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use super::Kernel;
use crate::{KernelAdd, KernelError, KernelMul};
use rayon::prelude::*;
use std::{ops::Add, ops::Mul};

fn weighted_norm_pow(params: &[f64], x: &Vec<f64>, xprime: &Vec<f64>) -> f64 {
  params
    .par_iter()
    .zip(x.par_iter())
    .zip(xprime.par_iter())
    .map(|((relevance, xi), xprimei)| relevance * (xi - xprimei).powi(2))
    .sum()
}

#[derive(Clone, Debug)]
pub struct ARD(pub usize);

impl Kernel<Vec<f64>> for ARD {
  fn params_len(&self) -> usize {
    self.0
  }

  fn value(&self, params: &[f64], x: &Vec<f64>, xprime: &Vec<f64>) -> Result<f64, KernelError> {
    if params.len() != self.0 {
      return Err(KernelError::ParametersLengthMismatch.into());
    }
    if x.len() != self.0 || xprime.len() != self.0 {
      return Err(KernelError::InvalidArgument.into());
    }

    let fx = (-weighted_norm_pow(&params, x, xprime)).exp();

    Ok(fx)
  }

  fn value_with_grad(
    &self,
    params: &[f64],
    x: &Vec<f64>,
    xprime: &Vec<f64>,
  ) -> Result<(f64, Vec<f64>), KernelError> {
    let fx = self.value(params, x, xprime)?;

    let gfx = x
      .par_iter()
      .zip(xprime.par_iter())
      .map(|(&xi, &xprimei)| -(xi - xprimei).powi(2))
      .collect::<Vec<_>>();

    Ok((fx, gfx))
  }
}

impl<R> Add<R> for ARD
where
  R: Kernel<Vec<f64>>,
{
  type Output = KernelAdd<Self, R, Vec<f64>>;

  fn add(self, rhs: R) -> Self::Output {
    Self::Output::new(self, rhs)
  }
}

impl<R> Mul<R> for ARD
where
  R: Kernel<Vec<f64>>,
{
  type Output = KernelMul<Self, R, Vec<f64>>;

  fn mul(self, rhs: R) -> Self::Output {
    Self::Output::new(self, rhs)
  }
}