Struct statrs::distribution::Uniform

pub struct Uniform { /* fields omitted */ }

Implements the Continuous Uniform distribution

Examples

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

let n = Uniform::new(0.0, 1.0).unwrap();
assert_eq!(n.mean().unwrap(), 0.5);
assert_eq!(n.pdf(0.5), 1.0);

Implementations

impl Uniform

pub fn new(min: f64, max: f64) -> Result<Uniform>

Constructs a new uniform distribution with a min of min and a max of max

Errors

Returns an error if min or max are NaN

Examples

use statrs::distribution::Uniform;
use std::f64;

let mut result = Uniform::new(0.0, 1.0);
assert!(result.is_ok());

result = Uniform::new(f64::NAN, f64::NAN);
assert!(result.is_err());

Trait Implementations

impl Clone for Uniform

fn clone(&self) -> Uniform

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Continuous<f64, f64> for Uniform

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

Calculates the probability density function for the continuous uniform distribution at x

Remarks

Returns 0.0 if x is not in [min, max]

Formula

1 / (max - min)

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

Calculates the log probability density function for the continuous uniform distribution at x

Remarks

Returns f64::NEG_INFINITY if x is not in [min, max]

Formula

ln(1 / (max - min))

impl ContinuousCDF<f64, f64> for Uniform

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

Calculates the cumulative distribution function for the uniform distribution at x

Formula

(x - min) / (max - min)

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 Uniform

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

Formats the value using the given formatter.

impl Distribution<f64> for Uniform

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

Returns the mean for the continuous uniform distribution

Formula

(min + max) / 2

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

Returns the variance for the continuous uniform distribution

Formula

(max - min)^2 / 12

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

Returns the entropy for the continuous uniform distribution

Formula

ln(max - min)

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

Returns the skewness for the continuous uniform distribution

Formula

0

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

Returns the standard deviation, if it exists.

impl Distribution<f64> for Uniform

fn sample<R: Rng + ?Sized>(&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.

impl Max<f64> for Uniform

fn max(&self) -> f64

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

impl Median<f64> for Uniform

fn median(&self) -> f64

Returns the median for the continuous uniform distribution

Formula

(min + max) / 2

impl Min<f64> for Uniform

fn min(&self) -> f64

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

impl Mode<Option<f64>> for Uniform

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

Returns the mode for the continuous uniform distribution

Remarks

Since every element has an equal probability, mode simply returns the middle element

Formula

N/A // (max + min) / 2 for the middle element

impl PartialEq<Uniform> for Uniform

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

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

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

This method tests for !=.

impl Copy for Uniform

impl StructuralPartialEq for Uniform