Function opt_hypothesis

src/opt.rs:267–296

TODO duplicated code

(h: &Hypothesis, x: &Matrix<f64>, y: &[f64], opts: OptParams<f64>)

// TODO duplicated code
pub fn opt_hypothesis(h: &Hypothesis, x: &Matrix<f64>, y: &[f64], opts: OptParams<f64>) -> OptResult<f64> {

    let alpha = opts.alpha.unwrap_or(0.1);
    let iter = opts.iter.unwrap_or(1000);
    let eps = opts.eps;

    let mut r = vec![];
    let mut p = h.params();
    let mut stopped = false;

    let mut hx = Hypothesis::from_params(&p);

    for _ in (0..iter) {
        let d = hx.derivatives(x, y);
        let i = p.sub(&d.mul_scalar(alpha));
        hx = Hypothesis::from_params(&i);
        r.push((i.clone(), hx.error(&x, &y)));
        stopped = eps.is_some() && i.iter().zip(p.iter()).all(|(&x, &y)| num::abs(x - y) <= eps.unwrap());
        p = i;
        if stopped {
            break;
        }
    }

    OptResult {
        params: p.to_vec(),
        fvals: r,
        stopped: stopped
    }
}

/// Plots the learning curve from an optimization result.
///