mod.rs

//! Defines common interfaces for interacting with statistical distributions
//! and provides
//! concrete implementations for a variety of distributions.

pub use self::bernoulli::Bernoulli;
pub use self::beta::Beta;
pub use self::binomial::Binomial;
pub use self::categorical::Categorical;
pub use self::cauchy::Cauchy;
pub use self::chi::Chi;
pub use self::chi_squared::ChiSquared;
pub use self::dirac::Dirac;
pub use self::dirichlet::Dirichlet;
pub use self::discrete_uniform::DiscreteUniform;
pub use self::erlang::Erlang;
pub use self::exponential::Exponential;
pub use self::fisher_snedecor::FisherSnedecor;
pub use self::gamma::Gamma;
pub use self::negative_binomial::NegativeBinomial;
pub use self::geometric::Geometric;
pub use self::hypergeometric::Hypergeometric;
pub use self::inverse_gamma::InverseGamma;
pub use self::log_normal::LogNormal;
pub use self::multinomial::Multinomial;
pub use self::multivariate_normal::MultivariateNormal;
pub use self::normal::Normal;
pub use self::pareto::Pareto;
pub use self::poisson::Poisson;
pub use self::students_t::StudentsT;
pub use self::triangular::Triangular;
pub use self::uniform::Uniform;
pub use self::weibull::Weibull;
use crate::statistics::{Max, Min};

mod bernoulli;
mod beta;
mod binomial;
mod categorical;
mod cauchy;
mod chi;
mod chi_squared;
mod dirac;
mod dirichlet;
mod discrete_uniform;
mod erlang;
mod exponential;
mod fisher_snedecor;
mod gamma;
mod geometric;
mod hypergeometric;
mod internal;
mod inverse_gamma;
mod log_normal;
mod multinomial;
mod multivariate_normal;
mod normal;
mod negative_binomial;
mod pareto;
mod poisson;
mod students_t;
mod triangular;
mod uniform;
mod weibull;
mod ziggurat;
mod ziggurat_tables;

use crate::Result;

/// The `Univariate` trait is used to specify an interface for univariate
/// distributions e.g. distributions that have a closed form cumulative
/// distribution
/// function
pub trait Univariate<T, K>: Min<T> + Max<T> {
    /// Returns the cumulative distribution function calculated
    /// at `x` for a given distribution. May panic depending
    /// on the implementor.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::{Univariate, Uniform};
    ///
    /// let n = Uniform::new(0.0, 1.0).unwrap();
    /// assert_eq!(0.5, n.cdf(0.5));
    /// ```
    fn cdf(&self, x: K) -> K;
}

/// The `InverseCDF` trait is used to specify an interface for distributions
/// with a closed form solution to the inverse cumulative distribution function.
/// This trait will probably be merged into `Univariate` in a future release
/// when already implemented distributions have `InverseCDF` back ported
pub trait InverseCDF<T> {
    /// Returns the inverse cumulative distribution function
    /// calculated at `x` for a given distribution. May panic
    /// depending on the implementor.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::InverseCDF;
    /// use statrs::distribution::Categorical;
    ///
    /// let n = Categorical::new(&[0.0, 1.0, 2.0]).unwrap();
    /// assert_eq!(n.inverse_cdf(0.5), 2.0);
    /// ```
    fn inverse_cdf(&self, x: T) -> T;
}

/// The `CheckedInverseCDF` trait is used to specify an interface
/// for  distributions with a closed form solution to the inverse
/// cumulative distribution function with possible failure modes.
/// This trait should be merged into a `CheckedUnivarite` trait
/// alongside `InverseCDF` in a future release.
pub trait CheckedInverseCDF<T> {
    /// Returns the inverse cumulative distribution function
    /// calculated at `x` for a given distribution. May panic
    /// depending on the implementor.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::CheckedInverseCDF;
    /// use statrs::distribution::Categorical;
    ///
    /// let n = Categorical::new(&[0.0, 1.0, 2.0]).unwrap();
    /// assert!(n.checked_inverse_cdf(-1.0).is_err());
    /// ```
    fn checked_inverse_cdf(&self, x: T) -> Result<T>;
}

