Struct faiss::Clustering1D¶

struct Clustering1D : public faiss::Clustering

Exact 1D clustering algorithm

Since it does not use an index, it does not overload the train() function

Public Types

typedef Index::idx_t idx_t

Public Functions

explicit Clustering1D(int k)

Clustering1D(int k, const ClusteringParameters &cp)

void train_exact(idx_t n, const float *x)

inline virtual ~Clustering1D()

virtual void train(idx_t n, const float *x, faiss::Index &index, const float *x_weights = nullptr)

run k-means training

Parameters:

x – training vectors, size n * d
index – index used for assignment
x_weights – weight associated to each vector: NULL or size n

void train_encoded(idx_t nx, const uint8_t *x_in, const Index *codec, Index &index, const float *weights = nullptr)

run with encoded vectors

win addition to train()’s parameters takes a codec as parameter to decode the input vectors.

Parameters:: codec – codec used to decode the vectors (nullptr = vectors are in fact floats) *

void post_process_centroids(): Post-process the centroids after each centroid update. includes optional L2 normalization and nearest integer rounding

Public Members

size_t d: dimension of the vectors

size_t k: nb of centroids

std::vector<float> centroids: centroids (k * d) if centroids are set on input to train, they will be used as initialization

std::vector<ClusteringIterationStats> iteration_stats: stats at every iteration of clustering

int niter: clustering iterations

int nredo: redo clustering this many times and keep best

bool verbose

bool spherical: do we want normalized centroids?

bool int_centroids: round centroids coordinates to integer

bool update_index: re-train index after each iteration?

bool frozen_centroids: use the centroids provided as input and do not change them during iterations

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

size_t decode_block_size: how many vectors at a time to decode