Struct sprs_rand::rand_distr::Pert
source · [−]pub struct Pert<N> { /* private fields */ }
Expand description
The PERT distribution.
Similar to the Triangular
distribution, the PERT distribution is
parameterised by a range and a mode within that range. Unlike the
Triangular
distribution, the probability density function of the PERT
distribution is smooth, with a configurable weighting around the mode.
Example
use rand_distr::{Pert, Distribution};
let d = Pert::new(0., 5., 2.5).unwrap();
let v = d.sample(&mut rand::thread_rng());
println!("{} is from a PERT distribution", v);
Implementations
sourceimpl<N> Pert<N> where
N: Float,
StandardNormal: Distribution<N>,
Exp1: Distribution<N>,
Open01: Distribution<N>,
impl<N> Pert<N> where
N: Float,
StandardNormal: Distribution<N>,
Exp1: Distribution<N>,
Open01: Distribution<N>,
sourcepub fn new(min: N, max: N, mode: N) -> Result<Pert<N>, PertError>
pub fn new(min: N, max: N, mode: N) -> Result<Pert<N>, PertError>
Set up the PERT distribution with defined min
, max
and mode
.
This is equivalent to calling Pert::new_shape
with shape == 4.0
.
sourcepub fn new_with_shape(
min: N,
max: N,
mode: N,
shape: N
) -> Result<Pert<N>, PertError>
pub fn new_with_shape(
min: N,
max: N,
mode: N,
shape: N
) -> Result<Pert<N>, PertError>
Set up the PERT distribution with defined min
, max
, mode
and
shape
.
Trait Implementations
sourceimpl<N> Distribution<N> for Pert<N> where
N: Float,
StandardNormal: Distribution<N>,
Exp1: Distribution<N>,
Open01: Distribution<N>,
impl<N> Distribution<N> for Pert<N> where
N: Float,
StandardNormal: Distribution<N>,
Exp1: Distribution<N>,
Open01: Distribution<N>,
sourcefn sample<R>(&self, rng: &mut R) -> N where
R: Rng + ?Sized,
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.
sourcefn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>ⓘNotable 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;
where
R: Rng,
fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>ⓘNotable 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;
where
R: Rng,
D: Distribution<T>,
R: Rng, type Item = T;
Create an iterator that generates random values of T
, using rng
as
the source of randomness. Read more
impl<N> Copy for Pert<N> where
N: Copy,
Auto Trait Implementations
impl<N> RefUnwindSafe for Pert<N> where
N: RefUnwindSafe,
impl<N> Send for Pert<N> where
N: Send,
impl<N> Sync for Pert<N> where
N: Sync,
impl<N> Unpin for Pert<N> where
N: Unpin,
impl<N> UnwindSafe for Pert<N> where
N: UnwindSafe,
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
impl<T> Pointable for T
impl<T> Pointable for T
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
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 more
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).
unsafe fn to_subset_unchecked(&self) -> SS
unsafe fn to_subset_unchecked(&self) -> SS
Use with care! Same as self.to_subset
but without any property checks. Always succeeds.
fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts self
to the equivalent element of its superset.