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use crate::{Kernel, KernelError};
use std::{error::Error, fmt::Debug};

pub trait Convolutable: Clone + Debug + Sync + Send {
    fn parts_len(&self) -> usize;
    fn part(&self, index: usize) -> &Vec<f64>;
    fn data_len(&self) -> usize;
}

impl Convolutable for Vec<f64> {
    fn parts_len(&self) -> usize {
        1
    }

    fn part(&self, _: usize) -> &Vec<f64> {
        self
    }

    fn data_len(&self) -> usize {
        self.len()
    }
}

#[derive(Clone, Debug)]
pub struct Convolutional<K>
where
    K: Kernel<Vec<f64>>,
{
    kernel: K,
}

impl<K> Convolutional<K>
where
    K: Kernel<Vec<f64>>,
{
    pub fn new(kernel: K) -> Self {
        Self { kernel }
    }

    pub fn kernel_ref(&self) -> &K {
        &self.kernel
    }
}

impl<T, K> Kernel<T> for Convolutional<K>
where
    T: Convolutable,
    K: Kernel<Vec<f64>>,
{
    fn params_len(&self) -> usize {
        self.kernel.params_len()
    }

    fn value(
        &self,
        params: &[f64],
        x: &T,
        xprime: &T,
        with_grad: bool,
    ) -> Result<(f64, Vec<f64>), Box<dyn Error>> {
        if params.len() != self.kernel.params_len() {
            return Err(KernelError::ParametersLengthMismatch.into());
        }
        let p = x.parts_len();
        if p != xprime.parts_len() {
            return Err(KernelError::InvalidArgument.into());
        }

        let (fx, gfx): (f64, Vec<f64>) = (0..p)
            .into_iter()
            .map(|pi| {
                self.kernel
                    .value(params, x.part(pi), xprime.part(pi), with_grad)
            })
            .try_fold::<(f64, Vec<f64>), _, Result<(f64, Vec<f64>), Box<dyn Error>>>(
                (0.0, vec![]),
                |a, b| {
                    let b = b?;
                    Ok((a.0 + b.0, [a.1, b.1].concat()))
                },
            )?;

        Ok((fx, gfx))
    }
}