# File IndexIVFSpectralHash.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.

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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. Implements a few neural net layers, mainly to support QINCo

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.

struct IndexIVFSpectralHash : public faiss::IndexIVF
#include <IndexIVFSpectralHash.h>

Inverted list that stores binary codes of size nbit. Before the binary conversion, the dimension of the vectors is transformed from dim d into dim nbit by vt (a random rotation by default).

Each coordinate is subtracted from a value determined by threshold_type, and split into intervals of size period. Half of the interval is a 0 bit, the other half a 1.

Public Types

enum ThresholdType

Values:

enumerator Thresh_global

global threshold at 0

enumerator Thresh_centroid

compare to centroid

enumerator Thresh_centroid_half

central interval around centroid

enumerator Thresh_median

median of training set

Public Functions

IndexIVFSpectralHash(Index *quantizer, size_t d, size_t nlist, int nbit, float period)
IndexIVFSpectralHash()
virtual void train_encoder(idx_t n, const float *x, const idx_t *assign) override

Train the encoder for the vectors.

If by_residual then it is called with residuals and corresponding assign array, otherwise x is the raw training vectors and assign=nullptr

virtual void encode_vectors(idx_t n, const float *x, const idx_t *list_nos, uint8_t *codes, bool include_listnos = false) const override

Encodes a set of vectors as they would appear in the inverted lists

Parameters:
• list_nos – inverted list ids as returned by the quantizer (size n). -1s are ignored.

• codes – output codes, size n * code_size

• include_listno – include the list ids in the code (in this case add ceil(log8(nlist)) to the code size)

virtual InvertedListScanner *get_InvertedListScanner(bool store_pairs, const IDSelector *sel) const override

Get a scanner for this index (store_pairs means ignore labels)

The default search implementation uses this to compute the distances

void replace_vt(VectorTransform *vt, bool own = false)

replace the vector transform for an empty (and possibly untrained) index

void replace_vt(IndexPreTransform *index, bool own = false)

convenience function to get the VT from an index constucted by an index_factory (should end in “LSH”)

~IndexIVFSpectralHash() override

Public Members

VectorTransform *vt = nullptr

transformation from d to nbit dim

bool own_fields = true

own the vt

int nbit = 0

nb of bits of the binary signature

float period = 0

interval size for 0s and 1s

ThresholdType threshold_type = Thresh_global
std::vector<float> trained

Trained threshold. size nlist * nbit or 0 if Thresh_global