Struct average::WeightedMean

pub struct WeightedMean { /* fields omitted */ }

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

Example

use average::WeightedMean;

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

Methods

impl WeightedMean

fn new() -> WeightedMean

Create a new weighted and unweighted mean estimator.

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

Add an observation sampled from the population.

fn is_empty(&self) -> bool

Determine whether the sample is empty.

Might be a false positive if the sum of weights is zero.

fn sum_weights(&self) -> f64

Return the sum of the weights.

Returns 0 for an empty sample.

fn mean(&self) -> f64

Estimate the weighted mean of the population.

Returns 0 for an empty sample.

Trait Implementations

impl Debug for WeightedMean

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

Formats the value using the given formatter.

impl Clone for WeightedMean

fn clone(&self) -> WeightedMean

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Default for WeightedMean

fn default() -> WeightedMean

Returns the "default value" for a type.

impl FromIterator<(f64, f64)> for WeightedMean

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

Creates a value from an iterator.

impl Merge for WeightedMean

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

Merge another sample into this one.

Example

use average::{WeightedMean, Merge};

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: WeightedMean = weighted_sequence.iter().map(|&x| x).collect();
let mut avg_left: WeightedMean = left.iter().map(|&x| x).collect();
let avg_right: WeightedMean = right.iter().map(|&x| x).collect();
avg_left.merge(&avg_right);
assert!((avg_total.mean() - avg_left.mean()).abs() < 1e-15);