Struct rv::dist::InvGaussian

pub struct InvGaussian { /* fields omitted */ }

Inverse Gaussian distribution, N^-1(μ, λ) over real values.

Implementations

impl InvGaussian

pub fn new(mu: f64, lambda: f64) -> Result<Self, InvGaussianError>

Create a new Inverse Gaussian distribution

Aruments

• mu: mean > 0
• lambda: shape > 0

use rv::dist::InvGaussian;
let invgauss = InvGaussian::new(1.0, 3.0).unwrap();

Mu and lambda must be finite and greater than 0.

use std::f64::{NAN, INFINITY};
assert!(InvGaussian::new(0.0, 3.0).is_err());
assert!(InvGaussian::new(NAN, 3.0).is_err());
assert!(InvGaussian::new(INFINITY, 3.0).is_err());

assert!(InvGaussian::new(1.0, 0.0).is_err());
assert!(InvGaussian::new(1.0, NAN).is_err());
assert!(InvGaussian::new(1.0, INFINITY).is_err());

pub fn new_unchecked(mu: f64, lambda: f64) -> Self

Creates a new InvGaussian without checking whether the parameters are valid.

pub fn mu(&self) -> f64

Get mu parameter

Example

let ig = InvGaussian::new(2.0, 1.5).unwrap();

assert_eq!(ig.mu(), 2.0);

pub fn set_mu(&mut self, mu: f64) -> Result<(), InvGaussianError>

Set the value of mu

Example

let mut ig = InvGaussian::new(2.0, 1.5).unwrap();
assert_eq!(ig.mu(), 2.0);

ig.set_mu(1.3).unwrap();
assert_eq!(ig.mu(), 1.3);

Will error for invalid values

assert!(ig.set_mu(1.3).is_ok());
assert!(ig.set_mu(0.0).is_err());
assert!(ig.set_mu(-1.0).is_err());
assert!(ig.set_mu(std::f64::NEG_INFINITY).is_err());
assert!(ig.set_mu(std::f64::INFINITY).is_err());
assert!(ig.set_mu(std::f64::NAN).is_err());

pub fn set_mu_unchecked(&mut self, mu: f64)

Set the value of mu without input validation

pub fn lambda(&self) -> f64

Get lambda parameter

Example

let ig = InvGaussian::new(2.0, 1.5).unwrap();

assert_eq!(ig.lambda(), 1.5);

pub fn set_lambda(&mut self, lambda: f64) -> Result<(), InvGaussianError>

Set the value of lambda

Example

let mut ig = InvGaussian::new(1.0, 2.0).unwrap();
assert_eq!(ig.lambda(), 2.0);

ig.set_lambda(2.3).unwrap();
assert_eq!(ig.lambda(), 2.3);

Will error for invalid values

assert!(ig.set_lambda(2.3).is_ok());
assert!(ig.set_lambda(0.0).is_err());
assert!(ig.set_lambda(-1.0).is_err());
assert!(ig.set_lambda(std::f64::INFINITY).is_err());
assert!(ig.set_lambda(std::f64::NEG_INFINITY).is_err());
assert!(ig.set_lambda(std::f64::NAN).is_err());

pub fn set_lambda_unchecked(&mut self, lambda: f64)

Set the value of lambda without input validation

pub fn params(&self) -> (f64, f64)

Return (mu, lambda)

Trait Implementations

impl Cdf<f32> for InvGaussian

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 InvGaussian

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 InvGaussian

fn clone(&self) -> Self

Returns a copy of the value.

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

Performs copy-assignment from source.

impl ContinuousDistr<f32> for InvGaussian

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 InvGaussian

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 InvGaussian

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

Formats the value using the given formatter.

impl Display for InvGaussian

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

Formats the value using the given formatter.

impl HasSuffStat<f32> for InvGaussian

type Stat = InvGaussianSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl HasSuffStat<f64> for InvGaussian

type Stat = InvGaussianSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Kurtosis for InvGaussian

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

impl Mean<f32> for InvGaussian

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

Returns None if the mean is undefined

impl Mean<f64> for InvGaussian

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

Returns None if the mean is undefined

impl Mode<f32> for InvGaussian

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

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

impl Mode<f64> for InvGaussian

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

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

impl PartialEq<InvGaussian> for InvGaussian

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

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

#[must_use]

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

This method tests for !=.

impl Rv<f32> for InvGaussian

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

Probability function

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

Single draw from the Rv

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

Probability function

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

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 Rv<f64> for InvGaussian

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

Probability function

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

Single draw from the Rv

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

Probability function

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

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 Skewness for InvGaussian

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

impl Support<f32> for InvGaussian

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

Returns true if x is in the support of the Rv

impl Support<f64> for InvGaussian

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

Returns true if x is in the support of the Rv

impl Variance<f64> for InvGaussian

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

Returns None if the variance is undefined