Struct statrs::distribution::Weibull
source · pub struct Weibull { /* private fields */ }
Expand description
Implements the Weibull distribution
Examples
use statrs::distribution::{Weibull, Continuous};
use statrs::statistics::Distribution;
use statrs::prec;
let n = Weibull::new(10.0, 1.0).unwrap();
assert!(prec::almost_eq(n.mean().unwrap(),
0.95135076986687318362924871772654021925505786260884, 1e-15));
assert_eq!(n.pdf(1.0), 3.6787944117144232159552377016146086744581113103177);
Implementations§
source§impl Weibull
impl Weibull
sourcepub fn new(shape: f64, scale: f64) -> Result<Weibull>
pub fn new(shape: f64, scale: f64) -> Result<Weibull>
Constructs a new weibull distribution with a shape (k) of shape
and a scale (λ) of scale
Errors
Returns an error if shape
or scale
are NaN
.
Returns an error if shape <= 0.0
or scale <= 0.0
Examples
use statrs::distribution::Weibull;
let mut result = Weibull::new(10.0, 1.0);
assert!(result.is_ok());
result = Weibull::new(0.0, 0.0);
assert!(result.is_err());
Trait Implementations§
source§impl Continuous<f64, f64> for Weibull
impl Continuous<f64, f64> for Weibull
source§impl ContinuousCDF<f64, f64> for Weibull
impl ContinuousCDF<f64, f64> for Weibull
source§fn cdf(&self, x: f64) -> f64
fn cdf(&self, x: f64) -> f64
Calculates the cumulative distribution function for the weibull
distribution at x
Formula
ⓘ
1 - e^-((x/λ)^k)
where k
is the shape and λ
is the scale
source§fn inverse_cdf(&self, p: T) -> K
fn inverse_cdf(&self, p: T) -> K
Due to issues with rounding and floating-point accuracy the default
implementation may be ill-behaved.
Specialized inverse cdfs should be used whenever possible.
Performs a binary search on the domain of
cdf
to obtain an approximation
of F^-1(p) := inf { x | F(x) >= p }
. Needless to say, performance may
may be lacking. Read moresource§impl Distribution<f64> for Weibull
impl Distribution<f64> for Weibull
source§fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64
Generate a random value of
T
, using rng
as the source of randomness.source§impl Distribution<f64> for Weibull
impl Distribution<f64> for Weibull
source§fn mean(&self) -> Option<f64>
fn mean(&self) -> Option<f64>
Returns the mean of the weibull distribution
Formula
ⓘ
λΓ(1 + 1 / k)
where k
is the shape, λ
is the scale, and Γ
is
the gamma function
source§fn variance(&self) -> Option<f64>
fn variance(&self) -> Option<f64>
Returns the variance of the weibull distribution
Formula
ⓘ
λ^2 * (Γ(1 + 2 / k) - Γ(1 + 1 / k)^2)
where k
is the shape, λ
is the scale, and Γ
is
the gamma function
source§fn entropy(&self) -> Option<f64>
fn entropy(&self) -> Option<f64>
Returns the entropy of the weibull distribution
Formula
ⓘ
γ(1 - 1 / k) + ln(λ / k) + 1
where k
is the shape, λ
is the scale, and γ
is
the Euler-Mascheroni constant
impl Copy for Weibull
impl StructuralPartialEq for Weibull
Auto Trait Implementations§
impl RefUnwindSafe for Weibull
impl Send for Weibull
impl Sync for Weibull
impl Unpin for Weibull
impl UnwindSafe for Weibull
Blanket Implementations§
source§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
source§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct
self
from the equivalent element of its
superset. Read moresource§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
Checks if
self
is actually part of its subset T
(and can be converted to it).source§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
Use with care! Same as
self.to_subset
but without any property checks. Always succeeds.source§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts
self
to the equivalent element of its superset.