//! Intelligent algorithm selection system for FFT operations

use super::types::*;
use crate::error::FFTResult;
use scirs2_core::numeric::Float;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::time::Instant;

/// Intelligent algorithm selection system
#[derive(Debug)]
#[allow(dead_code)]
pub struct IntelligentAlgorithmSelector<F: Float + Debug> {
    /// Algorithm performance database
    pub(crate) algorithm_db: HashMap<AlgorithmKey, AlgorithmPerformanceData>,
    /// Current signal characteristics
    pub(crate) current_signal_profile: Option<SignalProfile<F>>,
    /// Learning model for algorithm selection
    pub(crate) selection_model: AlgorithmSelectionModel,
    /// Historical performance data
    pub(crate) performance_history: VecDeque<AlgorithmPerformanceRecord>,
}

/// Key for algorithm identification
#[derive(Debug, Clone, Hash, Eq, PartialEq)]
pub struct AlgorithmKey {
    /// Algorithm type
    pub algorithm_type: FftAlgorithmType,
    /// Input size characteristics
    pub size_class: SizeClass,
    /// Signal type
    pub signal_type: SignalType,
    /// Hardware profile
    pub hardware_profile: HardwareProfile,
}

/// Performance data for algorithms
#[derive(Debug, Clone)]
pub struct AlgorithmPerformanceData {
    /// Average execution time (microseconds)
    pub avg_execution_time: f64,
    /// Memory usage (bytes)
    pub memory_usage: usize,
    /// Accuracy score (0.0 - 1.0)
    pub accuracy_score: f64,
    /// Energy efficiency score
    pub energy_efficiency: f64,
    /// Number of samples
    pub sample_count: usize,
    /// Last update time
    pub last_update: Instant,
}

/// Algorithm selection model
#[derive(Debug)]
#[allow(dead_code)]
pub struct AlgorithmSelectionModel {
    /// Feature weights for algorithm selection
    pub(crate) feature_weights: HashMap<String, f64>,
    /// Decision tree for algorithm selection
    pub(crate) decision_tree: Vec<SelectionRule>,
    /// Learning rate for weight updates
    pub learning_rate: f64,
}

/// Selection rule for decision tree
#[derive(Debug, Clone)]
pub struct SelectionRule {
    /// Condition for rule activation
    pub condition: SelectionCondition,
    /// Recommended algorithm
    pub algorithm: FftAlgorithmType,
    /// Confidence score
    pub confidence: f64,
}

/// Condition for algorithm selection
#[derive(Debug, Clone)]
pub enum SelectionCondition {
    /// Size-based condition
    SizeRange { min: usize, max: usize },
    /// Sparsity-based condition
    SparsityThreshold { threshold: f64 },
    /// Signal type condition
    SignalTypeMatch { signal_type: SignalType },
    /// Hardware availability condition
    HardwareAvailable { hardware: HardwareProfile },
    /// Composite condition (AND)
    And { conditions: Vec<SelectionCondition> },
    /// Composite condition (OR)
    Or { conditions: Vec<SelectionCondition> },
}

/// Performance record for algorithms
#[derive(Debug, Clone)]
pub struct AlgorithmPerformanceRecord {
    /// Algorithm used
    pub algorithm: FftAlgorithmType,
    /// Signal profile
    pub signal_profile: String, // Serialized profile
    /// Execution time (microseconds)
    pub execution_time: f64,
    /// Memory usage (bytes)
    pub memory_usage: usize,
    /// Accuracy achieved
    pub accuracy: f64,
    /// Timestamp
    pub timestamp: Instant,
}

impl<F: Float + Debug> IntelligentAlgorithmSelector<F> {
    pub fn new() -> FFTResult<Self> {
        Ok(Self {
            algorithm_db: HashMap::new(),
            current_signal_profile: None,
            selection_model: AlgorithmSelectionModel::new()?,
            performance_history: VecDeque::new(),
        })
    }
}

impl AlgorithmSelectionModel {
    pub fn new() -> FFTResult<Self> {
        Ok(Self {
            feature_weights: HashMap::new(),
            decision_tree: Vec::new(),
            learning_rate: 0.01,
        })
    }
}
