Struct faiss::Clustering1D
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struct faiss::Clustering1D : public faiss::Clustering
Exact 1D clustering algorithm
Since it does not use an index, it does not overload the train() function
Public Functions
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explicit Clustering1D(int k)
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Clustering1D(int k, const ClusteringParameters &cp)
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void train_exact(idx_t n, const float *x)
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inline virtual ~Clustering1D()
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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
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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explicit Clustering1D(int k)