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/data/users/matthijs/github_faiss/faiss/IndexIVF.cpp
/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the CC-by-NC license found in the
* LICENSE file in the root directory of this source tree.
*/
/* Copyright 2004-present Facebook. All Rights Reserved.
Inverted list structure.
*/
#include "IndexIVF.h"
#include <cstdio>
#include "utils.h"
#include "hamming.h"
#include "FaissAssert.h"
#include "IndexFlat.h"
#include "AuxIndexStructures.h"
namespace faiss {
/*****************************************
* IndexIVF implementation
******************************************/
IndexIVF::IndexIVF (Index * quantizer, size_t d, size_t nlist,
MetricType metric):
Index (d, metric),
nlist (nlist),
nprobe (1),
quantizer (quantizer),
quantizer_trains_alone (false),
own_fields (false),
ids (nlist),
maintain_direct_map (false)
{
FAISS_THROW_IF_NOT (d == quantizer->d);
is_trained = quantizer->is_trained && (quantizer->ntotal == nlist);
// Spherical by default if the metric is inner_product
if (metric_type == METRIC_INNER_PRODUCT) {
cp.spherical = true;
}
// here we set a low # iterations because this is typically used
// for large clusterings (nb this is not used for the MultiIndex,
// for which quantizer_trains_alone = true)
cp.niter = 10;
cp.verbose = verbose;
}
IndexIVF::IndexIVF ():
nlist (0), nprobe (1), quantizer (nullptr),
quantizer_trains_alone (false), own_fields (false),
maintain_direct_map (false)
{}
void IndexIVF::add (idx_t n, const float * x)
{
add_with_ids (n, x, nullptr);
}
void IndexIVF::make_direct_map (bool new_maintain_direct_map)
{
// nothing to do
if (new_maintain_direct_map == maintain_direct_map)
return;
if (new_maintain_direct_map) {
direct_map.resize (ntotal, -1);
for (size_t key = 0; key < nlist; key++) {
const std::vector<long> & idlist = ids[key];
for (long ofs = 0; ofs < idlist.size(); ofs++) {
FAISS_THROW_IF_NOT_MSG (
0 <= idlist [ofs] && idlist[ofs] < ntotal,
"direct map supported only for seuquential ids");
direct_map [idlist [ofs]] = key << 32 | ofs;
}
}
} else {
direct_map.clear ();
}
maintain_direct_map = new_maintain_direct_map;
}
void IndexIVF::reset ()
{
ntotal = 0;
direct_map.clear();
for (size_t i = 0; i < ids.size(); i++)
ids[i].clear();
}
void IndexIVF::train (idx_t n, const float *x)
{
if (quantizer->is_trained && (quantizer->ntotal == nlist)) {
if (verbose)
printf ("IVF quantizer does not need training.\n");
} else if (quantizer_trains_alone) {
if (verbose)
printf ("IVF quantizer trains alone...\n");
quantizer->train (n, x);
FAISS_THROW_IF_NOT_MSG (quantizer->ntotal == nlist,
"nlist not consistent with quantizer size");
} else {
if (verbose)
printf ("Training IVF quantizer on %ld vectors in %dD\n",
n, d);
Clustering clus (d, nlist, cp);
quantizer->reset();
clus.train (n, x, *quantizer);
quantizer->is_trained = true;
}
if (verbose)
printf ("Training IVF residual\n");
train_residual (n, x);
is_trained = true;
}
void IndexIVF::train_residual(idx_t /*n*/, const float* /*x*/) {
if (verbose)
printf("IndexIVF: no residual training\n");
// does nothing by default
}
double IndexIVF::imbalance_factor () const
{
std::vector<int> hist (nlist);
for (int i = 0; i < nlist; i++) {
hist[i] = ids[i].size();
}
return faiss::imbalance_factor (nlist, hist.data());
}
void IndexIVF::print_stats () const
{
std::vector<int> sizes(40);
