struct Clustering : public faiss::ClusteringParameters
K-means 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.
Subclassed by faiss::Clustering1D
Clustering(int d, int k)
Clustering(int d, int k, const ClusteringParameters &cp)
virtual void train(idx_t n, const float *x, faiss::Index &index, const float *x_weights = nullptr)
run k-means training
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.
codec – codec used to decode the vectors (nullptr = vectors are in fact floats)
Post-process the centroids after each centroid update. includes optional L2 normalization and nearest integer rounding
inline virtual ~Clustering()
dimension of the vectors
nb of centroids
centroids (k * d) if centroids are set on input to train, they will be used as initialization
int niter = 25
number of clustering iterations
int nredo = 1
redo clustering this many times and keep the clusters with the best objective
bool verbose = false
bool spherical = false
whether to normalize centroids after each iteration (useful for inner product clustering)
bool int_centroids = false
round centroids coordinates to integer after each iteration?
bool update_index = false
re-train index after each iteration?
bool frozen_centroids = false
Use the subset of centroids provided as input and do not change them during iterations
int min_points_per_centroid = 39
If fewer than this number of training vectors per centroid are provided, writes a warning. Note that fewer than 1 point per centroid raises an exception.
int max_points_per_centroid = 256
to limit size of dataset, otherwise the training set is subsampled
int seed = 1234
seed for the random number generator
size_t decode_block_size = 32768
when the training set is encoded, batch size of the codec decoder
- Clustering(int d, int k)