//! Performance metrics and results for autotuning.

use std::collections::HashMap;
use std::time::Duration;

use serde::{Deserialize, Serialize};

/// Performance measurement result
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    pub execution_time: Duration,
    pub gflops: f64,
    pub memory_bandwidth: f64,
    pub occupancy: f64,
    pub energy_efficiency: f64,
}

/// Statistical analysis of performance measurements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceStatistics {
    pub mean: f64,
    pub std_dev: f64,
    pub confidence_interval: (f64, f64),
    pub sample_count: usize,
    pub outliers_detected: Option<bool>,
}

/// Gaussian process prediction
#[derive(Debug, Clone)]
pub struct GPPrediction {
    pub mean: f64,
    pub variance: f64,
    pub confidence_interval: (f64, f64),
    pub multi_objective_score: Option<f64>,
}

/// Observation for Bayesian optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Observation {
    pub parameters: HashMap<String, i32>,
    pub metrics: PerformanceMetrics,
    pub timestamp: Duration, // Using Duration for serialization
    pub hardware_hash: u64,
}

/// Acquisition history entry
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AcquisitionHistoryEntry {
    pub parameters: HashMap<String, i32>,
    pub metrics: PerformanceMetrics,
    pub acquisition_value: f64,
    pub iteration: usize,
}

/// Tuning result with optimal parameters and metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TuningResult {
    pub optimal_parameters: HashMap<String, i32>,
    pub best_metrics: PerformanceMetrics,
    pub iterations_completed: usize,
    pub total_time: Duration,
    pub convergence_achieved: bool,
    pub pareto_frontier: Option<Vec<(HashMap<String, i32>, PerformanceMetrics)>>,
    pub acquisition_history: Option<Vec<AcquisitionHistoryEntry>>,
}