Module easy_ml::distributions

Models of distributions that samples can be drawn from.

These structs and methods require numerical types that can be treated as real numbers, ie unsigned and signed numbers cannot be used here.

Example of plotting a Gaussian

extern crate rand;
extern crate rand_chacha;
extern crate textplots;
extern crate easy_ml;

use rand::{Rng, SeedableRng};
use textplots::{Chart, Plot, Shape};
use easy_ml::distributions::Gaussian;

const SAMPLES: usize = 10000;

// create a normal distribution, note that the mean and variance are
// given in floating point notation as this will be a f64 Gaussian
let normal_distribution = Gaussian::new(0.0, 1.0);

// first create random numbers between 0 and 1
// using a fixed seed non cryptographically secure random
// generator from the rand crate
let mut random_generator = rand_chacha::ChaCha8Rng::seed_from_u64(10);

let mut random_numbers = Vec::with_capacity(SAMPLES);
for _ in 0..SAMPLES {
    random_numbers.push(random_generator.gen::<f64>());
}

// draw samples from the normal distribution
let samples: Vec<f64> = normal_distribution.draw(&mut random_numbers.drain(..), SAMPLES).unwrap();

// create a [(f32, f32)] list to plot a histogram of
let histogram_points = {
    let x = 0..SAMPLES;
    let mut y = samples;
    let mut points = Vec::with_capacity(SAMPLES);
    for (x, y) in y.drain(..).zip(x).map(|(y, x)| (x as f32, y as f32)) {
        points.push((x, y));
    }
    points
};

// plot a histogram from -3 to 3 with 30 bins
// to check that this distribution looks like a gaussian
// this will show a bell curve for large enough SAMPLES.
let histogram = textplots::utils::histogram(&histogram_points, -3.0, 3.0, 30);
Chart::new(180, 60, -3.0, 3.0)
    .lineplot( Shape::Bars(&histogram) )
    .nice();

Structs

Gaussian	A Gaussian probability density function of a normally distributed random variable with expected value / mean μ, and variance σ2.
MultivariateGaussian	A multivariate Gaussian distribution with mean vector μ, and covariance matrix Σ.