Struct Mixture

pub struct Mixture<C: Distribution> {
    pub weights: SimplexVector,
    pub components: Vec<C>,
    /* private fields */
}

Probability distribution derived from a linear sum of random variables.

Examples

use rstat::{
    Probability, Mixture,
    univariate::{normal::Normal, Moments}
};

let gmm = Mixture::new(
    vec![0.2, 0.5, 0.3],
    vec![
        Normal::new(-2.0, 1.2_f64.powi(2))?,
        Normal::new(0.0, 1.0_f64.powi(2))?,
        Normal::new(3.0, 2.5_f64.powi(2))?,
    ],
)?;

assert!((gmm.mean() - 0.5).abs() < 1e-7);
assert!((gmm.variance() - 5.913).abs() < 1e-7);
assert!((gmm.standard_deviation() - gmm.variance().sqrt()).abs() < 1e-7);

Fields

weights: SimplexVector

The weights of the linear sum.

components: Vec<C>

The distribution components of the linear sum.

Implementations

impl<C: Distribution> Mixture<C>

pub fn new(weights: Vec<f64>, components: Vec<C>) -> Result<Mixture<C>, Error>

where C::Support: Union,

pub fn new_unchecked(weights: Vec<f64>, components: Vec<C>) -> Mixture<C>

where C::Support: Union,

pub fn n_components(&self) -> usize

Trait Implementations

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: ContinuousDistribution> ContinuousDistribution for Mixture<C>

where C::Support: Union + Clone,

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

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

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

Evaluates the log PDF at \(x\).

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.

impl<C: Distribution> Distribution for Mixture<C>

where C::Support: Union + Clone,

type Support = <C as Distribution>::Support

Support of sample elements.

type Params = Params<<C as Distribution>::Params>

Parameter set uniquely defining the instance.

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

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

fn params(&self) -> Params<C::Params>

Returns an instance of the distribution parameters, Self::Params.

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

Draw a random value from the distribution support.

fn into_support(self) -> Self::Support

Converts self into an instance of Self::Support.

fn into_params(self) -> Self::Params

Converts self into an instance of 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<C: UnivariateMoments> UnivariateMoments for Mixture<C>

where C::Support: Union + 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.