use crate::stats::univariate::kde::kernel::Gaussian;

use crate::stats::univariate::kde::{Bandwidth, Kde};

use crate::stats::univariate::Sample;

pub fn sweep(

    sample: &Sample<f64>,

    npoints: usize,

    range: Option<(f64, f64)>,

) -> (Box<[f64]>, Box<[f64]>) {

    let (xs, ys, _) = sweep_and_estimate(sample, npoints, range, sample[0]);

    (xs, ys)

}

pub fn sweep_and_estimate(

    sample: &Sample<f64>,

    npoints: usize,

    range: Option<(f64, f64)>,

    point_to_estimate: f64,

) -> (Box<[f64]>, Box<[f64]>, f64) {

    let x_min = sample.min();

    let x_max = sample.max();

    let kde = Kde::new(sample, Gaussian, Bandwidth::Silverman);

    let h = kde.bandwidth();

    let (start, end) = match range {

        Some((start, end)) => (start, end),

        None => (x_min - 3. * h, x_max + 3. * h),

    };

    let mut xs: Vec<f64> = Vec::with_capacity(npoints);

    let step_size = (end - start) / (npoints - 1) as f64;

    for n in 0..npoints {

        xs.push(start + (step_size * n as f64));

    }

    let ys = kde.map(&xs);

    let point_estimate = kde.estimate(point_to_estimate);

    (xs.into_boxed_slice(), ys, point_estimate)

}