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/data/users/matthijs/github_faiss/faiss/Clustering.h
1 /**
2 * Copyright (c) 2015-present, Facebook, Inc.
3 * All rights reserved.
4 *
5 * This source code is licensed under the BSD+Patents license found in the
6 * LICENSE file in the root directory of this source tree.
7 */
8
9 // Copyright 2004-present Facebook. All Rights Reserved
10 // -*- c++ -*-
11
12 #ifndef FAISS_CLUSTERING_H
13 #define FAISS_CLUSTERING_H
14 #include "Index.h"
15
16 #include <vector>
17
18 namespace faiss {
19
20
21 /** Class for the clustering parameters. Can be passed to the
22 * constructor of the Clustering object.
23 */
24 struct ClusteringParameters {
25 int niter; ///< clustering iterations
26 int nredo; ///< redo clustering this many times and keep best
27
28 bool verbose;
29 bool spherical; ///< do we want normalized centroids?
30 bool update_index; ///< update index after each iteration?
31 bool frozen_centroids; ///< use the centroids provided as input and do not change them during iterations
32
33 int min_points_per_centroid; ///< otherwise you get a warning
34 int max_points_per_centroid; ///< to limit size of dataset
35
36 int seed; ///< seed for the random number generator
37
38 /// sets reasonable defaults
39 ClusteringParameters ();
40 };
41
42
43 /** clustering based on assignment - centroid update iterations
44 *
45 * The clustering is based on an Index object that assigns training
46 * points to the centroids. Therefore, at each iteration the centroids
47 * are added to the index.
48 *
49 * On output, the centoids table is set to the latest version
50 * of the centroids and they are also added to the index. If the
51 * centroids table it is not empty on input, it is also used for
52 * initialization.
53 *
54 * To do several clusterings, just call train() several times on
55 * different training sets, clearing the centroid table in between.
56 */
57 struct Clustering: ClusteringParameters {
58 typedef Index::idx_t idx_t;
59 size_t d; ///< dimension of the vectors
60 size_t k; ///< nb of centroids
61
62 /// centroids (k * d)
63 std::vector<float> centroids;
64
65 /// objective values (sum of distances reported by index) over
66 /// iterations
67 std::vector<float> obj;
68
69 /// the only mandatory parameters are k and d
70 Clustering (int d, int k);
71 Clustering (int d, int k, const ClusteringParameters &cp);
72
73 /// Index is used during the assignment stage
74 virtual void train (idx_t n, const float * x, faiss::Index & index);
75
76 virtual ~Clustering() {}
77 };
78
79
80 /** simplified interface
81 *
82 * @param d dimension of the data
83 * @param n nb of training vectors
84 * @param k nb of output centroids
85 * @param x training set (size n * d)
86 * @param centroids output centroids (size k * d)
87 * @return final quantization error
88 */
89 float kmeans_clustering (size_t d, size_t n, size_t k,
90 const float *x,
91 float *centroids);
92
93
94
95 }
96
97
98 #endif
faiss::ClusteringParameters::niter
int niter
clustering iterations
Definition: Clustering.h:25
faiss::ClusteringParameters::nredo
int nredo
redo clustering this many times and keep best
Definition: Clustering.h:26
faiss::ClusteringParameters::ClusteringParameters
ClusteringParameters()
sets reasonable defaults
Definition: Clustering.cpp:27
faiss::Clustering::Clustering
Clustering(int d, int k)
the only mandatory parameters are k and d
Definition: Clustering.cpp:40
faiss::Clustering::k
size_t k
nb of centroids
Definition: Clustering.h:60
faiss::ClusteringParameters::seed
int seed
seed for the random number generator
Definition: Clustering.h:36
faiss::ClusteringParameters::frozen_centroids
bool frozen_centroids
use the centroids provided as input and do not change them during iterations
Definition: Clustering.h:31
faiss::ClusteringParameters::min_points_per_centroid
int min_points_per_centroid
otherwise you get a warning
Definition: Clustering.h:33
faiss::Clustering
Definition: Clustering.h:57
faiss::Clustering::obj
std::vector< float > obj
Definition: Clustering.h:67
faiss::Index::idx_t
long idx_t
all indices are this type
Definition: Index.h:62
faiss::kmeans_clustering
float kmeans_clustering(size_t d, size_t n, size_t k, const float *x, float *centroids)
Definition: Clustering.cpp:241
faiss::Clustering::centroids
std::vector< float > centroids
centroids (k * d)
Definition: Clustering.h:63
faiss::Clustering::d
size_t d
dimension of the vectors
Definition: Clustering.h:59
faiss::ClusteringParameters::update_index
bool update_index
update index after each iteration?
Definition: Clustering.h:30
faiss::ClusteringParameters
Definition: Clustering.h:24
faiss::Index
Definition: Index.h:60
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:67
faiss::ClusteringParameters::spherical
bool spherical
do we want normalized centroids?
Definition: Clustering.h:29
faiss::ClusteringParameters::max_points_per_centroid
int max_points_per_centroid
to limit size of dataset
Definition: Clustering.h:34