Struct faiss::Clustering¶
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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
Public Functions
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Clustering(int d, int k)
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Clustering(int d, int k, const ClusteringParameters &cp)
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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
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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) *
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void post_process_centroids()
Post-process the centroids after each centroid update. includes optional L2 normalization and nearest integer rounding
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inline virtual ~Clustering()
Public Members
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size_t d
dimension of the vectors
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size_t k
nb of centroids
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std::vector<float> centroids
centroids (k * d) if centroids are set on input to train, they will be used as initialization
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std::vector<ClusteringIterationStats> iteration_stats
stats at every iteration of clustering
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int niter
clustering iterations
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int nredo
redo clustering this many times and keep best
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bool verbose
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bool spherical
do we want normalized centroids?
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bool int_centroids
round centroids coordinates to integer
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bool update_index
re-train index after each iteration?
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bool frozen_centroids
use the centroids provided as input and do not change them during iterations
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int min_points_per_centroid
otherwise you get a warning
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int max_points_per_centroid
to limit size of dataset
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int seed
seed for the random number generator
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size_t decode_block_size
how many vectors at a time to decode
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Clustering(int d, int k)