for (int i = 0; i < nlist; i++) {
for (int j = 0; j < sizes.size(); j++) {
if ((ids[i].size() >> j) == 0) {
sizes[j]++;
break;
}
}
}
for (int i = 0; i < sizes.size(); i++) {
if (sizes[i]) {
printf ("list size in < %d: %d instances\n",
1 << i, sizes[i]);
}
}
}
void IndexIVF::merge_from (IndexIVF &other, idx_t add_id)
{
// minimal sanity checks
FAISS_THROW_IF_NOT (other.d == d);
FAISS_THROW_IF_NOT (other.nlist == nlist);
FAISS_THROW_IF_NOT_MSG ((!maintain_direct_map &&
!other.maintain_direct_map),
"direct map copy not implemented");
FAISS_THROW_IF_NOT_MSG (typeid (*this) == typeid (other),
"can only merge indexes of the same type");
for (long i = 0; i < nlist; i++) {
std::vector<idx_t> & src = other.ids[i];
std::vector<idx_t> & dest = ids[i];
for (long j = 0; j < src.size(); j++)
dest.push_back (src[j] + add_id);
src.clear();
}
merge_from_residuals (other);
ntotal += other.ntotal;
other.ntotal = 0;
}
IndexIVF::~IndexIVF()
{
if (own_fields) delete quantizer;
}
/*****************************************
* IndexIVFFlat implementation
******************************************/
IndexIVFFlat::IndexIVFFlat (Index * quantizer,
size_t d, size_t nlist, MetricType metric):
IndexIVF (quantizer, d, nlist, metric)
{
vecs.resize (nlist);
}
void IndexIVFFlat::add_with_ids (idx_t n, const float * x, const long *xids)
{
add_core (n, x, xids, nullptr);
}
void IndexIVFFlat::add_core (idx_t n, const float * x, const long *xids,
const long *precomputed_idx)
{
FAISS_THROW_IF_NOT (is_trained);
FAISS_THROW_IF_NOT_MSG (!(maintain_direct_map && xids),
"cannot have direct map and add with ids");
const long * idx;
ScopeDeleter<long> del;
if (precomputed_idx) {
idx = precomputed_idx;
} else {
long * idx0 = new long [n];
quantizer->assign (n, x, idx0);
idx = idx0;
del.set (idx);
}
long n_add = 0;
for (size_t i = 0; i < n; i++) {
long id = xids ? xids[i] : ntotal + i;
long list_no = idx [i];
if (list_no < 0)
continue;
assert (list_no < nlist);
ids[list_no].push_back (id);
const float *xi = x + i * d;
/* store the vectors */
for (size_t j = 0 ; j < d ; j++)
vecs[list_no].push_back (xi [j]);
if (maintain_direct_map)
direct_map.push_back (list_no << 32 | (ids[list_no].size() - 1));
n_add++;
}
if (verbose) {
printf("IndexIVFFlat::add_core: added %ld / %ld vectors\n",
n_add, n);
}
ntotal += n_add;
}
void IndexIVFFlatStats::reset()
{
memset ((void*)this, 0, sizeof (*this));
}
IndexIVFFlatStats indexIVFFlat_stats;
void IndexIVFFlat::search_knn_inner_product (
size_t nx,
const float * x,
const long * __restrict keys,
float_minheap_array_t * res) const
{
const size_t k = res->k;
size_t nlistv = 0, ndis = 0;
#pragma omp parallel for reduction(+: nlistv, ndis)
for (size_t i = 0; i < nx; i++) {
const float * xi = x + i * d;
const long * keysi = keys + i * nprobe;
float * __restrict simi = res->get_val (i);
long * __restrict idxi = res->get_ids (i);
minheap_heapify (k, simi, idxi);
for (size_t ik = 0; ik < nprobe; ik++) {
long key = keysi[ik]; /* select the list */
if (key < 0) {
// not enough centroids for multiprobe
continue;
}
if (key >= (long) nlist) {
fprintf (stderr, "Invalid key=%ld at ik=%ld nlist=%ld\n",
key, ik, nlist);
throw;
}
nlistv++;
const size_t list_size = ids[key].size();
const float * list_vecs = vecs[key].data();
for (size_t j = 0; j < list_size; j++) {
const float * yj = list_vecs + d * j;
