//! Event-Driven Optimization
//!
//! Implementation of event-driven optimization algorithms for neuromorphic computing.

use scirs2_core::error::CoreResult as Result;
use scirs2_core::ndarray::{Array1, ArrayView1};
use std::cmp::Ordering;
use std::collections::BinaryHeap;

/// Event in the optimization process
#[derive(Debug, Clone)]
pub struct OptimizationEvent {
    /// Time of the event
    pub time: f64,
    /// Type of event
    pub event_type: EventType,
    /// Associated data
    pub data: Array1<f64>,
}

/// Types of optimization events
#[derive(Debug, Clone)]
pub enum EventType {
    /// Parameter update event
    ParameterUpdate,
    /// Gradient computation event
    GradientComputation,
    /// Objective evaluation event
    ObjectiveEvaluation,
}

impl PartialEq for OptimizationEvent {
    fn eq(&self, other: &Self) -> bool {
        self.time == other.time
    }
}

impl Eq for OptimizationEvent {}

impl PartialOrd for OptimizationEvent {
    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
        // Reverse ordering for min-heap behavior
        other.time.partial_cmp(&self.time)
    }
}

impl Ord for OptimizationEvent {
    fn cmp(&self, other: &Self) -> Ordering {
        self.partial_cmp(other).unwrap_or(Ordering::Equal)
    }
}

/// Event-driven optimization scheduler
#[derive(Debug, Clone)]
pub struct EventDrivenOptimizer {
    /// Event queue
    pub event_queue: BinaryHeap<OptimizationEvent>,
    /// Current time
    pub current_time: f64,
    /// Current parameters
    pub parameters: Array1<f64>,
}

impl EventDrivenOptimizer {
    /// Create new event-driven optimizer
    pub fn new(initial_params: Array1<f64>) -> Self {
        Self {
            event_queue: BinaryHeap::new(),
            current_time: 0.0,
            parameters: initial_params,
        }
    }

    /// Schedule an event
    pub fn schedule_event(&mut self, event: OptimizationEvent) {
        self.event_queue.push(event);
    }

    /// Process next event
    pub fn process_next_event<F>(&mut self, objective: &F) -> Result<bool>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        if let Some(event) = self.event_queue.pop() {
            self.current_time = event.time;

            match event.event_type {
                EventType::ParameterUpdate => {
                    // Update parameters with event data
                    for (i, &update) in event.data.iter().enumerate() {
                        if i < self.parameters.len() {
                            self.parameters[i] += update;
                        }
                    }
                }
                EventType::GradientComputation => {
                    // Compute gradient (simplified)
                    let _gradient = self.compute_finite_difference_gradient(objective);
                }
                EventType::ObjectiveEvaluation => {
                    let _obj_val = objective(&self.parameters.view());
                }
            }

            Ok(true)
        } else {
            Ok(false)
        }
    }

    /// Compute finite difference gradient
    fn compute_finite_difference_gradient<F>(&self, objective: &F) -> Array1<f64>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        let n = self.parameters.len();
        let mut gradient = Array1::zeros(n);
        let h = 1e-6;
        let f0 = objective(&self.parameters.view());

        for i in 0..n {
            let mut params_plus = self.parameters.clone();
            params_plus[i] += h;
            let f_plus = objective(&params_plus.view());
            gradient[i] = (f_plus - f0) / h;
        }

        gradient
    }
}

/// Event-driven optimization function
#[allow(dead_code)]
pub fn event_driven_optimize<F>(
    objective: F,
    initial_params: &ArrayView1<f64>,
    max_events: usize,
) -> Result<Array1<f64>>
where
    F: Fn(&ArrayView1<f64>) -> f64,
{
    let mut optimizer = EventDrivenOptimizer::new(initial_params.to_owned());

    // Schedule initial events
    for i in 0..10 {
        let event = OptimizationEvent {
            time: i as f64 * 0.1,
            event_type: EventType::ParameterUpdate,
            data: Array1::from(vec![0.01; initial_params.len()]),
        };
        optimizer.schedule_event(event);
    }

    // Process events
    for _ in 0..max_events {
        if !optimizer.process_next_event(&objective)? {
            break;
        }
    }

    Ok(optimizer.parameters)
}

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