File MetricType.h

namespace faiss

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)

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.

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.

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.

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

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.

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

Implements a few neural net layers, mainly to support QINCo

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

Typedefs

using idx_t = int64_t

all vector indices are this type

Enums

enum MetricType

The metric space for vector comparison for Faiss indices and algorithms.

Most algorithms support both inner product and L2, with the flat (brute-force) indices supporting additional metric types for vector comparison.

Values:

enumerator METRIC_INNER_PRODUCT

maximum inner product search

enumerator METRIC_L2

squared L2 search

enumerator METRIC_L1

L1 (aka cityblock)

enumerator METRIC_Linf

infinity distance

enumerator METRIC_Lp

L_p distance, p is given by a faiss::Index metric_arg

enumerator METRIC_Canberra

some additional metrics defined in scipy.spatial.distance

enumerator METRIC_BrayCurtis

enumerator METRIC_JensenShannon

enumerator METRIC_Jaccard

sum_i(min(a_i, b_i)) / sum_i(max(a_i, b_i)) where a_i, b_i > 0

enumerator METRIC_NaNEuclidean

Squared Eucliden distance, ignoring NaNs.

enumerator METRIC_ABS_INNER_PRODUCT

abs(x | y): the distance to a hyperplane

Functions

constexpr bool is_similarity_metric(MetricType metric_type)

this function is used to distinguish between min and max indexes since we need to support similarity and dis-similarity metrics in a flexible way