float ip = fvec_inner_product (xi, yj, d);
if (ip > simi[0]) {
minheap_pop (k, simi, idxi);
minheap_push (k, simi, idxi, ip, ids[key][j]);
}
}
ndis += list_size;
}
minheap_reorder (k, simi, idxi);
}
indexIVFFlat_stats.nq += nx;
indexIVFFlat_stats.nlist += nlistv;
indexIVFFlat_stats.ndis += ndis;
}
void IndexIVFFlat::search_knn_L2sqr (
size_t nx,
const float * x,
const long * __restrict keys,
float_maxheap_array_t * res) const
{
const size_t k = res->k;
size_t nlistv = 0, ndis = 0;
#pragma omp parallel for reduction(+: nlistv, ndis)
for (size_t i = 0; i < nx; i++) {
const float * xi = x + i * d;
const long * keysi = keys + i * nprobe;
float * __restrict disi = res->get_val (i);
long * __restrict idxi = res->get_ids (i);
maxheap_heapify (k, disi, idxi);
for (size_t ik = 0; ik < nprobe; ik++) {
long key = keysi[ik]; /* select the list */
if (key < 0) {
// not enough centroids for multiprobe
continue;
}
if (key >= (long) nlist) {
fprintf (stderr, "Invalid key=%ld at ik=%ld nlist=%ld\n",
key, ik, nlist);
throw;
}
nlistv++;
const size_t list_size = ids[key].size();
const float * list_vecs = vecs[key].data();
for (size_t j = 0; j < list_size; j++) {
const float * yj = list_vecs + d * j;
float disij = fvec_L2sqr (xi, yj, d);
if (disij < disi[0]) {
maxheap_pop (k, disi, idxi);
maxheap_push (k, disi, idxi, disij, ids[key][j]);
}
}
ndis += list_size;
}
maxheap_reorder (k, disi, idxi);
}
indexIVFFlat_stats.nq += nx;
indexIVFFlat_stats.nlist += nlistv;
indexIVFFlat_stats.ndis += ndis;
}
void IndexIVFFlat::search (idx_t n, const float *x, idx_t k,
float *distances, idx_t *labels) const
{
idx_t * idx = new idx_t [n * nprobe];
ScopeDeleter <idx_t> del (idx);
quantizer->assign (n, x, idx, nprobe);
search_preassigned (n, x, k, idx, distances, labels);
}
void IndexIVFFlat::search_preassigned (idx_t n, const float *x, idx_t k,
const idx_t *idx,
float *distances, idx_t *labels) const
{
if (metric_type == METRIC_INNER_PRODUCT) {
float_minheap_array_t res = {
size_t(n), size_t(k), labels, distances};
search_knn_inner_product (n, x, idx, &res);
} else if (metric_type == METRIC_L2) {
float_maxheap_array_t res = {
size_t(n), size_t(k), labels, distances};
search_knn_L2sqr (n, x, idx, &res);
}
}
void IndexIVFFlat::range_search (idx_t nx, const float *x, float radius,
RangeSearchResult *result) const
{
idx_t * keys = new idx_t [nx * nprobe];
ScopeDeleter<idx_t> del (keys);
quantizer->assign (nx, x, keys, nprobe);
#pragma omp parallel
{
RangeSearchPartialResult pres(result);
for (size_t i = 0; i < nx; i++) {
const float * xi = x + i * d;
const long * keysi = keys + i * nprobe;
RangeSearchPartialResult::QueryResult & qres =
pres.new_result (i);
for (size_t ik = 0; ik < nprobe; ik++) {
long key = keysi[ik]; /* select the list */
if (key < 0 || key >= (long) nlist) {
fprintf (stderr, "Invalid key=%ld at ik=%ld nlist=%ld\n",
key, ik, nlist);
throw;
}
const size_t list_size = ids[key].size();
const float * list_vecs = vecs[key].data();
for (size_t j = 0; j < list_size; j++) {
const float * yj = list_vecs + d * j;
if (metric_type == METRIC_L2) {
float disij = fvec_L2sqr (xi, yj, d);
if (disij < radius) {
qres.add (disij, ids[key][j]);
}
} else if (metric_type == METRIC_INNER_PRODUCT) {
float disij = fvec_inner_product(xi, yj, d);
if (disij > radius) {
qres.add (disij, ids[key][j]);
}
}
}
}
}
pres.finalize ();
