Crate classify

Structs

Bin

Represents a single bin in a classification, including the bin’s lowest (inclusive) and highest (exclusive) values and the number of points within it

Functions

breaks_to_classification

Returns a Classification object given a set of breaks between bins and the original dataset

classify_val

Returns an Option containing the index of the Bin within which a value should fall given the value and a Classification (returns None if the value is outside of the Classification’s range)

get_equal_interval_breaks

Returns a vector of breaks generated through the Equal Interval Breaks algorithm given the desired number of bins and a dataset

get_equal_interval_classification

Returns a Classification object following the Equal Interval Breaks algorithm given the desired number of bins and one-dimensional data

get_head_tail_breaks

Returns a vector of breaks generated through the Head-Tail Breaks algorithm given a dataset

get_head_tail_classification

Returns a Classification object following the Head-Tail Breaks algorithm given one-dimensional data

get_hinge_breaks

Returns a vector of breaks generated through the Hinge Breaks algorithm given the desired number of bins and a dataset

get_hinge_classification

Returns a Classification object following the Hinge Breaks algorithm given the desired number of bins and one-dimensional data

get_jenks_breaks

Returns a vector of breaks generated through the Jenks Natural Breaks algorithm given the desired number of bins and a dataset

get_jenks_classification

Returns a Classification object following the Jenks Natural Breaks algorithm given the desired number of bins and one-dimensional data

get_quantile_breaks

Returns a vector of breaks generated through the Quantile Breaks algorithm given the desired number of bins and a dataset

get_quantile_classification

Returns a Classification object following the Quantile Breaks algorithm given the desired number of bins and one-dimensional data

get_st_dev_breaks

Returns a vector of breaks generated through the Standard Deviation Breaks algorithm given the desired bin size as a proportion of a standard deviation and a dataset Note: This algorithm calculates Standard Deviation with Bessel’s correction

get_st_dev_classification

Returns a Classification object following the Standard Deviation Breaks algorithm given the desired bin size as a proportion of a standard deviation and one-dimensional data Note: This algorithm calculates Standard Deviation with Bessel’s correction

Type Aliases

Classification

Represents a full classification, which is a collection of Bin objects