Struct fitting::gaussian::Gaussian

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pub struct Gaussian<F: Float> { /* private fields */ }

Expand description

Gaussian Distribution

Implementations§

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impl<F: Float> Gaussian<F>

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pub fn new(mu: F, sigma: F, a: F) -> Gaussian<F>

Create a new Gaussian with given parameters.

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pub fn mu(&self) -> &F

Return a reference to mu.

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pub fn mu_mut(&mut self) -> &mut F

Return a mutable reference to mu.

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pub fn sigma(&self) -> &F

Return a reference to sigma.

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pub fn sigma_mut(&mut self) -> &mut F

Return a mutable reference to sigma.

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pub fn a(&self) -> &F

Return a reference to a.

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pub fn a_mut(&mut self) -> &mut F

Return a mutable reference to a.

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pub fn as_tuple(&self) -> (&F, &F, &F)

Return a reference to self as a tuple (mu, sigma, a).

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pub fn as_mut_tuple(&mut self) -> (&mut F, &mut F, &mut F)

Return a mutable reference to self as a tuple (mu, sigma, a).

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pub fn to_tuple(&self) -> (F, F, F)

Returns a copy of self as a new tuple (mu, sigma, a).

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pub fn into_tuple(self) -> (F, F, F)

Consume self and returns a tuple (mu, sigma, a).

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pub fn value(&self, x: F) -> F

Returns a value of gaussian function.

§Examples

Returns a value of gaussian function.

use fitting::Gaussian;

let gaussian = Gaussian::new(5., 3., 1.);
let x = 5.;
let y = gaussian.value(x);
assert_eq!(&y, gaussian.a());

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pub fn values(&self, x_vec: Array1<F>) -> Array1<F>

Returns the values of gaussian function.

§Examples

Returns the values of gaussian function.

use fitting::approx::assert_abs_diff_eq;
use fitting::Gaussian;
use fitting::ndarray::{array, Array, Array1};

let gaussian = Gaussian::new(5., 3., 1.);
let x_vec: Array1<f64> = Array::range(1., 10., 1.);
let y_vec: Array1<f64> = gaussian.values(x_vec);
let expected_ans = array![
    0.41111229050718745,
    0.6065306597126334,
    0.8007374029168081,
    0.9459594689067654,
    1.,
    0.9459594689067654,
    0.8007374029168081,
    0.6065306597126334,
    0.41111229050718745
];
assert_abs_diff_eq!(&y_vec, &expected_ans, epsilon = 1e-9);

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pub fn fit( x_vec: Array1<F>, y_vec: Array1<F> ) -> Result<Gaussian<F>, GaussianError>

Estimates the parameters of gaussian function for generic data. The return value is (mu, sigma, a)

This function implements the Guos Algorithm.

§Examples

Estimates the parameters for sample data.

use fitting::approx::assert_abs_diff_eq;
use fitting::Gaussian;
use fitting::ndarray::{array, Array, Array1};

let gaussian = Gaussian::new(5., 3., 1.);
let x_vec: Array1<f64> = Array::range(1., 10., 1.);
let y_vec: Array1<f64> = gaussian.values(x_vec.clone());
let estimated = Gaussian::fit(x_vec, y_vec).unwrap();
assert_abs_diff_eq!(gaussian, estimated, epsilon = 1e-9);

§Limitations

x_vec must have at least one element
All elements of y_vec must be positive numbers
If the distribution is “long-tailed”, it affects the fitting accuracy.
- A detailed description can be found in Reference 1.
- Also, you can see the reproduction and workaround in GitHub.