Trait rv::traits::Support

pub trait Support<X> {
    fn supports(&self, x: &X) -> bool;
}

Identifies the support of the Rv

Required methods

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

Returns true if x is in the support of the Rv

Example

use rv::dist::Uniform;
use rv::traits::Support;

// Create uniform with support on the interval [0, 1]
let u = Uniform::new(0.0, 1.0).unwrap();

assert!(u.supports(&0.5_f64));
assert!(!u.supports(&-0.1_f64));
assert!(!u.supports(&1.1_f64));

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Implementors

impl Support<f32> for Beta

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

impl Support<f32> for Cauchy

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

impl Support<f32> for ChiSquared

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

impl Support<f32> for Exponential

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

impl Support<f32> for Gamma

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

impl Support<f32> for Gaussian

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

impl Support<f32> for Gev

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

impl Support<f32> for InvChiSquared

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

impl Support<f32> for InvGamma

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

impl Support<f32> for InvGaussian

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

impl Support<f32> for KsTwoAsymptotic

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

impl Support<f32> for Kumaraswamy

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

impl Support<f32> for Laplace

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

impl Support<f32> for LogNormal

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

impl Support<f32> for Pareto

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

impl Support<f32> for ScaledInvChiSquared

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

impl Support<f32> for StudentsT

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

impl Support<f32> for Uniform

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

impl Support<f32> for VonMises

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

impl Support<f64> for Beta

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

impl Support<f64> for Cauchy

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

impl Support<f64> for ChiSquared

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

impl Support<f64> for Exponential

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

impl Support<f64> for Gamma

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

impl Support<f64> for Gaussian

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

impl Support<f64> for Gev

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

impl Support<f64> for InvChiSquared

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

impl Support<f64> for InvGamma

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

impl Support<f64> for InvGaussian

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

impl Support<f64> for KsTwoAsymptotic

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

impl Support<f64> for Kumaraswamy

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

impl Support<f64> for Laplace

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

impl Support<f64> for LogNormal

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

impl Support<f64> for Pareto

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

impl Support<f64> for ScaledInvChiSquared

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

impl Support<f64> for StudentsT

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

impl Support<f64> for Uniform

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

impl Support<f64> for VonMises

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

impl Support<i8> for BetaBinomial

fn supports(&self, k: &i8) -> bool

impl Support<i8> for Binomial

fn supports(&self, k: &i8) -> bool

impl Support<i8> for Skellam

fn supports(&self, _x: &i8) -> bool

impl Support<i16> for BetaBinomial

fn supports(&self, k: &i16) -> bool

impl Support<i16> for Binomial

fn supports(&self, k: &i16) -> bool

impl Support<i16> for Skellam

fn supports(&self, _x: &i16) -> bool

impl Support<i32> for BetaBinomial

fn supports(&self, k: &i32) -> bool

impl Support<i32> for Binomial

fn supports(&self, k: &i32) -> bool

impl Support<i32> for Skellam

fn supports(&self, _x: &i32) -> bool

impl Support<i64> for BetaBinomial

fn supports(&self, k: &i64) -> bool

impl Support<i64> for Binomial

fn supports(&self, k: &i64) -> bool

impl Support<u8> for BetaBinomial

fn supports(&self, k: &u8) -> bool

impl Support<u8> for Binomial

fn supports(&self, k: &u8) -> bool

impl Support<u8> for NegBinomial

fn supports(&self, _x: &u8) -> bool

impl Support<u8> for Poisson

fn supports(&self, x: &u8) -> bool

impl Support<u16> for BetaBinomial

fn supports(&self, k: &u16) -> bool

impl Support<u16> for Binomial

fn supports(&self, k: &u16) -> bool

impl Support<u16> for NegBinomial

fn supports(&self, _x: &u16) -> bool

impl Support<u16> for Poisson

fn supports(&self, x: &u16) -> bool

impl Support<u32> for BetaBinomial

fn supports(&self, k: &u32) -> bool

impl Support<u32> for Binomial

fn supports(&self, k: &u32) -> bool

impl Support<u32> for NegBinomial

fn supports(&self, _x: &u32) -> bool

impl Support<u32> for Poisson

fn supports(&self, x: &u32) -> bool

impl Support<u64> for BetaBinomial

fn supports(&self, k: &u64) -> bool

impl Support<u64> for Binomial

fn supports(&self, k: &u64) -> bool

impl Support<usize> for BetaBinomial

fn supports(&self, k: &usize) -> bool

impl Support<usize> for Binomial

fn supports(&self, k: &usize) -> bool

impl Support<Partition> for Crp

fn supports(&self, _x: &Partition) -> bool

impl Support<Bernoulli> for Beta

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

impl Support<Gaussian> for NormalGamma

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

impl Support<MvGaussian> for NormalInvWishart

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

impl Support<Poisson> for Gamma

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

impl Support<Vec<f64, Global>> for Dirichlet

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

impl Support<Vec<f64, Global>> for SymmetricDirichlet

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

impl Support<Matrix<f64, Dynamic, Dynamic, <DefaultAllocator as Allocator<f64, Dynamic, Dynamic>>::Buffer>> for InvWishart

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

impl Support<Matrix<f64, Dynamic, U1, VecStorage<f64, Dynamic, U1>>> for MvGaussian

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

impl<Fx, X> Support<X> for Fx where
    Fx: Deref,
    Fx::Target: Support<X>, 

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

impl<X> Support<X> for Geometric where
    X: Unsigned + Integer, 

fn supports(&self, k: &X) -> bool

impl<X, Fx> Support<X> for Mixture<Fx> where
    Fx: Rv<X> + Support<X>, 

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

impl<X, T> Support<X> for DiscreteUniform<T> where
    X: Integer + From<T>,
    T: DuParam, 

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

impl<X: Booleable> Support<X> for Bernoulli

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

impl<X: CategoricalDatum> Support<X> for Categorical

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

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