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/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include <cstdio>
#include <cstdlib>
#include <gtest/gtest.h>
#include <faiss/IndexBinaryFlat.h>
#include <faiss/hamming.h>
TEST(BinaryFlat, accuracy) {
// dimension of the vectors to index
int d = 64;
// size of the database we plan to index
size_t nb = 1000;
// make the index object and train it
faiss::IndexBinaryFlat index(d);
srand(35);
std::vector<uint8_t> database(nb * (d / 8));
for (size_t i = 0; i < nb * (d / 8); i++) {
database[i] = rand() % 0x100;
}
{ // populating the database
index.add(nb, database.data());
}
size_t nq = 200;
{ // searching the database
std::vector<uint8_t> queries(nq * (d / 8));
for (size_t i = 0; i < nq * (d / 8); i++) {
queries[i] = rand() % 0x100;
}
int k = 5;
std::vector<faiss::IndexBinary::idx_t> nns(k * nq);
std::vector<int> dis(k * nq);
index.search(nq, queries.data(), k, dis.data(), nns.data());
for (size_t i = 0; i < nq; ++i) {
faiss::HammingComputer8 hc(queries.data() + i * (d / 8), d / 8);
hamdis_t dist_min = hc.hamming(database.data());
for (size_t j = 1; j < nb; ++j) {
hamdis_t dist = hc.hamming(database.data() + j * (d / 8));
if (dist < dist_min) {
dist_min = dist;
}
}
EXPECT_EQ(dist_min, dis[k * i]);
}
}
}