Struct Variance

pub struct Variance { /* private fields */ }

Estimate the arithmetic mean and the variance of a sequence of numbers (“population”).

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

Example

use average::Variance;

let a: Variance = (1..6).map(f64::from).collect();
println!("The mean is {} ± {}.", a.mean(), a.error());

Implementations

impl Variance

pub fn new() -> Variance

Create a new variance estimator.

pub fn is_empty(&self) -> bool

Determine whether the sample is empty.

pub fn mean(&self) -> f64

Estimate the mean of the population.

Returns NaN for an empty sample.

pub fn len(&self) -> u64

Return the sample size.

pub fn sample_variance(&self) -> f64

Calculate the sample variance.

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

Returns NaN for samples of size 1 or less.

pub fn population_variance(&self) -> f64

Calculate the population variance of the sample.

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

Returns NaN for an empty sample.

pub fn variance_of_mean(&self) -> f64

Estimate the variance of the mean of the population.

Returns NaN for an empty sample.

pub fn error(&self) -> f64

Available on crate features std or libm only.

Estimate the standard error of the mean of the population.

Returns NaN for an empty sample.

Trait Implementations

impl Clone for Variance

fn clone(&self) -> Variance

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Variance

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for Variance

fn default() -> Variance

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Variance

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Estimate for Variance

fn add(&mut self, sample: f64)

Add an observation sampled from the population.

fn estimate(&self) -> f64

Estimate the statistic of the population.

impl<'a> Extend<&'a f64> for Variance

fn extend<T>(&mut self, iter: T)

where T: IntoIterator<Item = &'a f64>,

Extends a collection with the contents of an iterator.

fn extend_one(&mut self, item: A)

🔬This is a nightly-only experimental API. (extend_one)

Extends a collection with exactly one element.

fn extend_reserve(&mut self, additional: usize)

🔬This is a nightly-only experimental API. (extend_one)

Reserves capacity in a collection for the given number of additional elements.

impl Extend<f64> for Variance

fn extend<T>(&mut self, iter: T)

where T: IntoIterator<Item = f64>,

Extends a collection with the contents of an iterator.

fn extend_one(&mut self, item: A)

🔬This is a nightly-only experimental API. (extend_one)

Extends a collection with exactly one element.

fn extend_reserve(&mut self, additional: usize)

🔬This is a nightly-only experimental API. (extend_one)

Reserves capacity in a collection for the given number of additional elements.

impl<'a> FromIterator<&'a f64> for Variance

fn from_iter<T>(iter: T) -> Variance

where T: IntoIterator<Item = &'a f64>,

Creates a value from an iterator.

impl FromIterator<f64> for Variance

fn from_iter<T>(iter: T) -> Variance

where T: IntoIterator<Item = f64>,

Creates a value from an iterator.

impl<'a> FromParallelIterator<&'a f64> for Variance

Available on crate feature rayon only.

fn from_par_iter<I>(par_iter: I) -> Variance

where I: IntoParallelIterator<Item = &'a f64>, Self: Merge,

Creates an instance of the collection from the parallel iterator par_iter.

impl FromParallelIterator<f64> for Variance

Available on crate feature rayon only.

fn from_par_iter<I>(par_iter: I) -> Variance

where I: IntoParallelIterator<Item = f64>, Self: Merge,

Creates an instance of the collection from the parallel iterator par_iter.

impl Merge for Variance

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

Merge another sample into this one.

Example

use average::{Variance, Merge};

let sequence: &[f64] = &[1., 2., 3., 4., 5., 6., 7., 8., 9.];
let (left, right) = sequence.split_at(3);
let avg_total: Variance = sequence.iter().collect();
let mut avg_left: Variance = left.iter().collect();
let avg_right: Variance = right.iter().collect();
avg_left.merge(&avg_right);
assert_eq!(avg_total.mean(), avg_left.mean());
assert_eq!(avg_total.sample_variance(), avg_left.sample_variance());

impl Serialize for Variance

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.