Melvin Alvarez
1e88ba5bb6
- Add kalman_filter.rs with LinearKF, RTSSmoother, UKF implementations - Add kalman_py_bindings.rs for Python API exposure - Create tutorial notebook with 3 real-world examples (vehicle tracking, financial time series, multi-sensor fusion) - All tests passing with excellent results (63-88% RMSE improvements) - Fix compilation errors in py_bindings.rs (type annotations, imports) - Successfully builds with maturin develop --release
123 lines
4.4 KiB
Rust
123 lines
4.4 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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//! - `sparse_optimization`: Sparse PCA, Box-Tao, Elastic Net
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//! - `risk_metrics`: Portfolio risk analysis and Hurst exponent
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#[cfg(feature = "python-bindings")]
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use pyo3::prelude::*;
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#[cfg(feature = "python-bindings")]
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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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pub mod maths_toolkit; // Mathematical utilities
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pub mod timeseries_utils; // Time-series integration helpers
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pub mod rust_objectives; // Rust-native objectives for parallel evaluation
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pub mod shade; // SHADE adaptive DE algorithm
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// Modular structure (trait-based, generic)
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pub mod de;
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pub mod hmm;
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pub mod mcmc;
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pub mod optimal_control;
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pub mod risk_metrics;
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pub mod sparse_optimization;
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pub mod mean_field; // Mean Field Games and Mean Field Type Control
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// Python bindings for legacy compatibility
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#[cfg(feature = "python-bindings")]
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mod differential_evolution;
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#[cfg(feature = "python-bindings")]
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mod grid_search;
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#[cfg(feature = "python-bindings")]
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mod information_theory;
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/// OptimizR Python module
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#[cfg(feature = "python-bindings")]
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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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// ===== Additional Algorithms =====
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// Optimization functions
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m.add_function(wrap_pyfunction!(
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differential_evolution::differential_evolution,
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m
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)?)?;
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m.add_function(wrap_pyfunction!(
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differential_evolution::parallel_differential_evolution_rust,
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m
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)?)?;
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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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// ===== New Optimization Algorithms =====
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// Sparse optimization functions
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m.add_function(wrap_pyfunction!(sparse_optimization::sparse_pca_py, m)?)?;
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m.add_function(wrap_pyfunction!(
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sparse_optimization::box_tao_decomposition_py,
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m
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)?)?;
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m.add_function(wrap_pyfunction!(sparse_optimization::elastic_net_py, m)?)?;
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// Risk metrics functions
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m.add_function(wrap_pyfunction!(risk_metrics::hurst_exponent_py, m)?)?;
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m.add_function(wrap_pyfunction!(risk_metrics::compute_risk_metrics_py, m)?)?;
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m.add_function(wrap_pyfunction!(risk_metrics::estimate_half_life_py, m)?)?;
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m.add_function(wrap_pyfunction!(risk_metrics::bootstrap_returns_py, m)?)?;
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// Time-series utility functions
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timeseries_utils::python_bindings::register_python_functions(m)?;
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// Rust-native benchmark functions
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rust_objectives::register_benchmark_functions(m)?;
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// Mean Field Games functions
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mean_field::python_bindings::register_python_functions(m)?;
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// Optimal Control functions (includes Kalman Filter)
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optimal_control::py_bindings::register_py_module(m)?;
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Ok(())
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}
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