[−][src]Struct mathru::statistics::distrib::Normal
Normal distribution
Fore more information: https://en.wikipedia.org/wiki/Normal_distribution
Implementations
impl<T> Normal<T> where
T: Real,
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T: Real,
pub fn new(mean: T, variance: T) -> Self
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Creates a probability distribution
Arguments
mean
: Expected valuevariance
: variance > 0.0
Panics
if variance <= 0.0
Example
use mathru::statistics::distrib::Normal; let distrib: Normal<f64> = Normal::new(0.3, 0.2);
pub fn from_data<'a>(data: &'a Vec<T>) -> Self
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It is assumed that data are normal distributed.
data.len() >= 2
Trait Implementations
impl<T> Continuous<T> for Normal<T> where
T: Real + Gamma + Error,
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T: Real + Gamma + Error,
fn pdf(&self, x: T) -> T
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Probability density function
Arguments
x
: x ∈ ℕ
Example
use mathru::statistics::distrib::{Continuous, Normal}; let distrib: Normal<f64> = Normal::new(0.3, 0.2); let x: f64 = 5.0; let p: f64 = distrib.pdf(x);
fn cdf(&self, x: T) -> T
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Cumulative distribution function
Arguments
x
:
Example
use mathru::statistics::distrib::{Continuous, Normal}; let distrib: Normal<f64> = Normal::new(0.3, 0.2); let x: f64 = 0.4; let p: f64 = distrib.cdf(x);
fn quantile(&self, p: T) -> T
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Quantile: function of inverse cdf
The Percentage Points of the Normal Distribution Author(s): Michael J. Wichura Year 1988 Journal of the Royal Statistical Society 0.0 < p < 1.0
Panics
if p <= 0.0 || p >= 1.0
fn mean(&self) -> T
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Expected value
Example
use mathru::{ self, statistics::distrib::{Continuous, Normal}, }; let distrib: Normal<f64> = Normal::new(0.0, 0.2); let mean: f64 = distrib.mean();
fn variance(&self) -> T
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Variance
Example
use mathru::{ self, statistics::distrib::{Continuous, Normal}, }; let distrib: Normal<f64> = Normal::new(0.0, 0.2); let var: f64 = distrib.variance();
fn skewness(&self) -> T
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Skewness
Example
use mathru::{ self, statistics::distrib::{Continuous, Normal}, }; let mean: f64 = 1.0; let variance: f64 = 0.5; let distrib: Normal<f64> = Normal::new(mean, variance); assert_eq!(0.0, distrib.skewness());
fn median(&self) -> T
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Median
Example
use mathru::{ self, statistics::distrib::{Continuous, Normal}, }; let mean: f64 = 0.0; let distrib: Normal<f64> = Normal::new(mean, 0.2); let median: f64 = distrib.median(); assert_eq!(median, mean);
fn entropy(&self) -> T
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Entropy
Example
use mathru::{ self, statistics::distrib::{Continuous, Normal}, }; use std::f64::consts::{E, PI}; let mean: f64 = 1.0; let variance: f64 = 0.5; let distrib: Normal<f64> = Normal::new(mean, variance); assert_eq!(2.0 * PI * E * variance, distrib.entropy());
impl<T> Distribution<T> for Normal<T> where
T: Real,
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T: Real,
Auto Trait Implementations
impl<T> RefUnwindSafe for Normal<T> where
T: RefUnwindSafe,
T: RefUnwindSafe,
impl<T> Send for Normal<T> where
T: Send,
T: Send,
impl<T> Sync for Normal<T> where
T: Sync,
T: Sync,
impl<T> Unpin for Normal<T> where
T: Unpin,
T: Unpin,
impl<T> UnwindSafe for Normal<T> where
T: UnwindSafe,
T: UnwindSafe,
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<V, T> VZip<V> for T where
V: MultiLane<T>,
V: MultiLane<T>,