Struct statrs::distribution::Empirical

pub struct Empirical { /* private fields */ }

Implements the Empirical Distribution

Examples

use statrs::distribution::{Continuous, Empirical};
use statrs::statistics::Distribution;

let samples = vec![0.0, 5.0, 10.0];

let empirical = Empirical::from_vec(samples);
assert_eq!(empirical.mean().unwrap(), 5.0);

Implementations

impl Empirical

pub fn new() -> Result<Empirical>

Constructs a new discrete uniform distribution with a minimum value of min and a maximum value of max.

Examples

use statrs::distribution::Empirical;

let mut result = Empirical::new();
assert!(result.is_ok());

pub fn from_vec(src: Vec<f64>) -> Empirical

pub fn add(&mut self, data_point: f64)

pub fn remove(&mut self, data_point: f64)

Trait Implementations

impl Clone for Empirical

fn clone(&self) -> Empirical

Returns a copy of the value.

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

Performs copy-assignment from source.

impl ContinuousCDF<f64, f64> for Empirical

fn cdf(&self, x: f64) -> f64

Returns the cumulative distribution function calculated at x for a given distribution. May panic depending on the implementor.

fn inverse_cdf(&self, p: T) -> K

Due to issues with rounding and floating-point accuracy the default implementation may be ill-behaved. Specialized inverse cdfs should be used whenever possible. Performs a binary search on the domain of cdf to obtain an approximation of F^-1(p) := inf { x | F(x) >= p }. Needless to say, performance may may be lacking.

impl Debug for Empirical

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

Formats the value using the given formatter.

impl Distribution<f64> for Empirical

fn sample<R: ?Sized + Rng>(&self, rng: &mut R) -> f64

Generate a random value of T, using rng as the source of randomness.

fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> where
    R: Rng, 

Create an iterator that generates random values of T, using rng as the source of randomness.

fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S> where
    F: Fn(T) -> S, 

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F

impl Distribution<f64> for Empirical

fn mean(&self) -> Option<f64>

Returns the mean, if it exists. The default implementation returns an estimation based on random samples. This is a crude estimate for when no further information is known about the distribution. More accurate statements about the mean can and should be given by overriding the default implementation.

fn variance(&self) -> Option<f64>

Returns the variance, if it exists. The default implementation returns an estimation based on random samples. This is a crude estimate for when no further information is known about the distribution. More accurate statements about the variance can and should be given by overriding the default implementation.

fn std_dev(&self) -> Option<T>

Returns the standard deviation, if it exists.

fn entropy(&self) -> Option<T>

Returns the entropy, if it exists.

fn skewness(&self) -> Option<T>

Returns the skewness, if it exists.

impl Max<f64> for Empirical

Panics if number of samples is zero

fn max(&self) -> f64

Returns the maximum value in the domain of a given distribution if it exists, otherwise None.

impl Min<f64> for Empirical

Panics if number of samples is zero

fn min(&self) -> f64

Returns the minimum value in the domain of a given distribution if it exists, otherwise None.

impl PartialEq<Empirical> for Empirical

fn eq(&self, other: &Empirical) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Empirical) -> bool

This method tests for !=.

impl StructuralPartialEq for Empirical