File IndexBinaryHash.h

namespace faiss

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.

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. Implementation of k-means clustering with many variants.

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. IDSelector is intended to define a subset of vectors to handle (for removal or as subset to search)

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. PQ4 SIMD packing and accumulation functions

The basic kernel accumulates nq query vectors with bbs = nb * 2 * 16 vectors and produces an output matrix for that. It is interesting for nq * nb <= 4, otherwise register spilling becomes too large.

The implementation of these functions is spread over 3 cpp files to reduce parallel compile times. Templates are instantiated explicitly.

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. This file contains callbacks for kernels that compute distances.

Throughout the library, vectors are provided as float * pointers. Most algorithms can be optimized when several vectors are processed (added/searched) together in a batch. In this case, they are passed in as a matrix. When n vectors of size d are provided as float * x, component j of vector i is

x[ i * d + j ]

where 0 <= i < n and 0 <= j < d. In other words, matrices are always compact. When specifying the size of the matrix, we call it an n*d matrix, which implies a row-major storage.

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. I/O functions can read/write to a filename, a file handle or to an object that abstracts the medium.

The read functions return objects that should be deallocated with delete. All references within these objectes are owned by the object.

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. Definition of inverted lists + a few common classes that implement the interface.

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. Since IVF (inverted file) indexes are of so much use for large-scale use cases, we group a few functions related to them in this small library. Most functions work both on IndexIVFs and IndexIVFs embedded within an IndexPreTransform.

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. In this file are the implementations of extra metrics beyond L2 and inner product

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. Defines a few objects that apply transformations to a set of vectors Often these are pre-processing steps.

Variables

FAISS_API IndexBinaryHashStats indexBinaryHash_stats

struct IndexBinaryHash : public faiss::IndexBinary 

#include <IndexBinaryHash.h>

just uses the b first bits as a hash value

Public Types

using InvertedListMap = std::unordered_map<idx_t, InvertedList>

Public Functions

IndexBinaryHash(int d, int b)

IndexBinaryHash()

virtual void reset() override: Removes all elements from the database.

virtual void add(idx_t n, const uint8_t *x) override

Add n vectors of dimension d to the index.

Vectors are implicitly assigned labels ntotal .. ntotal + n - 1

Parameters:: x – input matrix, size n * d / 8

virtual void add_with_ids(idx_t n, const uint8_t *x, const idx_t *xids) override

Same as add, but stores xids instead of sequential ids.

The default implementation fails with an assertion, as it is not supported by all indexes.

Parameters:: xids – if non-null, ids to store for the vectors (size n)

virtual void range_search(idx_t n, const uint8_t *x, int radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const override

Query n vectors of dimension d to the index.

return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory). The distances are converted to float to reuse the RangeSearchResult structure, but they are integer. By convention, only distances < radius (strict comparison) are returned, ie. radius = 0 does not return any result and 1 returns only exact same vectors.

Parameters:

x – input vectors to search, size n * d / 8
radius – search radius
result – result table

virtual void search(idx_t n, const uint8_t *x, idx_t k, int32_t *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

Query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters:

x – input vectors to search, size n * d / 8
labels – output labels of the NNs, size n*k
distances – output pairwise distances, size n*k

void display() const

size_t hashtable_size() const

Public Members

InvertedListMap invlists

int b

int nflip

struct InvertedList

Public Functions

void add(idx_t id, size_t code_size, const uint8_t *code)

Public Members

std::vector<idx_t> ids

std::vector<uint8_t> vecs

struct IndexBinaryHashStats

Public Functions

inline IndexBinaryHashStats()

void reset()

Public Members

size_t nq

size_t n0

size_t nlist

size_t ndis

struct IndexBinaryMultiHash : public faiss::IndexBinary 

#include <IndexBinaryHash.h>

just uses the b first bits as a hash value

Public Types

using Map = std::unordered_map<idx_t, std::vector<idx_t>>

Public Functions

IndexBinaryMultiHash(int d, int nhash, int b)

IndexBinaryMultiHash()

~IndexBinaryMultiHash()

virtual void reset() override: Removes all elements from the database.

virtual void add(idx_t n, const uint8_t *x) override

Add n vectors of dimension d to the index.

Vectors are implicitly assigned labels ntotal .. ntotal + n - 1

Parameters:: x – input matrix, size n * d / 8

virtual void range_search(idx_t n, const uint8_t *x, int radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const override

Query n vectors of dimension d to the index.

return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory). The distances are converted to float to reuse the RangeSearchResult structure, but they are integer. By convention, only distances < radius (strict comparison) are returned, ie. radius = 0 does not return any result and 1 returns only exact same vectors.

Parameters:

x – input vectors to search, size n * d / 8
radius – search radius
result – result table

virtual void search(idx_t n, const uint8_t *x, idx_t k, int32_t *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

Query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters:

x – input vectors to search, size n * d / 8
labels – output labels of the NNs, size n*k
distances – output pairwise distances, size n*k

size_t hashtable_size() const

Public Members

IndexBinaryFlat *storage

bool own_fields

std::vector<Map> maps

int nhash: nb of hash maps

int b: nb bits per hash map

int nflip: nb bit flips to use at search time