Struct ContinuousSampler 

pub struct ContinuousSampler<D> { /* private fields */ }

Continuous distribution sampler.

Sampling uses SampleMethod (inverse transform or TDR). ppf and statistics always use numerical inverse CDF.

Implementations

impl<P> ContinuousSampler<IntegratedPdf<P>>

where P: Pdf + HasSupport,

pub fn from_pdf(pdf: P, opts: BuildOptions) -> Result<Self, BuildError>

where P: HasSupport,

pub fn from_pdf_with_dpdf<DpdfT>( pdf: P, dpdf: DpdfT, opts: BuildOptions, ) -> Result<ContinuousSampler<IntegratedPdf<P>>, BuildError>

where P: HasSupport, DpdfT: Dpdf + HasSupport,

impl<C> ContinuousSampler<AffineCdf<C>>

where C: Cdf,

pub fn from_cdf(cdf: C, opts: BuildOptions) -> Result<Self, BuildError>

where C: HasSupport,

impl<D> ContinuousSampler<D>

where D: CdfSource,

pub fn set_hermite_table(&mut self, grid_size: usize)

Rebuild or replace the Hermite PPF table (inverse-transform path only).

pub fn clear_hermite_table(&mut self)

Use bisection-only PPF (removes Hermite table).

pub fn uses_hermite_table(&self) -> bool

pub fn uses_tdr(&self) -> bool

pub fn support(&self) -> Interval

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

pub fn pdf_at(&self, x: f64) -> Option<f64>

pub fn has_pdf(&self) -> bool

pub fn ppf(&self, u: f64) -> Result<f64, SampleError>

pub fn sample(&self) -> Result<f64, SampleError>

pub fn sample_with_rng<R: Rng + ?Sized>( &self, rng: &mut R, ) -> Result<f64, SampleError>

pub fn sample_n(&self, n: usize) -> Result<Vec<f64>, SampleError>

pub fn sample_n_with_rng<R: Rng + ?Sized>( &self, rng: &mut R, n: usize, ) -> Result<Vec<f64>, SampleError>

impl<D> ContinuousSampler<D>

where D: CdfSource,

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

Probability density at x (zero outside support). Requires an underlying PDF.

pub fn logpdf(&self, x: f64) -> Option<f64>

Natural logarithm of the PDF; None if PDF unavailable or non-positive.

pub fn logcdf(&self, x: f64) -> f64

Log of the CDF.

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

Survival function sf(x) = 1 - cdf(x), stable for large cdf.

pub fn logsf(&self, x: f64) -> f64

Log survival function.

pub fn isf(&self, q: f64) -> Result<f64, SampleError>

Inverse survival function isf(q) = ppf(1 - q).

pub fn median(&self) -> Result<f64, SampleError>

Median ppf(0.5).

pub fn mean(&self) -> Result<f64, SampleError>

Mean E[X] via expect(|x| x).

pub fn var(&self) -> Result<f64, SampleError>

Variance Var[X].

pub fn std(&self) -> Result<f64, SampleError>

Standard deviation.

pub fn entropy(&self) -> Result<f64, SampleError>

Differential entropy H = -∫ f log f dx (nats).

pub fn expect( &self, func: impl Fn(f64) -> f64, tol: f64, ) -> Result<f64, SampleError>

E[func(X)] on the support.

pub fn interval(&self, confidence: f64) -> Result<(f64, f64), SampleError>

Equal-tailed confidence interval (ppf((1-p)/2), ppf(1-(1-p)/2)).

Trait Implementations

impl<D> Distribution<f64> for ContinuousSampler<D>

where D: CdfSource,

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) -> Iter<Self, R, T>

where R: Rng, Self: Sized,

Create an iterator that generates random values of T, using rng as the source of randomness.

fn map<F, S>(self, func: F) -> Map<Self, F, T, S>

where F: Fn(T) -> S, Self: Sized,

Map sampled values to type S