auradb 0.1.0

High-performance Rust storage engine with WAL-time KV separation, RL-driven compaction, and learned indexes
Documentation 
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//! Learned index module for machine learning-based indexing
//! 
//! This module will implement piecewise linear regression models,
//! online tuning, and fallback search methods.
//! 
//! Planned for M4 milestone.

use crate::error::{Error, Result};

/// Learned index model type
#[derive(Debug, Clone)]
pub enum ModelType {
    /// Piecewise linear regression
    PiecewiseLinear,
    /// Recursive Model Index (RMI)
    Rmi,
    /// Tiny neural network
    TinyNn,
}

/// Learned index model
pub struct LearnedIndex {
    // TODO: Implement learned index functionality
}

impl LearnedIndex {
    /// Create a new learned index
    pub fn new(_model_type: ModelType) -> Self {
        Self {}
    }
    
    /// Train the model on data
    pub fn train(&mut self, _keys: &[Vec<u8>], _positions: &[u64]) -> Result<()> {
        // TODO: Implement
        Ok(())
    }
    
    /// Predict position for a key
    pub fn predict(&self, _key: &[u8]) -> Result<u64> {
        // TODO: Implement
        Ok(0)
    }
    
    /// Validate model accuracy
    pub fn validate(&self, _test_keys: &[Vec<u8>], _test_positions: &[u64]) -> Result<f64> {
        // TODO: Implement
        Ok(0.0)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    
    #[test]
    fn test_learned_index_creation() {
        let index = LearnedIndex::new(ModelType::PiecewiseLinear);
        assert!(index.predict(b"test").is_ok());
    }
}