i elements |
||
55 | $m[$i][1] = $p[$i]; // sum of {s1, ..., si} -- optimized using pi |
||
56 | } |
||
57 | |||
58 | for ($j = 1; $j <= $k; ++$j) { |
||
59 | // The only possible partitioning of 1 element into j ranges is a single one-element range |
||
60 | // The cost of that partitioning is the value of first element |
||
61 | $m[1][$j] = $s[1]; |
||
62 | } |
||
63 | // 3) Main recurrence (fill the rest of values in table M) |
||
64 | for ($i = 2; $i <= $n; ++$i) { |
||
65 | for ($j = 2; $j <= $k; ++$j) { |
||
66 | $solutions = []; |
||
67 | for ($x = 1; ($i - 1) >= $x; ++$x) { |
||
68 | $solutions[] = [ |
||
69 | 0 => $this->getPartitionItemFactory()::create( |
||
70 | \max( |
||
71 | $m[$x][$j - 1]->getWeight(), |
||
72 | $p[$i]->getWeight() - $p[$x]->getWeight() |
||
73 | ) |
||
74 | ), |
||
75 | 1 => $x, |
||
76 | ]; |
||
77 | } |
||
78 | |||
79 | \usort( |
||
80 | $solutions, |
||
81 | static function (array $x, array $y) { |
||
82 | return $x[0] <=> $y[0]; |
||
83 | } |
||
84 | ); |
||
85 | |||
86 | $best_solution = $solutions[0]; |
||
87 | $m[$i][$j] = $best_solution[0]; |
||
88 | $d[$i][$j] = $best_solution[1]; |
||
89 | } |
||
90 | } |
||
91 | |||
92 | // 4) Reconstruct partitioning |
||
93 | $i = $n; |
||
94 | $j = $k; |
||
95 | $partition = []; |
||
96 | while (0 < $j) { |
||
97 | // delimiter position |
||
98 | $dp = $d[$i][$j] ?? 0; |
||
99 | // Add elements after delimiter {sdp, ..., si} to resulting $partition. |
||
100 | $partition[] = \array_slice($s, $dp + 1, $i - $dp); |
||
101 | // Step forward: look for delimiter position for partitioning M[$dp, $j-1] |
||
102 | $i = $dp; |
||
103 | --$j; |
||
104 | } |
||
105 | |||
106 | foreach ($partition as $i => $p) { |
||
107 | $partitions->partition($i)->exchangeArray($p); |
||
108 | } |
||
111 |