//! Meta-Learning for Optimization
//!
//! Learning to optimize across different problem classes and domains.

use crate::error::OptimizeResult;
use crate::result::OptimizeResults;
use scirs2_core::ndarray::{Array1, ArrayView1};
// Unused import
// use scirs2_core::error::CoreResult;
use std::collections::HashMap;

/// Meta-learning optimizer that learns across multiple tasks
#[derive(Debug, Clone)]
pub struct MetaLearningOptimizer {
    /// Task-specific learned parameters
    pub task_parameters: HashMap<String, Array1<f64>>,
    /// Meta-parameters learned across tasks
    pub meta_parameters: Array1<f64>,
    /// Learning rate for meta-updates
    pub meta_learning_rate: f64,
    /// Task counter
    pub task_count: usize,
}

impl MetaLearningOptimizer {
    /// Create new meta-learning optimizer
    pub fn new(_param_size: usize, meta_learning_rate: f64) -> Self {
        Self {
            task_parameters: HashMap::new(),
            meta_parameters: Array1::zeros(_param_size),
            meta_learning_rate,
            task_count: 0,
        }
    }

    /// Learn on a single task
    pub fn learn_task<F>(
        &mut self,
        task_id: String,
        objective: &F,
        initial_params: &ArrayView1<f64>,
        num_steps: usize,
    ) -> OptimizeResult<Array1<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        // Initialize task parameters with meta-parameters
        let mut task_params = if let Some(existing) = self.task_parameters.get(&task_id) {
            existing.clone()
        } else {
            &self.meta_parameters + initial_params
        };

        // Task-specific optimization (simplified gradient descent)
        for _step in 0..num_steps {
            let current_obj = objective(&task_params.view());

            // Finite difference gradient
            let mut gradient = Array1::zeros(task_params.len());
            let h = 1e-6;

            for i in 0..task_params.len() {
                let mut params_plus = task_params.clone();
                params_plus[i] += h;
                let obj_plus = objective(&params_plus.view());
                gradient[i] = (obj_plus - current_obj) / h;
            }

            // Update task parameters
            task_params = &task_params - &(0.01 * &gradient);
        }

        // Store task parameters
        self.task_parameters.insert(task_id, task_params.clone());
        self.task_count += 1;

        Ok(task_params)
    }

    /// Update meta-parameters based on task experiences
    pub fn update_meta_parameters(&mut self) {
        if self.task_parameters.is_empty() {
            return;
        }

        // Simple meta-update: average of all task parameters
        let mut sum = Array1::zeros(self.meta_parameters.len());
        for task_params in self.task_parameters.values() {
            sum = &sum + task_params;
        }

        let average = &sum / self.task_parameters.len() as f64;

        // Update meta-parameters with momentum
        self.meta_parameters = &((1.0 - self.meta_learning_rate) * &self.meta_parameters)
            + &(self.meta_learning_rate * &average);
    }

    /// Optimize new task using learned meta-parameters
    pub fn optimize_new_task<F>(
        &mut self,
        objective: &F,
        initial_params: &ArrayView1<f64>,
        num_steps: usize,
    ) -> OptimizeResult<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        let task_id = format!("task_{}", self.task_count);
        let result_params = self.learn_task(task_id, objective, initial_params, num_steps)?;

        // Update meta-parameters
        self.update_meta_parameters();

        Ok(OptimizeResults::<f64> {
            x: result_params.clone(),
            fun: objective(&result_params.view()),
            success: true,
            nit: num_steps,
            message: "Meta-learning optimization completed".to_string(),
            jac: None,
            hess: None,
            constr: None,
            nfev: num_steps * (self.task_count + 1), // Steps * tasks
            njev: 0,
            nhev: 0,
            maxcv: 0,
            status: 0,
        })
    }
}

/// Meta-learning optimization function
#[allow(dead_code)]
pub fn meta_learning_optimize<F>(
    objective: F,
    initial_params: &ArrayView1<f64>,
    num_tasks: usize,
    steps_per_task: usize,
) -> OptimizeResult<OptimizeResults<f64>>
where
    F: Fn(&ArrayView1<f64>) -> f64,
{
    let mut meta_optimizer = MetaLearningOptimizer::new(initial_params.len(), 0.1);

    // Train on multiple similar _tasks (variations of the objective)
    for task_idx in 0..num_tasks {
        let task_id = format!("training_task_{}", task_idx);

        // Create variation of the objective (simple shift)
        let shift = (task_idx as f64 - num_tasks as f64 * 0.5) * 0.1;
        let task_objective = |x: &ArrayView1<f64>| objective(x) + shift;

        meta_optimizer.learn_task(task_id, &task_objective, initial_params, steps_per_task)?;
        meta_optimizer.update_meta_parameters();
    }

    // Optimize on the original objective
    meta_optimizer.optimize_new_task(&objective, initial_params, steps_per_task)
}

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