Struct MultivarNormDist

pub struct MultivarNormDist<T> { /* private fields */ }

Multivariate normal distribution for given average vectors and covariance matrices.

Example

use multivariate_optimization::distributions::MultivarNormDist;
use multivariate_optimization::triangular::Triangular;
use rand::{thread_rng, Rng};

let averages: Vec<f64> = vec![1.51, 1.57, -0.29];
let covariances = Triangular::<f64>::new(3, |(i, j)| {
    // needs to handle only cases for i >= j
    // assert!(i >= j);
    if i == j { 1.0 } else { 0.5 }
});
let dist = MultivarNormDist::new(averages, covariances);

let mut rng = thread_rng();
let value = rng.sample(dist);
println!("Sample vector: {:?}", value);

Implementations

impl<T: Num> MultivarNormDist<T>

pub fn new(averages: Vec<T>, covariances: Triangular<T>) -> MultivarNormDist<T>

Create multivariate normal distribution for given averages and covariances.

Trait Implementations

impl<T: Debug> Debug for MultivarNormDist<T>

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

Formats the value using the given formatter.

impl<T: Num> Distribution<Vec<T>> for MultivarNormDist<T>

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

Generate a random value of T, using rng as the source of randomness.

fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>

where R: Rng, Self: Sized,

Create an iterator that generates random values of T, using rng as the source of randomness.

fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>

where F: Fn(T) -> S, Self: Sized,

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F

impl<T: Num> MultivarDist<T> for MultivarNormDist<T>

fn dim(&self) -> usize

Dimensionality