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test_binary_flat.cpp
1 /**
2 * Copyright (c) 2015-present, Facebook, Inc.
3 * All rights reserved.
4 *
5 * This source code is licensed under the BSD+Patents license found in the
6 * LICENSE file in the root directory of this source tree.
7 */
8
9 #include <cstdio>
10 #include <cstdlib>
11
12 #include <gtest/gtest.h>
13
14 #include <faiss/IndexBinaryFlat.h>
15 #include <faiss/hamming.h>
16
17 TEST(BinaryFlat, accuracy) {
18 // dimension of the vectors to index
19 int d = 64;
20
21 // size of the database we plan to index
22 size_t nb = 1000;
23
24 // make the index object and train it
25 faiss::IndexBinaryFlat index(d);
26
27 srand(35);
28
29 std::vector<uint8_t> database(nb * (d / 8));
30 for (size_t i = 0; i < nb * (d / 8); i++) {
31 database[i] = rand() % 0x100;
32 }
33
34 { // populating the database
35 index.add(nb, database.data());
36 }
37
38 int nq = 200;
39
40 { // searching the database
41
42 std::vector<uint8_t> queries(nq * (d / 8));
43 for (size_t i = 0; i < nq * (d / 8); i++) {
44 queries[i] = rand() % 0x100;
45 }
46
47 int k = 5;
48 std::vector<faiss::IndexBinary::idx_t> nns(k * nq);
49 std::vector<int> dis(k * nq);
50
51 index.search(nq, queries.data(), k, dis.data(), nns.data());
52
53 for (size_t i = 0; i < nq; ++i) {
54 faiss::HammingComputer8 hc(queries.data() + i * (d / 8), d / 8);
55 hamdis_t dist_min = hc.hamming(database.data());
56 for (size_t j = 1; j < nb; ++j) {
57 hamdis_t dist = hc.hamming(database.data() + j * (d / 8));
58 if (dist < dist_min) {
59 dist_min = dist;
60 }
61 }
62 EXPECT_EQ(dist_min, dis[k * i]);
63 }
64 }
65 }
faiss::IndexBinaryFlat
Definition: IndexBinaryFlat.h:22
faiss::HammingComputer8
Definition: hamming.h:201