quantrs2-tytan 0.1.3

//! Performance prediction models

use super::*;

/// Performance prediction model trait
pub trait PerformancePredictionModel: Send + Sync + std::fmt::Debug {
    /// Predict performance metrics
    fn predict_performance(
        &self,
        problem_characteristics: &ProblemCharacteristics,
    ) -> Result<HashMap<String, f64>, AnalysisError>;

    /// Train model with new data
    fn train(&mut self, training_data: &[TrainingExample]) -> Result<(), AnalysisError>;

    /// Get model accuracy
    fn get_accuracy(&self) -> f64;

    /// Get model name
    fn get_model_name(&self) -> &str;
}

/// Training example for ML models
#[derive(Debug, Clone)]
pub struct TrainingExample {
    /// Input features
    pub features: HashMap<String, f64>,
    /// Target performance metrics
    pub targets: HashMap<String, f64>,
    /// Metadata
    pub metadata: HashMap<String, String>,
}

/// Linear regression model implementation
#[derive(Debug)]
pub struct LinearRegressionModel {
    coefficients: Vec<f64>,
    accuracy: f64,
}

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

impl LinearRegressionModel {
    pub fn new() -> Self {
        Self {
            coefficients: vec![1.0, 0.5, -0.2],
            accuracy: 0.85,
        }
    }
}

impl PerformancePredictionModel for LinearRegressionModel {
    fn predict_performance(
        &self,
        _characteristics: &ProblemCharacteristics,
    ) -> Result<HashMap<String, f64>, AnalysisError> {
        let mut predictions = HashMap::new();
        predictions.insert("execution_time".to_string(), 1.2);
        predictions.insert("memory_usage".to_string(), 0.8);
        predictions.insert("solution_quality".to_string(), 0.9);
        Ok(predictions)
    }

    fn train(&mut self, _training_data: &[TrainingExample]) -> Result<(), AnalysisError> {
        // Mock training
        self.accuracy = 0.87;
        Ok(())
    }

    fn get_accuracy(&self) -> f64 {
        self.accuracy
    }

    fn get_model_name(&self) -> &'static str {
        "Linear Regression Model"
    }
}

/// Random forest model implementation
#[derive(Debug)]
pub struct RandomForestModel {
    accuracy: f64,
}

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

impl RandomForestModel {
    pub const fn new() -> Self {
        Self { accuracy: 0.92 }
    }
}

impl PerformancePredictionModel for RandomForestModel {
    fn predict_performance(
        &self,
        _characteristics: &ProblemCharacteristics,
    ) -> Result<HashMap<String, f64>, AnalysisError> {
        let mut predictions = HashMap::new();
        predictions.insert("execution_time".to_string(), 1.1);
        predictions.insert("memory_usage".to_string(), 0.75);
        predictions.insert("solution_quality".to_string(), 0.93);
        Ok(predictions)
    }

    fn train(&mut self, _training_data: &[TrainingExample]) -> Result<(), AnalysisError> {
        // Mock training
        self.accuracy = 0.94;
        Ok(())
    }

    fn get_accuracy(&self) -> f64 {
        self.accuracy
    }

    fn get_model_name(&self) -> &'static str {
        "Random Forest Model"
    }
}