scirs2-integrate 0.4.2

Numerical integration module for SciRS2 (scirs2-integrate)
Documentation
//! Performance prediction implementation
//!
//! This module contains the implementation for predictive performance modeling
//! and machine learning-based optimization.

use super::types::*;
use crate::common::IntegrateFloat;
use crate::error::{IntegrateError, IntegrateResult};

impl<F: IntegrateFloat> PerformancePredictor<F> {
    /// Create a new performance predictor
    pub fn new() -> Self {
        Self {
            model_registry: ModelRegistry::default(),
            feature_engineering: FeatureEngineering::default(),
            model_trainer: ModelTrainer::default(),
            accuracy_tracker: PredictionAccuracyTracker::default(),
        }
    }

    /// Predict performance for given configuration
    pub fn predict_performance(
        &self,
        _config: &AdaptationStrategy<F>,
    ) -> IntegrateResult<PredictedPerformance> {
        // Implementation would go here
        Ok(PredictedPerformance::default())
    }

    /// Update models with new performance data
    pub fn update_models(&mut self, metrics: &PerformanceMetrics) -> IntegrateResult<()> {
        // Implementation would go here
        Ok(())
    }
}

impl<F: IntegrateFloat + Default> MachineLearningOptimizer<F> {
    /// Create a new ML optimizer
    pub fn new() -> Self {
        Self {
            rl_agent: ReinforcementLearningAgent::default(),
            bayesian_optimizer: BayesianOptimizer::default(),
            nas_engine: NeuralArchitectureSearch::default(),
            hyperopt_engine: HyperparameterOptimizer::default(),
        }
    }

    /// Optimize parameters using ML
    pub fn optimize_parameters(
        &mut self,
        _metrics: &PerformanceMetrics,
    ) -> IntegrateResult<OptimizationResult<F>> {
        // Implementation would go here
        Ok(OptimizationResult::default())
    }
}

impl<F: IntegrateFloat + Default> Default for PerformancePredictor<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: IntegrateFloat + Default> Default for MachineLearningOptimizer<F> {
    fn default() -> Self {
        Self::new()
    }
}