pub struct Gaussian<T: Real> {
pub mean: T,
pub variance: T,
}Expand description
A Gaussian probability density function of a normally distributed random variable with expected value / mean μ, and variance σ2.
Fields§
§mean: TThe mean is the expected value of this gaussian.
variance: TThe variance is a measure of the spread of values around the mean, high variance means one standard deviation encompasses a larger spread of values from the mean.
Implementations§
Source§impl<T: Real> Gaussian<T>
impl<T: Real> Gaussian<T>
pub fn new(mean: T, variance: T) -> Gaussian<T>
Sourcepub fn approximating<I>(data: I) -> Gaussian<T>where
I: Iterator<Item = T>,
pub fn approximating<I>(data: I) -> Gaussian<T>where
I: Iterator<Item = T>,
Creates a Gaussian approximating the mean and variance in the provided data.
Note that this will always be an approximation, if you generate some data according to some mean and variance then construct a Gaussian from the mean and variance of that generated data the approximated mean and variance is unlikely to be exactly the same as the parameters the data was generated with, though as the amout of data increases you can expect the approximation to be more close.
Source§impl<T: Real> Gaussian<T>
impl<T: Real> Gaussian<T>
Sourcepub fn probability(&self, x: &T) -> T
pub fn probability(&self, x: &T) -> T
Computes g(x) for some x, the probability density of a normally distributed random variable x, or in other words how likely x is to be drawn from this normal distribution.
g(x) is largest for x equal to this distribution’s mean and g(x) will tend towards zero as x is further from this distribution’s mean, at a rate corresponding to this distribution’s variance.
Sourcepub fn draw<I>(&self, source: &mut I, max_samples: usize) -> Option<Vec<T>>where
I: Iterator<Item = T>,
pub fn draw<I>(&self, source: &mut I, max_samples: usize) -> Option<Vec<T>>where
I: Iterator<Item = T>,
Given a source of random variables in the uniformly distributed
range [0, 1] inclusive, draws max_samples of independent
random numbers according to this Gaussian distribution’s mean and
variance using the Box-Muller transform:
https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
The source of random variables must provide at least as many values
as max_samples if max_samples is even, and one more than max_samples
if max_samples is odd. If fewer are provided None is returned.
As all randomness is provided to this method, this code is deterministic and will always compute the same samples given the same random source of numbers.
pub fn map(&self, x: &T) -> T
probability