Struct statrs::distribution::Bernoulli

pub struct Bernoulli { /* fields omitted */ }

Implements the Bernoulli distribution which is a special case of the Binomial distribution where n = 1 (referenced Here)

Examples

use statrs::distribution::{Bernoulli, Discrete};
use statrs::statistics::Mean;

let n = Bernoulli::new(0.5).unwrap();
assert_eq!(n.mean(), 0.5);
assert_eq!(n.pmf(0), 0.5);
assert_eq!(n.pmf(1), 0.5);

Implementations

impl Bernoulli

pub fn new(p: f64) -> Result<Bernoulli>

Constructs a new bernoulli distribution with the given p probability of success.

Errors

Returns an error if p is NaN, less than 0.0 or greater than 1.0

Examples

use statrs::distribution::Bernoulli;

let mut result = Bernoulli::new(0.5);
assert!(result.is_ok());

result = Bernoulli::new(-0.5);
assert!(result.is_err());

pub fn p(&self) -> f64

Returns the probability of success p of the bernoulli distribution.

Examples

use statrs::distribution::Bernoulli;

let n = Bernoulli::new(0.5).unwrap();
assert_eq!(n.p(), 0.5);

pub fn n(&self) -> u64

Returns the number of trials n of the bernoulli distribution. Will always be 1.0.

Examples

use statrs::distribution::Bernoulli;

let n = Bernoulli::new(0.5).unwrap();
assert_eq!(n.n(), 1);

Trait Implementations

impl Clone for Bernoulli

fn clone(&self) -> Bernoulli

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Copy for Bernoulli

impl Debug for Bernoulli

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

Formats the value using the given formatter.

impl Discrete<u64, f64> for Bernoulli

fn pmf(&self, x: u64) -> f64

Calculates the probability mass function for the bernoulli distribution at x.

Formula

if x == 0 { 1 - p }
else { p }

fn ln_pmf(&self, x: u64) -> f64

Calculates the log probability mass function for the bernoulli distribution at x.

Formula

else if x == 0 { ln(1 - p) }
else { ln(p) }

impl Distribution<f64> for Bernoulli

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

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

pub 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.

impl Entropy<f64> for Bernoulli

fn entropy(&self) -> f64

Returns the entropy of the bernoulli distribution

Formula

q = (1 - p)
-q * ln(q) - p * ln(p)

impl Max<u64> for Bernoulli

fn max(&self) -> u64

Returns the maximum value in the domain of the bernoulli distribution representable by a 64- bit integer

Formula

1

impl Mean<f64> for Bernoulli

fn mean(&self) -> f64

Returns the mean of the bernoulli distribution

Formula

p

impl Median<f64> for Bernoulli

fn median(&self) -> f64

Returns the median of the bernoulli distribution

Formula

if p < 0.5 { 0 }
else if p > 0.5 { 1 }
else { 0.5 }

impl Min<u64> for Bernoulli

fn min(&self) -> u64

Returns the minimum value in the domain of the bernoulli distribution representable by a 64- bit integer

Formula

0

impl Mode<u64> for Bernoulli

fn mode(&self) -> u64

Returns the mode of the bernoulli distribution

Formula

if p < 0.5 { 0 }
else { 1 }

impl PartialEq<Bernoulli> for Bernoulli

fn eq(&self, other: &Bernoulli) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Bernoulli) -> bool

This method tests for !=.

impl Skewness<f64> for Bernoulli

fn skewness(&self) -> f64

Returns the skewness of the bernoulli distribution

Formula

q = (1 - p)
(1 - 2p) / sqrt(p * q)

impl StructuralPartialEq for Bernoulli

impl Univariate<u64, f64> for Bernoulli

fn cdf(&self, x: f64) -> f64

Calculates the cumulative distribution function for the bernoulli distribution at x.

Formula

if x < 0 { 0 }
else if x >= 1 { 1 }
else { 1 - p }

impl Variance<f64> for Bernoulli

fn variance(&self) -> f64

Returns the variance of the bernoulli distribution

Formula

p * (1 - p)

fn std_dev(&self) -> f64

Returns the standard deviation of the bernoulli distribution

Formula

sqrt(p * (1 - p))