oxirs-stream 0.2.4

//! Classical processor for hybrid quantum-classical operations

use anyhow::Result;
use std::collections::HashMap;

use super::QuantumProcessingResult;
use crate::error::StreamResult;
use crate::event::StreamEvent;

/// Classical processor for hybrid operations
pub struct ClassicalProcessor {
    optimization_algorithms: Vec<ClassicalOptimizer>,
    ml_models: HashMap<String, ClassicalMLModel>,
    preprocessing_pipelines: Vec<PreprocessingPipeline>,
    postprocessing_pipelines: Vec<PostprocessingPipeline>,
}

impl Default for ClassicalProcessor {
    fn default() -> Self {
        Self::new()
    }
}

impl ClassicalProcessor {
    pub fn new() -> Self {
        Self {
            optimization_algorithms: vec![ClassicalOptimizer::Adam, ClassicalOptimizer::BFGS],
            ml_models: HashMap::new(),
            preprocessing_pipelines: Vec::new(),
            postprocessing_pipelines: Vec::new(),
        }
    }

    pub async fn preprocess_event(&self, event: &StreamEvent) -> Result<StreamEvent> {
        // Classical preprocessing logic
        Ok(event.clone())
    }

    pub async fn postprocess_result(
        &self,
        _quantum_result: QuantumProcessingResult,
        processed_event: StreamEvent,
    ) -> StreamResult<StreamEvent> {
        // Classical postprocessing logic
        // For now, return the processed event as-is
        Ok(processed_event)
    }
}

/// Classical optimization algorithms
#[derive(Debug, Clone)]
pub enum ClassicalOptimizer {
    GradientDescent,
    Adam,
    BFGS,
    NelderMead,
    SimulatedAnnealing,
    GeneticAlgorithm,
    ParticleSwarm,
    BayesianOptimization,
}

/// Classical ML models
#[derive(Debug, Clone)]
pub struct ClassicalMLModel {
    pub model_type: ClassicalMLType,
    pub parameters: Vec<f64>,
    pub training_accuracy: f64,
    pub inference_time_ms: f64,
}

/// Classical ML model types
#[derive(Debug, Clone)]
pub enum ClassicalMLType {
    LinearRegression,
    LogisticRegression,
    RandomForest,
    SVM,
    NeuralNetwork,
    DecisionTree,
    KMeans,
    PCA,
}

/// Preprocessing pipeline
#[derive(Debug, Clone)]
pub struct PreprocessingPipeline {
    pub steps: Vec<PreprocessingStep>,
}

/// Preprocessing steps
#[derive(Debug, Clone)]
pub enum PreprocessingStep {
    Normalization,
    Standardization,
    FeatureSelection,
    DimensionalityReduction,
    NoiseFiltering,
}

/// Postprocessing pipeline
#[derive(Debug, Clone)]
pub struct PostprocessingPipeline {
    pub steps: Vec<PostprocessingStep>,
}

/// Postprocessing steps
#[derive(Debug, Clone)]
pub enum PostprocessingStep {
    ResultValidation,
    ErrorCorrection,
    StatisticalAnalysis,
    Visualization,
}