Functions
def	estimate_radii_values (data, subset_sizes, distance_fct, rel_offset=0.05, rel_cutoff=0.05)
	Heuristically determine region in which useful bin/radii values are located. More...

def	choose_bins (distance_list, possible_bins, n_bins=10, choose_type="uniform_y_dist", min_value_shift=None, max_value_shift=None)
	Heuristically determine reasonable bin/radii values from larger choice. More...

Function Documentation

◆ choose_bins()

def ecdf_estimator.select_bins.choose_bins	(	distance_list,
		possible_bins,
		n_bins = `10`,
		choose_type = `"uniform_y_dist"`,
		min_value_shift = `None`,
		max_value_shift = `None`
	)

Heuristically determine reasonable bin/radii values from larger choice.

Parameters

distance_list	List of distances to be grouped into the bins.
bins	List of possible bin values.
n_bins	Maximum amount of bins, which are to be selected. Defaults to 10.
choose_typ	Heurustic that is used to select bins.
min_value_shift	Exclude values that are smaller than min value of distances plus this.
max_value_shift	Exclude values that are largr than max value of distances plus this.

Return values

target_val The value of the target function.

def ecdf_estimator.select_bins.estimate_radii_values	(	data,
		subset_sizes,
		distance_fct,
		rel_offset = `0.05`,
		rel_cutoff = `0.05`
	)

Heuristically determine region in which useful bin/radii values are located.

Parameters

data	The whole training data from which some subsets are selected.
subset_sizes	The size of the subsets.
distance_fct	Functions that evaluates (generalized) distance between subset members.
rel_offset	Relative offset to determin interval of reasonable bin values.
rel_cutoff	Relative cutoff of the interval of reasonable bin values.

Return values

target_val The value of the target function.