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/data/users/matthijs/github_faiss/faiss/IndexLSH.cpp
1
2 /**
3 * Copyright (c) 2015-present, Facebook, Inc.
4 * All rights reserved.
5 *
6 * This source code is licensed under the CC-by-NC license found in the
7 * LICENSE file in the root directory of this source tree.
8 */
9
10 // Copyright 2004-present Facebook. All Rights Reserved.
11
12 #include "IndexLSH.h"
13
14 #include <cstdio>
15 #include <cstring>
16
17 #include <algorithm>
18
19 #include "utils.h"
20 #include "hamming.h"
21 #include "FaissAssert.h"
22
23 namespace faiss {
24
25 /***************************************************************
26 * IndexLSH
27 ***************************************************************/
28
29
30 IndexLSH::IndexLSH (idx_t d, int nbits, bool rotate_data, bool train_thresholds):
31 Index(d), nbits(nbits), rotate_data(rotate_data),
32 train_thresholds (train_thresholds), rrot(d, nbits)
33 {
34 is_trained = !train_thresholds;
35
36 bytes_per_vec = (nbits + 7) / 8;
37
38 if (rotate_data) {
39 rrot.init(5);
40 } else {
41 FAISS_ASSERT(d >= nbits);
42 }
43 set_typename();
44 }
45
46 IndexLSH::IndexLSH ():
47 nbits (0), bytes_per_vec(0), rotate_data (false), train_thresholds (false)
48 {
49 }
50
51
52 void IndexLSH::set_typename()
53 {
54 std::stringstream s;
55 s << "LSH_" << nbits << (rotate_data ? "r" : "");
56 index_typename = s.str();
57 }
58
59 const float * IndexLSH::apply_preprocess (idx_t n, const float *x) const
60 {
61
62 float *xt = nullptr;
63 if (rotate_data) {
64 // also applies bias if exists
65 xt = rrot.apply (n, x);
66 } else if (d != nbits) {
67 xt = new float [nbits * n];
68 float *xp = xt;
69 for (idx_t i = 0; i < n; i++) {
70 const float *xl = x + i * d;
71 for (int j = 0; j < nbits; j++)
72 *xp++ = xl [j];
73 }
74 }
75
76 if (train_thresholds) {
77
78 if (xt == NULL) {
79 xt = new float [nbits * n];
80 memcpy (xt, x, sizeof(*x) * n * nbits);
81 }
82
83 float *xp = xt;
84 for (idx_t i = 0; i < n; i++)
85 for (int j = 0; j < nbits; j++)
86 *xp++ -= thresholds [j];
87 }
88
89 return xt ? xt : x;
90 }
91
92
93
94 void IndexLSH::train (idx_t n, const float *x)
95 {
96 if (train_thresholds) {
97 thresholds.resize (nbits);
98 train_thresholds = false;
99 const float *xt = apply_preprocess (n, x);
100 train_thresholds = true;
101
102 float * transposed_x = new float [n * nbits];
103
104 for (idx_t i = 0; i < n; i++)
105 for (idx_t j = 0; j < nbits; j++)
106 transposed_x [j * n + i] = xt [i * nbits + j];
107 if (xt != x) delete [] xt;
108
109 for (idx_t i = 0; i < nbits; i++) {
110 float *xi = transposed_x + i * n;
111 // std::nth_element
112 std::sort (xi, xi + n);
113 if (n % 2 == 1)
114 thresholds [i] = xi [n / 2];
115 else
116 thresholds [i] = (xi [n / 2 - 1] + xi [n / 2]) / 2;
117
118 }
119 }
120 is_trained = true;
121 }
122
123
124 void IndexLSH::add (idx_t n, const float *x)
125 {
126 FAISS_ASSERT (is_trained);
127 const float *xt = apply_preprocess (n, x);
128
129 codes.resize ((ntotal + n) * bytes_per_vec);
130 fvecs2bitvecs (xt, &codes[ntotal * bytes_per_vec], nbits, n);
131 if (x != xt)
132 delete [] xt;
133 ntotal += n;
134 }
135
136
137 void IndexLSH::search (
138 idx_t n,
139 const float *x,
140 idx_t k,
