Melvin Avarez
b87fe2eeec
- Split HMM module into separate files (emission.rs, config.rs, model.rs, viterbi.rs, python_bindings.rs) - Split MCMC module into separate files (proposal.rs, config.rs, likelihood.rs, sampler.rs, python_bindings.rs) - Create organized src/hmm/ and src/mcmc/ directory structure - Rename legacy files to hmm_legacy.rs and mcmc_legacy.rs for backward compatibility - Update lib.rs to use new modular structure - Reduce file sizes: largest file now 171 lines (previously 583 lines) - Improve code reusability and maintainability - All Python bindings remain backward compatible
77 lines
2.6 KiB
Rust
77 lines
2.6 KiB
Rust
//! OptimizR - High-Performance Optimization Algorithms
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//! ===================================================
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//!
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//! This library provides fast, reliable implementations of advanced optimization
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//! and statistical inference algorithms, with Python bindings via PyO3.
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//!
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//! # Architecture
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//!
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//! The library is designed with modularity, functional programming patterns,
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//! and trait-based abstractions:
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//!
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//! - `core`: Core traits (Optimizer, Sampler, InformationMeasure) and error types
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//! - `functional`: Functional programming utilities (composition, memoization, pipes)
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//! - Refactored modules with trait-based design and parallel support
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//! - Original modules maintained for backward compatibility
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//!
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//! # Modules
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//!
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//! - `hmm`: Hidden Markov Model training and inference
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//! - `mcmc`: Markov Chain Monte Carlo sampling
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//! - `differential_evolution`: Global optimization algorithm
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//! - `grid_search`: Exhaustive parameter space search
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//! - `information_theory`: Mutual information and entropy calculations
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use pyo3::prelude::*;
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use pyo3::types::PyModule;
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// Core modules with trait-based architecture
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pub mod core;
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pub mod functional;
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// New modular structure (recommended)
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pub mod hmm;
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pub mod mcmc;
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pub mod de;
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// Legacy modules for backward compatibility
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mod hmm_legacy;
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mod mcmc_legacy;
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mod hmm_refactored;
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mod mcmc_refactored;
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mod de_refactored;
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mod differential_evolution;
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mod grid_search;
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mod information_theory;
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/// OptimizR Python module
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#[pymodule]
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fn _core(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
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// ===== New Modular API (Recommended) =====
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// HMM functions (modular structure)
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m.add_class::<hmm::HMMParams>()?;
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m.add_function(wrap_pyfunction!(hmm::fit_hmm, m)?)?;
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m.add_function(wrap_pyfunction!(hmm::viterbi_decode, m)?)?;
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// MCMC functions (modular structure)
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m.add_function(wrap_pyfunction!(mcmc::mcmc_sample, m)?)?;
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m.add_function(wrap_pyfunction!(mcmc::adaptive_mcmc_sample, m)?)?;
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// DE functions (modular structure - uses de_refactored for now)
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m.add_class::<de::DEResult>()?;
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m.add_function(wrap_pyfunction!(de::differential_evolution, m)?)?;
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// ===== Legacy API (Backward Compatible) =====
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// Legacy optimization functions
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m.add_function(wrap_pyfunction!(differential_evolution::differential_evolution, m)?)?;
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m.add_function(wrap_pyfunction!(grid_search::grid_search, m)?)?;
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// Information theory functions
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m.add_function(wrap_pyfunction!(information_theory::mutual_information, m)?)?;
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m.add_function(wrap_pyfunction!(information_theory::shannon_entropy, m)?)?;
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Ok(())
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}
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