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use crate::Value;
use crate::{KernelError, PositiveDefiniteKernel};
use rayon::prelude::*;
use std::fmt::Debug;
pub trait Convolutable: Value {
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: PositiveDefiniteKernel<Vec<f64>>,
{
kernel: K,
}
impl<K> Convolutional<K>
where
K: PositiveDefiniteKernel<Vec<f64>>,
{
pub fn new(kernel: K) -> Self {
Self { kernel }
}
pub fn kernel_ref(&self) -> &K {
&self.kernel
}
}
impl<T, K> PositiveDefiniteKernel<T> for Convolutional<K>
where
T: Convolutable,
K: PositiveDefiniteKernel<Vec<f64>>,
{
fn params_len(&self) -> usize {
self.kernel.params_len()
}
fn value(&self, params: &[f64], x: &T, xprime: &T) -> Result<f64, KernelError> {
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 = (0..p)
.into_par_iter()
.map(|pi| self.kernel.value(params, x.part(pi), xprime.part(pi)))
.sum::<Result<f64, KernelError>>()?;
Ok(fx)
}
}
#[cfg(test)]
mod tests {
use crate::*;
#[test]
fn it_works() {
let kernel = Convolutional::new(RBF);
let test_value = kernel.value(&[1.0], &vec![0.0, 0.0, 0.0], &vec![0.0, 0.0, 0.0]);
match test_value {
Err(KernelError::ParametersLengthMismatch) => (),
_ => panic!(),
};
}
}