Enum Dist 

pub enum Dist {
    Lognormal(Lognormal),
    Pareto(Pareto),
    PiecewisePareto(PiecewisePareto),
    LogAffinePareto(LogAffinePareto),
    GeneralizedPareto(Gpd),
    Gamma(Gamma),
    Tweedie(Tweedie),
    Weibull(Weibull),
    Loglogistic(Loglogistic),
    Mixture(Arc<Mixture>),
    Grid(Grid),
    Sampled(Sampled),
    Custom(Custom),
}

Any native univariate distribution.

Every variant is a Distribution; all but Dist::Sampled are also a Severity, which Dist::as_severity exposes. A Mixture is held in an Arc (its components are trait objects), so cloning a Dist never copies one.

use prospicio_prob::{Dist, Distribution, Lognormal, Sampled};

let dists = vec![
    Dist::from(Lognormal::from_mean_cv(1000.0, 0.5).unwrap()),
    Dist::from(Sampled::new(vec![800.0, 1000.0, 1200.0]).unwrap()),
];
for d in &dists {
    assert!((d.mean() - 1000.0).abs() < 1e-9);
}
// Limited expected values exist for the parametric family only.
assert!(dists[0].as_severity().is_some());
assert!(dists[1].as_severity().is_none());

Variants

Lognormal(Lognormal)

Pareto(Pareto)

PiecewisePareto(PiecewisePareto)

LogAffinePareto(LogAffinePareto)

GeneralizedPareto(Gpd)

Gamma(Gamma)

Tweedie(Tweedie)

Weibull(Weibull)

Loglogistic(Loglogistic)

Mixture(Arc<Mixture>)

Grid(Grid)

Sampled(Sampled)

Custom(Custom)

A user-defined severity (a Python or R callback): the slow path.

Implementations

impl Dist

pub fn family(&self) -> &'static str

Short name of the family: "lognormal", "pareto", "piecewise_pareto", "log_affine_pareto", "generalized_pareto", "gamma", "tweedie", "weibull", "loglogistic", "mixture", "grid", "sampled" or "custom".

pub fn as_severity(&self) -> Option<&(dyn Severity + Send + Sync)>

The distribution as a Severity (limited expected values, layers), or None for Dist::Sampled: draws have no exact layer moments (docs/design/distributions.md).

impl Dist

pub fn to_json(&self) -> Result<String>

The distribution as a JSON document; from_json reads it back to an equal distribution.

Fails for a Custom (a function in the caller’s language) and for a mixture built from trait objects rather than with Mixture::from_dists.

use prospicio_prob::{Dist, Distribution, Lognormal};

let d = Dist::from(Lognormal::new(7.0, 0.5).unwrap());
let back = Dist::from_json(&d.to_json().unwrap()).unwrap();
assert_eq!(back.family(), "lognormal");
assert_eq!(back.mean(), d.mean());

pub fn from_json(text: &str) -> Result<Self>

Reads a document written by to_json. Fails on malformed JSON, another format, a newer format version, an unknown family, or parameters the family’s constructor refuses.

Trait Implementations

impl Clone for Dist

fn clone(&self) -> Dist

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Dist

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

Formats the value using the given formatter.

impl Distribution for Dist

fn mean(&self) -> f64

Expected value.

fn variance(&self) -> f64

Variance.

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

P(X <= x).

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

P(X > x). Representations with a direct form override the default 1 - cdf(x), which loses all precision far in the tail.

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

Smallest x with cdf(x) >= p.

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

n draws from stream rng, by inverse transform unless the family overrides it (crate::Gamma draws by Marsaglia and Tsang).

fn is_parallel_safe(&self) -> bool

Whether the distribution may be evaluated on several threads at once. True for every native family; false for a crate::Custom whose callbacks must stay on the calling thread (an R function), so the parallel simulations run single-threaded when they meet one.

fn std_dev(&self) -> f64

Standard deviation.

impl From<Arc<Mixture>> for Dist

fn from(d: Arc<Mixture>) -> Self

Converts to this type from the input type.

impl From<Custom> for Dist

fn from(d: Custom) -> Self

Converts to this type from the input type.

impl From<Gamma> for Dist

fn from(d: Gamma) -> Self

Converts to this type from the input type.

impl From<Gpd> for Dist

fn from(d: Gpd) -> Self

Converts to this type from the input type.

impl From<Grid> for Dist

fn from(d: Grid) -> Self

Converts to this type from the input type.

impl From<LogAffinePareto> for Dist

fn from(d: LogAffinePareto) -> Self

Converts to this type from the input type.

impl From<Loglogistic> for Dist

fn from(d: Loglogistic) -> Self

Converts to this type from the input type.

impl From<Lognormal> for Dist

fn from(d: Lognormal) -> Self

Converts to this type from the input type.

impl From<Mixture> for Dist

fn from(d: Mixture) -> Self

Converts to this type from the input type.

impl From<Pareto> for Dist

fn from(d: Pareto) -> Self

Converts to this type from the input type.

impl From<PiecewisePareto> for Dist

fn from(d: PiecewisePareto) -> Self

Converts to this type from the input type.

impl From<Sampled> for Dist

fn from(d: Sampled) -> Self

Converts to this type from the input type.

impl From<SeverityDist> for Dist

fn from(s: SeverityDist) -> Self

Converts to this type from the input type.

impl From<Tweedie> for Dist

fn from(d: Tweedie) -> Self

Converts to this type from the input type.

impl From<Weibull> for Dist

fn from(d: Weibull) -> Self

Converts to this type from the input type.

impl TryFrom<Dist> for SeverityDist

type Error = Dist

The distribution back, when it is Dist::Sampled.

fn try_from(d: Dist) -> Result<Self, Dist>

Performs the conversion.