Struct average::WeightedAverage

pub struct WeightedAverage { /* fields omitted */ }

Estimate the weighted and unweighted arithmetic mean and the unweighted 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.weighted_mean(), a.error());

Methods

impl WeightedAverage

fn new() -> WeightedAverage

Create a new weighted and unweighted 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 sample is empty.

fn sum_weights(&self) -> f64

Return the sum of the weights.

fn sum_weights_sq(&self) -> f64

Return the sum of the squared weights.

fn weighted_mean(&self) -> f64

Estimate the weighted mean of the sequence.

fn unweighted_mean(&self) -> f64

Estimate the unweighted mean of the sequence.

fn len(&self) -> u64

Return sample size.

fn effective_len(&self) -> f64

Calculate the effective sample size.

fn population_variance(&self) -> f64

Calculate the unweighted population variance of the sample.

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

fn sample_variance(&self) -> f64

Calculate the unweighted sample variance.

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

fn error(&self) -> f64

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

Returns 0 if the sum of weights is 0.

This unbiased estimator assumes that the samples were independently drawn from the same population with constant variance.

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.weighted_mean() - avg_left.weighted_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.