r2rs-stats 0.1.1

Statistics programming for Rust based on R's stats package
Documentation 
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// "Whatever you do, work at it with all your heart, as working for the Lord,
// not for human masters, since you know that you will receive an inheritance
// from the Lord as a reward. It is the Lord Christ you are serving."
// (Col 3:23-24)

use nalgebra::DMatrix;
use strafe_trait::ModelBuilder;
use strafe_type::{Alpha64, ModelMatrix};

use crate::regression::lm::{linear_regression_standardized, LeastSquaresRegression};

pub struct LeastSquaresRegressionBuilder {
    pub(crate) x: Option<ModelMatrix>,
    pub(crate) y: Option<ModelMatrix>,
    pub(crate) alpha: Alpha64,
    pub(crate) w: Option<ModelMatrix>,
}

impl LeastSquaresRegressionBuilder {
    pub fn new() -> Self {
        Self {
            x: None,
            y: None,
            alpha: 0.05.into(),
            w: None,
        }
    }
}

impl ModelBuilder for LeastSquaresRegressionBuilder {
    type Model = LeastSquaresRegression;

    fn with_x(self, x: &ModelMatrix) -> Self {
        Self {
            x: Some(x.clone()),
            ..self
        }
    }

    fn with_y(self, y: &ModelMatrix) -> Self {
        Self {
            y: Some(y.clone()),
            ..self
        }
    }

    fn with_weights(self, weights: &ModelMatrix) -> Self {
        Self {
            w: Some(weights.clone()),
            ..self
        }
    }

    fn with_alpha<A: Into<Alpha64>>(self, alpha: A) -> Self {
        Self {
            alpha: alpha.into(),
            ..self
        }
    }

    fn build(self) -> Self::Model {
        let b = linear_regression_standardized(
            &self.x.as_ref().unwrap().matrix(),
            &self.y.as_ref().unwrap().matrix(),
        );

        let x1 = self.x.as_ref().unwrap().matrix().insert_column(0, 1.0);
        let w = ModelMatrix::from(DMatrix::from_iterator(
            x1.shape().0,
            1,
            (0..x1.shape().0).map(|_| 1.0),
        ));

        let model_data = (
            ModelMatrix::from(x1.clone()),
            self.y.as_ref().unwrap().matrix(),
            b.clone(),
            w.matrix(),
        );

        LeastSquaresRegression {
            x: self.x.as_ref().unwrap().clone(),
            y: self.y.as_ref().unwrap().clone(),
            alpha: self.alpha,
            x1,
            w,
            b,
            model_data,
        }
    }
}