Struct ndarray_rand::rand_distr::Normal

pub struct Normal<N> { /* fields omitted */ }

The normal distribution N(mean, std_dev**2).

This uses the ZIGNOR variant of the Ziggurat method, see StandardNormal for more details.

Note that StandardNormal is an optimised implementation for mean 0, and standard deviation 1.

Example

use rand_distr::{Normal, Distribution};

// mean 2, standard deviation 3
let normal = Normal::new(2.0, 3.0).unwrap();
let v = normal.sample(&mut rand::thread_rng());
println!("{} is from a N(2, 9) distribution", v)

Methods

impl<N> Normal<N> where
    N: Float,
    StandardNormal: Distribution<N>, 

pub fn new(mean: N, std_dev: N) -> Result<Normal<N>, Error>

Construct a new Normal distribution with the given mean and standard deviation.

Trait Implementations

impl<N> Debug for Normal<N> where
    N: Debug, 

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

Formats the value using the given formatter.

impl<N> Copy for Normal<N> where
    N: Copy, 

impl<N> Clone for Normal<N> where
    N: Clone, 

fn clone(&self) -> Normal<N>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<N> Distribution<N> for Normal<N> where
    N: Float,
    StandardNormal: Distribution<N>, 

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

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

Important traits for DistIter<D, R, T>

impl<D, R, T> Iterator for DistIter<D, R, T> where
    D: Distribution<T>,
    R: Rng,  type Item = T;

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

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