tinguely 0.1.1

Machine learning library
Documentation 
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use mathru::algebra::linear::{Vector, Matrix};
use mathru::statistics::distrib::{Normal, Uniform, Distribution};
use mathru::optimization::Gradient;
use tinguely::regression::LinearRegression;
use tinguely::{SupervisedLearn};
use plotters::prelude::*;


/**
* x_2 = a + b * x_1 * e
*
*
* x_1 \in [0.0, 5.0]
*/
fn generate_data(a: f64, b: f64, samples: usize, sigma: f64) -> (Matrix<f64>, Vector<f64>) {
    assert!(samples > 2, "Number of samples must be greater or equal to two.");
    assert!(sigma >= 0.0f64, "Noise must be non-negative.");

    let uniform: Uniform<f64> = Uniform::new(0.0, 5.0);
    let normal: Normal<f64> = Normal::new(0.0f64, sigma);

    let mut x_1_vec: Vec<f64> = Vec::with_capacity(samples);
    let mut x_2_vec: Vec<f64> = Vec::with_capacity(samples);

    for _ in 0..samples
    {
        let x: f64 = uniform.random();
        let e: f64 = normal.random();

        let y: f64 = a + b * x + e;
        x_1_vec.push(x);
        x_2_vec.push(y);
    }

    let x_1: Matrix<f64> = Matrix::new(samples, 1, x_1_vec);
    let x_2: Vector<f64> = Vector::new_column(samples, x_2_vec);

    return (x_1, x_2)
}

fn main()
{
    let (x, y): (Matrix<f64>, Vector<f64>) = generate_data(1.0, 3.0/5.0, 200, 0.05);

    let optimizer: Gradient<f64> = Gradient::new(0.01, 100);

    let mut model: LinearRegression<f64> = LinearRegression::new(optimizer);
    model.train(&x, &y);

    let y_hat: Vector<f64> = model.predict(&x).unwrap();

    let root_area = BitMapBackend::new("./figures/linear_regression.png", (600, 400)).into_drawing_area();
    root_area.fill(&WHITE).unwrap();

    let mut ctx = ChartBuilder::on(&root_area)
        .margin(20)
        .set_label_area_size(LabelAreaPosition::Left, 40)
        .set_label_area_size(LabelAreaPosition::Bottom, 40)
        .build_cartesian_2d(0.0..5.0, 0.0..4.0)
        .unwrap();

    ctx.configure_mesh()
        .x_desc("x")
        .y_desc("y")
        .axis_desc_style(("sans-serif", 15).into_font())
        .draw()
        .unwrap();

    ctx.draw_series(
        x.row_into_iter().zip(y.iter())
            .map(|(x, y)|
                {
                    Cross::new((*x.get(0), *y), 2, RED.filled())
                }
            )
    ).unwrap();

    ctx.draw_series(
        x.row_into_iter().zip(y_hat.iter())
            .map(|(x, y)|
                {
                    Cross::new((*x.get(0), *y), 2, BLUE.filled())
                }
            )
    ).unwrap();
}