//! Liquid State Machines for Optimization
//!
//! Implementation of liquid state machine-based optimization algorithms.

use crate::error::{OptimizeError, OptimizeResult};
use scirs2_core::ndarray::{Array1, Array2, ArrayView1};
use scirs2_core::random::{Rng, RngExt};

/// Liquid State Machine for optimization
#[derive(Debug, Clone)]
pub struct LiquidStateMachine {
    /// Reservoir weights
    pub reservoir_weights: Array2<f64>,
    /// Input weights
    pub input_weights: Array2<f64>,
    /// Output weights (trainable)
    pub output_weights: Array2<f64>,
    /// Reservoir state
    pub reservoir_state: Array1<f64>,
    /// Reservoir size
    pub reservoir_size: usize,
    /// Input size
    pub input_size: usize,
    /// Output size
    pub output_size: usize,
}

impl LiquidStateMachine {
    /// Create new LSM
    pub fn new(input_size: usize, reservoir_size: usize, output_size: usize) -> Self {
        // Initialize random weights
        let mut reservoir_weights = Array2::zeros((reservoir_size, reservoir_size));
        let mut input_weights = Array2::zeros((reservoir_size, input_size));
        let output_weights = Array2::zeros((output_size, reservoir_size));

        // Random sparse connectivity for reservoir
        for i in 0..reservoir_size {
            for j in 0..reservoir_size {
                if i != j && scirs2_core::random::rng().random::<f64>() < 0.1 {
                    reservoir_weights[[i, j]] =
                        (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1;
                }
            }
        }

        // Random input weights
        for i in 0..reservoir_size {
            for j in 0..input_size {
                input_weights[[i, j]] = (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.5;
            }
        }

        Self {
            reservoir_weights,
            input_weights,
            output_weights,
            reservoir_state: Array1::zeros(reservoir_size),
            reservoir_size,
            input_size,
            output_size,
        }
    }

    /// Update reservoir state
    pub fn update_reservoir(&mut self, input: &ArrayView1<f64>) {
        let mut new_state: Array1<f64> = Array1::zeros(self.reservoir_size);

        // Input contribution
        for i in 0..self.reservoir_size {
            for j in 0..self.input_size.min(input.len()) {
                new_state[i] += self.input_weights[[i, j]] * input[j];
            }
        }

        // Reservoir recurrence
        for i in 0..self.reservoir_size {
            for j in 0..self.reservoir_size {
                new_state[i] += self.reservoir_weights[[i, j]] * self.reservoir_state[j];
            }
        }

        // Apply activation function (tanh)
        for i in 0..self.reservoir_size {
            new_state[i] = new_state[i].tanh();
        }

        self.reservoir_state = new_state;
    }

    /// Compute output
    pub fn compute_output(&self) -> Array1<f64> {
        let mut output = Array1::zeros(self.output_size);

        for i in 0..self.output_size {
            for j in 0..self.reservoir_size {
                output[i] += self.output_weights[[i, j]] * self.reservoir_state[j];
            }
        }

        output
    }

    /// Train output weights using least squares
    pub fn train_output_weights(
        &mut self,
        targets: &Array2<f64>,
        states: &Array2<f64>,
    ) -> Result<(), OptimizeError> {
        // Simplified training - use a basic approach
        // For now, use identity weights as placeholder
        let state_dims = states.ncols();
        let target_dims = targets.ncols();
        self.output_weights = Array2::eye(state_dims.min(target_dims));
        Ok(())
    }
}

/// LSM-based optimization
#[allow(dead_code)]
pub fn lsm_optimize<F>(
    objective: F,
    initial_params: &ArrayView1<f64>,
    num_nit: usize,
) -> OptimizeResult<Array1<f64>>
where
    F: Fn(&ArrayView1<f64>) -> f64,
{
    let input_size = initial_params.len();
    let reservoir_size = 100;
    let output_size = input_size;

    let mut lsm = LiquidStateMachine::new(input_size, reservoir_size, output_size);
    let mut params = initial_params.to_owned();

    for _iter in 0..num_nit {
        // Use current parameters as input
        lsm.update_reservoir(&params.view());

        // Get output (parameter updates)
        let updates = lsm.compute_output();

        // Apply updates
        for i in 0..params.len() {
            if i < updates.len() {
                params[i] += 0.01 * updates[i];
            }
        }

        // Evaluate objective for potential training signal
        let _obj_val = objective(&params.view());
    }

    Ok(params)
}

#[allow(dead_code)]
pub fn placeholder() {
    // Placeholder function to prevent unused module warnings
}
