Struct rstat::Mixture

pub struct Mixture<C: Distribution> {
    pub dist: Categorical,
    pub components: Vec<C>,
    // some fields omitted
}

Fields

dist: Categorical

components: Vec<C>

Methods

impl<C: Distribution> Mixture<C> where
    C::Support: Enclose, 

pub fn new<T: Into<Categorical>>(prior: T, components: Vec<C>) -> Mixture<C>

pub fn homogeneous(components: Vec<C>) -> Mixture<C>

impl<C: Distribution> Mixture<C>

pub fn n_components(&self) -> usize

Trait Implementations

impl<C: Distribution> Distribution for Mixture<C> where
    C::Support: Clone, 

type Support = C::Support

fn support(&self) -> Self::Support

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

fn cdf(&self, x: <Self::Support as Space>::Value) -> Probability

Evaluates the cumulative distribution function (CDF) at x.

fn sample<R: Rng + ?Sized>(
    &self,
    rng: &mut R
) -> <Self::Support as Space>::Value

fn ccdf(&self, x: <Self::Support as Space>::Value) -> Probability

Evaluates the complementary cumulative distribution function at x.

fn logcdf(&self, x: <Self::Support as Space>::Value) -> f64

Evaluates the log CDF at x: ln F(x).

fn logccdf(&self, x: <Self::Support as Space>::Value) -> f64

Evaluates the log complementary CDF at x: ln (1 - F(x)).

fn cdf_batch(
    &self,
    xs: Vector<<Self::Support as Space>::Value>
) -> Vector<Probability>

Evaluates the CDF element-wise for a batch xs.

fn ccdf_batch(
    &self,
    xs: Vector<<Self::Support as Space>::Value>
) -> Vector<Probability>

Evaluates the complementary CDF element-wise for a batch xs.

fn logcdf_batch(
    &self,
    xs: Vector<<Self::Support as Space>::Value>
) -> Vector<f64>

Evaluates the log CDF element-wise for a batch xs.

fn logccdf_batch(
    &self,
    xs: Vector<<Self::Support as Space>::Value>
) -> Vector<f64>

Evaluates the log complementary CDF element-wise for a batch xs.

fn sample_n<D, Sh, R: ?Sized>(
    &self,
    rng: &mut R,
    shape: Sh
) -> Array<<Self::Support as Space>::Value, D> where
    D: Dimension,
    Sh: ShapeBuilder<Dim = D>,
    R: Rng, 

Important traits for Sampler<D, R>

impl<D, R> Iterator for Sampler<D, R> where
    D: Distribution,
    R: Rng,  type Item = <D::Support as Space>::Value;

fn sample_iter<R>(self, rng: R) -> Sampler<Self, R> where
    Self: Sized,
    R: Rng, 

impl<C: ContinuousDistribution> ContinuousDistribution for Mixture<C> where
    C::Support: Clone,
    <C::Support as Space>::Value: Clone, 

fn pdf(&self, x: <Self::Support as Space>::Value) -> f64

Evaluates the probability density function (PDF) at x.

fn logpdf(&self, x: <Self::Support as Space>::Value) -> f64

Evaluates the log PDF at x.

fn pdf_batch(&self, xs: Vector<<Self::Support as Space>::Value>) -> Vector<f64>

Evaluates the PDF element-wise for a batch xs.

fn logpdf_batch(
    &self,
    xs: Vector<<Self::Support as Space>::Value>
) -> Vector<f64>

Evaluates the log PDF element-wise for a batch xs.

fn loglikelihood(&self, xs: Vector<<Self::Support as Space>::Value>) -> f64

impl<C: UnivariateMoments> UnivariateMoments for Mixture<C> where
    C::Support: Clone, 

fn mean(&self) -> f64

Computes the expected value of the distribution.

fn variance(&self) -> f64

Computes the variance of the distribution.

fn skewness(&self) -> f64

Computes the skewness of the distribution.

fn kurtosis(&self) -> f64

Computes the kurtosis of the distribution.

fn standard_deviation(&self) -> f64

Computes the standard deviation of the distribution.

fn excess_kurtosis(&self) -> f64

Computes the excess kurtosis of the distribution.

impl<C: Clone + Distribution> Clone for Mixture<C> where
    C::Support: Clone, 

fn clone(&self) -> Mixture<C>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<C: Debug + Distribution> Debug for Mixture<C> where
    C::Support: Debug, 

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

Formats the value using the given formatter.