}
}
void IndexIVFFlat::merge_from_residuals (IndexIVF &other_in)
{
IndexIVFFlat &other = dynamic_cast< IndexIVFFlat &> (other_in);
for (int i = 0; i < nlist; i++) {
std::vector<float> & src = other.vecs[i];
std::vector<float> & dest = vecs[i];
for (int j = 0; j < src.size(); j++)
dest.push_back (src[j]);
src.clear();
}
}
void IndexIVFFlat::copy_subset_to (IndexIVFFlat & other, int subset_type,
long a1, long a2) const
{
FAISS_THROW_IF_NOT (nlist == other.nlist);
FAISS_THROW_IF_NOT (!other.maintain_direct_map);
for (long list_no = 0; list_no < nlist; list_no++) {
const std::vector<idx_t> & ids_in = ids[list_no];
std::vector<idx_t> & ids_out = other.ids[list_no];
const std::vector<float> & vecs_in = vecs[list_no];
std::vector<float> & vecs_out = other.vecs[list_no];
for (long i = 0; i < ids_in.size(); i++) {
idx_t id = ids_in[i];
if (subset_type == 0 && a1 <= id && id < a2) {
ids_out.push_back (id);
vecs_out.insert (vecs_out.end(),
vecs_in.begin() + i * d,
vecs_in.begin() + (i + 1) * d);
other.ntotal++;
}
}
}
}
void IndexIVFFlat::update_vectors (int n, idx_t *new_ids, const float *x)
{
FAISS_THROW_IF_NOT (maintain_direct_map);
FAISS_THROW_IF_NOT (is_trained);
std::vector<idx_t> assign (n);
quantizer->assign (n, x, assign.data());
for (int i = 0; i < n; i++) {
idx_t id = new_ids[i];
FAISS_THROW_IF_NOT_MSG (0 <= id && id < ntotal,
"id to update out of range");
{ // remove old one
long dm = direct_map[id];
long ofs = dm & 0xffffffff;
long il = dm >> 32;
size_t l = ids[il].size();
if (ofs != l - 1) {
long id2 = ids[il].back();
ids[il][ofs] = id2;
direct_map[id2] = (il << 32) | ofs;
memcpy (vecs[il].data() + ofs * d,
vecs[il].data() + (l - 1) * d,
d * sizeof(vecs[il][0]));
}
ids[il].pop_back();
vecs[il].resize((l - 1) * d);
}
{ // insert new one
long il = assign[i];
size_t l = ids[il].size();
long dm = (il << 32) | l;
direct_map[id] = dm;
ids[il].push_back (id);
vecs[il].resize((l + 1) * d);
memcpy (vecs[il].data() + l * d,
x + i * d,
d * sizeof(vecs[il][0]));
}
}
}
void IndexIVFFlat::reset()
{
IndexIVF::reset();
for (size_t key = 0; key < nlist; key++) {
vecs[key].clear();
}
}
long IndexIVFFlat::remove_ids (const IDSelector & sel)
{
FAISS_THROW_IF_NOT_MSG (!maintain_direct_map,
"direct map remove not implemented");
long nremove = 0;
#pragma omp parallel for reduction(+: nremove)
for (long i = 0; i < nlist; i++) {
std::vector<idx_t> & idsi = ids[i];
float *vecsi = vecs[i].data();
long l = idsi.size(), j = 0;
while (j < l) {
if (sel.is_member (idsi[j])) {
l--;
idsi [j] = idsi [l];
memmove (vecsi + j * d,
vecsi + l * d, d * sizeof (float));
} else {
j++;
}
}
if (l < idsi.size()) {
nremove += idsi.size() - l;
idsi.resize (l);
vecs[i].resize (l * d);
}
}
ntotal -= nremove;
return nremove;
}
void IndexIVFFlat::reconstruct (idx_t key, float * recons) const
{
FAISS_THROW_IF_NOT_MSG (direct_map.size() == ntotal,
"direct map is not initialized");
int list_no = direct_map[key] >> 32;
int ofs = direct_map[key] & 0xffffffff;
memcpy (recons, &vecs[list_no][ofs * d], d * sizeof(recons[0]));
}
/*****************************************
* IndexIVFFlatIPBounds implementation
******************************************/
IndexIVFFlatIPBounds::IndexIVFFlatIPBounds (
Index * quantizer, size_t d, size_t nlist,
size_t fsize):
IndexIVFFlat(quantizer, d, nlist, METRIC_INNER_PRODUCT), fsize(fsize)
{
