Class for the clustering parameters. Can be passed to the constructor of the Clustering object.
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
- int niter = 25