Struct statrs::distribution::Normal
source · [−]pub struct Normal { /* private fields */ }
Expand description
Implementations
sourceimpl Normal
impl Normal
sourcepub fn new(mean: f64, std_dev: f64) -> Result<Normal>
pub fn new(mean: f64, std_dev: f64) -> Result<Normal>
Constructs a new normal distribution with a mean of mean
and a standard deviation of std_dev
Errors
Returns an error if mean
or std_dev
are NaN
or if
std_dev <= 0.0
Examples
use statrs::distribution::Normal;
let mut result = Normal::new(0.0, 1.0);
assert!(result.is_ok());
result = Normal::new(0.0, 0.0);
assert!(result.is_err());
Trait Implementations
sourceimpl Continuous<f64, f64> for Normal
impl Continuous<f64, f64> for Normal
sourceimpl ContinuousCDF<f64, f64> for Normal
impl ContinuousCDF<f64, f64> for Normal
sourcefn cdf(&self, x: f64) -> f64
fn cdf(&self, x: f64) -> f64
Calculates the cumulative distribution function for the
normal distribution at x
Formula
(1 / 2) * (1 + erf((x - μ) / (σ * sqrt(2))))
where μ
is the mean, σ
is the standard deviation, and
erf
is the error function
sourcefn sf(&self, x: f64) -> f64
fn sf(&self, x: f64) -> f64
Calculates the survival function for the
normal distribution at x
Formula
(1 / 2) * (1 + erf(-(x - μ) / (σ * sqrt(2))))
where μ
is the mean, σ
is the standard deviation, and
erf
is the error function
note that this calculates the complement due to flipping the sign of the argument error function with respect to the cdf.
the normal cdf Φ (and internal error function) as the following property:
Φ(-x) + Φ(x) = 1
Φ(-x) = 1 - Φ(x)
sourcefn inverse_cdf(&self, x: f64) -> f64
fn inverse_cdf(&self, x: f64) -> f64
sourceimpl Distribution<f64> for Normal
impl Distribution<f64> for Normal
sourcefn 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.
sourcefn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> where
R: Rng,
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. Read more
sourceimpl Distribution<f64> for Normal
impl Distribution<f64> for Normal
sourcefn mean(&self) -> Option<f64>
fn mean(&self) -> Option<f64>
Returns the mean of the normal distribution
Remarks
This is the same mean used to construct the distribution
sourceimpl PartialEq<Normal> for Normal
impl PartialEq<Normal> for Normal
impl Copy for Normal
impl StructuralPartialEq for Normal
Auto Trait Implementations
impl RefUnwindSafe for Normal
impl Send for Normal
impl Sync for Normal
impl Unpin for Normal
impl UnwindSafe for Normal
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<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).
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.
fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts self
to the equivalent element of its superset.