part_norms.resize(nlist);
}
void IndexIVFFlatIPBounds::add_core (idx_t n, const float * x, const long *xids,
const long *precomputed_idx) {
FAISS_THROW_IF_NOT (is_trained);
const long * idx;
ScopeDeleter<long> del;
if (precomputed_idx) {
idx = precomputed_idx;
} else {
long * idx0 = new long [n];
quantizer->assign (n, x, idx0);
idx = idx0;
del.set (idx);
}
IndexIVFFlat::add_core(n, x, xids, idx);
// compute
const float * xi = x + fsize;
for (size_t i = 0; i < n; i++) {
float norm = std::sqrt (fvec_norm_L2sqr (xi, d - fsize));
part_norms[idx[i]].push_back(norm);
xi += d;
}
}
namespace {
void search_bounds_knn_inner_product (
const IndexIVFFlatIPBounds & ivf,
const float *x,
const long *keys,
float_minheap_array_t *res,
const float *qnorms)
{
size_t k = res->k, nx = res->nh, nprobe = ivf.nprobe;
size_t d = ivf.d;
int fsize = ivf.fsize;
size_t nlistv = 0, ndis = 0, npartial = 0;
#pragma omp parallel for reduction(+: nlistv, ndis, npartial)
for (size_t i = 0; i < nx; i++) {
const float * xi = x + i * d;
const long * keysi = keys + i * nprobe;
float qnorm = qnorms[i];
float * __restrict simi = res->get_val (i);
long * __restrict idxi = res->get_ids (i);
minheap_heapify (k, simi, idxi);
for (size_t ik = 0; ik < nprobe; ik++) {
long key = keysi[ik]; /* select the list */
if (key < 0) {
// not enough centroids for multiprobe
continue;
}
assert (key < (long) ivf.nlist);
nlistv++;
const size_t list_size = ivf.ids[key].size();
const float * yj = ivf.vecs[key].data();
const float * bnorms = ivf.part_norms[key].data();
for (size_t j = 0; j < list_size; j++) {
float ip_part = fvec_inner_product (xi, yj, fsize);
float bound = ip_part + bnorms[j] * qnorm;
if (bound > simi[0]) {
float ip = ip_part + fvec_inner_product (
xi + fsize, yj + fsize, d - fsize);
if (ip > simi[0]) {
minheap_pop (k, simi, idxi);
minheap_push (k, simi, idxi, ip, ivf.ids[key][j]);
}
ndis ++;
}
yj += d;
}
npartial += list_size;
}
minheap_reorder (k, simi, idxi);
}
indexIVFFlat_stats.nq += nx;
indexIVFFlat_stats.nlist += nlistv;
indexIVFFlat_stats.ndis += ndis;
indexIVFFlat_stats.npartial += npartial;
}
}
void IndexIVFFlatIPBounds::search (
idx_t n, const float *x, idx_t k,
float *distances, idx_t *labels) const
{
// compute query remainder norms and distances
idx_t * idx = new idx_t [n * nprobe];
ScopeDeleter<idx_t> del (idx);
quantizer->assign (n, x, idx, nprobe);
float * qnorms = new float [n];
ScopeDeleter <float> del2 (qnorms);
#pragma omp parallel for
for (size_t i = 0; i < n; i++) {
qnorms[i] = std::sqrt (fvec_norm_L2sqr (
x + i * d + fsize, d - fsize));
}
float_minheap_array_t res = {
size_t(n), size_t(k), labels, distances};
search_bounds_knn_inner_product (*this, x, idx, &res, qnorms);
}
} // namespace faiss
faiss::IndexIVF
Definition: IndexIVF.h:45
faiss::IndexIVFFlat::search_preassigned
void search_preassigned(idx_t n, const float *x, idx_t k, const idx_t *assign, float *distances, idx_t *labels) const
perform search, without computing the assignment to the quantizer
Definition: IndexIVF.cpp:382
faiss::ClusteringParameters::niter
int niter
clustering iterations
Definition: Clustering.h:25
faiss::RangeSearchPartialResult::QueryResult
result structure for a single query
Definition: AuxIndexStructures.h:145
faiss::fvec_L2sqr
float fvec_L2sqr(const float *x, const float *y, size_t d)
Squared L2 distance between two vectors.
