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| H A D | kmeans_index.h | 126 … DistanceType sq = distance_(dataset_[centers[index]], dataset_[centers[j]], dataset_.cols); in chooseCentersRandom()
163 … DistanceType dist = distance_(dataset_[centers[0]],dataset_[indices[j]],dataset_.cols); in chooseCentersGonzales()
165 … DistanceType tmp_dist = distance_(dataset_[centers[i]],dataset_[indices[j]],dataset_.cols); in chooseCentersGonzales()
212 … closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols); in chooseCentersKMeanspp()
240 … DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols); in chooseCentersKMeanspp()
255 …DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols… in chooseCentersKMeanspp()
348 : dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d) in dataset_()
683 variance += distance_(vec, ZeroIterator<ElementType>(), veclen_); in computeNodeStatistics()
689 variance -= distance_(mean, ZeroIterator<ElementType>(), veclen_); in computeNodeStatistics()
693 tmp = distance_(mean, dataset_[indices[i]], veclen_); in computeNodeStatistics()
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| H A D | autotuned_index.h | 79 dataset_(inputData), distance_(d) in dataset_()
113 bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_); in buildIndex()
144 bestIndex_ = create_index_by_type<Distance>(dataset_, params, distance_); in loadIndex()
234 KMeansIndex<Distance> kmeans(sampledDataset_, cost.params, distance_); in evaluate_kmeans()
242 …sion(kmeans, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn); in evaluate_kmeans()
259 KDTreeIndex<Distance> kdtree(sampledDataset_, cost.params, distance_); in evaluate_kdtree()
267 …sion(kdtree, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn); in evaluate_kdtree()
441 compute_ground_truth<Distance>(sampledDataset_, testDataset_, gt_matches_, 0, distance_); in estimateBuildParams()
512 compute_ground_truth<Distance>(dataset_, testDataset, gt_matches, 1, distance_); in estimateSearchParams()
529 …precision(*kmeans, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1); in estimateSearchParams()
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| H A D | linear_index.h | 59 dataset_(inputData), index_params_(params), distance_(d) in dataset_()
110 DistanceType dist = distance_(data, vec, dataset_.cols); in findNeighbors()
126 Distance distance_; variable
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| H A D | lsh_index.h | 91 dataset_(input_data), index_params_(params), distance_(d) in dataset_()
288 hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols); in getNeighbors()
327 hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols); in getNeighbors()
360 … hamming_distance = distance_(vec, dataset_[*training_index], (int)dataset_.cols); in getNeighbors()
388 Distance distance_; variable
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| H A D | kdtree_index.h | 86 dataset_(inputData), index_params_(params), distance_(d) in dataset_()
491 DistanceType dist = distance_(dataset_[index], vec, veclen_); in searchLevel()
511 DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat); in searchLevel()
529 DistanceType dist = distance_(dataset_[index], vec, veclen_); in searchLevelExact()
548 DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat); in searchLevelExact()
619 Distance distance_; variable
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| H A D | kdtree_single_index.h | 86 dataset_(inputData), index_params_(params), distance_(d) in dataset_()
528 dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].low, (int)i); in computeInitialDistances()
532 dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].high, (int)i); in computeInitialDistances()
551 DistanceType dist = distance_(vec, data_[index], dim_, worst_dist); in searchLevel()
571 cut_dist = distance_.accum_dist(val, node->divhigh, idx); in searchLevel()
576 cut_dist = distance_.accum_dist( val, node->divlow, idx); in searchLevel()
630 Distance distance_; variable
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