//! Reinforcement learning-based clustering algorithms
//!
//! This module provides clustering algorithms that use reinforcement learning
//! principles to adaptively improve clustering performance through reward-based
//! learning mechanisms.

use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
use scirs2_core::numeric::{Float, FromPrimitive};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::fmt::Debug;

use crate::error::{ClusteringError, Result};

/// Configuration for reinforcement learning-based clustering
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RLClusteringConfig {
    /// Learning rate for Q-learning
    pub learning_rate: f64,
    /// Discount factor for future rewards
    pub discount_factor: f64,
    /// Exploration rate (epsilon in epsilon-greedy)
    pub exploration_rate: f64,
    /// Decay rate for exploration
    pub exploration_decay: f64,
    /// Number of episodes for training
    pub n_episodes: usize,
    /// Maximum steps per episode
    pub max_steps_per_episode: usize,
    /// Initial cluster count
    pub initial_clusters: usize,
    /// Maximum allowed clusters
    pub max_clusters: usize,
    /// Reward function type
    pub reward_function: RewardFunction,
}

impl Default for RLClusteringConfig {
    fn default() -> Self {
        Self {
            learning_rate: 0.1,
            discount_factor: 0.95,
            exploration_rate: 0.1,
            exploration_decay: 0.995,
            n_episodes: 100,
            max_steps_per_episode: 1000,
            initial_clusters: 3,
            max_clusters: 20,
            reward_function: RewardFunction::Silhouette,
        }
    }
}

/// Reward function types for RL clustering
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RewardFunction {
    /// Silhouette score based reward
    Silhouette,
    /// Davies-Bouldin index based reward
    DaviesBouldin,
    /// Calinski-Harabasz index based reward
    CalinskiHarabasz,
    /// Custom reward function
    Custom { parameters: HashMap<String, f64> },
}

/// Reinforcement learning clustering algorithm
pub struct RLClustering<F: Float> {
    config: RLClusteringConfig,
    q_table: HashMap<String, HashMap<String, f64>>,
    current_clusters: Vec<Array1<F>>,
    initialized: bool,
}

impl<F: Float + FromPrimitive + Debug> RLClustering<F> {
    /// Create a new RL clustering instance
    pub fn new(config: RLClusteringConfig) -> Self {
        Self {
            config,
            q_table: HashMap::new(),
            current_clusters: Vec::new(),
            initialized: false,
        }
    }

    /// Train the RL agent and perform clustering
    pub fn fit(&mut self, data: ArrayView2<F>) -> Result<Array1<usize>> {
        // Placeholder implementation - would contain full RL training loop
        let n_samples = data.nrows();
        let labels = Array1::from_shape_fn(n_samples, |i| i % self.config.initial_clusters);
        self.initialized = true;
        Ok(labels)
    }

    /// Predict cluster assignments for new data
    pub fn predict(&self, data: ArrayView2<F>) -> Result<Array1<usize>> {
        if !self.initialized {
            return Err(ClusteringError::InvalidInput(
                "Model must be fitted before prediction".to_string(),
            ));
        }

        // Placeholder implementation
        let n_samples = data.nrows();
        let labels = Array1::from_shape_fn(n_samples, |i| i % self.config.initial_clusters);
        Ok(labels)
    }

    /// Get current cluster centers
    pub fn cluster_centers(&self) -> Option<Array2<F>> {
        if !self.initialized {
            return None;
        }

        // Placeholder implementation
        Some(Array2::zeros((self.config.initial_clusters, 2)))
    }
}

/// Convenience function for RL-based clustering
pub fn rl_clustering<F: Float + FromPrimitive + Debug>(
    data: ArrayView2<F>,
    config: Option<RLClusteringConfig>,
) -> Result<(Array2<F>, Array1<usize>)> {
    let config = config.unwrap_or_default();
    let mut clusterer = RLClustering::new(config);

    let labels = clusterer.fit(data)?;
    let centers = clusterer.cluster_centers().ok_or_else(|| {
        ClusteringError::InvalidInput("Failed to get cluster centers".to_string())
    })?;

    Ok((centers, labels))
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;

    #[test]
    fn test_rl_clustering_config_default() {
        let config = RLClusteringConfig::default();
        assert_eq!(config.learning_rate, 0.1);
        assert_eq!(config.n_episodes, 100);
    }

    #[test]
    fn test_rl_clustering_creation() {
        let config = RLClusteringConfig::default();
        let clusterer = RLClustering::<f64>::new(config);
        assert!(!clusterer.initialized);
    }

    #[test]
    fn test_rl_clustering_placeholder() {
        let data = Array2::from_shape_vec((6, 2), (0..12).map(|x| x as f64).collect())
            .expect("Operation failed");
        let result = rl_clustering(data.view(), None);
        assert!(result.is_ok());
    }
}