Definition: utils.cpp:481
faiss::IndexIVFFlat::search_knn_L2sqr
void search_knn_L2sqr(size_t nx, const float *x, const long *keys, float_maxheap_array_t *res) const
Implementation of the search for the L2 metric.
Definition: IndexIVF.cpp:322
faiss::HeapArray::get_val
T * get_val(size_t key)
Return the list of values for a heap.
Definition: Heap.h:360
faiss::IndexIVF::imbalance_factor
double imbalance_factor() const
1= perfectly balanced, &gt;1: imbalanced
Definition: IndexIVF.cpp:139
faiss::Index::reset
virtual void reset()=0
removes all elements from the database.
faiss::IndexIVF::nprobe
size_t nprobe
number of probes at query time
Definition: IndexIVF.h:47
faiss::Index::train
virtual void train(idx_t, const float *)
Definition: Index.h:89
faiss::IndexIVFFlat::reconstruct
void reconstruct(idx_t key, float *recons) const override
Definition: IndexIVF.cpp:572
faiss::Index::assign
void assign(idx_t n, const float *x, idx_t *labels, idx_t k=1)
Definition: Index.cpp:23
faiss::IDSelector
Definition: AuxIndexStructures.h:52
faiss::IndexIVF::quantizer_trains_alone
bool quantizer_trains_alone
just pass over the trainset to quantizer
Definition: IndexIVF.h:50
faiss::IndexIVFFlat::range_search
void range_search(idx_t n, const float *x, float radius, RangeSearchResult *result) const override
Definition: IndexIVF.cpp:400
faiss::IndexIVFFlat::copy_subset_to
void copy_subset_to(IndexIVFFlat &other, int subset_type, long a1, long a2) const
Definition: IndexIVF.cpp:462
faiss::IndexIVFFlat::merge_from_residuals
void merge_from_residuals(IndexIVF &other) override
Definition: IndexIVF.cpp:450
faiss::Index::add_with_ids
virtual void add_with_ids(idx_t n, const float *x, const long *xids)
Definition: Index.cpp:30
faiss::IndexIVF::train_residual
virtual void train_residual(idx_t n, const float *x)
Definition: IndexIVF.cpp:131
faiss::HeapArray::k
size_t k
allocated size per heap
Definition: Heap.h:355
faiss::ScopeDeleter
Definition: FaissException.h:39
faiss::imbalance_factor
double imbalance_factor(int n, int k, const long *assign)
a balanced assignment has a IF of 1
Definition: utils.cpp:1593
faiss::IndexIVFFlat::remove_ids
long remove_ids(const IDSelector &sel) override
Definition: IndexIVF.cpp:540
faiss::IndexIVFFlat
Definition: IndexIVF.h:132
faiss::IndexIVF::ids
std::vector< std::vector< long > > ids
Inverted lists for indexes.
Definition: IndexIVF.h:55
faiss::Index::d
int d
vector dimension
Definition: Index.h:64
faiss::IndexIVF::quantizer
Index * quantizer
quantizer that maps vectors to inverted lists
Definition: IndexIVF.h:49
faiss::IndexIVF::train
void train(idx_t n, const float *x) override
Trains the quantizer and calls train_residual to train sub-quantizers.
Definition: IndexIVF.cpp:102
faiss::IndexIVF::cp
ClusteringParameters cp
to override default clustering params
Definition: IndexIVF.h:53
faiss::IndexIVFFlat::add_with_ids
void add_with_ids(idx_t n, const float *x, const long *xids) override
implemented for all IndexIVF* classes
Definition: IndexIVF.cpp:215
faiss::Clustering
Definition: Clustering.h:56
faiss::IndexIVF::own_fields
bool own_fields
whether object owns the quantizer
Definition: IndexIVF.h:51
faiss::HeapArray
Definition: Heap.h:350
faiss::Index::idx_t
long idx_t
all indices are this type
Definition: Index.h:62
faiss::IndexIVFFlat::reset
void reset() override
removes all elements from the database.
