Wrapper function to generate k-means parameters

Usage

params_kmeans(
  metric = c("euclidean", "cosine"),
  max_iters = 1000L,
  batch_size = 4096L,
  drift_threshold = 1e-04,
  lr_alpha = 1,
  init = c("parallel", "random"),
  use_hamerly = NULL,
  use_gemm = NULL
)

Arguments

metric: String. Distance metric to use. One of c("euclidean", "cosine"). Defaults to "euclidean".
max_iters: Integer. Maximum number of iterations. Defaults to 1000L.
batch_size: Integer. Mini-batch size. Only used when method = "minibatch". Defaults to 4096L.
drift_threshold: Numeric. Below which centroid drift the mini-batch k-means is considered converged. Only used when method = "minibatch". Defaults to 1e-04.
lr_alpha: Numeric. Learning rate decay for the mini-batch k-means. Original paper uses 1.0. Defaults to 1.0.
init: String. The initialisation of the centroids. One of c("parallel", "random"). Defaults to "parallel".
use_hamerly: Boolean or NULL. Shall Hamerly's method be used (only if metric == "euclidean"). Defaults to NULL.
use_gemm: Boolean or NULL. Shall the GEMM path be used. Useful on high dimensional data. If NULL, choice will be based on heuristics. Defaults to NULL.

Value

A named list with the following elements:

metric - String. Distance metric to use. One of c("euclidean", "cosine"). Defaults to "euclidean".
max_iters - Integer. Maximum number of iterations. Defaults to 1000L.
batch_size - Integer. Mini-batch size. Only used when method = "minibatch". Defaults to 4096L.
drift_threshold - Numeric. Below which centroid drift the mini-batch k-means is considered converged. Only used when method = "minibatch". Defaults to 1e-04.
lr_alpha - Numeric. Learning rate decay for the mini-batch k-means. Original paper uses 1.0. Defaults to 1.0.
init - String. The initialisation of the centroids. One of c("parallel", "random"). Defaults to "parallel".
use_hamerly - Boolean or NULL. Shall Hamerly's method be used (only if metric == "euclidean"). Defaults to NULL.
use_gemm - Boolean or NULL. Shall the GEMM path be used. Useful on high dimensional data. If NULL, choice will be based on heuristics. Defaults to NULL.