Struct statrs::distribution::Exp

pub struct Exp { /* fields omitted */ }

Implements the Exp distribution and is a special case of the Gamma distribution (referenced here)

Examples

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

let n = Exp::new(1.0).unwrap();
assert_eq!(n.mean().unwrap(), 1.0);
assert_eq!(n.pdf(1.0), 0.3678794411714423215955);

Implementations

impl Exp

pub fn new(rate: f64) -> Result<Exp>

Constructs a new exponential distribution with a rate (λ) of rate.

Errors

Returns an error if rate is NaN or rate <= 0.0

Examples

use statrs::distribution::Exp;

let mut result = Exp::new(1.0);
assert!(result.is_ok());

result = Exp::new(-1.0);
assert!(result.is_err());

pub fn rate(&self) -> f64

Returns the rate of the exponential distribution

Examples

use statrs::distribution::Exp;

let n = Exp::new(1.0).unwrap();
assert_eq!(n.rate(), 1.0);

Trait Implementations

impl Clone for Exp

fn clone(&self) -> Exp

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Continuous<f64, f64> for Exp

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

Calculates the probability density function for the exponential distribution at x

Formula

λ * e^(-λ * x)

where λ is the rate

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

Calculates the log probability density function for the exponential distribution at x

Formula

ln(λ * e^(-λ * x))

where λ is the rate

impl ContinuousCDF<f64, f64> for Exp

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

Calculates the cumulative distribution function for the exponential distribution at x

Formula

1 - e^(-λ * x)

where λ is the rate

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 Exp

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

Formats the value using the given formatter.

impl Distribution<f64> for Exp

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

Returns the mean of the exponential distribution

Formula

1 / λ

where λ is the rate

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

Returns the variance of the exponential distribution

Formula

1 / λ^2

where λ is the rate

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

Returns the entropy of the exponential distribution

Formula

1 - ln(λ)

where λ is the rate

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

Returns the skewness of the exponential distribution

Formula

2

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

Returns the standard deviation, if it exists.

impl Distribution<f64> for Exp

fn sample<R: Rng + ?Sized>(&self, r: &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 Max<f64> for Exp

fn max(&self) -> f64

Returns the maximum value in the domain of the exponential distribution representable by a double precision float

Formula

INF

impl Median<f64> for Exp

fn median(&self) -> f64

Returns the median of the exponential distribution

Formula

(1 / λ) * ln2

where λ is the rate

impl Min<f64> for Exp

fn min(&self) -> f64

Returns the minimum value in the domain of the exponential distribution representable by a double precision float

Formula

0

impl Mode<Option<f64>> for Exp

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

Returns the mode of the exponential distribution

Formula

0

impl PartialEq<Exp> for Exp

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

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

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

This method tests for !=.

impl Copy for Exp

impl StructuralPartialEq for Exp