Definition: IndexIVF.cpp:532
faiss::Index::ntotal
idx_t ntotal
total nb of indexed vectors
Definition: Index.h:65
faiss::Index::verbose
bool verbose
verbosity level
Definition: Index.h:66
faiss::IndexIVF::reset
void reset() override
removes all elements from the database.
Definition: IndexIVF.cpp:93
faiss::RangeSearchPartialResult::new_result
QueryResult & new_result(idx_t qno)
begin a new result
Definition: AuxIndexStructures.cpp:113
faiss::IndexIVFFlat::update_vectors
void update_vectors(int nv, idx_t *idx, const float *v)
Definition: IndexIVF.cpp:487
faiss::IndexIVFFlatIPBounds::search
void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const override
Definition: IndexIVF.cpp:693
faiss::IndexIVFFlatIPBounds::part_norms
std::vector< std::vector< float > > part_norms
norm of remainder (dimensions fsize:d)
Definition: IndexIVF.h:213
faiss::fvec_norm_L2sqr
float fvec_norm_L2sqr(const float *x, size_t d)
Definition: utils.cpp:538
faiss::IndexIVFFlatIPBounds
Definition: IndexIVF.h:207
faiss::IndexIVFFlatIPBounds::fsize
size_t fsize
nb of dimensions of pre-filter
Definition: IndexIVF.h:210
faiss::RangeSearchPartialResult
the entries in the buffers are split per query
Definition: AuxIndexStructures.h:139
faiss::RangeSearchResult
Definition: AuxIndexStructures.h:29
faiss::IndexIVF::merge_from_residuals
virtual void merge_from_residuals(IndexIVF &other)=0
faiss::IndexIVF::make_direct_map
void make_direct_map(bool new_maintain_direct_map=true)
Definition: IndexIVF.cpp:68
faiss::HeapArray::get_ids
TI * get_ids(size_t key)
Correspponding identifiers.
Definition: Heap.h:363
faiss::Index::metric_type
MetricType metric_type
type of metric this index uses for search
Definition: Index.h:72
faiss::IndexIVF::print_stats
void print_stats() const
display some stats about the inverted lists
Definition: IndexIVF.cpp:148
faiss::IndexIVFFlatIPBounds::add_core
void add_core(idx_t n, const float *x, const long *xids, const long *precomputed_idx) override
same as add_with_ids, with precomputed coarse quantizer
Definition: IndexIVF.cpp:598
faiss::HeapArray::nh
size_t nh
number of heaps
Definition: Heap.h:354
faiss::IndexIVF::nlist
size_t nlist
number of possible key values
Definition: IndexIVF.h:46
faiss::Index
Definition: Index.h:60
faiss::IndexIVF::add
void add(idx_t n, const float *x) override
Quantizes x and calls add_with_key.
Definition: IndexIVF.cpp:63
faiss::Clustering::train
virtual void train(idx_t n, const float *x, faiss::Index &index)
Index is used during the assignment stage.
Definition: Clustering.cpp:66
faiss::Index::is_trained
bool is_trained
set if the Index does not require training, or if training is done already
Definition: Index.h:69
faiss::IndexIVFFlat::search_knn_inner_product
void search_knn_inner_product(size_t nx, const float *x, const long *keys, float_minheap_array_t *res) const
Implementation of the search for the inner product metric.
Definition: IndexIVF.cpp:271
faiss::IndexIVF::maintain_direct_map
bool maintain_direct_map
map for direct access to the elements. Enables reconstruct().
Definition: IndexIVF.h:58
faiss::ClusteringParameters::spherical
bool spherical
do we want normalized centroids?
Definition: Clustering.h:29
faiss::IndexIVF::merge_from
virtual void merge_from(IndexIVF &other, idx_t add_id)
Definition: IndexIVF.cpp:168
faiss::MetricType
MetricType
Some algorithms support both an inner product vetsion and a L2 search version.
Definition: Index.h:43
faiss::IndexIVFFlat::vecs
std::vector< std::vector< float > > vecs
Definition: IndexIVF.h:135
faiss::IndexIVFFlat::add_core
virtual void add_core(idx_t n, const float *x, const long *xids, const long *precomputed_idx)
same as add_with_ids, with precomputed coarse quantizer
Definition: IndexIVF.cpp:220
faiss::IndexIVFFlat::search
void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const override
Definition: IndexIVF.cpp:372