//! Multi-Armed Bandit Optimization
//!
//! Bandit-based approaches for hyperparameter and strategy selection.

use crate::error::OptimizeResult;
use crate::result::OptimizeResults;
use scirs2_core::ndarray::{Array1, ArrayView1};
use scirs2_core::random::{rng, Rng, RngExt};
// Unused import
// use scirs2_core::error::CoreResult;

/// Multi-armed bandit for optimization strategy selection
#[derive(Debug, Clone)]
pub struct BanditOptimizer {
    /// Number of arms (strategies)
    pub num_arms: usize,
    /// Arm rewards
    pub arm_rewards: Array1<f64>,
    /// Arm counts
    pub arm_counts: Array1<usize>,
}

impl BanditOptimizer {
    /// Create new bandit optimizer
    pub fn new(num_arms: usize) -> Self {
        Self {
            num_arms,
            arm_rewards: Array1::zeros(num_arms),
            arm_counts: Array1::zeros(num_arms),
        }
    }

    /// Select arm using UCB1
    pub fn select_arm(&self) -> usize {
        let total_counts: usize = self.arm_counts.sum();
        if total_counts == 0 {
            return scirs2_core::random::rng().random_range(0..self.num_arms);
        }

        let mut best_arm = 0;
        let mut best_ucb = f64::NEG_INFINITY;

        for arm in 0..self.num_arms {
            if self.arm_counts[arm] == 0 {
                return arm; // Explore unvisited arms
            }

            let average_reward = self.arm_rewards[arm] / self.arm_counts[arm] as f64;
            let confidence_interval =
                (2.0 * (total_counts as f64).ln() / self.arm_counts[arm] as f64).sqrt();
            let ucb = average_reward + confidence_interval;

            if ucb > best_ucb {
                best_ucb = ucb;
                best_arm = arm;
            }
        }

        best_arm
    }

    /// Update arm with reward
    pub fn update_arm(&mut self, arm: usize, reward: f64) {
        if arm < self.num_arms {
            self.arm_rewards[arm] += reward;
            self.arm_counts[arm] += 1;
        }
    }
}

/// Bandit-based optimization function
#[allow(dead_code)]
pub fn bandit_optimize<F>(
    objective: F,
    initial_params: &ArrayView1<f64>,
    num_nit: usize,
) -> OptimizeResult<OptimizeResults<f64>>
where
    F: Fn(&ArrayView1<f64>) -> f64,
{
    let mut bandit = BanditOptimizer::new(3); // 3 strategies
    let mut params = initial_params.to_owned();
    let mut best_obj = objective(initial_params);

    for _iter in 0..num_nit {
        let arm = bandit.select_arm();

        // Apply different strategies based on arm
        let step_size = match arm {
            0 => 0.01,  // Small steps
            1 => 0.1,   // Medium steps
            _ => 0.001, // Very small steps
        };

        // Simple gradient-like update
        for i in 0..params.len() {
            params[i] += (scirs2_core::random::rng().random::<f64>() - 0.5) * step_size;
        }

        let new_obj = objective(&params.view());
        let reward = if new_obj < best_obj { 1.0 } else { 0.0 };

        bandit.update_arm(arm, reward);

        if new_obj < best_obj {
            best_obj = new_obj;
        }
    }

    Ok(OptimizeResults::<f64> {
        x: params,
        fun: best_obj,
        success: true,
        nit: num_nit,
        message: "Bandit optimization completed".to_string(),
        jac: None,
        hess: None,
        constr: None,
        nfev: num_nit * 3, // Each iteration evaluates multiple arms
        njev: 0,
        nhev: 0,
        maxcv: 0,
        status: 0,
    })
}

#[allow(dead_code)]
pub fn placeholder() {}