/// The `Continuous` trait  provides an interface for interacting with
/// continuous statistical distributions
///
/// # Remarks
///
/// All methods provided by the `Continuous` trait are unchecked, meaning
/// they can panic if in an invalid state or encountering invalid input
/// depending on the implementing distribution.
pub trait Continuous<T, K> {
    /// Returns the probability density function calculated at `x` for a given
    /// distribution.
    /// May panic depending on the implementor.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::{Continuous, Uniform};
    ///
    /// let n = Uniform::new(0.0, 1.0).unwrap();
    /// assert_eq!(1.0, n.pdf(0.5));
    /// ```
    fn pdf(&self, x: T) -> K;

    /// Returns the log of the probability density function calculated at `x`
    /// for a given distribution.
    /// May panic depending on the implementor.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::{Continuous, Uniform};
    ///
    /// let n = Uniform::new(0.0, 1.0).unwrap();
    /// assert_eq!(0.0, n.ln_pdf(0.5));
    /// ```
    fn ln_pdf(&self, x: T) -> K;
}

/// The `CheckedContinuous` trait provides an interface for
/// interacting with continuous statistical distributions with possible
/// failure modes
pub trait CheckedContinuous<T, K> {
    /// Returns the probability density function calculated at `x` for a given
    /// distribution.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::{CheckedContinuous, Dirichlet};
    ///
    /// let n = Dirichlet::new(&[1.0, 2.0, 3.0]).unwrap();
    /// assert!(n.checked_pdf(&[0.0]).is_err());
    /// ```
    fn checked_pdf(&self, x: T) -> Result<K>;

    /// Returns the log of the probability density function calculated at `x`
    /// for a given distribution.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::{CheckedContinuous, Dirichlet};
    ///
    /// let n = Dirichlet::new(&[1.0, 2.0, 3.0]).unwrap();
    /// assert!(n.checked_ln_pdf(&[0.0]).is_err());
    /// ```
    fn checked_ln_pdf(&self, x: T) -> Result<K>;
}

/// The `Discrete` trait provides an interface for interacting with discrete
/// statistical distributions
///
/// # Remarks
///
/// All methods provided by the `Discrete` trait are unchecked, meaning
/// they can panic if in an invalid state or encountering invalid input
/// depending on the implementing distribution.
pub trait Discrete<T, K> {
    /// Returns the probability mass function calculated at `x` for a given
    /// distribution.
    /// May panic depending on the implementor.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::{Discrete, Binomial};
    /// use statrs::prec;
    ///
    /// let n = Binomial::new(0.5, 10).unwrap();
    /// assert!(prec::almost_eq(n.pmf(5), 0.24609375, 1e-15));
    /// ```
    fn pmf(&self, x: T) -> K;

    /// Returns the log of the probability mass function calculated at `x` for
    /// a given distribution.
    /// May panic depending on the implementor.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::{Discrete, Binomial};
    /// use statrs::prec;
    ///
    /// let n = Binomial::new(0.5, 10).unwrap();
    /// assert!(prec::almost_eq(n.ln_pmf(5), (0.24609375f64).ln(), 1e-15));
    /// ```
    fn ln_pmf(&self, x: T) -> K;
}

/// The `CheckedDiscrete` trait provides an interface for interacting
/// with discrete statistical distributions with possible failure modes
pub trait CheckedDiscrete<T, K> {
    /// Returns the probability mass function calculated at `x` for a given
    /// distribution.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::{CheckedDiscrete, Multinomial};
    /// use statrs::prec;
    ///
    /// let n = Multinomial::new(&[0.3, 0.7], 5).unwrap();
    /// assert!(n.checked_pmf(&[1]).is_err());
    /// ```
    fn checked_pmf(&self, x: T) -> Result<K>;

    /// Returns the log of the probability mass function calculated at `x` for
    /// a given distribution.
    ///
    /// # Examples
    ///
    /// ```
    /// use statrs::distribution::{CheckedDiscrete, Multinomial};
    /// use statrs::prec;
    ///
    /// let n = Multinomial::new(&[0.3, 0.7], 5).unwrap();
    /// assert!(n.checked_ln_pmf(&[1]).is_err());
    /// ```
    fn checked_ln_pmf(&self, x: T) -> Result<K>;
}