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packages/leann-backend-hnsw/third_party/faiss/tests/test_binary_flat.cpp

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+/*
+ * Copyright (c) Meta Platforms, Inc. and 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/utils/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);
+
+    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::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]);
+        }
+    }
+}