Improve code design

src/lib.rs

@@ -4,6 +4,16 @@
 //! This library provides fast, reliable implementations of advanced optimization
 //! and statistical inference algorithms, with Python bindings via PyO3.
 //!
+//! # Architecture
+//!
+//! The library is designed with modularity, functional programming patterns,
+//! and trait-based abstractions:
+//!
+//! - `core`: Core traits (Optimizer, Sampler, InformationMeasure) and error types
+//! - `functional`: Functional programming utilities (composition, memoization, pipes)
+//! - Refactored modules with trait-based design and parallel support
+//! - Original modules maintained for backward compatibility
+//!
 //! # Modules
 //!
 //! - `hmm`: Hidden Markov Model training and inference
@@ -15,6 +25,16 @@
 use pyo3::prelude::*;
 use pyo3::types::PyModule;
 
+// Core modules with trait-based architecture
+pub mod core;
+pub mod functional;
+
+// Refactored modules with advanced patterns
+pub mod hmm_refactored;
+pub mod mcmc_refactored;
+pub mod de_refactored;
+
+// Original modules for backward compatibility
 mod hmm;
 mod mcmc;
 mod differential_evolution;
@@ -24,21 +44,38 @@ mod information_theory;
 /// OptimizR Python module
 #[pymodule]
 fn _core(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
-    // Register HMM functions
+    // ===== Original API (Backward Compatible) =====
+    
+    // HMM functions
     m.add_class::<hmm::HMMParams>()?;
     m.add_function(wrap_pyfunction!(hmm::fit_hmm, m)?)?;
     m.add_function(wrap_pyfunction!(hmm::viterbi_decode, m)?)?;
     
-    // Register MCMC functions
+    // MCMC functions
     m.add_function(wrap_pyfunction!(mcmc::mcmc_sample, m)?)?;
     
-    // Register optimization functions
+    // Optimization functions
     m.add_function(wrap_pyfunction!(differential_evolution::differential_evolution, m)?)?;
     m.add_function(wrap_pyfunction!(grid_search::grid_search, m)?)?;
     
-    // Register information theory functions
+    // Information theory functions
     m.add_function(wrap_pyfunction!(information_theory::mutual_information, m)?)?;
     m.add_function(wrap_pyfunction!(information_theory::shannon_entropy, m)?)?;
     
+    // ===== New Refactored API (Advanced Features) =====
+    
+    // Refactored HMM with trait-based design
+    m.add_class::<hmm_refactored::HMMParams>()?;
+    m.add_function(wrap_pyfunction!(hmm_refactored::fit_hmm, m)?)?;
+    m.add_function(wrap_pyfunction!(hmm_refactored::viterbi_decode, m)?)?;
+    
+    // Refactored MCMC with strategy pattern
+    m.add_function(wrap_pyfunction!(mcmc_refactored::mcmc_sample, m)?)?;
+    m.add_function(wrap_pyfunction!(mcmc_refactored::adaptive_mcmc_sample, m)?)?;
+    
+    // Refactored DE with parallel support and multiple strategies
+    m.add_class::<de_refactored::DEResult>()?;
+    m.add_function(wrap_pyfunction!(de_refactored::differential_evolution, m)?)?;
+    
     Ok(())
 }