Struct rstat::multivariate::dirichlet::Dirichlet

pub struct Dirichlet {
    pub alphas: Concentrations,
    // some fields omitted
}

Fields

alphas: Concentrations

Methods

impl Dirichlet

pub fn new(alphas: Array1<f64>) -> Result<Dirichlet, Error>

pub fn new_unchecked(alphas: Array1<f64>) -> Dirichlet

Trait Implementations

impl Clone for Dirichlet

fn clone(&self) -> Dirichlet

Returns a copy of the value.

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

Performs copy-assignment from source.

impl ContinuousDistribution for Dirichlet

fn pdf(&self, xs: &Vec<f64>) -> f64

Evaluates the probability density function (PDF) at (x).

fn log_pdf(&self, xs: &Vec<f64>) -> f64

Evaluates the log PDF at (x).

impl Debug for Dirichlet

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

Formats the value using the given formatter.

impl Display for Dirichlet

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

Formats the value using the given formatter.

impl Distribution for Dirichlet

type Support = ProductSpace<Interval>

Support of sample elements.

type Params = Concentrations

Parameter set uniquely defining the instance.

fn support(&self) -> ProductSpace<Interval>

Returns an instance of the support Space, Self::Support.

fn params(&self) -> Concentrations

Returns an instance of Self::Params matching the parameters of self.

fn cdf(&self, _: &Vec<f64>) -> Probability

Evaluates the cumulative distribution function (CDF) at (x).

fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<f64>

Draw a random value from the distribution support.

fn into_support(self) -> Self::Support

fn into_params(self) -> Self::Params

fn ccdf(&self, x: &Sample<Self>) -> Probability

Evaluates the complementary CDF at (x).

fn log_cdf(&self, x: &Sample<Self>) -> f64

Evaluates the log CDF at (x), i.e. (\ln{F(x)}).

fn log_ccdf(&self, x: &Sample<Self>) -> f64

Evaluates the log complementary CDF at (x), i.e. (\ln{(1 - F(x))}).

fn sample_n<R: Rng + ?Sized>(&self, rng: &mut R, n: usize) -> Vec<Sample<Self>>

Draw n random value from the distribution support.

fn sample_iter<'a, R: Rng + ?Sized>(
    &'a self,
    rng: &'a mut R
) -> Sampler<'a, Self, R>

Draw an indefinite number of random values from the distribution support.

impl From<Concentrations> for Dirichlet

fn from(alphas: Concentrations) -> Dirichlet

Performs the conversion.

impl Modes for Dirichlet

fn modes(&self) -> Vec<Vec<f64>>

Computes the mode(s) of the distribution.

impl MultivariateMoments for Dirichlet

fn mean(&self) -> Array1<f64>

Computes the vector of expected values of the distribution.

fn variance(&self) -> Array1<f64>

Computes the vector of variances of the distribution.

fn covariance(&self) -> Array2<f64>

Computes the covariance matrix of the distribution.

fn correlation(&self) -> Array2<f64>

Computes the correlation matrix of the distribution.