Struct average::WeightedAverage

pub struct WeightedAverage { /* fields omitted */ }

Estimate the weighted arithmetic mean and the weighted variance of a sequence of numbers ("population").

This can be used to estimate the standard error of the weighted mean.

Example

use average::WeightedAverage;

let a: WeightedAverage = (1..6).zip(1..6)
    .map(|(x, w)| (f64::from(x), f64::from(w))).collect();
println!("The weighted average is {} ± {}.", a.mean(), a.error());

Methods

impl WeightedAverage

fn new() -> WeightedAverage

Create a new weighted average estimator.

fn add(&mut self, sample: f64, weight: f64)

Add a weighted element sampled from the population.

fn is_empty(&self) -> bool

Determine whether the samples are empty.

fn sum_weights(&self) -> f64

Return the sum of the weights.

fn mean(&self) -> f64

Estimate the weighted mean of the population.

fn population_variance(&self) -> f64

Calculate the weighted population variance of the sample.

This is a biased estimator of the weighted variance of the population.

fn sample_variance(&self) -> f64

Calculate the weighted sample variance.

This is an unbiased estimator of the weighted variance of the population.

Note that this will return 0 if the sum of the weights is <= 1.

fn error(&self) -> f64

Estimate the standard error of the weighted mean of the population.

Note that this will return 0 if the sum of the weights is 0. For this estimator, the sum of weights should be larger than 1.

This biased estimator uses the weighted variance and the sum of weights. It considers the weights as (noninteger) counts of how often the sample has been observed, applying the standard formulas to calculate mean, variance and sample size across all "repeats".

fn merge(&mut self, other: &WeightedAverage)

Merge another sample into this one.

Example

use average::WeightedAverage;

let weighted_sequence: &[(f64, f64)] = &[
    (1., 0.1), (2., 0.2), (3., 0.3), (4., 0.4), (5., 0.5),
    (6., 0.6), (7., 0.7), (8., 0.8), (9., 0.9)];
let (left, right) = weighted_sequence.split_at(3);
let avg_total: WeightedAverage = weighted_sequence.iter().map(|&x| x).collect();
let mut avg_left: WeightedAverage = left.iter().map(|&x| x).collect();
let avg_right: WeightedAverage = right.iter().map(|&x| x).collect();
avg_left.merge(&avg_right);
assert!((avg_total.mean() - avg_left.mean()).abs() < 1e-15);
assert!((avg_total.error() - avg_left.error()).abs() < 1e-15);

Trait Implementations

impl Debug for WeightedAverage

fn fmt(&self, __arg_0: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Clone for WeightedAverage

fn clone(&self) -> WeightedAverage

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Default for WeightedAverage

fn default() -> WeightedAverage

Returns the "default value" for a type.

impl FromIterator<(f64, f64)> for WeightedAverage

fn from_iter<T>(iter: T) -> WeightedAverage where
    T: IntoIterator<Item = (f64, f64)>, 

Creates a value from an iterator.