Initial commit: OptimizR - High-performance optimization algorithms in Rust with Python bindings

src/lib.rs

@@ -0,0 +1,44 @@
+//! OptimizR - High-Performance Optimization Algorithms
+//! ===================================================
+//!
+//! This library provides fast, reliable implementations of advanced optimization
+//! and statistical inference algorithms, with Python bindings via PyO3.
+//!
+//! # Modules
+//!
+//! - `hmm`: Hidden Markov Model training and inference
+//! - `mcmc`: Markov Chain Monte Carlo sampling
+//! - `differential_evolution`: Global optimization algorithm
+//! - `grid_search`: Exhaustive parameter space search
+//! - `information_theory`: Mutual information and entropy calculations
+
+use pyo3::prelude::*;
+use pyo3::types::PyModule;
+
+mod hmm;
+mod mcmc;
+mod differential_evolution;
+mod grid_search;
+mod information_theory;
+
+/// OptimizR Python module
+#[pymodule]
+fn _core(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
+    // Register 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
+    m.add_function(wrap_pyfunction!(mcmc::mcmc_sample, m)?)?;
+    
+    // Register 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
+    m.add_function(wrap_pyfunction!(information_theory::mutual_information, m)?)?;
+    m.add_function(wrap_pyfunction!(information_theory::shannon_entropy, m)?)?;
+    
+    Ok(())
+}