Medians

Fast algorithm(s) for finding 1d medians, implemented in Rust.

Usage

use medians::{Med,MStats,Median};

Introduction

Finding the medians is a common task in statistics and general data analysis. At least it should be, if only it would not take so long. We argue in rstats that using the Geometric Median is the most stable way to characterise multidimensional data (nd). That leaves the one dimensional (1d) medians, addressed here. Medians are more stable measure of central tendency than means but they are not used nearly enough. One suspects that this is mostly due to being slower to compute than the arithmetic mean.

See tests.rs as examples of usage. Their automatically generated output can be seen by clicking the 'test' icon at the top of this document and then examining the latest log.

The Algorithms

• naive_median
  is a useful baseline for time comparisons in our performance benchmark (see tests.rs. The naive median is found simply by sorting the list of data and then picking the midpoint. In this case, the fastest standard Rust sort_unstable_by is used.

  The problem with this approach is that, even when using a good quality sort with guaranteed performance, its complexity is at best O(n log n). The quest for faster median algorithms, with complexity O(n), is motivated by the observation that not all items need to be fully sorted.

• w_median
  is a specialisation of n dimensional iterative gmedian from rstats to one dimensional case. It starts at about 84% of naive time for very short vecs. For orders of magnitude 2 to 3 it runs at about 45%. Then it starts slowing down. At the order of 5 and above it becomes slower than naive_median.

• r_median recursively partitions data around a pivot computed by a specialised secant method using passed down minimum and maximum values. Beats all other algorithms on vecs of lengths of about 60 upwards. At the order of magnitude 4 it runs at just over 12% and at 5 it runs at just over 10% of the 'naive' time (on f64 data). In other words, it is approaching the linear complexity.

• median is the main public entry point, implemented as a method of trait Median. It is just a 'switch' between w_median and r_median, depending on the length of the input vector. Thus it gives optimal performance over all lengths of data and is the recommended method to use.

Structs

• MStats - centre (here the median), dispersion, here MAD (median of absolute differences from median). MAD is the most stable measure of data spread.
• Med - median, lower and upper quartiles, MAD and standard error.

Trait Median

/// Finding 1D medians, quartiles, and MAD (median of absolute differences)
pub trait Median {
    /// Finds the median of `&[T]`, fast
    fn median(self) -> f64;
    /// Median of absolute differences (MAD).
    fn mad(self,median:f64) -> f64;
    /// Median and MAD.
    fn medstats(self) -> MStats;
    /// Median, quartiles, MAD, Stderr.
    fn medinfo(self) -> Med;
}

Release Notes

Version 1.0.8 - Added fully automated tests by github actions.

Version 1.0.7 - Updated to ran 1.0.4

Version 1.0.6 - Updated to times 1.0.4. Changed the comparison test accordingly.

Version 1.0.5 - Simplification. Deleted unnecessary w_median. Simplified error test. Updated dev-dependencies ran 1.0.3 and times 1.0.3.

Version 1.0.4 - Updated dependency indxvec v.1.4.2.

Version 1.0.3 - Added ratio mad/median (standard error) to struct Med and improved its Display.

Version 1.0.2 - Removed unnecessary extra reference from method median.

Version 1.0.1 - Added for convenience struct MStats and method medstats returning it. It holds here the median and MAD. More generally, any centre and dispersion. Moved the low level and private functions to module algos.rs. Updated times dev-dependency.

Version 1.0.0 - Updated to the latest indxvec dependency, v. 1.2.11. Added times crate for timing comparison test.

Version 0.1.2 - The public methods are now in trait Median.