Struct Exponential

pub struct Exponential { /* private fields */ }

Exponential distribution, Exp(λ) over x in [0, ∞).

Examples

Compute 50% confidence interval

use rv::prelude::*;

let expon = Exponential::new(1.5).unwrap();
let interval: (f64, f64) = expon.interval(0.5);  // (0.19, 0.92)

Implementations

impl Exponential

pub fn new(rate: f64) -> Result<Self, ExponentialError>

Create a new exponential distribution

Arguments

• rate: λ > 0, rate or inverse scale

pub fn new_unchecked(rate: f64) -> Self

Creates a new Exponential without checking whether the parameter is valid.

pub fn rate(&self) -> f64

Get the rate parameter

Example

let expon = Exponential::new(1.3).unwrap();
assert_eq!(expon.rate(), 1.3);

pub fn set_rate(&mut self, rate: f64) -> Result<(), ExponentialError>

Set the rate parameter

Example

let mut expon = Exponential::new(1.3).unwrap();
assert_eq!(expon.rate(), 1.3);

expon.set_rate(2.1).unwrap();
assert_eq!(expon.rate(), 2.1);

Will error for invalid values

assert!(expon.set_rate(2.1).is_ok());
assert!(expon.set_rate(0.1).is_ok());
assert!(expon.set_rate(0.0).is_err());
assert!(expon.set_rate(-1.0).is_err());
assert!(expon.set_rate(f64::INFINITY).is_err());
assert!(expon.set_rate(f64::NEG_INFINITY).is_err());
assert!(expon.set_rate(f64::NAN).is_err());

pub fn set_rate_unchecked(&mut self, rate: f64)

Set the rate parameter without input validation

Trait Implementations

impl Cdf<f32> for Exponential

fn cdf(&self, x: &f32) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Cdf<f64> for Exponential

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

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Clone for Exponential

fn clone(&self) -> Exponential

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl ContinuousDistr<f32> for Exponential

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

The value of the Probability Density Function (PDF) at x

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

The value of the log Probability Density Function (PDF) at x

impl ContinuousDistr<f64> for Exponential

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

The value of the Probability Density Function (PDF) at x

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

The value of the log Probability Density Function (PDF) at x

impl Debug for Exponential

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

Formats the value using the given formatter.

impl Default for Exponential

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Exponential

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for Exponential

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

Formats the value using the given formatter.

impl Entropy for Exponential

fn entropy(&self) -> f64

The entropy, H(X)

impl From<&Exponential> for String

fn from(expon: &Exponential) -> String

Converts to this type from the input type.

impl HasDensity<f32> for Exponential

fn ln_f(&self, x: &f32) -> f64

Probability function

fn f(&self, x: &X) -> f64

Probability function

impl HasDensity<f64> for Exponential

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

Probability function

fn f(&self, x: &X) -> f64

Probability function

impl InverseCdf<f32> for Exponential

fn invcdf(&self, p: f64) -> f32

The value of the x at the given probability in the CDF

fn quantile(&self, p: f64) -> X

Alias for invcdf

fn interval(&self, p: f64) -> (X, X)

Interval containing p proportion for the probability

impl InverseCdf<f64> for Exponential

fn invcdf(&self, p: f64) -> f64

The value of the x at the given probability in the CDF

fn quantile(&self, p: f64) -> X

Alias for invcdf

fn interval(&self, p: f64) -> (X, X)

Interval containing p proportion for the probability

impl KlDivergence for Exponential

fn kl(&self, other: &Self) -> f64

The KL divergence, KL(P|Q) between this distribution, P, and another, Q

fn kl_sym(&self, other: &Self) -> f64

Symmetrized divergence, KL(P|Q) + KL(Q|P)

impl Kurtosis for Exponential

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

impl Mean<f32> for Exponential

fn mean(&self) -> Option<f32>

Returns None if the mean is undefined

impl Mean<f64> for Exponential

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

Returns None if the mean is undefined

impl Median<f32> for Exponential

fn median(&self) -> Option<f32>

Returns None if the median is undefined

impl Median<f64> for Exponential

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

Returns None if the median is undefined

impl Mode<f32> for Exponential

fn mode(&self) -> Option<f32>

Returns None if the mode is undefined or is not a single value

impl Mode<f64> for Exponential

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

Returns None if the mode is undefined or is not a single value

impl Parameterized for Exponential

type Parameters = f64

fn emit_params(&self) -> Self::Parameters

fn from_params(rate: Self::Parameters) -> Self

impl PartialEq for Exponential

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

Tests for self and other values to be equal, and is used by ==.

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

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Sampleable<f32> for Exponential

fn draw<R: Rng>(&self, rng: &mut R) -> f32

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>

Multiple draws of the Rv

fn sample_stream<'r, R: Rng>( &'r self, rng: &'r mut R, ) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl Sampleable<f64> for Exponential

fn draw<R: Rng>(&self, rng: &mut R) -> f64

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>

Multiple draws of the Rv

fn sample_stream<'r, R: Rng>( &'r self, rng: &'r mut R, ) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl Serialize for Exponential

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Skewness for Exponential

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

impl Support<f32> for Exponential

fn supports(&self, x: &f32) -> bool

Returns true if x is in the support of the Rv

impl Support<f64> for Exponential

fn supports(&self, x: &f64) -> bool

Returns true if x is in the support of the Rv

impl Variance<f32> for Exponential

fn variance(&self) -> Option<f32>

Returns None if the variance is undefined

impl Variance<f64> for Exponential

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

Returns None if the variance is undefined

impl StructuralPartialEq for Exponential