Struct statrs::distribution::Normal
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pub struct Normal { /* fields omitted */ }
Implements the Normal distribution
Examples
use statrs::distribution::{Normal, Continuous}; use statrs::statistics::Mean; let n = Normal::new(0.0, 1.0).unwrap(); assert_eq!(n.mean(), 0.0); assert_eq!(n.pdf(1.0), 0.2419707245191433497978);
Methods
impl Normal
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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
impl Debug for Normal
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impl Copy for Normal
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impl Clone for Normal
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fn clone(&self) -> Normal
Returns a copy of the value. Read more
fn clone_from(&mut self, source: &Self)
1.0.0
Performs copy-assignment from source
. Read more
impl PartialEq for Normal
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fn eq(&self, __arg_0: &Normal) -> bool
This method tests for self
and other
values to be equal, and is used by ==
. Read more
fn ne(&self, __arg_0: &Normal) -> bool
This method tests for !=
.
impl Sample<f64> for Normal
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fn sample<R: Rng>(&mut self, r: &mut R) -> f64
Generate a random sample from a normal
distribution using r
as the source of randomness.
Refer here for implementation details
impl IndependentSample<f64> for Normal
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fn ind_sample<R: Rng>(&self, r: &mut R) -> f64
Generate a random independent sample from a normal
distribution using r
as the source of randomness.
Refer here for implementation details
impl Distribution<f64> for Normal
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fn sample<R: Rng>(&self, r: &mut R) -> f64
Generate a random sample from the normal distribution
using r
as the source of randomness. Uses the Box-Muller
algorithm
Examples
use rand::StdRng; use statrs::distribution::{Normal, Distribution}; let mut r = rand::StdRng::new().unwrap(); let n = Normal::new(0.0, 1.0).unwrap(); print!("{}", n.sample::<StdRng>(&mut r));
impl Univariate<f64, f64> for Normal
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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
impl Min<f64> for Normal
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fn min(&self) -> f64
Returns the minimum value in the domain of the normal distribution representable by a double precision float
Formula
-INF
impl Max<f64> for Normal
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fn max(&self) -> f64
Returns the maximum value in the domain of the normal distribution representable by a double precision float
Formula
INF
impl Mean<f64> for Normal
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fn mean(&self) -> f64
Returns the mean of the normal distribution
Remarks
This is the same mean used to construct the distribution
impl Variance<f64> for Normal
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fn variance(&self) -> f64
fn std_dev(&self) -> f64
Returns the standard deviation of the normal distribution
Remarks
This is the same standard deviation used to construct the distribution
impl Entropy<f64> for Normal
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fn entropy(&self) -> f64
Returns the entropy of the normal distribution
Formula
(1 / 2) * ln(2σ^2 * π * e)
where σ
is the standard deviation
impl Skewness<f64> for Normal
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impl Median<f64> for Normal
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impl Mode<f64> for Normal
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impl Continuous<f64, f64> for Normal
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fn pdf(&self, x: f64) -> f64
Calculates the probability density function for the normal distribution
at x
Formula
(1 / sqrt(2σ^2 * π)) * e^(-(x - μ)^2 / 2σ^2)
where μ
is the mean and σ
is the standard deviation
fn ln_pdf(&self, x: f64) -> f64
Calculates the log probability density function for the normal distribution
at x
Formula
ln((1 / sqrt(2σ^2 * π)) * e^(-(x - μ)^2 / 2σ^2))
where μ
is the mean and σ
is the standard deviation