141 float *distances,
142 idx_t *labels) const
143 {
144 FAISS_ASSERT (is_trained);
145 const float *xt = apply_preprocess (n, x);
146
147 uint8_t * qcodes = new uint8_t [n * bytes_per_vec];
148 fvecs2bitvecs (xt, qcodes, nbits, n);
149
150 if (x != xt)
151 delete [] xt;
152
153 int * idistances = new int [n * k];
154 int_maxheap_array_t res = { size_t(n), size_t(k), labels, idistances};
155
156 hammings_knn (&res, qcodes, codes.data(),
157 ntotal, bytes_per_vec, true);
158
159 delete [] qcodes;
160
161 // convert distances to floats
162 for (int i = 0; i < k * n; i++)
163 distances[i] = idistances[i];
164 delete [] idistances;
165
166 }
167
168
169 void IndexLSH::transfer_thresholds (LinearTransform *vt) {
170 if (!train_thresholds) return;
171 FAISS_ASSERT (nbits == vt->d_out);
172 if (!vt->have_bias) {
173 vt->b.resize (nbits, 0);
174 vt->have_bias = true;
175 }
176 for (int i = 0; i < nbits; i++)
177 vt->b[i] -= thresholds[i];
178 train_thresholds = false;
179 thresholds.clear();
180 }
181
182 void IndexLSH::reset() {
183 codes.clear();
184 ntotal = 0;
185 }
186
187
188
189 } // namespace faiss
faiss::IndexLSH::bytes_per_vec
int bytes_per_vec
nb of 8-bits per encoded vector
Definition: IndexLSH.h:29
faiss::IndexLSH::thresholds
std::vector< float > thresholds
thresholds to compare with
Definition: IndexLSH.h:35
faiss::IndexLSH::search
virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const override
Definition: IndexLSH.cpp:137
faiss::Index::d
int d
vector dimension
Definition: Index.h:66
faiss::LinearTransform::b
std::vector< float > b
bias vector, size d_out
Definition: VectorTransform.h:87
faiss::IndexLSH::rrot
RandomRotationMatrix rrot
optional random rotation
Definition: IndexLSH.h:33
faiss::IndexLSH::transfer_thresholds
void transfer_thresholds(LinearTransform *vt)
Definition: IndexLSH.cpp:169
faiss::LinearTransform
Definition: VectorTransform.h:78
faiss::HeapArray
Definition: Heap.h:351
faiss::Index::idx_t
long idx_t
all indices are this type
Definition: Index.h:64
faiss::hammings_knn
void hammings_knn(int_maxheap_array_t *ha, const uint8_t *a, const uint8_t *b, size_t nb, size_t ncodes, int order)
Definition: hamming.cpp:475
faiss::Index::ntotal
idx_t ntotal
total nb of indexed vectors
Definition: Index.h:67
faiss::IndexLSH::reset
virtual void reset() override
removes all elements from the database.
Definition: IndexLSH.cpp:182
faiss::IndexLSH::add
virtual void add(idx_t n, const float *x) override
Definition: IndexLSH.cpp:124
faiss::IndexLSH::train
virtual void train(idx_t n, const float *x) override
Definition: IndexLSH.cpp:94
faiss::VectorTransform::d_out
int d_out
! input dimension
Definition: VectorTransform.h:34
faiss::IndexLSH::nbits
int nbits
nb of bits per vector
Definition: IndexLSH.h:28
faiss::IndexLSH::apply_preprocess
const float * apply_preprocess(idx_t n, const float *x) const
Definition: IndexLSH.cpp:59
faiss::Index::is_trained
bool is_trained
set if the Index does not require training, or if training is done already
Definition: Index.h:71
faiss::VectorTransform::apply
float * apply(idx_t n, const float *x) const
Definition: VectorTransform.cpp:68
faiss::IndexLSH::codes
std::vector< uint8_t > codes
encoded dataset
Definition: IndexLSH.h:38