use itertools_num;
use stats::univariate::Sample;
use stats::univariate::kde::kernel::Gaussian;
use stats::univariate::kde::{Bandwidth, Kde};
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.as_slice()[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 xs: Vec<_> = itertools_num::linspace(start, end, npoints).collect();
let ys = kde.map(&xs);
let point_estimate = kde.estimate(point_to_estimate);
(xs.into_boxed_slice(), ys, point_estimate)
}