Struct statrs::distribution::Geometric

pub struct Geometric { /* fields omitted */ }

Implements the Geometric distribution

Examples

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

let n = Geometric::new(0.3).unwrap();
assert_eq!(n.mean(), 1.0 / 0.3);
assert_eq!(n.pmf(1), 0.3);
assert_eq!(n.pmf(2), 0.21);

Methods

impl Geometric

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

Constructs a new shifted geometric distribution with a probability of p

Errors

Returns an error if p is not in (0, 1]

Examples

use statrs::distribution::Geometric;

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

result = Geometric::new(0.0);
assert!(result.is_err());

pub fn p(&self) -> f64

Returns the probability p of the geometric distribution

Examples

use statrs::distribution::Geometric;

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

Trait Implementations

impl Distribution<f64> for Geometric

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

Generates a random sample from the geometric distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{Geometric, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Geometric::new(0.5).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Univariate<u64, f64> for Geometric

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

Calculates the cumulative distribution function for the geometric distribution at x

Formula

1 - (1 - p) ^ (x + 1)

impl Discrete<u64, f64> for Geometric

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

Calculates the probability mass function for the geometric distribution at x

Formula

(1 - p)^(x - 1) * p

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

Calculates the log probability mass function for the geometric distribution at x

Formula

ln((1 - p)^(x - 1) * p)

impl Min<u64> for Geometric

fn min(&self) -> u64

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

Formula

1

impl Max<u64> for Geometric

fn max(&self) -> u64

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

Formula

2^63 - 1

impl Mean<f64> for Geometric

fn mean(&self) -> f64

Returns the mean of the geometric distribution

Formula

1 / p

impl Variance<f64> for Geometric

fn variance(&self) -> f64

Returns the standard deviation of the geometric distribution

Formula

(1 - p) / p^2

fn std_dev(&self) -> f64

Returns the standard deviation of the geometric distribution

Remarks

Returns NAN if p is 1

Formula

sqrt(1 - p) / p

impl Entropy<f64> for Geometric

fn entropy(&self) -> f64

Returns the entropy of the geometric distribution

Formula

(-(1 - p) * log_2(1 - p) - p * log_2(p)) / p

impl Skewness<f64> for Geometric

fn skewness(&self) -> f64

Returns the skewness of the geometric distribution

Formula

(2 - p) / sqrt(1 - p)

impl Median<f64> for Geometric

fn median(&self) -> f64

Returns the median of the geometric distribution

Remarks

Returns 1 if p is 1

Formula

ceil(-1 / log_2(1 - p))

impl Mode<u64> for Geometric

fn mode(&self) -> u64

Returns the mode of the geometric distribution

Formula

1

impl Copy for Geometric

impl Clone for Geometric

fn clone(&self) -> Geometric

Returns a copy of the value.

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

Performs copy-assignment from source.

impl PartialEq<Geometric> for Geometric

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

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

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

This method tests for !=.

impl Debug for Geometric

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

Formats the value using the given formatter.

impl IndependentSample<f64> for Geometric

fn ind_sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random independent sample from a geometric distribution using r as the source of randomness. Refer here for implementation details

impl Sample<f64> for Geometric

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

Generate a random sample from a geometric distribution using r as the source of randomness. Refer here for implementation details