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faiss::Clustering Struct Reference

`#include <Clustering.h>`

Inheritance diagram for faiss::Clustering:

## Public Types
typedef Index::idx_t | idx_t

## Public Member Functions
| Clustering (int d, int k)
| the only mandatory parameters are k and d
| Clustering (int d, int k, const ClusteringParameters &cp)
virtual void | train (idx_t n, const float *x, faiss::Index &index)
| Index is used during the assignment stage.
Public Member Functions inherited from faiss::ClusteringParameters
| ClusteringParameters ()
| sets reasonable defaults

## Public Attributes
size_t | d
| dimension of the vectors
size_t | k
| nb of centroids
std::vector< float > | centroids
| centroids (k * d)
std::vector< float > | obj
Public Attributes inherited from faiss::ClusteringParameters
int | niter
| clustering iterations
int | nredo
| redo clustering this many times and keep best
bool | verbose
bool | spherical
| do we want normalized centroids?
bool | update_index
| update index after each iteration?
int | min_points_per_centroid
| otherwise you get a warning
int | max_points_per_centroid
| to limit size of dataset
int | seed
| seed for the random number generator

## Detailed Description

clustering based on assignment - centroid update iterations

The clustering is based on an Index object that assigns training points to the centroids. Therefore, at each iteration the centroids are added to the index.

On output, the centoids table is set to the latest version of the centroids and they are also added to the index. If the centroids table it is not empty on input, it is also used for initialization.

To do several clusterings, just call train() several times on different training sets, clearing the centroid table in between.

Definition at line 58 of file Clustering.h.

## Member Data Documentation

std::vector<float> faiss::Clustering::obj

objective values (sum of distances reported by index) over iterations

Definition at line 68 of file Clustering.h.

The documentation for this struct was generated from the following files:

- Clustering.h
- Clustering.cpp