Struct faiss::PolysemousTraining

struct PolysemousTraining : public faiss::SimulatedAnnealingParameters

optimizes the order of indices in a ProductQuantizer

Public Types

enum Optimization_type_t

Values:

enumerator OT_None

enumerator OT_ReproduceDistances_affine

default

enumerator OT_Ranking_weighted_diff

same as _2, but use rank of y+ - rank of y-

Public Functions

PolysemousTraining()

void optimize_pq_for_hamming(ProductQuantizer &pq, size_t n, const float *x) const

reorder the centroids so that the Hamming distance becomes a good approximation of the SDC distance (called by train)

void optimize_ranking(ProductQuantizer &pq, size_t n, const float *x) const

called by optimize_pq_for_hamming

void optimize_reproduce_distances(ProductQuantizer &pq) const

called by optimize_pq_for_hamming

size_t memory_usage_per_thread(const ProductQuantizer &pq) const

make sure we don’t blow up the memory

Public Members

Optimization_type_t optimization_type

int ntrain_permutation

use 1/4 of the training points for the optimization, with max. ntrain_permutation. If ntrain_permutation == 0: train on centroids

double dis_weight_factor

decay of exp that weights distance loss

size_t max_memory

refuse to train if it would require more than that amount of RAM

std::string log_pattern

double init_temperature = 0.7

double temperature_decay = 0.9997893011688015

int n_iter = 500000

int n_redo = 2

int seed = 123

int verbose = 0

bool only_bit_flips = false

bool init_random = false