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 std::marker::PhantomData;

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

use crate::regression::glm::{
    family::core::Family, generalized_linear_model, link::core::Link, GeneralizedLinearRegression,
};

pub struct GeneralizedLinearRegressionBuilder<L: Link, F: Family<L>> {
    pub(crate) x: Option<ModelMatrix>,
    pub(crate) y: Option<ModelMatrix>,
    pub(crate) w: Option<ModelMatrix>,
    pub(crate) alpha: Alpha64,
    pub(crate) family: F,
    pub(crate) _link: PhantomData<L>,
}

impl<L: Link, F: Family<L>> GeneralizedLinearRegressionBuilder<L, F> {
    pub fn new(family: F) -> Self {
        Self {
            x: None,
            y: None,
            w: None,
            alpha: 0.05.into(),
            family,
            _link: PhantomData::default(),
        }
    }
}

impl<L: Link, F: Family<L>> ModelBuilder for GeneralizedLinearRegressionBuilder<L, F> {
    type Model = GeneralizedLinearRegression<L, F>;

    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) -> GeneralizedLinearRegression<L, F> {
        let (b, aic, null_deviance, residual_deviance, final_weights, eta, mu) =
            generalized_linear_model(
                &self.y.as_ref().unwrap().matrix(),
                &self.x.as_ref().unwrap().matrix(),
                &self.w.as_ref().unwrap().matrix(),
                &self.family,
            );

        let x1 = ModelMatrix::from(
            self.x
                .as_ref()
                .unwrap()
                .matrix()
                .to_owned()
                .insert_column(0, 1.0),
        );

        let model_data = (
            x1.clone(),
            self.y.as_ref().unwrap().matrix().clone(),
            b.clone(),
            final_weights.clone(),
        );

        GeneralizedLinearRegression {
            x: self.x.as_ref().unwrap().clone(),
            y: self.y.as_ref().unwrap().clone(),
            w: self.w.as_ref().unwrap().clone(),
            alpha: self.alpha,
            family: self.family,
            _link: self._link,
            x1,
            b,
            model_data,
            eta,
            mu,
            aic,
            null_deviance,
            residual_deviance,
            final_weights,
        }
    }
}