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use opensrdk_symbolic_computation::Expression;

use super::ActivationFunction;
use crate::{KernelAdd, KernelError, KernelMul, PositiveDefiniteKernel};
use std::{
    fmt::Debug,
    ops::{Add, Mul},
};

/// https://arxiv.org/abs/1711.00165
#[derive(Clone, Debug)]
pub struct DeepNeuralNetwork<'a> {
    layers: Vec<&'a dyn ActivationFunction>,
}

impl<'a> DeepNeuralNetwork<'a> {
    pub fn new(layers: Vec<&'a dyn ActivationFunction>) -> Self {
        Self { layers }
    }
}
impl<'a> PositiveDefiniteKernel for DeepNeuralNetwork<'a> {
    fn params_len(&self) -> usize {
        2 * (1 + self.layers.len())
    }

    fn expression(
        &self,
        x: Expression,
        x_prime: Expression,
        params: &[Expression],
    ) -> Result<Expression, KernelError> {
        if params.len() != self.params_len() {
            return Err(KernelError::ParametersLengthMismatch.into());
        }
        // if x.len() != x_prime.len() {
        //     return Err(KernelError::InvalidArgument.into());
        // }
        todo!()
    }

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

    //     let layer0 = Constant + Constant * Linear;
    //     let mut previous_layer_kernel = (
    //         layer0.value(&params[0..2], x, xprime)?,
    //         layer0.value(&params[0..2], x, x)?,
    //         layer0.value(&params[0..2], xprime, xprime)?,
    //     );
    //     let params = &params[2..];

    //     for (i, &layer) in self.layers.iter().enumerate() {
    //         let sigma_b = params[(i + 1) * 2];
    //         let sigma_w = params[(i + 1) * 2 + 1];
    //         let f = layer.f(previous_layer_kernel);
    //         let fxx = layer.f((
    //             previous_layer_kernel.1,
    //             previous_layer_kernel.1,
    //             previous_layer_kernel.1,
    //         ));
    //         let fxpxp = layer.f((
    //             previous_layer_kernel.2,
    //             previous_layer_kernel.2,
    //             previous_layer_kernel.2,
    //         ));

    //         previous_layer_kernel = (
    //             sigma_b + sigma_w * f,
    //             sigma_b + sigma_w * fxx,
    //             sigma_b + sigma_w * fxpxp,
    //         );
    //     }

    //     Ok(previous_layer_kernel.0)
    // }
}

impl<'a, R> Add<R> for DeepNeuralNetwork<'a>
where
    R: PositiveDefiniteKernel,
{
    type Output = KernelAdd<Self, R>;
    fn add(self, rhs: R) -> Self::Output {
        Self::Output::new(self, rhs)
    }
}

impl<'a, R> Mul<R> for DeepNeuralNetwork<'a>
where
    R: PositiveDefiniteKernel,
{
    type Output = KernelMul<Self, R>;

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

// #[cfg(test)]
// mod tests {
//     use crate::*;

//     #[test]
//     fn it_works() {
//         let activfunc = ReLU;
//         let kernel = DeepNeuralNetwork::new(vec![&activfunc]);

//         let test_value = kernel.value(
//             &[1.0, 1.0, 3.0, 4.0, 6.0],
//             &vec![0.0, 0.0, 0.0],
//             &vec![0.0, 0.0, 0.0],
//         );

//         match test_value {
//             Err(KernelError::ParametersLengthMismatch) => (),
//             _ => panic!(),
//         };
//     }
// }