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<?php |
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declare(strict_types=1); |
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/** |
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* @copyright Copyright (c) 2023, Matias De lellis |
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* |
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* @author Matias De lellis <[email protected]> |
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* |
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* @license AGPL-3.0-or-later |
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* |
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* This code is free software: you can redistribute it and/or modify |
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* it under the terms of the GNU Affero General Public License, version 3, |
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* as published by the Free Software Foundation. |
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* |
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* This program is distributed in the hope that it will be useful, |
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* but WITHOUT ANY WARRANTY; without even the implied warranty of |
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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* GNU Affero General Public License for more details. |
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* |
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* You should have received a copy of the GNU Affero General Public License, version 3, |
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* along with this program. If not, see <http://www.gnu.org/licenses/> |
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* |
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*/ |
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namespace OCA\FaceRecognition\Clusterer; |
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/** |
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* This class implements the graph clustering algorithm described in the |
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* paper: Chinese Whispers - an Efficient Graph Clustering Algorithm and its |
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* Application to Natural Language Processing Problems by Chris Biemann. |
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* |
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* In particular, it tries to be a shameless copy of the original dlib |
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* implementation. |
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* - https://github.com/davisking/dlib/blob/master/dlib/clustering/chinese_whispers.h |
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*/ |
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class ChineseWhispers { |
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/** |
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* Cluster the dataset by assigning a label to each sample.from the edges |
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*/ |
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static public function predict(array &$edges, array &$labels, int $num_iterations = 100) |
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{ |
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// To improve the stability of the clusters, we must |
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// iterate the neighbors in a pseudo-random way. |
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mt_srand(2023); |
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$labels = []; |
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if (count($edges) == 0) |
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return 0; |
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$neighbors = []; |
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self::find_neighbor_ranges($edges, $neighbors); |
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// Initialize the labels, each node gets a different label. |
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for ($i = 0; $i < count($neighbors); ++$i) |
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$labels[$i] = $i; |
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for ($iter = 0; $iter < count($neighbors)*$num_iterations; ++$iter) |
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{ |
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// Pick a random node. |
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$idx = mt_rand()%count($neighbors); |
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// Count how many times each label happens amongst our neighbors. |
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$labels_to_counts = []; |
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$end = $neighbors[$idx][1]; |
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for ($i = $neighbors[$idx][0]; $i != $end; ++$i) |
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{ |
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$iLabelFirst = $edges[$i][1]; |
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$iLabel = $labels[$iLabelFirst]; |
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if (isset($labels_to_counts[$iLabel])) |
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$labels_to_counts[$iLabel]++; |
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else |
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$labels_to_counts[$iLabel] = 1; |
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} |
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// find the most common label |
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// std::map<unsigned long, double>::iterator i; |
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$best_score = PHP_INT_MIN; |
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$best_label = $labels[$idx]; |
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foreach ($labels_to_counts as $key => $value) |
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{ |
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if ($value > $best_score) |
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{ |
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$best_score = $value; |
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$best_label = $key; |
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} |
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} |
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$labels[$idx] = $best_label; |
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} |
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// Remap the labels into a contiguous range. First we find the |
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// mapping. |
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$label_remap = []; |
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for ($i = 0; $i < count($labels); ++$i) |
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{ |
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$next_id = count($label_remap); |
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if (!isset($label_remap[$labels[$i]])) |
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$label_remap[$labels[$i]] = $next_id; |
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} |
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// now apply the mapping to all the labels. |
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for ($i = 0; $i < count($labels); ++$i) |
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{ |
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$labels[$i] = $label_remap[$labels[$i]]; |
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} |
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return count($label_remap); |
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} |
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static function find_neighbor_ranges (&$edges, &$neighbors) { |
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// setup neighbors so that [neighbors[i].first, neighbors[i].second) is the range |
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// within edges that contains all node i's edges. |
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$num_nodes = self::max_index_plus_one($edges); |
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for ($i = 0; $i < $num_nodes; ++$i) $neighbors[$i] = [0, 0]; |
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$cur_node = 0; |
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$start_idx = 0; |
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for ($i = 0; $i < count($edges); ++$i) |
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{ |
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if ($edges[$i][0] != $cur_node) |
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{ |
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$neighbors[$cur_node] = [$start_idx, $i]; |
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$start_idx = $i; |
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$cur_node = $edges[$i][0]; |
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} |
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} |
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if (count($neighbors) !== 0) |
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$neighbors[$cur_node] = [$start_idx, count($edges)]; |
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} |
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static function max_index_plus_one ($pairs): int { |
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if (count($pairs) === 0) |
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{ |
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return 0; |
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} |
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else { |
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$max_idx = 0; |
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for ($i = 0; $i < count($pairs); ++$i) |
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{ |
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if ($pairs[$i][0] > $max_idx) |
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$max_idx = $pairs[$i][0]; |
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if ($pairs[$i][1] > $max_idx) |
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$max_idx = $pairs[$i][1]; |
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} |
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return $max_idx + 1; |
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} |
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} |
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static function convert_unordered_to_ordered (&$edges, &$out_edges) |
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{ |
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$out_edges = []; |
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for ($i = 0; $i < count($edges); ++$i) |
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{ |
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$out_edges[] = [$edges[$i][0], $edges[$i][1]]; |
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if ($edges[$i][0] != $edges[$i][1]) |
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$out_edges[] = [$edges[$i][1], $edges[$i][0]]; |
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} |
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} |
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} |
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If the size of the collection does not change during the iteration, it is generally a good practice to compute it beforehand, and not on each iteration: