Wrapper function to generate SNF parameters

Usage

params_snf(
  k = 20L,
  t = 20L,
  mu = 0.5,
  alpha = 1,
  normalise = TRUE,
  distance_metric = c("euclidean", "manhattan", "canberra", "cosine")
)

Arguments

k: Integer. Number of neighbours to consider. Defaults to 20L.
t: Integer. Number of iterations for the SNF algorithm. Defaults to 20L.
mu: Numeric. Normalisation factor for the Gaussian kernel width. Defaults to 0.5.
alpha: Numeric. Normalisation parameter controlling the fusion strength. Defaults to 1.0.
normalise: Boolean. Shall continuous values be Z-scored. Defaults to TRUE.
distance_metric: String. Which distance metric to use for the continuous calculations. In case of pure categorical, Hamming will be used, for mixed data types Gower distance is used. One of c("euclidean", "manhattan", "canberra", "cosine"). Defaults to "euclidean".

Value

A named list with the following elements:

k - Integer. Number of neighbours to consider. Defaults to 20L.
t - Integer. Number of iterations for the SNF algorithm. Defaults to 20L.
mu - Numeric. Normalisation factor for the Gaussian kernel width. Defaults to 0.5.
alpha - Numeric. Normalisation parameter controlling the fusion strength. Defaults to 1.0.
distance_metric - String. Which distance metric to use for the continuous calculations. In case of pure categorical, Hamming will be used, for mixed data types Gower distance is used. One of c("euclidean", "manhattan", "canberra", "cosine"). Defaults to "euclidean".
normalise - Boolean. Shall continuous values be Z-scored. Defaults to TRUE.