Struct average::Covariance

pub struct Covariance { /* private fields */ }

Estimate the arithmetic means and the covariance of a sequence of number pairs (“population”).

Because the variances are calculated as well, this can be used to calculate the Pearson correlation coefficient.

Example

use average::Covariance;

let a: Covariance = [(1., 5.), (2., 4.), (3., 3.), (4., 2.), (5., 1.)].iter().cloned().collect();
assert_eq!(a.mean_x(), 3.);
assert_eq!(a.mean_y(), 3.);
assert_eq!(a.population_covariance(), -2.0);
assert_eq!(a.sample_covariance(), -2.5);

Implementations

impl Covariance

pub fn new() -> Covariance

Create a new covariance estimator.

pub fn add(&mut self, x: f64, y: f64)

Add an observation sampled from the population.

pub fn population_covariance(&self) -> f64

Calculate the population covariance of the sample.

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

Returns NaN for an empty sample.

pub fn sample_covariance(&self) -> f64

Calculate the sample covariance.

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

Returns NaN for samples of size 1 or less.

pub fn pearson(&self) -> f64

Available on crate features std or libm only.

Calculate the population Pearson correlation coefficient.

Returns NaN for an empty sample.

pub fn len(&self) -> u64

Return the sample size.

pub fn is_empty(&self) -> bool

Determine whether the sample is empty.

pub fn mean_x(&self) -> f64

Estimate the mean of the x population.

Returns NaN for an empty sample.

pub fn mean_y(&self) -> f64

Estimate the mean of the y population.

Returns NaN for an empty sample.

pub fn sample_variance_x(&self) -> f64

Calculate the sample variance of x.

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

Returns NaN for samples of size 1 or less.

pub fn population_variance_x(&self) -> f64

Calculate the population variance of the sample for x.

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

Returns NaN for an empty sample.

pub fn sample_variance_y(&self) -> f64

Calculate the sample variance of y.

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

Returns NaN for samples of size 1 or less.

pub fn population_variance_y(&self) -> f64

Calculate the population variance of the sample for y.

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

Returns NaN for an empty sample.

Trait Implementations

impl Clone for Covariance

fn clone(&self) -> Covariance

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Covariance

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

Formats the value using the given formatter.

impl Default for Covariance

fn default() -> Covariance

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Covariance

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<'a> Extend<&'a (f64, f64)> for Covariance

fn extend<T: IntoIterator<Item = &'a (f64, f64)>>(&mut self, iter: T)

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, f64)> for Covariance

fn extend<T: IntoIterator<Item = (f64, f64)>>(&mut self, iter: T)

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, f64)> for Covariance

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

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

Creates a value from an iterator.

impl FromIterator<(f64, f64)> for Covariance

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

where T: IntoIterator<Item = (f64, f64)>,

Creates a value from an iterator.

impl Merge for Covariance

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

Merge another sample into this one.

Example

use average::{Covariance, Merge};

let sequence: &[(f64, f64)] = &[(1., 2.), (3., 4.), (5., 6.), (7., 8.), (9., 10.)];
let (left, right) = sequence.split_at(3);
let cov_total: Covariance = sequence.iter().collect();
let mut cov_left: Covariance = left.iter().collect();
let cov_right: Covariance = right.iter().collect();
cov_left.merge(&cov_right);
assert_eq!(cov_total.population_covariance(), cov_left.population_covariance());

impl Serialize for Covariance

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

where __S: Serializer,

Serialize this value into the given Serde serializer.