Summarise a training set for dissimilarity-index computation.

Computed once and reused for every prediction chunk: the training data does not change, and the pairwise mean is the expensive part.

Usage

di_reference(x_train, weights = NULL, max_pairs = 2e+06, seed = 42L)

Arguments

x_train

Numeric matrix of SCALED training predictors (n x p), in the model's channel order.

weights

Optional per-predictor weights, e.g. variable importance. NULL gives every predictor the same weight, which is what Meyer & Pebesma fall back to when no importance is available.

max_pairs

Cap on the number of point pairs used to estimate avg_dist. The full n^2 is unnecessary for a mean and quadratic in memory; a large random sample estimates it to more precision than the quantity deserves.

seed

Draw seed for that sample.

Value

An object of class "di_reference".

Examples

set.seed(1)
x_train <- matrix(rnorm(200), 100, 2)    # 100 training rows of 2 scaled predictors
ref <- di_reference(x_train)
ref
#> <di_reference> 100 training row(s) x 2 predictor(s) | unweighted
#>   mean pairwise distance (the DI's unit): 1.656