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/data/users/matthijs/github_faiss/faiss/Clustering.h
1
2 /**
3 * Copyright (c) 2015-present, Facebook, Inc.
4 * All rights reserved.
5 *
6 * This source code is licensed under the CC-by-NC license found in the
7 * LICENSE file in the root directory of this source tree.
8 */
9
10 // Copyright 2004-present Facebook. All Rights Reserved
11 // -*- c++ -*-
12
13 #ifndef FAISS_CLUSTERING_H
14 #define FAISS_CLUSTERING_H
15 #include "Index.h"
16
17 #include <vector>
18
19 namespace faiss {
20
21
22 /** Class for the clustering parameters. Can be passed to the
23 * constructor of the Clustering object.
24 */
25 struct ClusteringParameters {
26 int niter; ///< clustering iterations
27 int nredo; ///< redo clustering this many times and keep best
28
29
30 bool verbose;
31 bool spherical; ///< do we want normalized centroids?
32 bool update_index; ///< update index after each iteration?
33
34 int min_points_per_centroid; ///< otherwise you get a warning
35 int max_points_per_centroid; ///< to limit size of dataset
36
37 int seed; ///< seed for the random number generator
38
39 /// sets reasonable defaults
40 ClusteringParameters ();
41 };
42
43
44 /** clustering based on assignment - centroid update iterations
45 *
46 * The clustering is based on an Index object that assigns training
47 * points to the centroids. Therefore, at each iteration the centroids
48 * are added to the index.
49 *
50 * On output, the centoids table is set to the latest version
51 * of the centroids and they are also added to the index. If the
52 * centroids table it is not empty on input, it is also used for
53 * initialization.
54 *
55 * To do several clusterings, just call train() several times on
56 * different training sets, clearing the centroid table in between.
57 */
58 struct Clustering: ClusteringParameters {
59 typedef Index::idx_t idx_t;
60 size_t d; ///< dimension of the vectors
61 size_t k; ///< nb of centroids
62
63 /// centroids (k * d)
64 std::vector<float> centroids;
65
66 /// objective values (sum of distances reported by index) over
67 /// iterations
68 std::vector<float> obj;
69
70 /// the only mandatory parameters are k and d
71 Clustering (int d, int k);
72 Clustering (int d, int k, const ClusteringParameters &cp);
73
74 /// Index is used during the assignment stage
75 virtual void train (idx_t n, const float * x, faiss::Index & index);
76
77 virtual ~Clustering() {}
78 };
79
80
81 /** simplified interface
82 *
83 * @param d dimension of the data
84 * @param n nb of training vectors
85 * @param k nb of output centroids
86 * @param x training set (size n * d)
87 * @param centroids output centroids (size k * d)
88 * @return final quantization error
89 */
90 float kmeans_clustering (size_t d, size_t n, size_t k,
91 const float *x,
92 float *centroids);
93
94
95
96 }
97
98
99 #endif
faiss::ClusteringParameters::niter
int niter
clustering iterations
Definition: Clustering.h:26
faiss::ClusteringParameters::nredo
int nredo
redo clustering this many times and keep best
Definition: Clustering.h:27
faiss::ClusteringParameters::ClusteringParameters
ClusteringParameters()
sets reasonable defaults
Definition: Clustering.cpp:28
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:61
faiss::ClusteringParameters::seed
int seed
seed for the random number generator
Definition: Clustering.h:37
faiss::ClusteringParameters::min_points_per_centroid
int min_points_per_centroid
otherwise you get a warning
Definition: Clustering.h:34
faiss::Clustering
Definition: Clustering.h:58
faiss::Clustering::obj
std::vector< float > obj
Definition: Clustering.h:68
faiss::Index::idx_t
long idx_t
all indices are this type
Definition: Index.h:64
faiss::kmeans_clustering
float kmeans_clustering(size_t d, size_t n, size_t k, const float *x, float *centroids)
Definition: Clustering.cpp:204
faiss::Clustering::centroids
std::vector< float > centroids
centroids (k * d)
Definition: Clustering.h:64
faiss::Clustering::d
size_t d
dimension of the vectors
Definition: Clustering.h:60
faiss::ClusteringParameters::update_index
bool update_index
update index after each iteration?
Definition: Clustering.h:32
faiss::ClusteringParameters
Definition: Clustering.h:25
faiss::Index
Definition: Index.h:61
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:31
faiss::ClusteringParameters::max_points_per_centroid
int max_points_per_centroid
to limit size of dataset
Definition: Clustering.h:35