95 lines
2.3 KiB
C++
95 lines
2.3 KiB
C++
/**
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* Copyright (c) Facebook, Inc. and its affiliates.
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*
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* This source code is licensed under the MIT license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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#include <cstdio>
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#include <cstdlib>
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#include <random>
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#include <gtest/gtest.h>
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#include <faiss/IndexFlat.h>
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#include <faiss/IndexIVFPQ.h>
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#include <faiss/index_io.h>
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TEST(IVFPQ, accuracy) {
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// dimension of the vectors to index
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int d = 64;
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// size of the database we plan to index
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size_t nb = 1000;
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// make a set of nt training vectors in the unit cube
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// (could be the database)
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size_t nt = 1500;
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// make the index object and train it
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faiss::IndexFlatL2 coarse_quantizer(d);
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// a reasonable number of cetroids to index nb vectors
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int ncentroids = 25;
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faiss::IndexIVFPQ index(&coarse_quantizer, d, ncentroids, 16, 8);
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// index that gives the ground-truth
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faiss::IndexFlatL2 index_gt(d);
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std::mt19937 rng;
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std::uniform_real_distribution<> distrib;
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{ // training
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std::vector<float> trainvecs(nt * d);
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for (size_t i = 0; i < nt * d; i++) {
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trainvecs[i] = distrib(rng);
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}
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index.verbose = true;
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index.train(nt, trainvecs.data());
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}
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{ // populating the database
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std::vector<float> database(nb * d);
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for (size_t i = 0; i < nb * d; i++) {
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database[i] = distrib(rng);
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}
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index.add(nb, database.data());
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index_gt.add(nb, database.data());
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}
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int nq = 200;
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int n_ok;
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{ // searching the database
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std::vector<float> queries(nq * d);
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for (size_t i = 0; i < nq * d; i++) {
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queries[i] = distrib(rng);
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}
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std::vector<faiss::Index::idx_t> gt_nns(nq);
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std::vector<float> gt_dis(nq);
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index_gt.search(nq, queries.data(), 1, gt_dis.data(), gt_nns.data());
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index.nprobe = 5;
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int k = 5;
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std::vector<faiss::Index::idx_t> nns(k * nq);
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std::vector<float> dis(k * nq);
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index.search(nq, queries.data(), k, dis.data(), nns.data());
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n_ok = 0;
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for (int q = 0; q < nq; q++) {
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for (int i = 0; i < k; i++)
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if (nns[q * k + i] == gt_nns[q])
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n_ok++;
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}
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EXPECT_GT(n_ok, nq * 0.4);
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}
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}
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