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Fix some typos (#2645)
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2645 Reviewed By: mdouze Differential Revision: D42338453 Pulled By: algoriddle fbshipit-source-id: 1ea5c33960a42c9e8624b10eba067e722d712e34
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@@ -196,7 +196,7 @@ struct IndexBinaryIVF : IndexBinary {
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return invlists->list_size(list_no);
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}
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/** intialize a direct map
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/** initialize a direct map
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*
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* @param new_maintain_direct_map if true, create a direct map,
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* else clear it
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+1
-1
@@ -458,7 +458,7 @@ void IndexIVF::search_preassigned(
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* that are in common between the two
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******************************************************/
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// intialize + reorder a result heap
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// initialize + reorder a result heap
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auto init_result = [&](float* simi, idx_t* idxi) {
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if (!do_heap_init)
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+1
-1
@@ -339,7 +339,7 @@ struct IndexIVF : Index, Level1Quantizer {
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/// are the ids sorted?
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bool check_ids_sorted() const;
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/** intialize a direct map
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/** initialize a direct map
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*
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* @param new_maintain_direct_map if true, create a direct map,
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* else clear it
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@@ -169,7 +169,7 @@ struct IndexIVFFastScan : IndexIVF {
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size_t* nlist_out,
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const Scaler& scaler) const;
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// implem 14 is mukltithreaded internally across nprobes and queries
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// implem 14 is multithreaded internally across nprobes and queries
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template <class C, class Scaler>
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void search_implem_14(
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idx_t n,
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@@ -120,7 +120,7 @@ struct RandomRotationMatrix : LinearTransform {
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/// must be called before the transform is used
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void init(int seed);
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// intializes with an arbitrary seed
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// initializes with an arbitrary seed
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void train(idx_t n, const float* x) override;
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RandomRotationMatrix() {}
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@@ -192,7 +192,7 @@ void LocalSearchQuantizer::train(size_t n, const float* x) {
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// allocate memory for codebooks, size [M, K, d]
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codebooks.resize(M * K * d);
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// randomly intialize codes
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// randomly initialize codes
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std::mt19937 gen(random_seed);
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std::vector<int32_t> codes(n * M); // [n, M]
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random_int32(codes, 0, K - 1, gen);
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@@ -425,7 +425,7 @@ void NNDescent::search(
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// candidate pool, the K best items is the result.
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std::vector<Neighbor> retset(L + 1);
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// Randomly choose L points to intialize the candidate pool
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// Randomly choose L points to initialize the candidate pool
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std::vector<int> init_ids(L);
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std::mt19937 rng(random_seed);
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@@ -905,7 +905,7 @@ void PolysemousTraining::optimize_ranking(
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ScopeDeleter1<PermutationObjective> del(obj);
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if (verbose > 0) {
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printf(" m=%d, nq=%zd, nb=%zd, intialize RankingScore "
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printf(" m=%d, nq=%zd, nb=%zd, initialize RankingScore "
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"in %.3f ms\n",
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m,
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nq,
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@@ -36,8 +36,8 @@ struct ProductQuantizer : Quantizer {
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Train_default,
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Train_hot_start, ///< the centroids are already initialized
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Train_shared, ///< share dictionary accross PQ segments
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Train_hypercube, ///< intialize centroids with nbits-D hypercube
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Train_hypercube_pca, ///< intialize centroids with nbits-D hypercube
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Train_hypercube, ///< initialize centroids with nbits-D hypercube
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Train_hypercube_pca, ///< initialize centroids with nbits-D hypercube
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};
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train_type_t train_type;
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