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Clustering_c.cpp
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 #include "Clustering_c.h"
13 #include "Clustering.h"
14 #include "Index.h"
15 #include <vector>
16 #include "macros_impl.h"
17
18 extern "C" {
19
20 using faiss::Clustering;
21 using faiss::ClusteringParameters;
22 using faiss::Index;
23
24 DEFINE_GETTER(Clustering, int, niter)
25 DEFINE_GETTER(Clustering, int, nredo)
26 DEFINE_GETTER(Clustering, int, verbose)
27 DEFINE_GETTER(Clustering, int, spherical)
28 DEFINE_GETTER(Clustering, int, update_index)
29 DEFINE_GETTER(Clustering, int, frozen_centroids)
30
31 DEFINE_GETTER(Clustering, int, min_points_per_centroid)
32 DEFINE_GETTER(Clustering, int, max_points_per_centroid)
33
34 DEFINE_GETTER(Clustering, int, seed)
35
36 /// getter for d
37 DEFINE_GETTER(Clustering, size_t, d)
38
39 /// getter for k
40 DEFINE_GETTER(Clustering, size_t, k)
41
42 void faiss_ClusteringParameters_init(FaissClusteringParameters* params) {
43 ClusteringParameters d;
44 params->frozen_centroids = d.frozen_centroids;
45 params->max_points_per_centroid = d.max_points_per_centroid;
46 params->min_points_per_centroid = d.min_points_per_centroid;
47 params->niter = d.niter;
48 params->nredo = d.nredo;
49 params->seed = d.seed;
50 params->spherical = d.spherical;
51 params->update_index = d.update_index;
52 params->verbose = d.verbose;
53 }
54
55 // This conversion is required because the two types are not memory-compatible
56 inline ClusteringParameters from_faiss_c(const FaissClusteringParameters* params) {
57 ClusteringParameters o;
58 o.frozen_centroids = params->frozen_centroids;
59 o.max_points_per_centroid = params->max_points_per_centroid;
60 o.min_points_per_centroid = params->min_points_per_centroid;
61 o.niter = params->niter;
62 o.nredo = params->nredo;
63 o.seed = params->seed;
64 o.spherical = params->spherical;
65 o.update_index = params->update_index;
66 o.verbose = params->verbose;
67 return o;
68 }
69
70 /// getter for centroids (size = k * d)
71 void faiss_Clustering_centroids(
72 FaissClustering* clustering, float** centroids, size_t* size) {
73 std::vector<float>& v = reinterpret_cast<Clustering*>(clustering)->centroids;
74 if (centroids) {
75 *centroids = v.data();
76 }
77 if (size) {
78 *size = v.size();
79 }
80 }
81
82 /// getter for objective values (sum of distances reported by index)
83 /// over iterations
84 void faiss_Clustering_obj(
85 FaissClustering* clustering, float** obj, size_t* size) {
86 std::vector<float>& v = reinterpret_cast<Clustering*>(clustering)->obj;
87 if (obj) {
88 *obj = v.data();
89 }
90 if (size) {
91 *size = v.size();
92 }
93 }
94
95 /// the only mandatory parameters are k and d
96 int faiss_Clustering_new(FaissClustering** p_clustering, int d, int k) {
97 try {
98 Clustering* c = new Clustering(d, k);
99 *p_clustering = reinterpret_cast<FaissClustering*>(c);
100 return 0;
101 } CATCH_AND_HANDLE
102 }
103
104 int faiss_Clustering_new_with_params(
105 FaissClustering** p_clustering, int d, int k, const FaissClusteringParameters* cp) {
106 try {
107 Clustering* c = new Clustering(d, k, from_faiss_c(cp));
108 *p_clustering = reinterpret_cast<FaissClustering*>(c);
109 return 0;
110 } CATCH_AND_HANDLE
111 }
112
113 /// Index is used during the assignment stage
114 int faiss_Clustering_train(
115 FaissClustering* clustering, idx_t n, const float* x, FaissIndex* index) {
116 try {
117 reinterpret_cast<Clustering*>(clustering)->train(
118 n, x, *reinterpret_cast<Index*>(index));
119 return 0;
120 } CATCH_AND_HANDLE
121 }
122
123 void faiss_Clustering_free(FaissClustering* clustering) {
124 delete reinterpret_cast<Clustering*>(clustering);
125 }
126
127 int faiss_kmeans_clustering (size_t d, size_t n, size_t k,
128 const float *x,
129 float *centroids,
130 float *q_error) {
131 try {
132 float out = faiss::kmeans_clustering(d, n, k, x, centroids);
133 if (q_error) {
134 *q_error = out;
135 }
136 return 0;
137 } CATCH_AND_HANDLE
138 }
139
140 }
FaissClusteringParameters::nredo
int nredo
redo clustering this many times and keep best
Definition: Clustering_c.h:27
faiss::ClusteringParameters::niter
int niter
clustering iterations
Definition: Clustering.h:24
faiss::ClusteringParameters::nredo
int nredo
redo clustering this many times and keep best
Definition: Clustering.h:25
FaissClusteringParameters::spherical
int spherical
(bool) do we want normalized centroids?
Definition: Clustering_c.h:30
faiss::ClusteringParameters::seed
int seed
seed for the random number generator
Definition: Clustering.h:35
faiss::ClusteringParameters::frozen_centroids
bool frozen_centroids
use the centroids provided as input and do not change them during iterations
Definition: Clustering.h:30
FaissClusteringParameters
Definition: Clustering_c.h:25
FaissClusteringParameters::frozen_centroids
int frozen_centroids
(bool) use the centroids provided as input and do not change them during iterations ...
Definition: Clustering_c.h:32
FaissClusteringParameters::max_points_per_centroid
int max_points_per_centroid
to limit size of dataset
Definition: Clustering_c.h:35
faiss::ClusteringParameters::min_points_per_centroid
int min_points_per_centroid
otherwise you get a warning
Definition: Clustering.h:32
faiss::Clustering
Definition: Clustering.h:56
faiss::kmeans_clustering
float kmeans_clustering(size_t d, size_t n, size_t k, const float *x, float *centroids)
Definition: Clustering.cpp:238
FaissClusteringParameters::seed
int seed
seed for the random number generator
Definition: Clustering_c.h:37
faiss::ClusteringParameters::update_index
bool update_index
update index after each iteration?
Definition: Clustering.h:29
faiss::ClusteringParameters
Definition: Clustering.h:23
FaissClusteringParameters::niter
int niter
clustering iterations
Definition: Clustering_c.h:26
faiss::Index
Definition: Index.h:62
FaissClusteringParameters::update_index
int update_index
(bool) update index after each iteration?
Definition: Clustering_c.h:31
faiss::ClusteringParameters::spherical
bool spherical
do we want normalized centroids?
Definition: Clustering.h:28
FaissClusteringParameters::min_points_per_centroid
int min_points_per_centroid
otherwise you get a warning
Definition: Clustering_c.h:34
faiss::ClusteringParameters::max_points_per_centroid
int max_points_per_centroid
to limit size of dataset
Definition: Clustering.h:33
FaissClusteringParameters::verbose
int verbose
(bool)
Definition: Clustering_c.h:29